Weekend Effect in Stock Returns: Do Stocks perform differently on Mondays VS Fridays?

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SWALEH MOHD, VIKRAMADITYA CHAKRABORTY This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7580297/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The "Monday effect" or "weekend effect" is arguably one of the most documented calendar anomalies in the financial markets that is inconsistent with the underlying meaning of the efficient market hypothesis as stock returns exhibit systematic patterns based upon the day of week. The research presented examines whether stocks behave differently on Mondays relative to Fridays observing both the size and direction of returns on these trading days. Using quantitative research methodology examined stock returns on a daily frequency from the National Stock Exchange of India for a five-year period (2019 to 2024) for data on a sample of 500 companies from different sectors. The quantitative research methodology applies statistical tests including t-tests, ANOVA, and regression analysis to identify whether a statistically significant difference exists in mean returns on Mondays and Fridays. Our evidence provided a statistically significant weekend effect, the average Monday return of -0.23% and the average Friday return of + 0.18% gave a differential of 0.41 percentage points. The evidence shows a larger effect on mid-cap and small-cap stock relative to large-cap stock which suggests the weekend effect is a function of market capitalisation. The results of the study have important implications for the management of portfolios, trading strategies, and market efficiency. The study adds to the growing field of literature on calendar anomalies in emerging markets, and provides practical information for investors and fund managers who want to enhance their trading strategies in conformity with weekly market trends. Weekend Effect Calendar Anomaly Stock Returns Market Efficiency Behavioural Finance Trading Strategy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Traditionally, financial markets have been viewed as efficient systems where the pricing of assets reflects all relevant information, with that information being reflected instantaneously. The identification of various anomalies, however, has challenged this notion, with profits derived, in part, from predictable deviations from randomness. The weekend effect is the best known and documented, in that it relates to the fact that stock returns on Mondays are significantly lower than stock returns on any other day of the week, and specifically Fridays. The weekend effect was first recorded by Cross ( 1973 ), and then confirmed by French ( 1980 ). By definition, an anomaly such as the weekend effect is a systematic rejection of the efficient market hypothesis. In theory, investors could earn abnormal returns using the day of the week in their trade selection, that is, irrational behaviour closely resembling triviality. The weekend effect has been studied through multiple international markets and the phenomenon of predictable patterns in stock returns raises questions regarding explanations of such predictability. The weekend effect also provides a useful opportunity for research among emerging markets such as India. An emerging market, such as India, has distinctive areas of interest, such as greater volatility, lesser liquidity and a different culture of investor behaviour compared with a developed market. The Indian stock market is an exciting area to examine whether traditional calendar anomalies are still in place in fast-changing financial environments, due to its growing retail investor base and current institutional participation. The importance of the weekend effect is not only of academic interest. It can translate directly into portfolio management, institutional investment decisions, and for individual traders, create a framework for trading behaviour, risk management, and portfolio construction. For example, if there are systemic differences in returns on Monday and Friday, an investor might alter their trading schedule to accommodate their trading behaviour or manage a risky loss. Although many studies have examined the weekend effect in developed markets, there is a notable gap in understanding how the weekend effect exists in emerging markets, and particularly in the Indian context. The majority of the existing research either studies developed markets or examines the earlier time periods that do not necessarily reflect the current market. Finally, not many permits us to directly compare Monday and Friday returns, whilst controlling for a variety of market conditions and firm characteristics. The objective of the study is to fill these gaps by exploring the weekend effect on the Indian stock market as a whole, with particular emphasis on comparing weekend and weekday stock performance on Mondays and Fridays. Our research questions include: Do stocks experience significantly different returns on Mondays compared to Fridays? Are differences in returns across the weekend effect influenced by other factors? Does market capitalisation influence the weekend effect for different categories of stocks? The main aim with this research is to empirically identify the existence and magnitude of the weekend effect in Indian stock market by comparing stock returns between Monday and Friday. Secondary aims include identifying if the effect exists across different market capitalisations, how consistent the pattern is over time, and what the practical implications may be for trading strategies. We hypothesise that stocks will have significantly lower returns on Mondays, compared to Fridays, as existing studies on the weekend effect identifies in other countries. Also, we conclude that the weekend effect will establish higher output in the smaller capitalisation stocks because these are the higher volatility stocks, often with less institutional ownership. This research will contribute to the literature about the weekend effect, as it provides contemporary evidence of the weekend effect in an emerging markets context, while also providing market participants with practical aspects to improve understanding of and exploit calendar based positional trading. 2. Literature Review The weekend effect has been well established in the academic literature, with researchers repeatedly reporting that Monday returns are lower than other days of the week. This part summarizes the most important theoretical bases, empirical evidence, and explanations of the phenomenon in various markets and time frames. 2.1 Theoretical Foundations The efficient market hypothesis (EMH) of Fama ( 1970 ) states that financial markets are informationally efficient in the sense that asset prices reflect all available information. According to the strong form of EMH, there should not exist any investor who can generate abnormal returns from any type of information, such as historical patterns of prices. Evidence on calendar anomalies such as the weekend effect contradicts this basic assumption. Behavioural finance theory offers other explanations of market anomalies, proposing that psychological influences and cognitive biases on the decision-making of investors cause them. According to Shefrin and Statman ( 1985 ), such emotional states and behavioural traits of the investors can generate systematic departures from rational pricing, giving rise to salient market anomalies. The disposition effect, first documented by Shefrin and Statman ( 1985 ), implies that investors overstay losing positions and exit winning positions too prematurely. This psychological bias could account for the weekend effect if investors' moods systematically vary between the start and the end of the trading week. 2.2 International Evidence French ( 1980 ) performed one of the classic weekend effect studies on S&P 500 returns between 1953 and 1977. His results showed that Monday returns were − 0.17% on average relative to positive returns on all other days, with Fridays especially strong at + 0.12%. This laid the groundwork for future research into calendar anomalies. Gibbons and Hess ( 1981 ) built on French's research by analysing individual stock returns and verified that the Monday effect was not restricted to market indices but was widespread among individual securities. Their study of New York Stock Exchange (NYSE) stocks during 1962–1978 indicated that 16 of the 17 size-based portfolios had negative Monday returns. Keim and Stambaugh ( 1984 ) examined the weekend effect across market capitalisations and discovered that small firms had more significant Monday effects than big companies. This size difference in the weekend effect has been repeatedly evidenced in later studies and implies that market capitalisation plays a significant moderating role. Cross-sectional international evidence for the weekend effect has also been inconsistent, with some markets having strong patterns and others no or weak effects. The weekend effect in nine international market was tested by Solnik & Bousquet ( 1990 ), the result had detected significant effects in some European markets on Monday, although the effects were weaker in certain Asian markets. Another research was conducted by Agarwal and Tandon (1994) and found the weekend effect exists in most markets but they differed in magnitude and persistence after researching eighteen international stock markets. The results indicated that smaller and less developed markets had a stronger effect, which suggested that the size of calendar anomalies could be affected by market maturity. 2.3 Emerging Market Studies In recent times, emerging markets have shown intriguing patterns during the studies of the weekend effect and found contrasting findings in developed markets. When Chang et al ( 1993 ) was conducting a study on Asian markets, he discovered that some market had dissimilar patterns as compared to markets with usual Monday effect, indicating that certain factors could affect calendar anomalies like cultural & institutional factors. Choudhry ( 2000 ) while examining the seven Asian-Pacific markets, he observed that the effect is absent in a few time periods although the weekend effect was found in six markets. The weekend effects may be changing as markets become more efficient and mature as indicated by the time dependence. In 1980s the Indian equity market, which was researched upon by Kohli and Kohers ( 1992 ), detected weak signs of weekend effect, but the sample space and time frame used by them was inadequate. More recent evidence on calendar anomalies was presented after conducting subsequent research in Indian markets. 2.4 Indian Market Evidence Conflict ing results has been found upon studying calendar anomalies in the Indian stock market, while some established the existence of the weekend effect, others have reported scant evidence. Nageswari et al. ( 2011 ) observed poor evidence on the Monday effect and realised the anomalies could be weaker in the Indian market, after studying the BSE Sensex for the time frame 1991–2009 Mangala and Raj during their much recent study on Indian stock market, using data from2005 to 2014 uncovered strong evidence on the Weekend effect. The Monday returns were always lesser than the Friday returns, which concluded that the effect was stronger in the recent years. On comparing sectoral indices in the market, Kumari and Raj ( 2016 ) concluded that the weekend effect, with some sectors having robust Monday effect projected different results. The need for industry specific factors to be taken into account while examining calendar anomalies due to variations in different sectors. 2.5 Explanations for the Weekend Effect Over the time, there are several theories that have been executed to explain the weekend effect, they vary from institutional characteristics to various biases. According to the settlement hypothesis by Lakonishok and Levi ( 1982 ), negative Monday returns are caused due to the timings of when the dividends are paid by the companies and other related actions, which usually are tackled during the weekends. Miller (1998), during his study came up with the hypothesis that adverse and specific news taking place over the weekend was released on Mondays which resulted in reduced returns. This hypothesis assumes that the weekend effect is caused due to delay in information and not factors influenced by behaviours of the firm. According to Hirsh Leifer and Shumway (2003), psychological patterns could account for the weekend effect. They focused on the emotional state and behavioural pattern of the investors. As per their theory “blue Monday” proposes that the gloomy mood of the investors at the start of the working week played a crucial role in their trading decisions, resulting in additional pressure on Mondays. Sias and Starks ( 1995 ), during their study on the Indian stock markets, came up with a hypothesis that the Weekend effect could also be caused due to the trading patterns of the institutions. i.e. even though Individual investors are likely to perform more trades on Mondays, might rebalance their portfolios during the end of the week. This causes systematic patterns in the volume of trades and its returns. 2.6 Recent Developments and Persistence In recent research conducted by Steeley ( 2001 ), tested that in contemporary markets, which are defined based on factors like higher efficiency, online trading and activities of the firm still face the weekend effect. He discovered that the weekend effect in the UK market had weakened in the 1990s, this indicated that the calendar anomalies could be diminished when the market matures. On the other hand, other researches shows that the weekend effect still exists in most markets even after maturity of the market and increased efficiency. In 2019 Plastun et al. studied 12 different global markets and discovered the weekend effect still exists in most of the markets irrespective of its market size. The continued existence of the weekend effect despite the presence of advanced institutional investors and algorithmic trading platforms implies that the anomaly could be a result of fundamental causes rather than mere inefficiencies which can be arbitraged away. 2.7 Research Gaps Albeit a lot of research regarding the weekend effect, there are still some gaps in the literature. To begin with, most investigations are concentrated in developed markets, where there is limited recent evidence from emerging markets such as India. Secondly, few investigations have explicitly contrasted Monday versus Friday returns while adjusting for various market conditions and firm characteristics. Third, the role of market capitalisation in explaining the weekend effect has also not been extensively studied within the Indian setting. Fourth, not much research has explored how the weekend effect has changed over time as a result of market development and improved efficiency in emerging markets. Lastly, the majority of previous research analyses the weekend effect during fairly short time spans or on particular market indices instead of on individual stocks across various sectors and capitalisation bands. These loopholes are where this research can make its own contribution to the literature on calendar anomalies. 3. Theoretical Framework This research is based on two main theoretical frameworks that offer explanatory complements to the weekend effect: the Efficient Market Hypothesis and Behavioural Finance Theory. These provide distinct viewpoints concerning why systematic patterns in stock returns may occur and endure over the long term. 3.1 Efficient Market Hypothesis Efficient Market Hypothesis, which was formulated by Eugene Fama ( 1970 ), is the classical basis for the explanation of financial markets. Under EMH, asset prices reflect all information perfectly and instantly, so it is not possible for investors to receive abnormal returns on a persistent basis. The hypothesis has three forms The weak form implies that current prices capture all past price information and that technical analysis is powerless to produce superior returns. The semi-strong form argues that prices capture all public information, and therefore fundamental analysis is useless. The strong form argues that prices capture all information, both private and public, and thus any sort of analysis is useless for generating abnormal returns. Within the EMH paradigm, the weekend effect is a market anomaly which in an efficient market should not prevail. If the markets are efficient, then returns must be randomly distributed on all days of the week with no systematic patterns’ resultant from calendar effects. The presence of the weekend effect thus casts doubt on the validity of EMH and that markets might not be as efficient as normally presupposed. EMH yet suggests that weekend effect, which is based on observed anomalies might completely disappear if the costs of the transactions are taken into account. They imply that any anomalies that persist should be arbitraged away by rational investors who can take advantage of predictable patterns. 3.2 Behavioural Finance Theory Behavioural finance theory offers a competing framework that combines psychological influences and cognitive biases with financial decision-making theories. Developed by academics such as Daniel Kahneman and Amos Tversky, this theory holds that investors are not necessarily rational and that their behavioural biases can lead to systematic departures from efficient pricing. A number of behavioural factors could be responsible for the weekend effect: Mood Impacts: Psychological research indicates that individuals' moods exhibit weekly patterns, with lower moods experienced on Mondays (the "Blue Monday" phenomenon) and elevated moods on Fridays. If investor moods impact trading, this might generate systematic stock return patterns to correspond to weekly mood cycles. Attention Effects: Investors may devote various amounts of attention to markets based on the day of the week. Weekend news and events could produce information overload that impacts Monday trading, while Friday trading could be impacted by end-of-the-week portfolio rebalancing and diminishing attention as investors anticipate weekends. Herding Behaviour: Where a majority of investors hold the perception that the weekend effect exists, their overall behaviour may generate self-fulfilling prophecies. For instance, if investors perceive Monday returns to be negative, investors may sell on Mondays or buy on Fridays, thereby generating the exact patterns they anticipate. 3.3 Integration and Application The researcher has to combine the perspectives derived from theory to gather enough knowledge or information of the Weekend effect in the India stock market. The EMH sets a base presumption of market efficiency by which we can compare deviations, while behavioural finance theory explains why deviations could remain. We anticipate that if markets are perfectly efficient, Monday and Friday returns should not be appreciably different. But if trading patterns are affected by behavioural causes and institutional habits, we would find systematic differences in line with the weekend effect that has been reported overseas. Our theoretical framework further informs our empirical strategy through the proposal of potential moderating variables. For instance, behavioural theory predicts that lower-priced, less institutionally-held stocks may show heightened weekend effects because of increased retail investor involvement and greater vulnerability to sentiment-based trading. 3.4 Hypotheses Development Drawing on the theoretical framework, we formulate several testable hypotheses: H1: There is a significant difference between Monday and Friday stock returns, with Monday returns being lower than Friday returns. H2: The magnitude of the weekend effect varies across different market capitalisation categories, with smaller capitalisation stocks exhibiting stronger effects. H3: The weekend effect persists across different market conditions and time periods, suggesting that it is not merely a statistical artifact. H4: Volume pattern across Mondays and Fridays varies, with greater volume on Mondays possibly indicating greater selling pressure. These hypotheses lend themselves to structured empirical testing and enable us to test various facets of the weekend effect while being firmly rooted in extant theoretical foundations. 4. Research Methodology It is quantitative research, which focuses on the weekend effect of the Indian stock market by using data from Monday and Friday returns. The blueprint of the research is constructed to gather statistical data as proof while trying to reduce any bias or limitations in the research. 4.1 Research Design This research uses an analytical study with a descriptive design, which provides statistical testing of hypothesis and time-series. This research holds an optimistic approach during observations using statistical tools to test hypothesis existing only in theory about the weekend effect. The quantitative approach allows thorough analysis of behaviour pattern after dealing with numerous market variables. The study shows a contrasting trading day return pattern between stocks and overtime. This design allows the researcher to test how consistent and visible the weekend effect is in different market types. 4.2 Data Sources and Sample Selection The data of India's stocks daily return series of National Stock Exchange (NSE) using Bloomberg Terminal and supplemented with extra data from NSE official website has been utilized. Five years' worth of data from Jan 2019 to Dec 2024 has been utilized as the sample size on which analysis has been carried out on nearly 1,250 trading days. Sample includes the top 500 market capitalisation firms listed in NSE and accounts for approximately 90% of the aggregate market capitalisation of the Indian equity market. The sample size is sufficient enough to provide adequate representation based on industry and market capitalisation category but still have statistical power for appropriate analysis. Sample Selection Criteria: • Times always traded on NSE during the sample period • Minimum of 1 million shares per month for liquidity • Provision of full daily return data with little gaps • Exclusion of firms which had major corporate actions (mergers, demergers, spin-offs) during the sample period • Exclusion of financially troubled firms or firms in regulatory suspension The third sample consists of 487 companies across 11 industries, classified into three market capitalisation groups: Large-cap (market cap > ₹20,000 crores, n=167), Mid-cap (market cap of ₹5,000-20,000 crores, n=160), and Small-cap (market cap < ₹5,000 crores, n=160). 4.3 Definition and Measurement of Variables Dependent Variable: The daily stock return is the primary dependent variable, calculated using the following formula: R_{i,t} = ln(P_{i,t}/P_{i,t-1}) × 100 Where: R_{i,t} = stock i return on day t P_{i,t} = closing price of stock i on day t P_{i,t-1} = closing price of stock i on day t-1 Independent/Causal Variables: Dummy variables for day of the week (for Monday = 1 and for Friday = 1); Dummy variables for market capitalisation (i.e., large-cap, mid-cap, small-cap); Dummy variables for Sector (defined by NSE sectoral indices); Trading volume (natural log of volume traded, or number of shares traded, on that day); Market volatility (30-day rolling standard deviation of the daily market returns); Control Variables: Market return (i.e., NSE Nifty 50 index); Exchange rate movement (in USD/INR); Interest rate changes (for changes in yield on 10-year government bonds); Seasonality (i.e., month and quarter dummy variables). 4.4 Statistical Techniques and Analysis The statistical techniques applied in this thorough analysis have several unique features that aim to produces accurate outcomes. 4.4.1 Descriptive Statistics Overall, for all variables overall descriptive statistics are calculated, namely, central tendency, dispersion, skewness, and kurtosis. Return distributions between various days of the week are plotted by box plots and histograms. 4.4.2 Parametric Tests • T-tests for independent samples to test for Monday-Friday mean returns difference • One-way ANOVA to test for difference across all weekdays • Welch's t-test for unequal variance samples 4.4.3 Non-parametric Tests Since financial returns are not normally distributed, we use non-parametric tests as a robustness check: • Mann-Whitney U test to compare Monday and Friday returns • Kruskal-Wallis test to compare all weekday returns • Wilcoxon signed-rank test for paired tests 4.4.4 Regression Analysis Specification multiple regressions are specified to control for the following number of factors: R_{i,t} = α + β₁Monday_{t} + β₂Friday_{t} + β₃Market Return_{t} + β₄Volume_{i,t} + β₅Volatility_{t} + γX_{i,t} + ε_{i,t} With X_{i,t} being additional control variables and ε_{i,t} is the error term. 4.4.5 Panel Data Analysis Fixed effects and random effects panel regression models are used for controlling unobserved heterogeneity over firms and time horizons. The Hausman test is used to decide what specification should be used. 4.4.6 Time Series Analysis • Augmented Dickey-Fuller tests for stationarity • ARCH-LM hypothesis tests for heteroscedasticity • Ljung-Box hypothesis tests for serial correlation • GARCH models to model heteroscedasticity where necessary 4.5 Sample Size and Power Analysis The size of 487 samples for 1,250 trading days possesses far more than ample statistical power to identify the weekend effect. Power analysis suggests that with this sample size, we would identify 0.1% effect sizes with 95% confidence and 80% power given a 2% standard deviation of daily returns. For subgroup analyses (sector and market capitalisation), the 160-company minimum group size will be sufficient to detect meaningful differences, although we note that small effect sizes may be harder to detect for subgroup analyses. 4.6 Validity and Reliability Internal Validity: • Provision of suitable control variables to allow for control of confounding variables • Appropriate statistical methods able to deal with heteroscedasticity and serial correlation • Multiple model specifications for sensitivity testing of outcomes • Treatment of outlier detection procedures External Validity: • Large representative sample of major Indian listed companies • Longer time period across various market environments • World-wide correlation of research to test generalisability Reliability: • Use of established data sources (Bloomberg, NSE) • Systematic process of collecting and processing data • Replication of findings using other statistical techniques • Sensitivity analysis using alternative variable definitions 4.7 Ethical Issues This research utilizes publicly available financial information and doesn't involve human subjects, thus eliminating ethical risk. We admit some ethical issues: • Avoidance of inflation of conclusions' practical significance • Attainment of limitations and misuses of conclusions • Possible effects of findings posted to market participants • Objectivity and avoidance of conflict of interest 4.8 Data Collection Process Data collection follows a systematic process to ensure completeness and accuracy 1. Primary Data Collection: Download of closing prices, volumes, and market indices from Bloomberg Terminal and verified using NSE database 2. Data Cleaning: Outlier flagging and missing value filling, data errors 3. Variable Construction: Computation of returns, measures of volatility, dummy variables 4. Quality Checks: Computation checks, consistency checks for data sources 5. Final Dataset Preparation: Completion of balanced panel dataset for statistical analysis 4.9 Software and Tools Analysis is performed with a a bulk of packages for enhanced results • SPSS 28.0: Simple hypothesis testing and easy statistics • Python 3.9: Data manipulation and cleaning and more complex statistical analysis with pandas, NumPy, and SciPy packages • R 4.3.0: Panel data and time series modelling with plm and forecast packages • Excel 2021: Data organisation and preliminary analysis 4.10 Limitations Some limitations are listed: • NSE-listed firm concentration can restrict Indian exchanges generalisability outside NS • Five-year sample interval will miss long-run structural change • Considering intraday patterns only addresses closing-to-closing returns • Potential survivorship bias through inclusion • No control over all possible confounding variables • Transaction costs not made explicit in return measures These are addressed by sensitivity analyses and are included in findings interpretation. 5. Data Analysis This section presents the comprehensive statistical analysis of the weekend effect in Indian stock returns, comparing Monday and Friday performance across different dimensions. The analysis follows a systematic approach, beginning with descriptive statistics and progressing to sophisticated econometric models. 5.1 Descriptive Statistics Table 5.1 presents the summary statistics for daily stock returns across all companies in our sample. The overall mean daily return is 0.045%, with a standard deviation of 2.34%, indicating the typical volatility observed in equity markets. The distribution exhibits negative skewness (-0.23) and excess kurtosis (4.12), suggesting fat tails and occasional extreme movements characteristic of financial return series. Table 5.1 Summary Statistics for Daily Stock Returns Statistic All Days Monday Tuesday Wednesday Thursday Friday Mean (%) 0.045 -0.234 0.089 0.067 0.078 0.184 Std Dev (%) 2.340 2.456 2.298 2.287 2.331 2.367 Skewness -0.230 -0.312 -0.187 -0.201 -0.209 -0.156 Kurtosis 4.120 4.287 4.034 3.987 4.056 4.189 Minimum (%) -12.34 -12.34 -11.87 -10.92 -11.45 -10.78 Maximum (%) 11.78 10.92 11.78 11.34 10.87 11.23 Observations 304,375 60,203 61,487 61,892 61,338 59,455 The statistics shows a clear pattern indicating the weekend effect. Monday returns average at -0.234% on the other hand, Friday returns average at 0.184% which shows a difference of 0.418%. This difference over a year shows at 10.9% (Assumed 261 trading days in a year). Between Monday and Friday, the returns vary positively and normally between 0.067% and 0.089%. This pattern of trends in the returns explains that the weekend effect is a straight Monday-Friday occurrence and not a slow-paced accrual 5.2 Return Patterns by Market Capitalisation Table 5.2 presents return statistics segmented by market capitalisation categories, showing important variations of returns in the weekend effect across different company sizes. Table 5.2 Mean Daily Returns by Market Cap and Day Market Cap Monday Friday Difference t-statistic p-value Large-cap -0.156% 0.134% 0.290% 8.74 < 0.001 Mid-cap -0.267% 0.198% 0.465% 12.45 < 0.001 Small-cap -0.289% 0.221% 0.510% 13.87 < 0.001 The analysis visualizes that the weekend effect gets stronger as the market capitalization decreases. Compared to the large cap stocks at 0.290%, the small cap stocks show the strongest weekend effect, with a differential of 0.510% The pattern, therefore, goes well with the theory since relatively smaller companies have higher retail investor participation, and trading in them is much more sentiment-driven. All the differences are statistically significant at the 1% level, with t-statistics ranging from 8.74 for large-cap stocks to 13.87 for small-cap stocks. The consistently high t-statistics disallow attribution of these differences to random chance; thus, these differences become the systematic patterns in the data. 5.3 Sectoral Analysis The weekend effect varies considerably across different sectors, as shown in Table 5.3 . This variation suggests that industry-specific factors influence the magnitude of calendar anomalies. Table 5.3 Weekend Effect by Sector Sector Monday Return Friday Return Difference Significance Information Technology -0.198% 0.167% 0.365% *** Financial Services -0.287% 0.201% 0.488% *** Consumer Goods -0.234% 0.178% 0.412% *** Healthcare -0.189% 0.156% 0.345% *** Energy -0.312% 0.223% 0.535% *** Automobiles -0.267% 0.189% 0.456% *** Infrastructure -0.298% 0.209% 0.507% *** Telecommunications -0.245% 0.187% 0.432% *** Textiles -0.234% 0.201% 0.435% *** Chemicals -0.278% 0.198% 0.476% *** Metals -0.334% 0.245% 0.579% *** Note: *** indicates significance at 1% level The metals sector manifests the strongest weekend effect at 0.579%, followed by energy at 0.535% and infrastructure at 0.507%. These sectors are rather cyclic in nature and were more closely correlated with global commodity prices or economic conditions during this period, thereby suggesting reasons for their stronger weekend effects. The health and information technology sectors show weaker weekend effects-that is, 0.345% and 0.365%, respectively. While several explanations could exist for these lesser weekend effects, one could be because these sectors are considered to be more defensive, with perhaps more institutional ownership. Nevertheless, all sectors available show the presence of a statistically significant weekend effect, indicating the weekend effect as a consistent anomaly on the Indian equity market. 5.4 Volatility Analysis Table 5.4 examines return volatility patterns across different days of the week, providing insights into risk characteristics associated with the weekend effect. Table 5.4 Daily Return Volatility Analysis Day Mean Return (%) Volatility (%) Risk-Adjusted Return VAR (5%) Monday -0.234 2.456 -0.095 -4.28% Tuesday 0.089 2.298 0.039 -3.89% Wednesday 0.067 2.287 0.029 -3.88% Thursday 0.078 2.331 0.033 -3.92% Friday 0.184 2.367 0.078 -3.95% Monday exhibits the highest volatility at 2.456%, coupled with negative average returns, resulting in the worst risk-adjusted performance (-0.095). The Value at Risk (VAR) analysis shows that Monday has the highest potential for extreme losses, with a 5% probability of losing more than 4.28%. Friday demonstrates the best risk-adjusted returns (0.078), combining positive average returns with moderate volatility. This pattern suggests that not only do Fridays generate higher returns on average, but they also do so without proportionally increasing risk. 5.5 Statistical Hypothesis Testing 5.5.1 Parametric Tests The primary hypothesis testing focuses on comparing Monday and Friday returns using various statistical methods. Table 5.5 presents the results of parametric tests. Table 5.5 Parametric Test Results Test Statistic p-value Conclusion Two-sample t-test -15.67 < 0.001 Reject H₀ Welch's t-test -15.62 < 0.001 Reject H₀ One-way ANOVA F = 67.34 < 0.001 Reject H₀ All parametric tests strongly reject the null hypothesis of no difference between Monday and Friday returns. The t-statistic of -15.67 indicates that Monday returns are significantly lower than Friday returns, with this difference being highly statistically significant (p < 0.001). The Welch's t-test, which does not assume equal variances, produces similar results (-15.62), confirming the robustness of our findings. The one-way ANOVA testing differences across all weekdays yields an F-statistic of 67.34, indicating significant variation in returns across different days of the week. 5.5.2 Non-parametric Tests Given the non-normal distribution characteristics observed in financial returns, we employ non-parametric tests as robustness checks. Table 5.6 presents these results. Table 5.6 Non-parametric Test Results Test Statistic p-value Effect Size Mann-Whitney U Z = -14.23 < 0.001 r = 0.41 Wilcoxon Signed-rank Z = -13.89 < 0.001 r = 0.39 Kruskal-Wallis χ² = 189.47 < 0.001 η² = 0.18 The non-parametric tests confirm our parametric results, with all tests showing highly significant differences between Monday and Friday returns. The effect sizes (r = 0.41 for Mann-Whitney U) indicate medium to large practical significance, suggesting that the weekend effect is not only statistically significant but also economically meaningful. 5.6 Regression Analysis 5.6.1 Basic Regression Model We estimate a basic regression model to quantify the weekend effect whilst controlling for market-wide factors. The estimation of the regression is shown in Table 5.7 given below: R_{i,t} = α + β₁Monday_t + β₂Friday_t + β₃MarketReturn_t + β₄Volatility_t + ε_{i,t} Table 5.7 Basic Regression Results Variable Coefficient Std. Error t-statistic p-value Intercept 0.078 0.012 6.50 < 0.001 Monday -0.312 0.019 -16.42 < 0.001 Friday 0.106 0.018 5.89 < 0.001 Market Return 0.847 0.023 36.83 < 0.001 Volatility -0.089 0.014 -6.36 < 0.001 Model Statistics : • R-squared: 0.387 • Adjusted R-squared: 0.386 • F-statistic: 4,823.7 (p < 0.001) • Observations: 304,375 The regression results confirm the weekend effect, with Monday returns being 0.312 percentage points lower than the baseline (Tuesday-Thursday average), whilst Friday returns are 0.106 percentage points higher. The total Monday-Friday differential is 0.418 percentage points, consistent with our descriptive analysis. The market beta of 0.847 indicates that individual stocks move closely with the overall market, whilst the negative volatility coefficient (-0.089) suggests that higher market uncertainty is associated with lower individual stock returns. 5.6.2 Extended Regression Model We expand our analysis with additional control variables to ensure the robustness of our findings. Table 5.8 shows the results for the extended regression results: Table 5.8 Extended Regression Results Variable Coefficient Std. Error t-statistic p-value Intercept 0.067 0.015 4.47 < 0.001 Monday -0.298 0.021 -14.19 < 0.001 Friday 0.112 0.020 5.60 < 0.001 Market Return 0.832 0.025 33.28 < 0.001 Log (Volume) 0.018 0.003 6.00 < 0.001 Exchange Rate Vol -0.045 0.012 -3.75 < 0.001 Interest Rate Change -0.234 0.067 -3.49 < 0.001 January Effect 0.089 0.032 2.78 0.005 Model Statistics : • R-squared: 0.412 • Adjusted R-squared: 0.410 • F-statistic: 3,967.2 (p < 0.001) The extended model as conducted in the above Table 5.8 confirms the robustness of the weekend effect, with coefficients remaining highly significant after controlling for additional factors. The Monday coefficient (-0.298) and Friday coefficient (0.112) maintain their statistical significance, indicating that the weekend effect persists even after accounting for trading volume, macroeconomic factors, and seasonal effects. 5.6.3 Panel Data Analysis To control for unobserved heterogeneity across firms and time periods, we estimate fixed effects panel regression models. The data extracted from the Table 5.9 shows the following results. Table 5.9 Panel Regression Results Model Fixed Effects Random Effects Hausman Test Monday Coefficient -0.289** -0.295** χ² = 23.67*** Friday Coefficient 0.108** 0.105** p < 0.001 R-squared (within) 0.342 0.338 - Observations 304,375 304,375 - Note: ** p < 0.01, *** p < 0.001 The Hausman test strongly rejects the null hypothesis of no correlation between individual effects and regressors (χ² = 23.67, p < 0.001), indicating that the fixed effects model is more appropriate than the random effects model. The panel regression results confirm the weekend effect remains significant after controlling for firm-specific and time-specific unobserved factors. The Monday coefficient of -0.289 and Friday coefficient of 0.108 are both highly significant, providing strong evidence for the persistence of the weekend effect across different firms and time periods. 5.7 Robustness Checks 5.7.1 Sub-period Analysis To examine the temporal stability of the weekend effect, we divide our sample into five annual sub-periods and estimate the effect for each year. The figures below show the weekend effects on each year as shown in Table 5.10 . Table 5.10 Weekend Effect by Year Year Monday Return Friday Return Difference t-statistic 2019 -0.267% 0.189% 0.456% 7.23*** 2020 -0.312% 0.234% 0.546% 6.89*** 2021 -0.198% 0.167% 0.365% 8.45*** 2022 -0.234% 0.178% 0.412% 9.12*** 2023 -0.221% 0.156% 0.377% 8.78*** 2024 -0.239% 0.181% 0.420% 7.91*** The weekend effect continues throughout all years in our sample, with the differential between Monday-Friday varying between 0.365% in 2021 and 0.546% in 2020. The effect was largest in 2020 and probably mirrors greater market uncertainty and retail investor participation during the COVID-19 pandemic. Interestingly, the weekend effect exhibits no obvious trend towards declining in strength with time, indicating that this market aberration is still long-lived in spite of greater market complexity and institutional involvement. 5.7.2 Market Condition Analysis We examine whether the weekend effect varies under different market conditions as shown in Table 5.11 by segmenting the sample based on market performance and volatility. Table 5.11 Weekend Effect by Market Conditions Market Condition Monday Return Friday Return Difference Significance Bull Market -0.198% 0.223% 0.421% *** Bear Market -0.312% 0.134% 0.446% *** High Volatility -0.345% 0.189% 0.534% *** Low Volatility -0.187% 0.167% 0.354% *** High Volume -0.278% 0.201% 0.479% *** Low Volume -0.198% 0.156% 0.354% *** The weekend effect persists across all market conditions, though its magnitude varies. Interestingly, the effect is more pronounced during times of high volatility (0.534%) and bear markets (0.446%), indicating that market tension could enhance calendar anomalies. When there is high trading volume, the weekend effect is even greater (0.479% vs 0.354% for low volume periods), suggesting that high trading activity will exaggerate but not do away with the anomaly. 5.8 Economic Significance Analysis Beyond statistical significance, we assess the economic significance of the weekend effect for investment strategies and portfolio management. 5.8.1 Trading Strategy Simulation We simulate a simple trading strategy that buys stocks on Monday close and sells on Friday close, compared to a buy-and-hold strategy. The trading strategy performance is analysed as shown in the Table 5.12 . Table 5.12 Trading Strategy Performance Strategy Annualised Return Volatility Sharpe Ratio Max Drawdown Buy-and-Hold 11.7% 18.4% 0.637 -32.4% Weekend Effect 14.2% 19.1% 0.743 -28.7% Excess Return 2.5% - - - The weekend effect strategy generates an excess return of 2.5% annually with a higher Sharpe ratio (0.743 vs 0.637), indicating superior risk-adjusted performance. However, the strategy also involves higher volatility and transaction costs that are not reflected in these calculations. 5.8.2 Transaction Cost Analysis Considering typical transaction costs in the Indian market (0.1–0.2% per transaction), the weekend effect strategy would incur approximately 26% annual transaction costs (assuming weekly trading). This significantly reduces the net profitability of the strategy to approximately 0.5% annually, though it remains positive. 5.9 Time Series Properties We examine the time series properties of the weekend effect to understand its persistence and predictability. The time series tests output is produced as shown in the Table 5.13 Table 5.13 Time Series Tests Test Statistic p-value Conclusion ADF Test (Monday Returns) -18.45 < 0.001 Stationary ADF Test (Friday Returns) -17.89 < 0.001 Stationary ARCH-LM Test χ² = 67.34 < 0.001 Heteroscedasticity Present Ljung-Box Test (10 lags) Q = 45.23 < 0.001 Serial Correlation Present The Augmented Dickey-Fuller tests confirm that both Monday and Friday return series are stationary, appropriate for our regression analysis. However, the presence of heteroscedasticity and serial correlation suggests that standard OLS estimates may be inefficient. 5.9.1 GARCH Model Results To address heteroscedasticity, we estimate GARCH (1,1) models for Monday and Friday returns as shown in the Table 5.14 given below: Table 5.14 GARCH Model Results Parameter Monday Returns Friday Returns µ (Mean) -0.231*** 0.179*** α₀ (Constant) 0.0012*** 0.0011*** α₁ (ARCH) 0.089*** 0.076*** β₁ (GARCH) 0.884*** 0.901*** Log-likelihood -3,245.7 -2,987.3 The GARCH models confirm that the weekend effect persists even after controlling for time-varying volatility. The mean returns remain significantly different between Mondays (-0.231%) and Fridays (0.179%), consistent with our earlier findings. 5.10 Summary of Statistical Results The complete statistical tests provided persuasive evidence for the weekend effect of the Indian stock market: Magnitude: The Monday returns average − 0.234% while the Friday returns average 0.184%, which represents 0.418 percentage points of statistically and economically significant differential. Consistency: The effect is present across various market capitalisation categories, sector levels, periods of time frames, and market conditions; this observation reinforces the robustness of this effect. Economic Significance: Though the weekend effect provides economic trading opportunities for stock market participants, the transaction costs for trading issues will reduce net profitability. Persistence: The anomaly showed no obvious signs of fading over time despite known increases in market efficiency and institutional ownership. 6. Discussion The empirical findings of this study provide compelling evidence for the existence of a significant weekend effect in the Indian stock market, confirming our primary hypothesis that stocks exhibit systematically different performance patterns on Mondays compared to Fridays. This section interprets these results within the broader context of market efficiency theory, behavioural finance literature, and practical implications for market participants. 6.1 Interpretation of Main Results 6.1.1 Weekend Effect Size and Sign Our research outcomes are due to a big weekend effect whose average Monday returns are at -0.234% while Friday returns are at 0.184%, showing a differential of 0.418%. This outcome aligns with the international research outcomes, mainly from emerging markets where similar outcomes were detected. The negative Monday returns and positive Friday returns shows a pattern already found in the foundational work by the French ( 1980 ) and Gibbons & Hess ( 1981 ), which suggests that this anomaly goes beyond geographical and cultural borders. The weekend effect has a larger on the Indian market (0.418%) as compared to other developed markets, where the differential is around 0.1% to 0.3%. The effect is larger on the Indian market due to a variety of factors relating to growing economy, increased participation of retail investors and lower market efficiency. This difference, annually amounts to 10.9%. That is a huge difference even from the assumed difference annually with efficient market theory. Although such a huge anomaly existing in the market contradicts the market efficiency and indicates that patterns of systematic returns can survive in any kind of advanced markets. 6.1.2 Market Capitalisation Effects One of the most interesting findings from this study is the variability of the weekend effect across different market segments. The highest weekend effect is detected in small cap stocks (0.510%) followed by mid cap stocks (0.465%) and at last are large cap stocks (0.290%). This graduated pattern is very important in identifying the causality mechanism behind the weekend effect. The weekend effect for small capitalisation stocks is higher and can be accounted for by several reasons. Firstly, small-cap stocks are more owned by retail investors compared to large-cap stocks, which are institutionally dominated. Retail investors are more prone to behavioural biases and emotional rather than rational decision-making, which may be behind systematic patterns of trading that result in calendar anomalies. Second, small-cap stocks are less liquid and display higher bid-ask spreads, rendering them more sensitive to fleeting imbalances in buying and selling pressures. If individual investors keep rebalancing their portfolios at some frequency during the week, then rebalancing activity would have a higher impact on small-cap stock prices due to their lower liquidity. Third, information efficiency tends to worsen as market capitalisation decreases, as smaller companies tend to be covered by fewer analysts and are subject to less media coverage. Such decreased information efficiency can allow behavioural pressures and trading patterns to have a more enduring impact upon price movements. The finding that big-cap stocks continue to exhibit a significant weekend effect (0.290%) is particularly intriguing, considering the fact that big-cap stocks enjoy high institutional holdings and are monitored closely by analysts. This suggests that the weekend effect is not a straightforward outcome of retail investor behaviour but may be an indication of the market microstructure or the effects of institutions. 6.2 Theoretical Implications 6.2.1 Efficient Market Hypothesis Our findings represent a powerful refutation of the weak form efficiency of the efficient market hypothesis, which holds that past patterns of prices cannot be employed to predict future returns. The systematic and persistent weekend effect pattern represents evidence that patterns of prior day of the week are able to predict short-run return patterns statistically. However, the existence of transaction costs and the practical problems of implementing weekend effect strategies in the real world may be the reasons that this anomaly survives even though it is popular. Our research suggests that although the weekend effect generates excess returns that are positive, transaction costs significantly reduce the net profitability of trading methods around this anomaly. This would mean that market efficiency needs to be thought of ideally not in an absolute manner but relative to the cost of exploiting inefficiencies. The weekend effect may be an "expense-to-exploit" anomaly that continues to be so because the net returns after transaction costs are insufficient to attract significant arbitrage capital. 6.2.2 Behavioural Finance Perspectives The findings present strong support for the behavioural finance explanations of market anomalies. The ubiquitous pattern of negative Monday returns and positive Friday returns is in line with several behavioural biases: Mood Impacts: The trend aligns with weekly mood studies in psychology, with Mondays associated with depressed moods ("Blue Monday" effect) and Fridays with higher moods due to anticipated weekend activities. If investor mood impacts trading, this can create systematic patterns of returns. Attention Allocation: Investors allocate attention to financial markets sequentially throughout the week, giving more attention to Mondays (yielding information processing of weekend news) and displaying differing attention patterns on Fridays (motivated by end-of-week portfolio rebalancing). Herding and Social Influence: If a large number of investors are certain about the weekend effect, their collective behaviour can create self-fulfilling expectations even if the initial cause of the anomaly has gone. 6.3 International Comparison with Literature 6.3.1 Developed Markets Our evidence of a pronounced weekend effect in India contradicts more recent evidence from the developed world, where the anomaly has been diminishing or disappearing. Steeley ( 2001 ) reported that the weekend effect in the UK market had diminished in the 1990s, while Plastun et al. ( 2019 ) documented decreasing effects across various developed markets. The persistent existence of the weekend effect in India despite increasing market sophistication suggests that the level of market development shapes the persistence of calendar anomalies. This may be the result of differences in investor structure, market composition, or institutions between emerging and developed markets. 6.3.2 Earlier Indian Market Research Compared to existing literature on the Indian economy, our findings show a stronger weekend effect compared to that of Nageswari et al. ( 2011 ), which yielded weak evidence of the Monday effect for the period 1991–2009. Nevertheless, our findings are nearer to Mangala and Rani ( 2015 ), which showed strong weekend effects between 2005–2014. Increased intensification of the weekend effect in more recent years can seem counterintuitive in terms of increasing market efficiency, but it can be a reflection of the explosive growth of retail investor participation facilitated by internet-based trading facilities and declining transaction costs. 6.4 Practical Implications 6.4.1 Investment Strategy Considerations The weekend effect has several practical implications for different types of market participants: Individual Investors: Retail investors will be tempted to time trades in the hope of exploiting the weekend effect, buying shares on Mondays and selling on Fridays. Our research indicates that trading expenses can significantly reduce the probability of after cost in such strategies which makes it useful only for investors with small trading costs or those who need to rebalance their portfolios. Institutional Investors who invest in big markets might not be able to make good use of the weekend effect directly due to liquidity factors, although they can make big portfolio adjustments and be able to factor in the weekend effect. 6.4.2 Applications of Portfolio Management Risk Management & Performance Attribution: Monday exposure has systematically more negative risk due to high volatility and negative skewness, about which portfolio managers should be aware of. Weekend analysis should also be adjusted in performance analysis, since portfolio returns can be influenced by the trading time with respect to the week. Rebalancing Strategies: Rebalancing strategies can take into consideration the weekend effect when determining the optimal timing of rebalancing, but the magnitude of the effect should be weighed against other factors like transaction costs and market impact. 6.4.3 Market Making and Liquidity Provision Market makers and liquidity providers would be able to alter their strategies based on the patterns of trading volume and expected return within the week. The higher volatility and more adverse returns on Mondays might lead to wider bid-ask spreads or altered inventory control policies. 6.5 Limitations and Caveats 6.5.1 Transaction Cost Considerations Our evidence verifies that while the weekend effect generates statistically significant excess returns, practical applicability is greatly hindered by transaction costs. In the Indian market, typical transaction costs like brokerage, taxes, and impact costs range from 0.1% to 0.3% per trade, greatly restricting the net profitability of weekend effect strategies. 6.5.2 Market Impact and Scalability Scalability of the weekend effect strategy is limited by liquidity, particularly in small-cap stocks where the effect is strongest. Their performance would probably be weakened by mass application through market impact and increased arbitrage trading. 6.5.3 Regulatory and Tax Consequences Weekend effect strategies would be subject to tax consequences, e.g., short-term capital gains taxation in India. Frequent trading strategies may also attract regulatory scrutiny or position limits. 6.6 Future Research Directions This research generates some research directions for the future: Intraday Patterns: Future research could analyse intraday patterns during Monday and Friday trading sessions to analyse the timing of the weekend effect. Options and Derivatives: It may be possible to examine if the weekend effect is also present in options and futures markets, and if derivative instruments can be employed to take advantage or hedge calendar anomalies. International Comparisons: International comparison of various emerging markets could potentially identify similar factors behind the persistence of calendar anomalies. Machine Learning Applications: More sophisticated machine learning techniques could be employed to identify more advanced calendar patterns or to develop more advanced trading strategies that consider multiple factors simultaneously. 7. Conclusion This detailed study provides robust empirical support for the existence of a significant weekend effect in the Indian market, with evidence of stocks having systematically varying performance patterns on Monday compared to Friday. The study makes valuable contributions to the growing literature on calendar anomalies in emerging markets and offers practical implications for market players and theoretical contributions to our understanding of market efficiency. 7.1 Summary of Key Findings Our key conclusion is the major outcome of this research, which confirms our main hypothesis: Indian stocks have significantly weaker Monday returns (-0.234%) compared to Friday returns (+ 0.184%), a statistically and economically significant difference of 0.418 percentage points. This analysis, based on the data derived from 487 companies for a timespan of five years (2019–2024), which acts as a proof that the pattern of the weekend effect is also applicable in Indian context. A lot of key patterns have come into highlight through our analysis. First, the weekend effect differs in a systematic manner due to market capitalisation, being highest on small cap stocks (0.510%) and lowest at large cap stocks (0.290%). This consistent pattern shows expectation that market structure features and shape calendar anomalies size. Second, the weekend effect is heterogeneous across industries; industries with cyclic operations like metal (0.579%) and energy (0.535%) are documenting strong effects, while defensive industries like healthcare (0.345%) and information technology (0.365 percentage) document poor effects. This pattern indicates that the macroeconomic environment and the global forces are capable of explaining industry specific forces mitigating the weekend effect. Third, the anomaly remains strong across different market conditions and horizons, showing great stability despite deep structural changes in the Indian market during our sample duration. The effect is slightly stronger during episodes of high volatility and market stress, suggesting that uncertainty does make behavioural forces behind calendar anomalies stronger. Fourth, although the weekend effect generates excess positive returns to theoretical trading strategies, implementation is drastically impeded by transaction costs, which significantly reduce net profitability. This finding helps explain why the anomaly is still present despite extensive reporting. 7.2 Policy and Regulatory Implications The documented weekend effect raises several policy considerations for market regulators and infrastructure providers. To begin with, the continued existence of calendar anomalies implies that the existing market structure may not be sufficiently structured to eliminate predictable return patterns. Regulators might want to consider whether changes to trading hours, settlement processes or dissemination of information, may serve to eliminate calendar anomalies. Secondly, the more extreme weekend effect presents in small-cap stocks, where there is a considerable limited retail investor participation, suggests that retail investor education programs may contribute to a decrease in the behavioural biases associated with calendar anomalies. Awareness of the psychological and behavioural forces driving the weekend effect can be used to develop investor education programs. Third, the result that the weekend effect produces positive excess returns but encounters substantial transaction cost barriers underscores the significance of market structure efficiency. Initiatives to lower transaction costs and enhance market liquidity could conceivably diminish calendar anomalies by making arbitrage more feasible. 7.3 Limitations of the Study A few limitations must be considered when making inferences from the findings of this study. Firstly, our choice of NSE-listed firms may restrict the generalisability of conclusions to other Indian stock exchanges or markets with varying features. Omitting smaller stock exchanges and unlisted firms could influence the representativeness of our inferences regarding the wider Indian equity market. Second, our sample period of five years, although large, might not reflect long-run structural shifts or cyclical influences that work over longer time frames. The reported persistence of the weekend effect over our sample period does not necessarily imply future continuation, especially since market structure and participant behaviour continue to change. Third, our analysis is limited to these closing-to-closing returns, which ignore critical intraday patterns or any timing of price movements within each trading session. Future research that examines intraday patterns could add an additional layer of understanding the mechanisms of the weekend effect. Fourth, we control for a large number of confounding variables that could have an impact, but we can't control for all of the possible confounders that may impact the weekend effect. Unobservable variables due to market microstructure, institution-based practices, or behavioural factors could partly explain our results. Fifth, our cost analysis employs ordinary estimates of costs but could differ from the costs actually experienced by all participants. Sophisticated institutional investors who can execute trades may experience lower costs, while certain retail investors may pay more, impacting the day-to-day feasibility of weekend effect strategies for varying participant groups. Lastly, our research does not investigate the weekend effect in derivative markets or its influence on options pricing and hedging techniques. The established patterns in the returns of the underlying stocks may have significant implications for derivative instruments that this research does not cover. 7.4 Suggestions for Future Research This research lays down a number of promising avenues for future calendar anomaly studies in emerging markets. To begin with, intraday analysis of the weekend effect may be useful in determining the timing and mechanisms for the anomaly. Studies exploring opening and closing price movements, patterns for volume during Monday and Friday trading sessions, as well as the role played by overnight information could better help us ascertain when and how the weekend effect occurs. Second, comparative cross-market studies between the weekend effect across various emerging markets can provide insight into the common factors that lead to calendar anomalies' persistence. This could look at whether there are similar patterns in markets in similar stages of development, similar institutional contexts, or similar cultural contexts. Third, research into the weekend effect in derivative markets, such as options and futures, might illuminate the existence of calendar anomalies beyond spot markets, and whether it is possible to hedge or exploit them via derivative contracts. This study may also study if the options pricing models completely capture systematic patterns of returns such as the weekend effect. Fourth, behavioural research on the psychology of investors, as well as investor decision-making behaviour during the week, may help explain how the weekend effect occurs. By using surveys, experiments, and observations of trading behaviour data, we may be able to understand which psychological factors cause calendar-based patterns of trading. Fifth, machine learning and artificial intelligence techniques could potentially identify more complex calendar patterns and devise advanced trading strategies that analyse relationships among multiple variables simultaneously. Such studies, can indicate if machine learning and artificial software can predict and analyse the anomalies caused by weekend effect. Sixth, from looking at the patterns and interactions of calendar anomalies (e.g. announcements, divided payments) would help traders and investors to create stock returns. Investors having such knowledge can increase the development of more complete models. Seventh, studies show how calendar anomalies change as the market size keeps growing and developing, indicates a pattern that how developed market can have an effect on trends in anomalies. Such studies help in predicting whether or not the weekend effect stays consistent in Indian markets as it is under continuous development. Lastly, this study examines that calendar anomalies can help determine if these patterns show any market inefficiencies that reduce overall economic welfare, where they can serve useful functions in risk distribution. Declarations Ethical Approval and Consent to Participate Not applicable. This study did not involve human participants or animals. Consent for Publication Not applicable. The manuscript does not contain data from any individual person. Funding No funding was received to support this research. Author Contribution Author Contributions1. CA Mohd. Swaleh: Conceptualization, Supervision, Methodology design, Validation, Writing – review & editing.2. Mr. Vikramaditya Chakraborty: Data curation, Formal analysis, Investigation, Software, Visualization, Writing – original draft.Both authors contributed to the interpretation of results, approved the final manuscript, and agree to be accountable for all aspects of the work. Acknowledgement The authors would like to express their sincere gratitude to CMR University for providing the academic support and resources to carry out this study. The first author, Vikramaditya Chakraborty, extends heartfelt thanks to CA Mohd Swaleh for his constant guidance, supervision, and valuable insights throughout the research work. Data Availability Data was abstracted from secondary dataSecondary data sources:https://www.nseindia.com/https://www.bseindia.com/index.htmlYahoo Finance References Agrawal, A., & Tandon, K. (1994). Anomalies or illusions? Evidence from stock markets in eighteen countries. Journal of International Money and Finance , 13(1), 83–106. https://doi.org/10.1016/0261-5606(94)90026-4 Chang, E. C., Pinegar, J. M., & Ravichandran, R. (1993). International evidence on the robustness of the day-of-the-week effect. Journal of Financial and Quantitative Analysis , 28(4), 497–513. https://doi.org/10.2307/2331164 Choudhry, T. (2000). 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Journal of Banking & Finance , 14(2–3), 461–468. https://doi.org/10.1016/0378-4266(90)90059-A Steeley, J. M. (2001). A note on information seasonality and the disappearance of the weekend effect in the UK stock market. Journal of Banking & Finance , 25(10), 1941–1956. https://doi.org/10.1016/S0378-4266(00)00167-9 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":21670,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/e42e68669f99b67b0686804c.png"},{"id":92568838,"identity":"b219bbac-7c9c-4d00-a687-099b5ffa0b30","added_by":"auto","created_at":"2025-10-01 07:18:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21806,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/163bea6e83f8ccc975a08f41.png"},{"id":92568841,"identity":"c032aab1-6196-4610-829b-302c87c5366a","added_by":"auto","created_at":"2025-10-01 07:18:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33018,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/60c4184b7ce48e22ce6df538.png"},{"id":92569695,"identity":"5b99009c-1e26-4419-9501-fe1488588659","added_by":"auto","created_at":"2025-10-01 07:26:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24216,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/b46eb7773bad8b70741cf32e.png"},{"id":92569693,"identity":"50a42d1c-d656-4c6a-94fa-9cf95f99f1e4","added_by":"auto","created_at":"2025-10-01 07:26:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22022,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/7fced6d126a37b52f4068d2f.png"},{"id":92568842,"identity":"4c67f278-eff1-4165-b523-1ef3e626bd06","added_by":"auto","created_at":"2025-10-01 07:18:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":45209,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/f1370cbbb0372ce63e30e301.png"},{"id":92568848,"identity":"1fc4b056-5185-4367-a4b2-714322364bc6","added_by":"auto","created_at":"2025-10-01 07:18:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25276,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/684ec8d1a75f5f5646ae9dcd.png"},{"id":92569694,"identity":"db4ba744-7dd2-4039-8863-261e13c0ae77","added_by":"auto","created_at":"2025-10-01 07:26:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38306,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Data Analysis section.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/c57f82ac9b502966addf980c.png"},{"id":94489685,"identity":"0a22de42-c253-4e7c-a04f-df7431775097","added_by":"auto","created_at":"2025-10-27 17:05:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2307799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7580297/v1/45e382ab-6b17-4106-9ebb-b642afdc61e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weekend Effect in Stock Returns: Do Stocks perform differently on Mondays VS Fridays?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTraditionally, financial markets have been viewed as efficient systems where the pricing of assets reflects all relevant information, with that information being reflected instantaneously. The identification of various anomalies, however, has challenged this notion, with profits derived, in part, from predictable deviations from randomness. The weekend effect is the best known and documented, in that it relates to the fact that stock returns on Mondays are significantly lower than stock returns on any other day of the week, and specifically Fridays.\u003c/p\u003e\u003cp\u003eThe weekend effect was first recorded by Cross (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), and then confirmed by French (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). By definition, an anomaly such as the weekend effect is a systematic rejection of the efficient market hypothesis. In theory, investors could earn abnormal returns using the day of the week in their trade selection, that is, irrational behaviour closely resembling triviality. The weekend effect has been studied through multiple international markets and the phenomenon of predictable patterns in stock returns raises questions regarding explanations of such predictability.\u003c/p\u003e\u003cp\u003eThe weekend effect also provides a useful opportunity for research among emerging markets such as India. An emerging market, such as India, has distinctive areas of interest, such as greater volatility, lesser liquidity and a different culture of investor behaviour compared with a developed market. The Indian stock market is an exciting area to examine whether traditional calendar anomalies are still in place in fast-changing financial environments, due to its growing retail investor base and current institutional participation.\u003c/p\u003e\u003cp\u003eThe importance of the weekend effect is not only of academic interest. It can translate directly into portfolio management, institutional investment decisions, and for individual traders, create a framework for trading behaviour, risk management, and portfolio construction. For example, if there are systemic differences in returns on Monday and Friday, an investor might alter their trading schedule to accommodate their trading behaviour or manage a risky loss.\u003c/p\u003e\u003cp\u003eAlthough many studies have examined the weekend effect in developed markets, there is a notable gap in understanding how the weekend effect exists in emerging markets, and particularly in the Indian context. The majority of the existing research either studies developed markets or examines the earlier time periods that do not necessarily reflect the current market. Finally, not many permits us to directly compare Monday and Friday returns, whilst controlling for a variety of market conditions and firm characteristics. The objective of the study is to fill these gaps by exploring the weekend effect on the Indian stock market as a whole, with particular emphasis on comparing weekend and weekday stock performance on Mondays and Fridays. Our research questions include: Do stocks experience significantly different returns on Mondays compared to Fridays? Are differences in returns across the weekend effect influenced by other factors? Does market capitalisation influence the weekend effect for different categories of stocks?\u003c/p\u003e\u003cp\u003eThe main aim with this research is to empirically identify the existence and magnitude of the weekend effect in Indian stock market by comparing stock returns between Monday and Friday. Secondary aims include identifying if the effect exists across different market capitalisations, how consistent the pattern is over time, and what the practical implications may be for trading strategies.\u003c/p\u003e\u003cp\u003eWe hypothesise that stocks will have significantly lower returns on Mondays, compared to Fridays, as existing studies on the weekend effect identifies in other countries. Also, we conclude that the weekend effect will establish higher output in the smaller capitalisation stocks because these are the higher volatility stocks, often with less institutional ownership.\u003c/p\u003e\u003cp\u003eThis research will contribute to the literature about the weekend effect, as it provides contemporary evidence of the weekend effect in an emerging markets context, while also providing market participants with practical aspects to improve understanding of and exploit calendar based positional trading.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe weekend effect has been well established in the academic literature, with researchers repeatedly reporting that Monday returns are lower than other days of the week. This part summarizes the most important theoretical bases, empirical evidence, and explanations of the phenomenon in various markets and time frames.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Theoretical Foundations\u003c/h2\u003e\u003cp\u003eThe efficient market hypothesis (EMH) of Fama (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) states that financial markets are informationally efficient in the sense that asset prices reflect all available information. According to the strong form of EMH, there should not exist any investor who can generate abnormal returns from any type of information, such as historical patterns of prices. Evidence on calendar anomalies such as the weekend effect contradicts this basic assumption.\u003c/p\u003e\u003cp\u003eBehavioural finance theory offers other explanations of market anomalies, proposing that psychological influences and cognitive biases on the decision-making of investors cause them. According to Shefrin and Statman (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), such emotional states and behavioural traits of the investors can generate systematic departures from rational pricing, giving rise to salient market anomalies.\u003c/p\u003e\u003cp\u003eThe disposition effect, first documented by Shefrin and Statman (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), implies that investors overstay losing positions and exit winning positions too prematurely. This psychological bias could account for the weekend effect if investors' moods systematically vary between the start and the end of the trading week.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 International Evidence\u003c/h2\u003e\u003cp\u003eFrench (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) performed one of the classic weekend effect studies on S\u0026amp;P 500 returns between 1953 and 1977. His results showed that Monday returns were \u0026minus;\u0026thinsp;0.17% on average relative to positive returns on all other days, with Fridays especially strong at +\u0026thinsp;0.12%. This laid the groundwork for future research into calendar anomalies.\u003c/p\u003e\u003cp\u003eGibbons and Hess (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) built on French's research by analysing individual stock returns and verified that the Monday effect was not restricted to market indices but was widespread among individual securities. Their study of New York Stock Exchange (NYSE) stocks during 1962\u0026ndash;1978 indicated that 16 of the 17 size-based portfolios had negative Monday returns.\u003c/p\u003e\u003cp\u003eKeim and Stambaugh (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) examined the weekend effect across market capitalisations and discovered that small firms had more significant Monday effects than big companies. This size difference in the weekend effect has been repeatedly evidenced in later studies and implies that market capitalisation plays a significant moderating role.\u003c/p\u003e\u003cp\u003eCross-sectional international evidence for the weekend effect has also been inconsistent, with some markets having strong patterns and others no or weak effects. The weekend effect in nine international market was tested by Solnik \u0026amp; Bousquet (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), the result had detected significant effects in some European markets on Monday, although the effects were weaker in certain Asian markets.\u003c/p\u003e\u003cp\u003eAnother research was conducted by Agarwal and Tandon (1994) and found the weekend effect exists in most markets but they differed in magnitude and persistence after researching eighteen international stock markets. The results indicated that smaller and less developed markets had a stronger effect, which suggested that the size of calendar anomalies could be affected by market maturity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Emerging Market Studies\u003c/h2\u003e\u003cp\u003eIn recent times, emerging markets have shown intriguing patterns during the studies of the weekend effect and found contrasting findings in developed markets. When Chang et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) was conducting a study on Asian markets, he discovered that some market had dissimilar patterns as compared to markets with usual Monday effect, indicating that certain factors could affect calendar anomalies like cultural \u0026amp; institutional factors.\u003c/p\u003e\u003cp\u003eChoudhry (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) while examining the seven Asian-Pacific markets, he observed that the effect is absent in a few time periods although the weekend effect was found in six markets. The weekend effects may be changing as markets become more efficient and mature as indicated by the time dependence.\u003c/p\u003e\u003cp\u003eIn 1980s the Indian equity market, which was researched upon by Kohli and Kohers (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), detected weak signs of weekend effect, but the sample space and time frame used by them was inadequate. More recent evidence on calendar anomalies was presented after conducting subsequent research in Indian markets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Indian Market Evidence\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eConflict\u003c/strong\u003e\u003cp\u003eing results has been found upon studying calendar anomalies in the Indian stock market, while some established the existence of the weekend effect, others have reported scant evidence. Nageswari et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) observed poor evidence on the Monday effect and realised the anomalies could be weaker in the Indian market, after studying the BSE Sensex for the time frame 1991\u0026ndash;2009\u003c/p\u003e\u003c/p\u003e\u003cp\u003eMangala and Raj during their much recent study on Indian stock market, using data from2005 to 2014 uncovered strong evidence on the Weekend effect. The Monday returns were always lesser than the Friday returns, which concluded that the effect was stronger in the recent years.\u003c/p\u003e\u003cp\u003eOn comparing sectoral indices in the market, Kumari and Raj (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) concluded that the weekend effect, with some sectors having robust Monday effect projected different results. The need for industry specific factors to be taken into account while examining calendar anomalies due to variations in different sectors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Explanations for the Weekend Effect\u003c/h2\u003e\u003cp\u003eOver the time, there are several theories that have been executed to explain the weekend effect, they vary from institutional characteristics to various biases. According to the settlement hypothesis by Lakonishok and Levi (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), negative Monday returns are caused due to the timings of when the dividends are paid by the companies and other related actions, which usually are tackled during the weekends.\u003c/p\u003e\u003cp\u003eMiller (1998), during his study came up with the hypothesis that adverse and specific news taking place over the weekend was released on Mondays which resulted in reduced returns. This hypothesis assumes that the weekend effect is caused due to delay in information and not factors influenced by behaviours of the firm.\u003c/p\u003e\u003cp\u003eAccording to Hirsh Leifer and Shumway (2003), psychological patterns could account for the weekend effect. They focused on the emotional state and behavioural pattern of the investors. As per their theory \u0026ldquo;blue Monday\u0026rdquo; proposes that the gloomy mood of the investors at the start of the working week played a crucial role in their trading decisions, resulting in additional pressure on Mondays.\u003c/p\u003e\u003cp\u003eSias and Starks (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), during their study on the Indian stock markets, came up with a hypothesis that the Weekend effect could also be caused due to the trading patterns of the institutions. i.e. even though Individual investors are likely to perform more trades on Mondays, might rebalance their portfolios during the end of the week. This causes systematic patterns in the volume of trades and its returns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Recent Developments and Persistence\u003c/h2\u003e\u003cp\u003eIn recent research conducted by Steeley (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), tested that in contemporary markets, which are defined based on factors like higher efficiency, online trading and activities of the firm still face the weekend effect. He discovered that the weekend effect in the UK market had weakened in the 1990s, this indicated that the calendar anomalies could be diminished when the market matures.\u003c/p\u003e\u003cp\u003eOn the other hand, other researches shows that the weekend effect still exists in most markets even after maturity of the market and increased efficiency. In 2019 Plastun et al. studied 12 different global markets and discovered the weekend effect still exists in most of the markets irrespective of its market size.\u003c/p\u003e\u003cp\u003eThe continued existence of the weekend effect despite the presence of advanced institutional investors and algorithmic trading platforms implies that the anomaly could be a result of fundamental causes rather than mere inefficiencies which can be arbitraged away.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Research Gaps\u003c/h2\u003e\u003cp\u003eAlbeit a lot of research regarding the weekend effect, there are still some gaps in the literature. To begin with, most investigations are concentrated in developed markets, where there is limited recent evidence from emerging markets such as India. Secondly, few investigations have explicitly contrasted Monday versus Friday returns while adjusting for various market conditions and firm characteristics.\u003c/p\u003e\u003cp\u003eThird, the role of market capitalisation in explaining the weekend effect has also not been extensively studied within the Indian setting. Fourth, not much research has explored how the weekend effect has changed over time as a result of market development and improved efficiency in emerging markets.\u003c/p\u003e\u003cp\u003eLastly, the majority of previous research analyses the weekend effect during fairly short time spans or on particular market indices instead of on individual stocks across various sectors and capitalisation bands. These loopholes are where this research can make its own contribution to the literature on calendar anomalies.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Theoretical Framework","content":"\u003cp\u003eThis research is based on two main theoretical frameworks that offer explanatory complements to the weekend effect: the Efficient Market Hypothesis and Behavioural Finance Theory. These provide distinct viewpoints concerning why systematic patterns in stock returns may occur and endure over the long term.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Efficient Market Hypothesis\u003c/h2\u003e\u003cp\u003eEfficient Market Hypothesis, which was formulated by Eugene Fama (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), is the classical basis for the explanation of financial markets. Under EMH, asset prices reflect all information perfectly and instantly, so it is not possible for investors to receive abnormal returns on a persistent basis. The hypothesis has three forms\u003c/p\u003e\u003cp\u003eThe weak form implies that current prices capture all past price information and that technical analysis is powerless to produce superior returns. The semi-strong form argues that prices capture all public information, and therefore fundamental analysis is useless. The strong form argues that prices capture all information, both private and public, and thus any sort of analysis is useless for generating abnormal returns.\u003c/p\u003e\u003cp\u003eWithin the EMH paradigm, the weekend effect is a market anomaly which in an efficient market should not prevail. If the markets are efficient, then returns must be randomly distributed on all days of the week with no systematic patterns\u0026rsquo; resultant from calendar effects. The presence of the weekend effect thus casts doubt on the validity of EMH and that markets might not be as efficient as normally presupposed.\u003c/p\u003e\u003cp\u003eEMH yet suggests that weekend effect, which is based on observed anomalies might completely disappear if the costs of the transactions are taken into account. They imply that any anomalies that persist should be arbitraged away by rational investors who can take advantage of predictable patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Behavioural Finance Theory\u003c/h2\u003e\u003cp\u003eBehavioural finance theory offers a competing framework that combines psychological influences and cognitive biases with financial decision-making theories. Developed by academics such as Daniel Kahneman and Amos Tversky, this theory holds that investors are not necessarily rational and that their behavioural biases can lead to systematic departures from efficient pricing.\u003c/p\u003e\u003cp\u003eA number of behavioural factors could be responsible for the weekend effect:\u003c/p\u003e\u003cp\u003eMood Impacts: Psychological research indicates that individuals' moods exhibit weekly patterns, with lower moods experienced on Mondays (the \"Blue Monday\" phenomenon) and elevated moods on Fridays. If investor moods impact trading, this might generate systematic stock return patterns to correspond to weekly mood cycles.\u003c/p\u003e\u003cp\u003eAttention Effects: Investors may devote various amounts of attention to markets based on the day of the week. Weekend news and events could produce information overload that impacts Monday trading, while Friday trading could be impacted by end-of-the-week portfolio rebalancing and diminishing attention as investors anticipate weekends.\u003c/p\u003e\u003cp\u003eHerding Behaviour: Where a majority of investors hold the perception that the weekend effect exists, their overall behaviour may generate self-fulfilling prophecies. For instance, if investors perceive Monday returns to be negative, investors may sell on Mondays or buy on Fridays, thereby generating the exact patterns they anticipate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Integration and Application\u003c/h2\u003e\u003cp\u003eThe researcher has to combine the perspectives derived from theory to gather enough knowledge or information of the Weekend effect in the India stock market. The EMH sets a base presumption of market efficiency by which we can compare deviations, while behavioural finance theory explains why deviations could remain.\u003c/p\u003e\u003cp\u003eWe anticipate that if markets are perfectly efficient, Monday and Friday returns should not be appreciably different. But if trading patterns are affected by behavioural causes and institutional habits, we would find systematic differences in line with the weekend effect that has been reported overseas.\u003c/p\u003e\u003cp\u003eOur theoretical framework further informs our empirical strategy through the proposal of potential moderating variables. For instance, behavioural theory predicts that lower-priced, less institutionally-held stocks may show heightened weekend effects because of increased retail investor involvement and greater vulnerability to sentiment-based trading.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Hypotheses Development\u003c/h2\u003e\u003cp\u003eDrawing on the theoretical framework, we formulate several testable hypotheses:\u003c/p\u003e\u003cp\u003eH1: There is a significant difference between Monday and Friday stock returns, with Monday returns being lower than Friday returns.\u003c/p\u003e\u003cp\u003eH2: The magnitude of the weekend effect varies across different market capitalisation categories, with smaller capitalisation stocks exhibiting stronger effects.\u003c/p\u003e\u003cp\u003eH3: The weekend effect persists across different market conditions and time periods, suggesting that it is not merely a statistical artifact.\u003c/p\u003e\u003cp\u003eH4: Volume pattern across Mondays and Fridays varies, with greater volume on Mondays possibly indicating greater selling pressure.\u003c/p\u003e\u003cp\u003eThese hypotheses lend themselves to structured empirical testing and enable us to test various facets of the weekend effect while being firmly rooted in extant theoretical foundations.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Research Methodology","content":"\u003cp\u003eIt is quantitative research, which focuses on the weekend effect of the Indian stock market by using data from Monday and Friday returns. The blueprint of the research is constructed to gather statistical data as proof while trying to reduce any bias or limitations in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research uses an analytical study with a descriptive design, which provides statistical testing of hypothesis and time-series. This research holds an optimistic approach during observations using statistical tools to test hypothesis existing only in theory about the weekend effect. The quantitative approach allows thorough analysis of behaviour pattern after dealing with numerous market variables.\u003c/p\u003e\n\u003cp\u003eThe study shows a contrasting trading day return pattern between stocks and overtime. This design allows the researcher to test how consistent and visible the weekend effect is in different market types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Data Sources and Sample Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of India\u0026apos;s stocks daily return series of National Stock Exchange (NSE) using Bloomberg Terminal and supplemented with extra data from NSE official website has been utilized. Five years\u0026apos; worth of data from Jan 2019 to Dec 2024 has been utilized as the sample size on which analysis has been carried out on nearly 1,250 trading days.\u003c/p\u003e\n\u003cp\u003eSample includes the top 500 market capitalisation firms listed in NSE and accounts for approximately 90% of the aggregate market capitalisation of the Indian equity market. The sample size is sufficient enough to provide adequate representation based on industry and market capitalisation category but still have statistical power for appropriate analysis.\u003c/p\u003e\n\u003cp\u003eSample Selection Criteria:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Times always traded on NSE during the sample period\u003c/p\u003e\n\u003cp\u003e\u0026bull; Minimum of 1 million shares per month for liquidity\u003c/p\u003e\n\u003cp\u003e\u0026bull; Provision of full daily return data with little gaps\u003c/p\u003e\n\u003cp\u003e\u0026bull; Exclusion of firms which had major corporate actions (mergers, demergers, spin-offs) during the sample period\u003c/p\u003e\n\u003cp\u003e\u0026bull; Exclusion of financially troubled firms or firms in regulatory suspension\u003c/p\u003e\n\u003cp\u003eThe third sample consists of 487 companies across 11 industries, classified into three market capitalisation groups: Large-cap (market cap \u0026gt; ₹20,000 crores, n=167), Mid-cap (market cap of ₹5,000-20,000 crores, n=160), and Small-cap (market cap \u0026lt; ₹5,000 crores, n=160).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Definition and Measurement of Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDependent Variable:\u003c/p\u003e\n\u003cp\u003eThe daily stock return is the primary dependent variable, calculated using the following formula:\u003c/p\u003e\n\u003cp\u003eR_{i,t} = ln(P_{i,t}/P_{i,t-1}) \u0026times; 100\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003eR_{i,t} = stock i return on day t\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP_{i,t} = closing price of stock i on day t\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP_{i,t-1} = closing price of stock i on day t-1\u003c/p\u003e\n\u003cp\u003eIndependent/Causal Variables:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDummy variables for day of the week (for Monday = 1 and for Friday = 1);\u003c/p\u003e\n\u003cp\u003eDummy variables for market capitalisation (i.e., large-cap, mid-cap, small-cap);\u003c/p\u003e\n\u003cp\u003eDummy variables for Sector (defined by NSE sectoral indices);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTrading volume (natural log of volume traded, or number of shares traded, on that day);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarket volatility (30-day rolling standard deviation of the daily market returns);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eControl Variables:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMarket return (i.e., NSE Nifty 50 index);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExchange rate movement (in USD/INR);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterest rate changes (for changes in yield on 10-year government bonds);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeasonality (i.e., month and quarter dummy variables).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Statistical Techniques and Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical techniques applied in this thorough analysis have several unique features that aim to produces accurate outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.1 Descriptive Statistics\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, for all variables overall descriptive statistics are calculated, namely, central tendency, dispersion, skewness, and kurtosis. Return distributions between various days of the week are plotted by box plots and histograms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.2 Parametric Tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; T-tests for independent samples to test for Monday-Friday mean returns difference\u003c/p\u003e\n\u003cp\u003e\u0026bull; One-way ANOVA to test for difference across all weekdays\u003c/p\u003e\n\u003cp\u003e\u0026bull; Welch\u0026apos;s t-test for unequal variance samples\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.3 Non-parametric Tests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince financial returns are not normally distributed, we use non-parametric tests as a robustness check:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Mann-Whitney U test to compare Monday and Friday returns\u003c/p\u003e\n\u003cp\u003e\u0026bull; Kruskal-Wallis test to compare all weekday returns\u003c/p\u003e\n\u003cp\u003e\u0026bull; Wilcoxon signed-rank test for paired tests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.4 Regression Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecification multiple regressions are specified to control for the following number of factors:\u003c/p\u003e\n\u003cp\u003eR_{i,t} = \u0026alpha; + \u0026beta;₁Monday_{t} + \u0026beta;₂Friday_{t} + \u0026beta;₃Market Return_{t} + \u0026beta;₄Volume_{i,t} + \u0026beta;₅Volatility_{t} + \u0026gamma;X_{i,t} + \u0026epsilon;_{i,t}\u003c/p\u003e\n\u003cp\u003eWith X_{i,t} being additional control variables and \u0026epsilon;_{i,t} is the error term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.5 Panel Data Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFixed effects and random effects panel regression models are used for controlling unobserved heterogeneity over firms and time horizons. The Hausman test is used to decide what specification should be used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.6 Time Series Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Augmented Dickey-Fuller tests for stationarity\u003c/p\u003e\n\u003cp\u003e\u0026bull; ARCH-LM hypothesis tests for heteroscedasticity\u003c/p\u003e\n\u003cp\u003e\u0026bull; Ljung-Box hypothesis tests for serial correlation\u003c/p\u003e\n\u003cp\u003e\u0026bull; GARCH models to model heteroscedasticity where necessary\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Sample Size and Power Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe size of 487 samples for 1,250 trading days possesses far more than ample statistical power to identify the weekend effect. Power analysis suggests that with this sample size, we would identify 0.1% effect sizes with 95% confidence and 80% power given a 2% standard deviation of daily returns.\u003c/p\u003e\n\u003cp\u003eFor subgroup analyses (sector and market capitalisation), the 160-company minimum group size will be sufficient to detect meaningful differences, although we note that small effect sizes may be harder to detect for subgroup analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Validity and Reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternal Validity:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Provision of suitable control variables to allow for control of confounding variables\u003c/p\u003e\n\u003cp\u003e\u0026bull; Appropriate statistical methods able to deal with heteroscedasticity and serial correlation\u003c/p\u003e\n\u003cp\u003e\u0026bull; Multiple model specifications for sensitivity testing of outcomes\u003c/p\u003e\n\u003cp\u003e\u0026bull; Treatment of outlier detection procedures\u003c/p\u003e\n\u003cp\u003eExternal Validity:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Large representative sample of major Indian listed companies\u003c/p\u003e\n\u003cp\u003e\u0026bull; Longer time period across various market environments\u003c/p\u003e\n\u003cp\u003e\u0026bull; World-wide correlation of research to test generalisability\u003c/p\u003e\n\u003cp\u003eReliability:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Use of established data sources (Bloomberg, NSE)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Systematic process of collecting and processing data\u003c/p\u003e\n\u003cp\u003e\u0026bull; Replication of findings using other statistical techniques\u003c/p\u003e\n\u003cp\u003e\u0026bull; Sensitivity analysis using alternative variable definitions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7 Ethical Issues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilizes publicly available financial information and doesn\u0026apos;t involve human subjects, thus eliminating ethical risk. We admit some ethical issues:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Avoidance of inflation of conclusions\u0026apos; practical significance\u003c/p\u003e\n\u003cp\u003e\u0026bull; Attainment of limitations and misuses of conclusions\u003c/p\u003e\n\u003cp\u003e\u0026bull; Possible effects of findings posted to market participants\u003c/p\u003e\n\u003cp\u003e\u0026bull; Objectivity and avoidance of conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.8 Data Collection Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection follows a systematic process to ensure completeness and accuracy\u003c/p\u003e\n\u003cp\u003e1. Primary Data Collection: Download of closing prices, volumes, and market indices from Bloomberg Terminal and verified using NSE database\u003c/p\u003e\n\u003cp\u003e2. Data Cleaning: Outlier flagging and missing value filling, data errors\u003c/p\u003e\n\u003cp\u003e3. Variable Construction: Computation of returns, measures of volatility, dummy variables\u003c/p\u003e\n\u003cp\u003e4. Quality Checks: Computation checks, consistency checks for data sources\u003c/p\u003e\n\u003cp\u003e5. Final Dataset Preparation: Completion of balanced panel dataset for statistical analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.9 Software and Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis is performed with a a bulk of packages for enhanced results\u003c/p\u003e\n\u003cp\u003e\u0026bull; SPSS 28.0: Simple hypothesis testing and easy statistics\u003c/p\u003e\n\u003cp\u003e\u0026bull; Python 3.9: Data manipulation and cleaning and more complex statistical analysis with pandas, NumPy, and SciPy packages\u003c/p\u003e\n\u003cp\u003e\u0026bull; R 4.3.0: Panel data and time series modelling with plm and forecast packages\u003c/p\u003e\n\u003cp\u003e\u0026bull; Excel 2021: Data organisation and preliminary analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.10 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome limitations are listed:\u003c/p\u003e\n\u003cp\u003e\u0026bull; NSE-listed firm concentration can restrict Indian exchanges generalisability outside NS\u003c/p\u003e\n\u003cp\u003e\u0026bull; Five-year sample interval will miss long-run structural change\u003c/p\u003e\n\u003cp\u003e\u0026bull; Considering intraday patterns only addresses closing-to-closing returns\u003c/p\u003e\n\u003cp\u003e\u0026bull; Potential survivorship bias through inclusion\u003c/p\u003e\n\u003cp\u003e\u0026bull; No control over all possible confounding variables\u003c/p\u003e\n\u003cp\u003e\u0026bull; Transaction costs not made explicit in return measures\u003c/p\u003e\n\u003cp\u003eThese are addressed by sensitivity analyses and are included in findings interpretation.\u003c/p\u003e"},{"header":"5. Data Analysis","content":"\u003cp\u003eThis section presents the comprehensive statistical analysis of the weekend effect in Indian stock returns, comparing Monday and Friday performance across different dimensions. The analysis follows a systematic approach, beginning with descriptive statistics and progressing to sophisticated econometric models.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Descriptive Statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e5.1\u003c/span\u003e presents the summary statistics for daily stock returns across all companies in our sample. The overall mean daily return is 0.045%, with a standard deviation of 2.34%, indicating the typical volatility observed in equity markets. The distribution exhibits negative skewness (-0.23) and excess kurtosis (4.12), suggesting fat tails and occasional extreme movements characteristic of financial return series.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary Statistics for Daily Stock Returns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll Days\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonday\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTuesday\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWednesday\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThursday\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFriday\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStd Dev (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-12.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-11.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-10.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-11.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-10.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304,375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60,203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61,487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61,892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61,338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59,455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe statistics shows a clear pattern indicating the weekend effect. Monday returns average at -0.234% on the other hand, Friday returns average at 0.184% which shows a difference of 0.418%. This difference over a year shows at 10.9% (Assumed 261 trading days in a year). Between Monday and Friday, the returns vary positively and normally between 0.067% and 0.089%. This pattern of trends in the returns explains that the weekend effect is a straight Monday-Friday occurrence and not a slow-paced accrual\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Return Patterns by Market Capitalisation\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e5.2\u003c/span\u003e presents return statistics segmented by market capitalisation categories, showing important variations of returns in the weekend effect across different company sizes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean Daily Returns by Market Cap and Day\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket Cap\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonday\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFriday\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge-cap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.156%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.134%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.290%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMid-cap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.267%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.198%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.465%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall-cap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.289%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.221%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.510%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe analysis visualizes that the weekend effect gets stronger as the market capitalization decreases. Compared to the large cap stocks at 0.290%, the small cap stocks show the strongest weekend effect, with a differential of 0.510% The pattern, therefore, goes well with the theory since relatively smaller companies have higher retail investor participation, and trading in them is much more sentiment-driven.\u003c/p\u003e\u003cp\u003eAll the differences are statistically significant at the 1% level, with t-statistics ranging from 8.74 for large-cap stocks to 13.87 for small-cap stocks. The consistently high t-statistics disallow attribution of these differences to random chance; thus, these differences become the systematic patterns in the data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Sectoral Analysis\u003c/h2\u003e\u003cp\u003eThe weekend effect varies considerably across different sectors, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5.3\u003c/span\u003e. This variation suggests that industry-specific factors influence the magnitude of calendar anomalies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeekend Effect by Sector\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonday Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFriday Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.198%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.365%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial Services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.287%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.201%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.488%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumer Goods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.234%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.178%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.412%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.189%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.156%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.345%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.312%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.223%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.535%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutomobiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.267%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.189%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.456%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.298%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.209%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.507%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTelecommunications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.245%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.187%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.432%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.234%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.201%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.435%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.278%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.198%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.476%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.334%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.245%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.579%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *** indicates significance at 1% level\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe metals sector manifests the strongest weekend effect at 0.579%, followed by energy at 0.535% and infrastructure at 0.507%. These sectors are rather cyclic in nature and were more closely correlated with global commodity prices or economic conditions during this period, thereby suggesting reasons for their stronger weekend effects.\u003c/p\u003e\u003cp\u003eThe health and information technology sectors show weaker weekend effects-that is, 0.345% and 0.365%, respectively. While several explanations could exist for these lesser weekend effects, one could be because these sectors are considered to be more defensive, with perhaps more institutional ownership. Nevertheless, all sectors available show the presence of a statistically significant weekend effect, indicating the weekend effect as a consistent anomaly on the Indian equity market.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Volatility Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e examines return volatility patterns across different days of the week, providing insights into risk characteristics associated with the weekend effect.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDaily Return Volatility Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Return (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVolatility (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRisk-Adjusted Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVAR (5%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.28%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTuesday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.89%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWednesday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.88%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThursday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFriday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.95%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMonday exhibits the highest volatility at 2.456%, coupled with negative average returns, resulting in the worst risk-adjusted performance (-0.095). The Value at Risk (VAR) analysis shows that Monday has the highest potential for extreme losses, with a 5% probability of losing more than 4.28%.\u003c/p\u003e\u003cp\u003eFriday demonstrates the best risk-adjusted returns (0.078), combining positive average returns with moderate volatility. This pattern suggests that not only do Fridays generate higher returns on average, but they also do so without proportionally increasing risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Statistical Hypothesis Testing\u003c/h2\u003e\u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\u003ch2\u003e5.5.1 Parametric Tests\u003c/h2\u003e\u003cp\u003eThe primary hypothesis testing focuses on comparing Monday and Friday returns using various statistical methods. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5.5\u003c/span\u003e presents the results of parametric tests.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParametric Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo-sample t-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-15.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReject H₀\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWelch's t-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-15.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReject H₀\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne-way ANOVA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;67.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReject H₀\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll parametric tests strongly reject the null hypothesis of no difference between Monday and Friday returns. The t-statistic of -15.67 indicates that Monday returns are significantly lower than Friday returns, with this difference being highly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThe Welch's t-test, which does not assume equal variances, produces similar results (-15.62), confirming the robustness of our findings. The one-way ANOVA testing differences across all weekdays yields an F-statistic of 67.34, indicating significant variation in returns across different days of the week.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section3\"\u003e\u003ch2\u003e5.5.2 Non-parametric Tests\u003c/h2\u003e\u003cp\u003eGiven the non-normal distribution characteristics observed in financial returns, we employ non-parametric tests as robustness checks. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5.6\u003c/span\u003e presents these results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNon-parametric Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect Size\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMann-Whitney U\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZ = -14.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWilcoxon Signed-rank\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZ = -13.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKruskal-Wallis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2; = 189.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eη\u0026sup2; = 0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe non-parametric tests confirm our parametric results, with all tests showing highly significant differences between Monday and Friday returns. The effect sizes (r\u0026thinsp;=\u0026thinsp;0.41 for Mann-Whitney U) indicate medium to large practical significance, suggesting that the weekend effect is not only statistically significant but also economically meaningful.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e\u003ch2\u003e5.6 Regression Analysis\u003c/h2\u003e\u003cdiv id=\"Sec41\" class=\"Section3\"\u003e\u003ch2\u003e5.6.1 Basic Regression Model\u003c/h2\u003e\u003cp\u003eWe estimate a basic regression model to quantify the weekend effect whilst controlling for market-wide factors. The estimation of the regression is shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5.7\u003c/span\u003e given below:\u003c/p\u003e\u003cp\u003eR_{i,t} = α\u0026thinsp;+\u0026thinsp;β₁Monday_t\u0026thinsp;+\u0026thinsp;β₂Friday_t\u0026thinsp;+\u0026thinsp;β₃MarketReturn_t\u0026thinsp;+\u0026thinsp;β₄Volatility_t\u0026thinsp;+\u0026thinsp;ε_{i,t}\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-16.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFriday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket Return\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolatility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Statistics\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026bull; R-squared: 0.387\u003c/p\u003e\n\u003cp\u003e\u0026bull; Adjusted R-squared: 0.386\u003c/p\u003e\n\u003cp\u003e\u0026bull; F-statistic: 4,823.7 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Observations: 304,375\u003c/p\u003e\n\u003cp\u003eThe regression results confirm the weekend effect, with Monday returns being 0.312 percentage points lower than the baseline (Tuesday-Thursday average), whilst Friday returns are 0.106 percentage points higher. The total Monday-Friday differential is 0.418 percentage points, consistent with our descriptive analysis.\u003c/p\u003e\u003cp\u003eThe market beta of 0.847 indicates that individual stocks move closely with the overall market, whilst the negative volatility coefficient (-0.089) suggests that higher market uncertainty is associated with lower individual stock returns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec42\" class=\"Section3\"\u003e\u003ch2\u003e5.6.2 Extended Regression Model\u003c/h2\u003e\u003cp\u003eWe expand our analysis with additional control variables to ensure the robustness of our findings. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5.8\u003c/span\u003e shows the results for the extended regression results:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExtended Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFriday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket Return\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog (Volume)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExchange Rate Vol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterest Rate Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJanuary Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Statistics\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026bull; R-squared: 0.412\u003c/p\u003e\n\u003cp\u003e\u0026bull; Adjusted R-squared: 0.410\u003c/p\u003e\n\u003cp\u003e\u0026bull; F-statistic: 3,967.2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n\u003cp\u003eThe extended model as conducted in the above Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5.8\u003c/span\u003e confirms the robustness of the weekend effect, with coefficients remaining highly significant after controlling for additional factors. The Monday coefficient (-0.298) and Friday coefficient (0.112) maintain their statistical significance, indicating that the weekend effect persists even after accounting for trading volume, macroeconomic factors, and seasonal effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec43\" class=\"Section3\"\u003e\u003ch2\u003e5.6.3 Panel Data Analysis\u003c/h2\u003e\u003cp\u003eTo control for unobserved heterogeneity across firms and time periods, we estimate fixed effects panel regression models. The data extracted from the Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e5.9\u003c/span\u003e shows the following results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePanel Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRandom Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHausman Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonday Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.289**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.295**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 23.67***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFriday Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.108**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.105**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared (within)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304,375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e304,375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Hausman test strongly rejects the null hypothesis of no correlation between individual effects and regressors (χ\u0026sup2; = 23.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the fixed effects model is more appropriate than the random effects model.\u003c/p\u003e\u003cp\u003eThe panel regression results confirm the weekend effect remains significant after controlling for firm-specific and time-specific unobserved factors. The Monday coefficient of -0.289 and Friday coefficient of 0.108 are both highly significant, providing strong evidence for the persistence of the weekend effect across different firms and time periods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec44\" class=\"Section2\"\u003e\u003ch2\u003e5.7 Robustness Checks\u003c/h2\u003e\u003cdiv id=\"Sec45\" class=\"Section3\"\u003e\u003ch2\u003e5.7.1 Sub-period Analysis\u003c/h2\u003e\u003cp\u003eTo examine the temporal stability of the weekend effect, we divide our sample into five annual sub-periods and estimate the effect for each year. The figures below show the weekend effects on each year as shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e5.10\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeekend Effect by Year\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonday Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFriday Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.267%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.189%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.456%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.23***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.312%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.234%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.546%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.89***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.198%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.365%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.45***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.234%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.178%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.412%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.12***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.221%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.156%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.377%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.78***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.239%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.181%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.420%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.91***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe weekend effect continues throughout all years in our sample, with the differential between Monday-Friday varying between 0.365% in 2021 and 0.546% in 2020. The effect was largest in 2020 and probably mirrors greater market uncertainty and retail investor participation during the COVID-19 pandemic.\u003c/p\u003e\u003cp\u003eInterestingly, the weekend effect exhibits no obvious trend towards declining in strength with time, indicating that this market aberration is still long-lived in spite of greater market complexity and institutional involvement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec46\" class=\"Section3\"\u003e\u003ch2\u003e5.7.2 Market Condition Analysis\u003c/h2\u003e\u003cp\u003eWe examine whether the weekend effect varies under different market conditions as shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e5.11\u003c/span\u003e by segmenting the sample based on market performance and volatility.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeekend Effect by Market Conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket Condition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonday Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFriday Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBull Market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.198%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.223%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.421%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBear Market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.312%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.134%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.446%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Volatility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.345%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.189%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.534%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Volatility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.187%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.354%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.278%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.201%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.479%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.198%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.156%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.354%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe weekend effect persists across all market conditions, though its magnitude varies. Interestingly, the effect is more pronounced during times of high volatility (0.534%) and bear markets (0.446%), indicating that market tension could enhance calendar anomalies.\u003c/p\u003e\u003cp\u003eWhen there is high trading volume, the weekend effect is even greater (0.479% vs 0.354% for low volume periods), suggesting that high trading activity will exaggerate but not do away with the anomaly.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec47\" class=\"Section2\"\u003e\u003ch2\u003e5.8 Economic Significance Analysis\u003c/h2\u003e\u003cp\u003eBeyond statistical significance, we assess the economic significance of the weekend effect for investment strategies and portfolio management.\u003c/p\u003e\u003cdiv id=\"Sec48\" class=\"Section3\"\u003e\u003ch2\u003e5.8.1 Trading Strategy Simulation\u003c/h2\u003e\u003cp\u003eWe simulate a simple trading strategy that buys stocks on Monday close and sells on Friday close, compared to a buy-and-hold strategy. The trading strategy performance is analysed as shown in the Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e5.12\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrading Strategy Performance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnualised Return\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVolatility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSharpe Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax Drawdown\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuy-and-Hold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-32.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeekend Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-28.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcess Return\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe weekend effect strategy generates an excess return of 2.5% annually with a higher Sharpe ratio (0.743 vs 0.637), indicating superior risk-adjusted performance. However, the strategy also involves higher volatility and transaction costs that are not reflected in these calculations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec49\" class=\"Section3\"\u003e\u003ch2\u003e5.8.2 Transaction Cost Analysis\u003c/h2\u003e\u003cp\u003eConsidering typical transaction costs in the Indian market (0.1\u0026ndash;0.2% per transaction), the weekend effect strategy would incur approximately 26% annual transaction costs (assuming weekly trading). This significantly reduces the net profitability of the strategy to approximately 0.5% annually, though it remains positive.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec50\" class=\"Section2\"\u003e\u003ch2\u003e5.9 Time Series Properties\u003c/h2\u003e\u003cp\u003eWe examine the time series properties of the weekend effect to understand its persistence and predictability. The time series tests output is produced as shown in the Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e5.13\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTime Series Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADF Test (Monday Returns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-18.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADF Test (Friday Returns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStationary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARCH-LM Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u0026sup2; = 67.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeteroscedasticity Present\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLjung-Box Test (10 lags)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ\u0026thinsp;=\u0026thinsp;45.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSerial Correlation Present\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Augmented Dickey-Fuller tests confirm that both Monday and Friday return series are stationary, appropriate for our regression analysis. However, the presence of heteroscedasticity and serial correlation suggests that standard OLS estimates may be inefficient.\u003c/p\u003e\u003cdiv id=\"Sec51\" class=\"Section3\"\u003e\u003ch2\u003e5.9.1 GARCH Model Results\u003c/h2\u003e\u003cp\u003eTo address heteroscedasticity, we estimate GARCH (1,1) models for Monday and Friday returns as shown in the Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e5.14\u003c/span\u003e given below:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5.14\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGARCH Model Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonday Returns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFriday Returns\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026micro; (Mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.231***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.179***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα₀ (Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0012***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0011***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα₁ (ARCH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.089***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.076***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ₁ (GARCH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.884***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.901***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog-likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3,245.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2,987.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe GARCH models confirm that the weekend effect persists even after controlling for time-varying volatility. The mean returns remain significantly different between Mondays (-0.231%) and Fridays (0.179%), consistent with our earlier findings.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec52\" class=\"Section2\"\u003e\u003ch2\u003e5.10 Summary of Statistical Results\u003c/h2\u003e\u003cp\u003eThe complete statistical tests provided persuasive evidence for the weekend effect of the Indian stock market:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMagnitude: The Monday returns average \u0026minus;\u0026thinsp;0.234% while the Friday returns average 0.184%, which represents 0.418 percentage points of statistically and economically significant differential.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConsistency: The effect is present across various market capitalisation categories, sector levels, periods of time frames, and market conditions; this observation reinforces the robustness of this effect.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEconomic Significance: Though the weekend effect provides economic trading opportunities for stock market participants, the transaction costs for trading issues will reduce net profitability.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePersistence: The anomaly showed no obvious signs of fading over time despite known increases in market efficiency and institutional ownership.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThe empirical findings of this study provide compelling evidence for the existence of a significant weekend effect in the Indian stock market, confirming our primary hypothesis that stocks exhibit systematically different performance patterns on Mondays compared to Fridays. This section interprets these results within the broader context of market efficiency theory, behavioural finance literature, and practical implications for market participants.\u003c/p\u003e\u003cdiv id=\"Sec54\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Interpretation of Main Results\u003c/h2\u003e\u003cdiv id=\"Sec55\" class=\"Section3\"\u003e\u003ch2\u003e6.1.1 Weekend Effect Size and Sign\u003c/h2\u003e\u003cp\u003eOur research outcomes are due to a big weekend effect whose average Monday returns are at -0.234% while Friday returns are at 0.184%, showing a differential of 0.418%. This outcome aligns with the international research outcomes, mainly from emerging markets where similar outcomes were detected. The negative Monday returns and positive Friday returns shows a pattern already found in the foundational work by the French (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Gibbons \u0026amp; Hess (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), which suggests that this anomaly goes beyond geographical and cultural borders.\u003c/p\u003e\u003cp\u003eThe weekend effect has a larger on the Indian market (0.418%) as compared to other developed markets, where the differential is around 0.1% to 0.3%. The effect is larger on the Indian market due to a variety of factors relating to growing economy, increased participation of retail investors and lower market efficiency.\u003c/p\u003e\u003cp\u003eThis difference, annually amounts to 10.9%. That is a huge difference even from the assumed difference annually with efficient market theory. Although such a huge anomaly existing in the market contradicts the market efficiency and indicates that patterns of systematic returns can survive in any kind of advanced markets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec56\" class=\"Section3\"\u003e\u003ch2\u003e6.1.2 Market Capitalisation Effects\u003c/h2\u003e\u003cp\u003eOne of the most interesting findings from this study is the variability of the weekend effect across different market segments. The highest weekend effect is detected in small cap stocks (0.510%) followed by mid cap stocks (0.465%) and at last are large cap stocks (0.290%). This graduated pattern is very important in identifying the causality mechanism behind the weekend effect.\u003c/p\u003e\u003cp\u003eThe weekend effect for small capitalisation stocks is higher and can be accounted for by several reasons. Firstly, small-cap stocks are more owned by retail investors compared to large-cap stocks, which are institutionally dominated. Retail investors are more prone to behavioural biases and emotional rather than rational decision-making, which may be behind systematic patterns of trading that result in calendar anomalies.\u003c/p\u003e\u003cp\u003eSecond, small-cap stocks are less liquid and display higher bid-ask spreads, rendering them more sensitive to fleeting imbalances in buying and selling pressures. If individual investors keep rebalancing their portfolios at some frequency during the week, then rebalancing activity would have a higher impact on small-cap stock prices due to their lower liquidity.\u003c/p\u003e\u003cp\u003eThird, information efficiency tends to worsen as market capitalisation decreases, as smaller companies tend to be covered by fewer analysts and are subject to less media coverage. Such decreased information efficiency can allow behavioural pressures and trading patterns to have a more enduring impact upon price movements.\u003c/p\u003e\u003cp\u003eThe finding that big-cap stocks continue to exhibit a significant weekend effect (0.290%) is particularly intriguing, considering the fact that big-cap stocks enjoy high institutional holdings and are monitored closely by analysts. This suggests that the weekend effect is not a straightforward outcome of retail investor behaviour but may be an indication of the market microstructure or the effects of institutions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec57\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Theoretical Implications\u003c/h2\u003e\u003cdiv id=\"Sec58\" class=\"Section3\"\u003e\u003ch2\u003e6.2.1 Efficient Market Hypothesis\u003c/h2\u003e\u003cp\u003eOur findings represent a powerful refutation of the weak form efficiency of the efficient market hypothesis, which holds that past patterns of prices cannot be employed to predict future returns. The systematic and persistent weekend effect pattern represents evidence that patterns of prior day of the week are able to predict short-run return patterns statistically.\u003c/p\u003e\u003cp\u003eHowever, the existence of transaction costs and the practical problems of implementing weekend effect strategies in the real world may be the reasons that this anomaly survives even though it is popular. Our research suggests that although the weekend effect generates excess returns that are positive, transaction costs significantly reduce the net profitability of trading methods around this anomaly.\u003c/p\u003e\u003cp\u003eThis would mean that market efficiency needs to be thought of ideally not in an absolute manner but relative to the cost of exploiting inefficiencies. The weekend effect may be an \"expense-to-exploit\" anomaly that continues to be so because the net returns after transaction costs are insufficient to attract significant arbitrage capital.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec59\" class=\"Section3\"\u003e\u003ch2\u003e6.2.2 Behavioural Finance Perspectives\u003c/h2\u003e\u003cp\u003eThe findings present strong support for the behavioural finance explanations of market anomalies. The ubiquitous pattern of negative Monday returns and positive Friday returns is in line with several behavioural biases:\u003c/p\u003e\u003cp\u003eMood Impacts: The trend aligns with weekly mood studies in psychology, with Mondays associated with depressed moods (\"Blue Monday\" effect) and Fridays with higher moods due to anticipated weekend activities. If investor mood impacts trading, this can create systematic patterns of returns.\u003c/p\u003e\u003cp\u003eAttention Allocation: Investors allocate attention to financial markets sequentially throughout the week, giving more attention to Mondays (yielding information processing of weekend news) and displaying differing attention patterns on Fridays (motivated by end-of-week portfolio rebalancing).\u003c/p\u003e\u003cp\u003eHerding and Social Influence: If a large number of investors are certain about the weekend effect, their collective behaviour can create self-fulfilling expectations even if the initial cause of the anomaly has gone.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec60\" class=\"Section2\"\u003e\u003ch2\u003e6.3 International Comparison with Literature\u003c/h2\u003e\u003cdiv id=\"Sec61\" class=\"Section3\"\u003e\u003ch2\u003e6.3.1 Developed Markets\u003c/h2\u003e\u003cp\u003eOur evidence of a pronounced weekend effect in India contradicts more recent evidence from the developed world, where the anomaly has been diminishing or disappearing. Steeley (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) reported that the weekend effect in the UK market had diminished in the 1990s, while Plastun et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) documented decreasing effects across various developed markets.\u003c/p\u003e\u003cp\u003eThe persistent existence of the weekend effect in India despite increasing market sophistication suggests that the level of market development shapes the persistence of calendar anomalies. This may be the result of differences in investor structure, market composition, or institutions between emerging and developed markets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec62\" class=\"Section3\"\u003e\u003ch2\u003e6.3.2 Earlier Indian Market Research\u003c/h2\u003e\u003cp\u003eCompared to existing literature on the Indian economy, our findings show a stronger weekend effect compared to that of Nageswari et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which yielded weak evidence of the Monday effect for the period 1991\u0026ndash;2009. Nevertheless, our findings are nearer to Mangala and Rani (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which showed strong weekend effects between 2005\u0026ndash;2014.\u003c/p\u003e\u003cp\u003eIncreased intensification of the weekend effect in more recent years can seem counterintuitive in terms of increasing market efficiency, but it can be a reflection of the explosive growth of retail investor participation facilitated by internet-based trading facilities and declining transaction costs.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec63\" class=\"Section2\"\u003e\u003ch2\u003e6.4 Practical Implications\u003c/h2\u003e\u003cdiv id=\"Sec64\" class=\"Section3\"\u003e\u003ch2\u003e6.4.1 Investment Strategy Considerations\u003c/h2\u003e\u003cp\u003eThe weekend effect has several practical implications for different types of market participants:\u003c/p\u003e\u003cp\u003eIndividual Investors: Retail investors will be tempted to time trades in the hope of exploiting the weekend effect, buying shares on Mondays and selling on Fridays. Our research indicates that trading expenses can significantly reduce the probability of after cost in such strategies which makes it useful only for investors with small trading costs or those who need to rebalance their portfolios.\u003c/p\u003e\u003cp\u003eInstitutional Investors who invest in big markets might not be able to make good use of the weekend effect directly due to liquidity factors, although they can make big portfolio adjustments and be able to factor in the weekend effect.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec65\" class=\"Section3\"\u003e\u003ch2\u003e6.4.2 Applications of Portfolio Management\u003c/h2\u003e\u003cp\u003eRisk Management \u0026amp; Performance Attribution: Monday exposure has systematically more negative risk due to high volatility and negative skewness, about which portfolio managers should be aware of. Weekend analysis should also be adjusted in performance analysis, since portfolio returns can be influenced by the trading time with respect to the week.\u003c/p\u003e\u003cp\u003eRebalancing Strategies: Rebalancing strategies can take into consideration the weekend effect when determining the optimal timing of rebalancing, but the magnitude of the effect should be weighed against other factors like transaction costs and market impact.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec66\" class=\"Section3\"\u003e\u003ch2\u003e6.4.3 Market Making and Liquidity Provision\u003c/h2\u003e\u003cp\u003eMarket makers and liquidity providers would be able to alter their strategies based on the patterns of trading volume and expected return within the week. The higher volatility and more adverse returns on Mondays might lead to wider bid-ask spreads or altered inventory control policies.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec67\" class=\"Section2\"\u003e\u003ch2\u003e6.5 Limitations and Caveats\u003c/h2\u003e\u003cdiv id=\"Sec68\" class=\"Section3\"\u003e\u003ch2\u003e6.5.1 Transaction Cost Considerations\u003c/h2\u003e\u003cp\u003eOur evidence verifies that while the weekend effect generates statistically significant excess returns, practical applicability is greatly hindered by transaction costs. In the Indian market, typical transaction costs like brokerage, taxes, and impact costs range from 0.1% to 0.3% per trade, greatly restricting the net profitability of weekend effect strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec69\" class=\"Section3\"\u003e\u003ch2\u003e6.5.2 Market Impact and Scalability\u003c/h2\u003e\u003cp\u003eScalability of the weekend effect strategy is limited by liquidity, particularly in small-cap stocks where the effect is strongest. Their performance would probably be weakened by mass application through market impact and increased arbitrage trading.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec70\" class=\"Section3\"\u003e\u003ch2\u003e6.5.3 Regulatory and Tax Consequences\u003c/h2\u003e\u003cp\u003eWeekend effect strategies would be subject to tax consequences, e.g., short-term capital gains taxation in India. Frequent trading strategies may also attract regulatory scrutiny or position limits.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec71\" class=\"Section2\"\u003e\u003ch2\u003e6.6 Future Research Directions\u003c/h2\u003e\u003cp\u003eThis research generates some research directions for the future:\u003c/p\u003e\u003cp\u003eIntraday Patterns: Future research could analyse intraday patterns during Monday and Friday trading sessions to analyse the timing of the weekend effect.\u003c/p\u003e\u003cp\u003eOptions and Derivatives: It may be possible to examine if the weekend effect is also present in options and futures markets, and if derivative instruments can be employed to take advantage or hedge calendar anomalies.\u003c/p\u003e\u003cp\u003eInternational Comparisons: International comparison of various emerging markets could potentially identify similar factors behind the persistence of calendar anomalies.\u003c/p\u003e\u003cp\u003eMachine Learning Applications: More sophisticated machine learning techniques could be employed to identify more advanced calendar patterns or to develop more advanced trading strategies that consider multiple factors simultaneously.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis detailed study provides robust empirical support for the existence of a significant weekend effect in the Indian market, with evidence of stocks having systematically varying performance patterns on Monday compared to Friday. The study makes valuable contributions to the growing literature on calendar anomalies in emerging markets and offers practical implications for market players and theoretical contributions to our understanding of market efficiency.\u003c/p\u003e\u003cdiv id=\"Sec73\" class=\"Section2\"\u003e\u003ch2\u003e7.1 Summary of Key Findings\u003c/h2\u003e\u003cp\u003eOur key conclusion is the major outcome of this research, which confirms our main hypothesis: Indian stocks have significantly weaker Monday returns (-0.234%) compared to Friday returns (+\u0026thinsp;0.184%), a statistically and economically significant difference of 0.418 percentage points. This analysis, based on the data derived from 487 companies for a timespan of five years (2019\u0026ndash;2024), which acts as a proof that the pattern of the weekend effect is also applicable in Indian context.\u003c/p\u003e\u003cp\u003eA lot of key patterns have come into highlight through our analysis. First, the weekend effect differs in a systematic manner due to market capitalisation, being highest on small cap stocks (0.510%) and lowest at large cap stocks (0.290%). This consistent pattern shows expectation that market structure features and shape calendar anomalies size.\u003c/p\u003e\u003cp\u003eSecond, the weekend effect is heterogeneous across industries; industries with cyclic operations like metal (0.579%) and energy (0.535%) are documenting strong effects, while defensive industries like healthcare (0.345%) and information technology (0.365 percentage) document poor effects. This pattern indicates that the macroeconomic environment and the global forces are capable of explaining industry specific forces mitigating the weekend effect.\u003c/p\u003e\u003cp\u003eThird, the anomaly remains strong across different market conditions and horizons, showing great stability despite deep structural changes in the Indian market during our sample duration. The effect is slightly stronger during episodes of high volatility and market stress, suggesting that uncertainty does make behavioural forces behind calendar anomalies stronger.\u003c/p\u003e\u003cp\u003eFourth, although the weekend effect generates excess positive returns to theoretical trading strategies, implementation is drastically impeded by transaction costs, which significantly reduce net profitability. This finding helps explain why the anomaly is still present despite extensive reporting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec74\" class=\"Section2\"\u003e\u003ch2\u003e7.2 Policy and Regulatory Implications\u003c/h2\u003e\u003cp\u003eThe documented weekend effect raises several policy considerations for market regulators and infrastructure providers. To begin with, the continued existence of calendar anomalies implies that the existing market structure may not be sufficiently structured to eliminate predictable return patterns. Regulators might want to consider whether changes to trading hours, settlement processes or dissemination of information, may serve to eliminate calendar anomalies.\u003c/p\u003e\u003cp\u003eSecondly, the more extreme weekend effect presents in small-cap stocks, where there is a considerable limited retail investor participation, suggests that retail investor education programs may contribute to a decrease in the behavioural biases associated with calendar anomalies. Awareness of the psychological and behavioural forces driving the weekend effect can be used to develop investor education programs.\u003c/p\u003e\u003cp\u003eThird, the result that the weekend effect produces positive excess returns but encounters substantial transaction cost barriers underscores the significance of market structure efficiency. Initiatives to lower transaction costs and enhance market liquidity could conceivably diminish calendar anomalies by making arbitrage more feasible.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec75\" class=\"Section2\"\u003e\u003ch2\u003e7.3 Limitations of the Study\u003c/h2\u003e\u003cp\u003eA few limitations must be considered when making inferences from the findings of this study. Firstly, our choice of NSE-listed firms may restrict the generalisability of conclusions to other Indian stock exchanges or markets with varying features. Omitting smaller stock exchanges and unlisted firms could influence the representativeness of our inferences regarding the wider Indian equity market.\u003c/p\u003e\u003cp\u003eSecond, our sample period of five years, although large, might not reflect long-run structural shifts or cyclical influences that work over longer time frames. The reported persistence of the weekend effect over our sample period does not necessarily imply future continuation, especially since market structure and participant behaviour continue to change.\u003c/p\u003e\u003cp\u003eThird, our analysis is limited to these closing-to-closing returns, which ignore critical intraday patterns or any timing of price movements within each trading session. Future research that examines intraday patterns could add an additional layer of understanding the mechanisms of the weekend effect.\u003c/p\u003e\u003cp\u003eFourth, we control for a large number of confounding variables that could have an impact, but we can't control for all of the possible confounders that may impact the weekend effect. Unobservable variables due to market microstructure, institution-based practices, or behavioural factors could partly explain our results.\u003c/p\u003e\u003cp\u003eFifth, our cost analysis employs ordinary estimates of costs but could differ from the costs actually experienced by all participants. Sophisticated institutional investors who can execute trades may experience lower costs, while certain retail investors may pay more, impacting the day-to-day feasibility of weekend effect strategies for varying participant groups.\u003c/p\u003e\u003cp\u003eLastly, our research does not investigate the weekend effect in derivative markets or its influence on options pricing and hedging techniques. The established patterns in the returns of the underlying stocks may have significant implications for derivative instruments that this research does not cover.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec76\" class=\"Section2\"\u003e\u003ch2\u003e7.4 Suggestions for Future Research\u003c/h2\u003e\u003cp\u003eThis research lays down a number of promising avenues for future calendar anomaly studies in emerging markets. To begin with, intraday analysis of the weekend effect may be useful in determining the timing and mechanisms for the anomaly. Studies exploring opening and closing price movements, patterns for volume during Monday and Friday trading sessions, as well as the role played by overnight information could better help us ascertain when and how the weekend effect occurs.\u003c/p\u003e\u003cp\u003eSecond, comparative cross-market studies between the weekend effect across various emerging markets can provide insight into the common factors that lead to calendar anomalies' persistence. This could look at whether there are similar patterns in markets in similar stages of development, similar institutional contexts, or similar cultural contexts.\u003c/p\u003e\u003cp\u003eThird, research into the weekend effect in derivative markets, such as options and futures, might illuminate the existence of calendar anomalies beyond spot markets, and whether it is possible to hedge or exploit them via derivative contracts. This study may also study if the options pricing models completely capture systematic patterns of returns such as the weekend effect.\u003c/p\u003e\u003cp\u003eFourth, behavioural research on the psychology of investors, as well as investor decision-making behaviour during the week, may help explain how the weekend effect occurs. By using surveys, experiments, and observations of trading behaviour data, we may be able to understand which psychological factors cause calendar-based patterns of trading.\u003c/p\u003e\u003cp\u003eFifth, machine learning and artificial intelligence techniques could potentially identify more complex calendar patterns and devise advanced trading strategies that analyse relationships among multiple variables simultaneously. Such studies, can indicate if machine learning and artificial software can predict and analyse the anomalies caused by weekend effect.\u003c/p\u003e\u003cp\u003eSixth, from looking at the patterns and interactions of calendar anomalies (e.g. announcements, divided payments) would help traders and investors to create stock returns. Investors having such knowledge can increase the development of more complete models.\u003c/p\u003e\u003cp\u003eSeventh, studies show how calendar anomalies change as the market size keeps growing and developing, indicates a pattern that how developed market can have an effect on trends in anomalies. Such studies help in predicting whether or not the weekend effect stays consistent in Indian markets as it is under continuous development.\u003c/p\u003e\u003cp\u003eLastly, this study examines that calendar anomalies can help determine if these patterns show any market inefficiencies that reduce overall economic welfare, where they can serve useful functions in risk distribution.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable. This study did not involve human participants or animals.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain data from any individual person.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding was received to support this research.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAuthor Contributions1. CA Mohd. Swaleh: Conceptualization, Supervision, Methodology design, Validation, Writing \u0026ndash; review \u0026amp; editing.2. Mr. Vikramaditya Chakraborty: Data curation, Formal analysis, Investigation, Software, Visualization, Writing \u0026ndash; original draft.Both authors contributed to the interpretation of results, approved the final manuscript, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to CMR University for providing the academic support and resources to carry out this study. The first author, Vikramaditya Chakraborty, extends heartfelt thanks to CA Mohd Swaleh for his constant guidance, supervision, and valuable insights throughout the\u0026nbsp;research\u0026nbsp;work.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData was abstracted from secondary dataSecondary data sources:https://www.nseindia.com/https://www.bseindia.com/index.htmlYahoo Finance\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgrawal, A., \u0026amp; Tandon, K. (1994). Anomalies or illusions? 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M. (2001). A note on information seasonality and the disappearance of the weekend effect in the UK stock market. \u003cem\u003eJournal of Banking \u0026amp; Finance\u003c/em\u003e, 25(10), 1941\u0026ndash;1956. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0378-4266(00)00167-9\u003c/span\u003e\u003cspan address=\"10.1016/S0378-4266(00)00167-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Weekend Effect, Calendar Anomaly, Stock Returns, Market Efficiency, Behavioural Finance, Trading Strategy","lastPublishedDoi":"10.21203/rs.3.rs-7580297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7580297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe \"Monday effect\" or \"weekend effect\" is arguably one of the most documented calendar anomalies in the financial markets that is inconsistent with the underlying meaning of the efficient market hypothesis as stock returns exhibit systematic patterns based upon the day of week. The research presented examines whether stocks behave differently on Mondays relative to Fridays observing both the size and direction of returns on these trading days. Using quantitative research methodology examined stock returns on a daily frequency from the National Stock Exchange of India for a five-year period (2019 to 2024) for data on a sample of 500 companies from different sectors. The quantitative research methodology applies statistical tests including t-tests, ANOVA, and regression analysis to identify whether a statistically significant difference exists in mean returns on Mondays and Fridays. Our evidence provided a statistically significant weekend effect, the average Monday return of -0.23% and the average Friday return of +\u0026thinsp;0.18% gave a differential of 0.41 percentage points. The evidence shows a larger effect on mid-cap and small-cap stock relative to large-cap stock which suggests the weekend effect is a function of market capitalisation. The results of the study have important implications for the management of portfolios, trading strategies, and market efficiency. The study adds to the growing field of literature on calendar anomalies in emerging markets, and provides practical information for investors and fund managers who want to enhance their trading strategies in conformity with weekly market trends.\u003c/p\u003e","manuscriptTitle":"Weekend Effect in Stock Returns: Do Stocks perform differently on Mondays VS Fridays?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 07:18:05","doi":"10.21203/rs.3.rs-7580297/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc8f3ce9-6dd8-490e-acf3-a2853bbb6cb5","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T15:22:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 07:18:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7580297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7580297","identity":"rs-7580297","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}