An Inquiry into the Influence of Key Macroeconomic Variables on the Dhaka Stock Exchange Equity Returns

An Inquiry into the Influence of Key Macroeconomic Variables on the Dhaka Stock Exchange Equity Returns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Inquiry into the Influence of Key Macroeconomic Variables on the Dhaka Stock Exchange Equity Returns Mallik Rawshan Alam, Aoulad Hosen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8926796/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 This study is based on the performance of the Dhaka Stock Exchange (DSE) from January 2015 to December 2024 and empirically analyses the implications for key macroeconomic variables, including interest rates, inflation rates, and currency rates. The analysis uses monthly data from the Bangladesh Bank and employs time-series econometric methodologies, including unit root tests, Johansen cointegration, vector error correction models (VECM), and Granger causality tests. The results demonstrate that all variables are integrated in order one, with indications of a singular long-run cointegrating relationship. The results indicate that interest rates have a substantial negative long-term impact on the DSE index, aligning with theoretical predictions that increased borrowing and opportunity costs diminish equity prices. Conversely, inflation and exchange rates do not exhibit statistically significant long-term effects; nonetheless, short-term dynamics indicate that inflation positively influences the index, perhaps due to speculative market behaviour. Granger causality studies establish bidirectional causality between interest rates and the DSE index, while inflation is determined to affect exchange rates. These results underscore the DSE's inefficacy, wherein macroeconomic shocks may exert delayed or exacerbated impacts on pricing. The research offers pragmatic insights for investors, policymakers, and regulators, including the importance of monetary policy coordination and enhanced market efficiency in Bangladesh. JEL Codes: G12, E00, G28, E44, C32. Dhaka Stock Exchange (DSE) Macroeconomic Variables Stock Market Volatility Time-Series Models Figures Figure 1 1. Introduction There has been considerable discussion regarding the effectiveness of stock exchanges due to their significant role in the global economies and financial markets. Stock markets assist the economy in expanding by influencing how people and businesses spend and save their money. They achieve these objectives by making sure people spend their money wisely and pushing them to make investments that will pay dividends. As a result, market performance has become a key measure of economic fundamentals that has an effect on the investment decisions of both domestic and international investors (Islam et al., 2015). Macroeconomic variables are often considered crucial factors influencing stock market trends since they might impact future corporate cash flows and the risk-adjusted discount rates used in valuation models (Islam et al., 2015; Eldomiaty et al., 2020). Stock exchanges are also beneficial for firms because they are easy to trade and have a lot of cash flow (Adjasi & Biekpe, 2006). For this beneficial role to work, the stock market and key macroeconomic data must be closely connected. Theorists agree that macroeconomic factors are important, but their nature, size, and direction are unclear. This position is especially true in Emerging Markets (EM), where institutional and structural factors are completely unique (Islam et al., 2014; Islam et al., 2015; Current Market Valuation, 2025). This variation shows how vital it is to look at each country separately. There have been several ups and downs at the Dhaka Stock Exchange (DSE) in Bangladesh over the years (Islam et al., 2023a). Therefore, investors, regulators, and politicians need to figure out what macroeconomic conditions affect its performance. 2. Background of the Study Stock markets are pivotal for economic growth; they function as efficient mechanisms for mobilising capital and channelling it toward productive investment. In emerging economies, their roles are even more evident, as they intensify domestic financial systems, enhance resource allocation, and support industrialisation and economic development (Islam et al., 2014 ; Islam et al., 2015 ). The increasing interconnectedness of global financial markets further accentuates the importance of understanding their complex dynamics (Islam et al., 2015 ). This study focuses on the DSE, Bangladesh’s primary stock exchange, within the broader context of the country’s strong economic growth recently (Islam et al., 2014 ). National economic conditions closely tie the DSE's performance, serving as a barometer of basic fundamentals. However, the exchange is characterised by unique characteristics and risks, including frequent regulatory changes, a relatively small market size, low liquidity, and limited depth. Historically, the DSE has been prone to manipulation and sharp crashes, most notably in 1996 and 2011 (Gwala & Mashau, 2022 ; Islam et al., 2023a ). These features distinguish the DSE from developed markets and impact the transmission of macroeconomic shocks. Evidence indicates that the DSE is not weak-form efficient because its index follows a predictable pattern (Islam et al., 2017 ). Structural weaknesses, such as low market capitalisation and turnover, a narrow range of financial products, and a large proportion of inexperienced investors (Islam et al., 2023a )—undermine price discovery and slow the incorporation of information into stock prices. Consequently, macroeconomic announcements may exert a stronger or delayed effect compared to efficient markets. This inefficiency highlights the relevance of analysing macroeconomic-stock market linkages in Bangladesh. For investors, understanding these relationships provides valuable guidance for portfolio management. For policymakers, it sheds light on the interplay between financial markets and broader economic stability (Islam et al., 2015 ). 3. Theoretical Foundation and Literature Review Research indicates that macroeconomic factors contribute to approximately 30–35% of the variations in stock prices (Chandra, 2004 ). This study focuses on economic fundamentals, offering a thorough examination of fundamental analysis and illustrating the impact of root macroeconomic variables on stock price movements. Corporate earnings, a crucial determinant of stock prices, are affected by multiple factors, including economic growth, productivity, labour quality, and capital stock (Golob & Bishop, 1997 ). In addition, direct and indirect conditions such as interest rates, industrial output, inflation, exchange rates, and money supply also significantly affect stock market performance (Goswami & Jung, 1997 ). Maysami et al. (2005), applying cointegration analysis to Singapore’s stock market, confirmed the importance of these macroeconomic variables. Similar findings have been reported by Aga & Kocaman ( 2006 ), Abeyratna et al. ( 2004 ), Bilson et al. ( 1999 ), among others. 3.1 Theoretical Foundations of Macroeconomic Influences on Stock Markets The interaction between macroeconomic conditions and stock market performance is pivotal to financial economics, grounded in several theoretical constructs. The Efficient Market Hypothesis (EMH), articulated by Fama ( 1970 ), posits that stock prices fully reflect market expectations of a company’s future performance, which is inherently tied to broader macroeconomic conditions (Eldomiaty et al., 2020 ). In a truly efficient market, all available information, including macroeconomic data, is quickly and completely absorbed into prices. However, evidence indicates that the DSE does not conform to the weak form of EMH (Islam et al., 2017 ), implying that macroeconomic variables may exert stronger and more exploitable effects due to information asymmetries and delayed adjustments. Ross’s ( 1976 ) Arbitrage Pricing Theory (APT) further extends this view, proposing that asset returns are influenced by multiple macroeconomic risk factors, with investors earning risk premia in response. APT does not identify specific factors; however, it provides a comprehensive framework for the simultaneous analysis of multiple variables, in contrast to single-factor models like the Capital Asset Pricing Model (CAPM) (Eldomiaty et al., 2020 ). Interest rates are widely regarded as a major determinant of stock prices. They fluctuate with time, default risk, inflation, and capital productivity (Chandra, 2004 ). Shifts in interest rates influence investor behaviour, leading to reallocations between equity markets and fixed-income instruments and speculative activity. Central banks regulate interest rates through monetary policy (Kevin, 2000 ), while informal financial markets remain unregulated. Theory predicts an inverse relationship between interest rates and stock prices. Smith ( 1990 ) exhibited that U.S. stock prices often rose following interest rate cuts. In Korea, Goswami & Jung ( 1997 ) found an inverse relationship between stock prices and long-term rates, but a positive correlation with short-term rates due to liquidity effects. The cash-flow discounting model posits that elevated interest rates augment discount rates, consequently diminishing the present value of anticipated cash flows and, by extension, stock prices (Islam et al., 2017 ; Eldomiaty et al., 2020 ; Rahman et al., 2012 ). At the same time, higher borrowing costs reduce corporate profitability, suppressing valuations. Conversely, lower rates stimulate investment and economic activity, boosting stock returns. The opportunity cost channel reinforces this relationship: when interest rates rise, fixed-income assets become more attractive relative to equities, encouraging investors to shift capital away from the stock market (Islam et al., 2017 ; Rahman et al., 2012 ). Given the inefficiency of the DSE, this channel likely has an amplified effect, as investors may quickly reallocate funds into bank deposits promising higher guaranteed returns. Inflation also plays a pivotal role. While Chandra ( 2004 ) noticed its dual impact across industries, most studies indicate an inverse relationship between inflation and stock prices. Fama & Schwert ( 1977 ) demonstrated that both expected and unexpected inflation reduce stock returns. Feldstein ( 1979 , 1997 ) and Summers ( 1981 ) shaded the distortive impact of inflation on taxation and accounting rules, including understated depreciation and taxation of nominal gains, which depress real returns. Amadi & Odubo ( 2002 ) argued that mispricing often results from investors relying on nominal instead of real measures. Empirical evidence strongly supports the negative effect of inflation, which arises through purchasing power erosion, reduced profitability, tax distortions, and negative real returns (Eldomiaty et al., 2020 ; Gopinath, 2025 ). The exchange rate–stock price relationship is more intricate. Fluctuations directly affect import-dependent industries and corporate earnings. Maku & Atanda ( 2010 ) found a positive correlation between stock prices and a depreciating Naira in Nigeria. Exchange rates influence competitiveness, trade balances, and reserves, creating risks and opportunities (Osamwonyi, 2003 ). Channels include (i) international competitiveness and trade balance, where depreciation boosts exports (Eldomiaty et al., 2020 ; Phylaktis & Ravazzolo, 2005 ); (ii) transaction and translation exposure in trade and financial statements; and (iii) the economic activity channel, where depreciation can increase aggregate demand. Empirical findings are mixed—negative (Mfugale & Olomi, 2023 ), positive (Golder et al., 2020 ), or even reverse causality (Ullah et al., 2017 ). This variability underscores the need for country-specific analysis. 3.2 Empirical Evidence in Emerging Economies A substantial frame of work highlights the impact of macroeconomic factors on emerging markets (EM) (Islam et al., 2014 ; 2015 ; Eldomiaty et al., 2020 ). Garza-Garcia & Yue ( 2010 ) demonstrated U.S. influence on Chinese markets, while Sharma & Mahendru (2010) found similar dynamics in India. Asmy et al. ( 2010 ) noted differing pre- and post-crisis relationships among inflation, money supply, and exchange rates. Humpe & Macmillan ( 2009 ) found strong macro-market linkages in the U.S. and Japan. Qayyum & Anwar ( 2011 ) showed that monetary policy drives volatility in Pakistan. More recent studies confirm that GDP, interest rates, inflation, exchange rates, money supply, and capital flows are key factors, though results often conflict in direction and significance (Mfugale & Olomi, 2023 ). This inconsistency demonstrates the importance of localised analysis, as causality and magnitude vary across contexts (Adrian et al., 2024 ; Gopinath, 2025 ). 3.3 Literature on the Dhaka Stock Exchange For the DSE, multiple studies confirm a long-run cointegrating relationship between stock prices and macroeconomic variables (Islam et al., 2018). Inflation is consistently shown to negatively affect DSE returns (Gwala & Mashau, 2022 ; Golder et al., 2020 ; Mfugale & Olomi, 2023 ). Similarly, interest rates negatively affect prices, aligning with opportunity cost and profitability theories (Islam et al., 2017 ; Mfugale & Olomi, 2023 ). Exchange rate effects, however, are mixed: some studies demonstrate a positive link, others negative, and some indicate reverse causality, where stock prices Granger-cause exchange rates (Ullah et al., 2018). These contradictions highlight the DSE’s inefficiency, low liquidity, and heightened sensitivity to macro shocks. 3.4 Identification of the Research Gap While theory and empirical evidence suggest that macroeconomic factors influence stock markets, findings for emerging markets (EM) are inconsistent and context-dependent. In Bangladesh, the DSE’s inefficiency, volatility, and structural limitations suggest that macroeconomic shocks may exert stronger and delayed effects compared to developed markets. Empirical evidence on inflation and exchange rate impacts is particularly contradictory, revealing a need for context-specific verification. This study addresses these gaps by examining the effects of interest rates, inflation, and exchange rates on the DSE using updated data (2015–2024) and advanced econometric methods. It tests the applicability of established theories within Bangladesh’s unique market environment, which is characterised by import dependency and the opportunity cost of bank deposits. The findings aim to provide nuanced insights for investors, policymakers, and regulators. 3.5 Research Questions This study aims to address the following research questions: 3.5.1 Time-Series Properties : What are the stationarity characteristics and descriptive statistics of the DSE index, interest rate, inflation rate, and exchange rate of Bangladesh during 2015–2024? 3.5.2 Long-Run Relationships : Are the DSE index, interest rate, inflation rate, and exchange rate cointegrated in the long run over the study period? 3.5.3 Short-Run and Long-Run Effects : How do interest rates, inflation, and exchange rates influence the DSE index in both the short run and the long run? 3.5.4 Causality Dynamics : Does Granger causality exist between the DSE index and the selected macroeconomic variables? If so, what is the direction of causality? 3.6 Research Hypotheses 3.6.1 H1: Time-Series Properties H1a: The DSE index, interest rate, inflation rate, and exchange rate are non-stationary at their levels. H1b: These variables become stationary after first differencing, indicating integration of order one, I (1). 3.6.2 H2: Long-Run Relationships H2a: The DSE index, interest rate, inflation rate, and exchange rate share at least one cointegrating relationship in the long run. H2b: Interest rates exert a significant negative long-run effect on the DSE index. H2c: Inflation and exchange rates exert significant long-run effects on the DSE index. 3.6.3 H3: Short-Run Dynamics H3a: Interest rates have a significant short-run effect on the DSE index. H3b: Inflation has a significant short-run effect on the DSE index. H3c: Exchange rate fluctuations have a significant short-run effect on the DSE index. 3.6.4 H4: Granger Causality H4a: There exists bidirectional Granger causality between interest rates and the DSE index. H4b: Inflation Granger-causes the DSE index and/or the exchange rate. H4c: Exchange rate fluctuations Granger-cause the DSE index. 4. Data and Research Methods 4.1 Data Availability: This study uses monthly time-series data for the DSE Index (as the dependent variable) and three key macroeconomic variables: the interest rate, inflation rate, and exchange rate (as independent variables). The primary data source for all variables is the website of Bangladesh Bank, ensuring consistency and direct applicability to the research query. The dataset covers a continuous period of 10 years, from January 2015 to December 2024, providing a total of 120 monthly observations for each variable. This length of data is generally considered sufficient for robust time-series econometric analysis. Among the four variables, the first, i.e., the DSE Index datasets collected from the Dhaka Stock Exchange depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables, recognised were collected from the Bangladesh Bank's (BB) repository system (https://www.bb.org.bd/en/index.php). Both repositories are publicly available. The two data sources name and repositories are: 4.1.1 Name of data source: Dhaka Stock Exchange (DSE) and Repository: https://www.dsebd.org/ 4.1.2 Name of data source: Bangladesh Bank (BB) and Repository: https://www.bb.org.bd/en/index.php The variables are defined as follows: 4.1.1 DSE Index: Represents the overall performance of the Dhaka Stock Exchange, serving as a proxy for the aggregate stock market return. 4.1.2 Interest Rate: Reflects the general cost of borrowing in the economy and the opportunity cost of holding equity investments. 4.1.3 Inflation Rate: Measured by the Consumer Price Index (CPI), indicating the rate of change in the general price level of goods and services. 4.1.4 Exchange Rate: Represents the value of the Bangladeshi Taka against USD. 4.2 Model Specification: INDEX =A*INTEREST_RATE + B*INFLATION_RATE +C*EXCHANGE_RATE + D + E Where A, B and C are coefficients, D and E consist of a parameter and disturbance term, respectively, and INDEX means DSE Index. 4.3 General Tendency of Data The study seeks to empirically investigate the long-term and short-term effects through time series analysis of three principal macroeconomic variables—interest rate, inflation rate, and exchange rate—on the influence of the Dhaka Stock Exchange (DSE). To do so, the data of each variable are depicted individually by Fig. 1. In the figure the study plotted each variable and got each frequency curve, which presents the general trend line about the study. Among these four quartiles of the figure, the inflation rate and exchange rate showed an upward trend over time (3rd and 4th quartiles). With little fluctuation, the interest rate also found an upward tendency (2nd quartile), but the dependent variable of the research, the DSE index, did not show any trend line (1st quartile). As a matter of fact, the DSE index did not respond equally to the progress of the three independent variables. 4.4 Time Series Properties: a) Stationarity Tests: A fundamental prerequisite for many time-series econometric models, particularly regression analyses, is that the variables are stationary. Stationarity implies that the statistical properties of a time series (mean, variance, and autocorrelation) remain constant over time. To formally test for the presence of a unit root (indicating non-stationarity) in each variable, the Augmented Dickey-Fuller (ADF) Test and the Phillips-Perron (PP) Test are typically employed. The ADF test is a widely used and robust test that extends the basic Dickey-Fuller test by incorporating lagged differenced terms to account for potential serial correlation in the error terms. The null hypothesis (H 0 ) for the ADF test is the presence of a unit root (i.e., the series is non-stationary). The PP test is another powerful test for unit roots that addresses serial correlation and heteroskedasticity in the error terms through non-parametric corrections to the t-statistic. Similar to ADF, its null hypothesis (H 0 ) is the presence of a unit root. Using both tests provides a more robust assessment of stationarity. Unit root test results directly determine the appropriate econometric model for subsequent analysis. If all variables are stationary at levels, a standard Vector Autoregression (VAR) model can be employed. The test results reveal whether the series exhibit trends or random walks, providing insights into their underlying economic dynamics. b) Cointegration Analysis: If the variables are found to be non-stationary but integrated of the same order (e.g., I (1)), cointegration analysis is performed to determine if there is a long-run equilibrium relationship among them (Corporate Finance Institute, 2025). The Johansen Cointegration Test is typically employed for this purpose. This method is generally preferred over the Engle-Granger two-step method when dealing with more than two variables, as it allows for the identification of multiple cointegrating relationships (Corporate Finance Institute, 2025). It is also considered more robust for larger sample sizes, which is relevant for the 10-year monthly data used in this study. Both the trace statistic and the maximum eigenvalue statistic are used to test the null hypothesis of no cointegration against the alternative of one or more cointegrating relationships (Corporate Finance Institute, 2025). c) Vector Error Correction Model (VECM) Specification: The choice between a Vector Autoregression (VAR) model and a Vector Error Correction Model (VECM) is determined by the outcome of the cointegration tests. If no cointegration is found among the variables (or if variables are stationary at levels), a standard VAR model is applied to the appropriate (stationary) series (Buteikis, 2019). A VAR model captures the linear interdependencies among multiple time series, where each variable in the system is affected by its own past values (lags) and the past values of all other variables in the system. However, if cointegration is found, a VECM is used. A VECM is a restricted VAR model that explicitly incorporates the long-run cointegrating relationships through an error correction term while simultaneously modelling the short-run dynamics and adjustments towards the long-run equilibrium (Buteikis, 2019). The optimal lag length for the VAR/VECM is determined using various information criteria (e.g., Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan-Quinn Information Criterion (HQIC)) to ensure model parsimony and avoid overfitting while capturing sufficient dynamics. The selected VAR/VECM is then estimated using appropriate econometric software to obtain the coefficients, standard errors, and significance levels of the relationships between the DSE Index and the macroeconomic variables. d) Granger Causality Analysis: Granger causality analysis is conducted to investigate the predictive power between the DSE Index and the selected macroeconomic variables (interest rate, inflation rate, and exchange rate) (Buteikis, 2019). Granger causality tests determine whether past values of one variable can statistically help predict the current values of another variable, implying a causal relationship in a statistical, rather than philosophical, sense. Granger causality tests are conducted within the established VAR/VECM framework. The F-test is commonly employed to assess the joint significance of the lagged coefficients of the "causing" variable in the equation of the "effected" variable. A low p-value (typically below 0.05) indicates the rejection of the null hypothesis that the variable does not Granger-cause the other, suggesting a statistically significant causal link. The analysis explores all pairwise relationships (e.g., interest rate Granger-causes DSE, DSE Granger-causes interest rate, etc.). 4.5 Data Availability The study used two repository systems to collect the data and analyse the econometric model. Among the four variables: The datasets for all four variables—1. DSE Index, 2. Interest Rate, 3. Inflation Rate, and 4. Exchange Rate—covered a 10-year period from January 2015 to December 2024, consisting of 120 monthly point-to-point datasets for each variable. Among the four variables, the first, i.e., the DSE Index (independent variable) datasets collected from the Dhaka Stock Exchange (DSE) depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables, recognised as independent, were collected from the Bangladesh Bank's (BB) repository system (https://www.bb.org.bd/en/index.php). The datasets used in this study are available in the following repositories: a) Name: Dhaka Stock Exchange (DSE), Persistent Web Link to Datasets: https://www.dsebd.org b) Name: Bangladesh Bank (BB), Persistent Web Link to Datasets: https://www.bb.org.bd/en/index.php 5. Empirical Results and Discussions 5.1 Descriptive Statistics of Variables : Initial statistical summaries of each variable are computed to understand their basic characteristics, central tendencies, and dispersion over the study period. This step is crucial for gaining preliminary insights into the behaviour of the data. Measures calculated for each variable include the mean, median, standard deviation (as a proxy for volatility), minimum value, and maximum value (Table 1 ). The table below presents the calculated descriptive statistics for the DSE Index, interest rate, inflation rate, and exchange rate from January 2015 to December 2024. Table 1 Descriptive Statistics of Variables (January 2015 - December 2024) Particulars INDEX INTEREST_RATE INFLATION_RATE EXCHANGE_RATE Mean 5474.59 6.70 6.55 88.86 Median 5413.54 6.14 5.77 84.80 Maximum 7329.04 12.31 10.34 120.00 Minimum 3989.09 2.33 5.38 77.80 Std. Dev. 792.17 2.68 1.52 12.20 Skewness 0.02 0.61 1.38 1.34 Kurtosis 2.09 2.66 3.30 3.49 Observations 120 120 120 120 Source Bangladesh Bank (January 2015 to December 2024), Study Approximations. The DSE Index, for instance, shows a mean of approximately 5474.59, with a standard deviation of 792.17, indicating notable volatility throughout the decade. The minimum index value was 3989.09, while the maximum reached 7329.04, highlighting significant market swings (2nd column, Table 1 ). Interest rates in Bangladesh during this period averaged 6.70%, ranging from a low of 2.33% to a high of 12.31%. This wide range reflects dynamic monetary policy adjustments and economic conditions over the ten years (3rd column, Table 1 ). The inflation rate, measured by CPI, had a mean of 6.55% and a standard deviation of 1.52, indicating a relatively stable but upward-trending price level, especially towards the latter part of the study period (4th column, Table 1 ). The exchange rate showed a mean of 88.86, with a significant range from 77.80 to 120.00, underscoring a substantial depreciation of the Bangladeshi Taka, particularly evident from mid-2022 onwards (5th column). This depreciation is a key trend that warrants further in-depth analysis regarding its impact on the DSE. Observing the raw trends in the data provides preliminary visual clues about potential correlations before formal econometric testing. For instance, the DSE Index experienced a notable bull run from late 2020 to late 2021, a period during which interest rates were comparatively low (Data File, 2015–2024). Conversely, from mid-2022 to 2024, the DSE Index generally trended downwards, coinciding with rising interest rates and inflation and a sharp depreciation of the exchange rate (Data File, 2015–2024). These observed co-movements, even prior to formal statistical validation, establish an intuitive basis for the expected relationships and aid in contextualising the subsequent econometric findings, thereby allowing for a richer interpretation of the results. 5.2 Results of Unit Root Tests : Based on the results from the table, both the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests consistently indicate that all four variables – the DSE Index, Interest Rate, Inflation Rate, and Exchange Rate – are non-stationary at their levels. The p-values for the level series are generally above the conventional 0.05 significance level, leading to a failure to reject the null hypothesis of a unit root. This suggests that these series exhibit trends or random walk characteristics, meaning that shocks to these variables have permanent effects and they do not revert to a long-run mean. Table 2 Unit Root Test Results Variable Test Type ADF Statistic P-value PP Statistic P-value Conclusion Order of Integration DSE Index Level -1.887039 0.3374 -1.822736 0.3680 Non-stationary I (1) 1st Diff -9.471213 0.0000 -9.419641 0.0000 Stationary Interest Rate Level -1.364658 0.5973 -0.992641 0.7543 Non-stationary I (1) 1st Diff -4.116200 0.0014 -11.86208 0.0000 Stationary Inflation Rate Level -0.920426 0.9495 -0.759389 0.9656 Non-stationary I (1) 1st Diff -3.394659 0.0570 -3.410458 0.0549 Stationary Exchange Rate Level -0.297940 0.9899 0.090248 0.9969 Non-stationary I (1) 1st Diff -7.054045 0.000 -6.979780 0.0000 Stationary Source : Bangladesh Bank (January 2015 to December 2024), Study Approximations However, upon taking the first difference of each variable, both ADF and PP tests show highly significant p-values, leading to a strong rejection of the null hypothesis of a unit root (Table 2 ). This indicates that all variables become stationary after first differencing, classifying them as integrated of order one, or I (1) (8th column, Table 2 ). It suggests that shocks to the market have permanent effects, meaning the market does not automatically revert to a long-run mean after a disturbance. This is a critical characteristic for investors and risk managers, as it implies that market downturns or upturns might represent new, persistent levels rather than temporary deviations. Such a characteristic underscore the necessity of employing econometric models that can appropriately handle non-stationary data. 5.3 Results of Cointegration Tests : Based on the Trace Statistic, the null hypothesis of no cointegrating relationships (r = 0) is rejected at the 0.05 significance level (Trace Statistic = 51.46, Critical Value = 47.85, P-value = 0.0220). This indicates the presence of at least one cointegrating vector. Similarly, the Maximum Eigenvalue Statistic also rejects the null hypothesis of r = 0 (Max-Eigen Statistic = 30.19, Critical Value = 27.58, P-value = 0.0226), further confirming at least one cointegrating relationship (2nd row, Table 3 ). Table 3 Johansen Cointegration Test Results Hypothesized No. of CE(s) Trace Statistic 0.05 Critical value Prob. Max-Eigen Statistic 0.05 Critical value Prob. None * 51.46347 47.85613 0.0220 30.19204 27.58434 0.0226 At most 1 21.27143 29.79707 0.3409 14.33580 21.13162 0.3381 At most 2 6.935626 15.49471 0.5852 6.926842 14.26460 0.4978 At most 3 0.008785 3.841465 0.9250 0.008785 3.841465 0.9135 Source Bangladesh Bank (January 2015 to December 2024), Study Approximations However, for the hypothesis of "at most 1" cointegrating relationship (r ≤ 1), both the Trace Statistic (21.27) and Max-Eigen Statistic (14.34) are below their respective 0.05 critical values (29.80 and 21.13), with p-values less than 0.05 (3rd row, Table 3 ). This suggests that the null hypothesis of "at most 1" cointegrating relationship cannot be rejected. Therefore, the results indicate the existence of one cointegrating relationship among the variables. The finding that all variables are integrated of order one I (1)) and exhibit a single cointegrating relationship strongly supports the theoretical notion that macroeconomic fundamentals drive stock market performance in the long run, even in an emerging market like Bangladesh. This empirically validates the idea that the DSE is not merely subject to random fluctuations but is fundamentally tied to the broader economic environment, with an underlying equilibrium relationship that ensures long-term co-movement. 5.4 VAR/VECM Estimation Results : Given the confirmation of a single cointegrating relationship, a VECM is the appropriate choice for estimating the short-run and long-run impacts of the macroeconomic variables on the DSE Index. A summary of the results is shown in the following Table 4 . Output using EViews software is given in Appendix A . Table 4 Cointegrating Relationship (Long-run Equation) Variable Coefficient t-statistic Significance (at 5%) INTEREST_RATE (-1) + 290.79 4.58 Yes INFLATION_RATE (-1) -271.78 -1.38 No EXCHANGE_RATE (-1) -11.49 -0.49 No Constant (C) -4623.60 — — Source : Bangladesh Bank (January 2015 to December 2024), Study Approximations The cointegrating equation represents the long-run equilibrium relationship that the variables tend to revert to over time. The estimated long-run equation, normalised on INDEX (-1), is: INDEX (-1) + 290.7916 * INTEREST_RATE (-1) − 271.7757 * INFLATION_RATE (-1) − 11.49420 * EXCHANGE_RATE (-1) − 4623.596 = 0 (from the 1st and 2nd column of Table 4 ) Rearranging to express INDEX in terms of the other variables: INDEX (-1) = -290.7916 * INTEREST_RATE (-1) + 271.7757 * INFLATION_RATE (-1) + 11.49420 * EXCHANGE_RATE (-1) + 4623.596 5.4.1 Interest Rate : The coefficient for INTEREST_RATE (-1) (290.79) is statistically significant (t-statistic [4.58]) (2nd row, Table 4 ). This suggests a strong long-run inverse relationship: in the long run, a one-unit increase in the interest rate is associated with a substantial decrease in the Index, holding other factors constant. This highlights the central and robust role of interest rates as the primary macroeconomic determinant of the DSE's long-term equilibrium path among the variables studied. 5.4.2 Inflation Rate : The coefficient for INFLATION_RATE (-1) (-271.78) is not statistically significant (t-statistic [-1.38]) (3rd row, Table 4 ). This indicates that, within this specific long-run equilibrium, the inflation rate does not have a statistically significant long-term impact on the Index. 5.4.3 Exchange Rate : The coefficient for EXCHANGE_RATE (-1) (-11.49) is also not statistically significant (t-statistic [-0.49]) (4th row, Table 4 ). Similar to the inflation rate, the exchange rate does not appear to have a statistically significant long-term relationship with the index in this cointegrating vector. This could indicate that the long-run effects of inflation and exchange rates are more complex, possibly non-linear, or subject to regime shifts not captured by a single linear co-integrating vector. 5.4.4 Error Correction Terms (ECT) The error correction term (COINTEQ1) in each differenced equation indicates how quickly each variable adjusts to correct deviations from the long-run equilibrium. A negative and statistically significant coefficient suggests that the variable adjusts back towards equilibrium (Table 5 ). Table 5 Error Correction Terms (Speed of Adjustment) Dependent Variable ECT Coefficient t-statistic Adjustment to Long-run Equilibrium D(INDEX) -0.1529 -3.99 Significant, negative (adjusts) D(INTEREST_RATE) + 0.00029 2.58 Adjusts, but positive sign — suggests divergence D(INFLATION_RATE) + 0.0000198 2.83 Positive, may not support equilibrium D(EXCHANGE_RATE) + 0.000159 1.30 Not significant Source : Bangladesh Bank (January 2015 to December 2024), Study Approximations 5.4.4.1 D (INDEX) : The coefficient of the Error Correction Term (ECT) is -0.15 and is highly statistically significant (t-statistic [-3.99]) (2nd row, Table 5 ). The negative sign and statistical significance of the ECT confirm that the DSE Index adjusts towards its long-run equilibrium with the macroeconomic variables. The negative ECT confirms convergence, but the speed (15%) is unusually slow vs. mature markets (typically 20–30%). This aligns with DSE’s documented inefficiency (Uddin, M. H., 2009). 5.4.4.2 D(INTEREST_RATE) : The coefficient for COINTEQ1 is 0.00029 (t-statistic [2.58]). This coefficient is statistically significant, but it is positive (3rd row, Table 5 ). Typically, a negative coefficient is expected for an error correction mechanism, indicating an adjustment towards equilibrium. A positive sign here suggests that if the system deviates from the long-run equilibrium, the interest rate might move in a direction that increases the disequilibrium, or it could imply that the interest rate is a driving force of the disequilibrium rather than an adjusting variable. 5.4.4.3 D(INFLATION_RATE) : The coefficient for COINTEQ1 is 1.98E-05 (t-statistic [2.83]). This coefficient is also statistically significant and positive, similar to the interest rate (4th row, Table 5 ). This suggests a similar anomaly where the inflation rate does not adjust towards the equilibrium in the expected manner. 5.4.4.4 D(EXCHANGE_RATE) : The coefficient for COINTEQ1 is 0.000159 (t-statistic [1.30371]). This coefficient is not statistically significant 5th row, Table 5 ). 5.4.5 Short-Run Effects : The short-run dynamics capture the immediate impact of past changes in one variable on the current changes in another, after accounting for the long-run equilibrium. These are represented by the coefficients of the lagged differenced terms in each equation. Equation for D(INDEX) (Changes in Index) This equation models how the current change in the Index is influenced by past changes in the other variables. Table 6 Short-Run Effects (Selected Lagged Differences) Predictor (Lag 1) Δ(INDEX) Coef. (t-stat) Δ(INTEREST_RATE) Coef. (t-stat) Δ(INFLATION_RATE) Coef. (t-stat) Δ(EXCHANGE_RATE) Coef. (t-stat) Δ(INDEX) 0.1501 (1.69) -0.00031 (-1.18) -0.000017 (-1.05) -0.00029 (-1.03) Δ(INTEREST_RATE) 30.4485 (0.88) -0.2286 ( -2.26 ) 0.00049 (0.08) -0.1660 (-1.50) Δ(INFLATION_RATE) 607.7578 ( 2.12 ) -0.9871 (-1.18) 0.76199 ( 14.51 ) 2.0302 ( 2.22 ) Δ(EXCHANGE_RATE) -5.9414 (-0.22) 0.0990 (1.25) 0.00880 (1.78) 0.3437 ( 3.98 ) Constant -12.8684 (-0.57) 0.0335 (0.51) 0.00562 (1.36) 0.1838 ( 2.55 ) Source : Bangladesh Bank (January 2015 to December 2024), Study Approximations 6.4.5.1 D (INFLATION_RATE (-1)) : The coefficient is 607.7578, with a t-statistic of 2.12. This is statistically significant, indicating that a one-unit increase in the previous period's change in the inflation rate leads to a substantial positive change in the current period's index (4th row, Table 6 ). The result conflicts with Feldstein’s ( 1979 ) tax-channel theory, which predicts negative effects. 6.4.5.2 No other lagged differenced variables (D (INDEX (-1)), D (INTEREST_RATE (-1)), D (EXCHANGE_RATE (-1))) or the constant term show a statistically significant impact on D(INDEX) in this equation (Table 6 ). Overall, the findings of VECM largely corroborate previous research on the DSE and other EM concerning the general influence of macroeconomic variables. The consistent negative impact of interest rates on the DSE Index is also in agreement with established literature for Bangladesh (Gwala & Mashau, 2022 ; Islam et al., 2017 ; Mfugale & Olomi, 2023 ; Islam et al., 2023c ) and another emerging market (Eldomiaty et al., 2020 ). The lack of long-term significance for inflation and exchange rate despite short-term movements implies these are transient or driven by other forces (e.g., policy, foreign investment sentiment). 5.5 Granger Causality Test Results The Granger Causality Test results, as presented in Table 7 , reveal the direction of predictive power between the DSE Index and the selected macroeconomic variables. Table 7 Granger Causality Test Results Dependent Variable Excluded Variable Chi-Sq p-value Causality (at 5%) INDEX INTEREST_RATE 15.05 0.0005 Yes INFLATION_RATE 2.71 0.2583 No EXCHANGE_RATE 2.79 0.2476 No All 17.91 0.0065 Jointly Yes INTEREST_RATE INDEX 8.86 0.0119 Yes INFLATION_RATE 2.71 0.2574 No EXCHANGE_RATE 1.76 0.4138 No All 20.44 0.0023 Jointly Yes INFLATION_RATE INDEX 5.85 0.0538 Marginal (10%) INTEREST_RATE 5.15 0.0760 Marginal (10%) EXCHANGE_RATE 5.47 0.0648 Marginal (10%) All 19.05 0.0041 Jointly Yes EXCHANGE_RATE INDEX 3.36 0.1865 No INTEREST_RATE 1.70 0.4264 No INFLATION_RATE 6.28 0.0434 Yes All 16.06 0.0135 Jointly Yes Source : Bangladesh Bank (January 2015 to December 2024), Study Approximations Summary of Causal Relationships are- a) Interest Rate is a significant Granger cause of the Index. b) Index is a significant Granger cause of the Interest Rate. c) Inflation Rate is a significant Granger cause of the Exchange Rate. d) There is marginal evidence (at 10% significance) that the Index, Interest Rate, and Exchange Rate individually Granger cause the Inflation Rate. In all cases, the joint test for Granger causality from all other variables is significant , indicating that collectively, the other variables provide predictive power for each dependent variable. These results highlight the complex short-run interdependencies among these macroeconomic variables, suggesting that changes in one variable can indeed help predict changes in others. 6. Summary of Principal Findings This empirical research of the DSE from January 2015 to December 2024 demonstrates substantial macroeconomic impacts on its performance. All analysed variables—the DSE Index, interest rate, inflation rate, and exchange rate—were determined to be non-stationary at their levels but achieved stationarity following initial differencing, signifying they are integrated into order one, I(1). A solitary cointegrating link was discerned among these variables, affirming a stable long-term equilibrium in which they exhibit a tendency to co-move over time. The estimation of the Vector Error Correction Model (VECM) indicated that the interest rate exerts a statistically significant negative long-term effect on the DSE Index, aligning with acknowledged economic theories. In contrast, the inflation rate demonstrated a statistically significant positive short-term effect on the DSE Index, corroborating the idea of speculative rallies in emerging markets. The error correction term was substantial, validating the DSE Index's adjustment towards its long-term equilibrium. Granger causality experiments indicated bidirectional links between the interest rate and the DSE Index. The minimal influence of the exchange rate on the DSEX may result from the Bangladesh Bank's intervention in regulating the currency rate. 7. Constraints of the Research This work offers significant insights; however, it is restricted by multiple constraints. First , the scope of macroeconomic variables was limited to three: interest rate, inflation rate, and exchange rate. Other notable macroeconomic variables, including Gross Domestic Product (GDP) growth, industrial output, aggregate money supply, foreign direct investment (FDI), and global equity market indices, are considered to impact stock markets (Islam et al., 2014 ; Gwala & Mashau, 2022 ; Eldomiaty et al., 2020 ; Islam et al., 2023b) but were excluded due to limitations in data availability or scope delineation. Second , the use of monthly data, although suitable for my dataset, may conceal higher-frequency dynamics or prompt immediate daily market responses to macroeconomic news. Third , the VAR/VECM models employed presume linearity and stable parameters during the whole study duration. In actuality, structural fractures (e.g., resulting from significant policy alterations or economic crises) or non-linear interactions may be present, which these models might inadequately represent. Finally , the results are exclusive to the Dhaka Stock Exchange and the economic setting of Bangladesh. They may not be directly applicable to other emerging or developed markets, which display unique characteristics and sensitivities. The DSE's recorded vulnerability to "unethical and poorly objective-orientated conduct among market participants, insufficient due diligence, and manipulation" (Gwala & Mashau, 2022 ; Islam et al., 2023a ) suggests that just macroeconomic models may not comprehensively encompass all factors influencing market performance. This indicates that non-fundamental, market-specific factors may significantly influence DSE fluctuations, highlighting a shortcoming in the current study's scope. 8. Policy Recommendations In light of the empirical findings, the following policy recommendations are proposed: 8.1 Recommendations for the Bangladesh Bank (Central Bank) : In light of the considerable adverse effects of interest rates and inflation on the DSE, the Bangladesh Bank must meticulously evaluate the potential ramifications of its monetary policy actions on the stock market. Policies designed to provide price stability and regulate interest rates must be developed with consideration of their direct impact on the capital market, with the objective of promoting market expansion while regulating inflation. 8.2. For the Bangladesh Securities and Exchange Commission (BSEC) : The BSEC must persist in its efforts to improve market efficiency, liquidity, and depth. It is essential to address concerns, including regulatory failures, market manipulation, and the influx of ignorant investors, as emphasised in the literature (Gwala & Mashau, 2022 ; Islam et al., 2023a ). A more resilient and transparent market would facilitate the efficient pricing of macroeconomic information, potentially mitigating excessive volatility caused by non-fundamental issues. 8.3 Inter-Agency Coordination : The identified Granger causation from the DSE Index to the Exchange Rate indicates that the stock market is not solely a passive entity influenced by macroeconomic factors but can also serve as a precursor for currency fluctuations. This necessitates improved collaboration between the Bangladesh Bank and the BSEC. Policymakers ought to integrate DSE performance measurements into their comprehensive economic forecasts and policy development, especially with foreign currency management and capital movement oversight. 8.4 Investor Education : Considering the DSE's attributes and the observed "influx of numerous investors lacking pertinent knowledge and expertise" (Islam et al., 2023a ), ongoing investor education initiatives are essential. These programs need to clarify the connections between macroeconomic issues and stock market performance, enabling investors to make more informed decisions and diminishing their vulnerability to market manipulation. Declarations 9.1 Ethical Approval anda Consent to Participate This study did not involve human participants, human data, or human tissue. All the data used in this research was obtained from publicly available and anonymised secondary sources, including the official publications of the Bangladesh Bank (BB), the Dhaka Stock Exchange (DSE), the Bangladesh Security and Exchange Commission (BSEC), and the Bangladesh Bureau of Statistics (BBS). Formal ethical approval and individual consent to participate were unnecessary because this research did not involve primary data collection from individuals. 9.2 Consent for Publication Not applicable. This manuscript does not contain any individual person’s data in any form (including any individual details, images, or videos). 9.3 Funding No funding or financial assistance was received for conducting this study at any stage of the research. The research was carried out independently without any financial support from public, commercial, or not-for-profit funding agencies. Author Contribution A) Mallik Rowshan Alam is responsible for preparing the concept note and literature review, identifying research gaps, collecting data, organising the manuscript, and conducting the primary methodology and data analysis, as well as writing some parts of the conclusion.B) Dr Aoulad Hosen has contributed to the research objective determination, model setting, part of data analyses, research outcomes, conclusion, and policy recommendation. In addition to that, the final review and corresponding work were done by Dr Hosen. Acknowledgement To complete the research work, we took support from different institutes, such as Bangladesh Bank, Dhaka Stock Exchange (DSE), Bangladesh Security and Exchange Commission (BSEC), Investment Corporation of Bangladesh (ICB), Institute of Capital Market, and the Ministry of Finance. We humbly acknowledge these institutes for their invaluable support. As we have done the estimation, we have included four variables: 1. DSE Index, 2. Interest Rate, 3. Inflation Rate and 4. Exchange Rate. All datasets had covered 10-year periods, from January 2015 to December 2024, and they had considered monthly datasets. It consisted of 120 (10×12) point-to-point datasets for each variable. Among the four variables, the first, i.e., the DSE Index (independent variable) datasets collected from the Dhaka Stock Exchange depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables, recognised as independent, were collected from the Bangladesh Bank's (BB) repository system (https://www.bb.org.bd/en/index.php). Both repositories are publicly available, and the dataset is attached in the system as an Excel file. Data Availability Data Availability: This study uses monthly time-series data for the DSE Index (as the dependent variable) and three key macroeconomic variables: the interest rate, inflation rate, and exchange rate (as independent variables). The primary data source for all variables is the website of Bangladesh Bank, ensuring consistency and direct applicability to the research query. The dataset covers a continuous period of 10 years, from January 2015 to December 2024, providing a total of 120 monthly observations for each variable. This length of data is generally considered sufficient for robust time-series econometric analysis.Among the four variables, the first, i.e., the DSE Index datasets collected from the Dhaka Stock Exchange depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables recognised were collected from the Bangladesh Bank's (BB) repository system (https://www.bb.org.bd/en/index.php). Both repositories are publicly available. The two data source names and repositories are:4.1.1 Name of data source: Dhaka Stock Exchange (DSE) and Repository: https://www.dsebd.org/4.1.2 Name of data source: Bangladesh Bank (BB) and Repository: https://www.bb.org.bd/en/index.php References Abeyratna G, Pisedtasalasai A, Brown D. Macroeconomic influence on the stock market: evidence from an emerging market in South Asia. J Emerg Market Finance. 2004;3(3):285–304. Adjasi CKD, Biekpe NB. 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(2025), Understanding the theory: how interest rates impact stock prices, available at: https://www.currentmarketvaluation.com/models/10y-interest-rates.php (accessed 1 February 2025). Eldomiaty T, Saeed Y, Hammam R, Soud AS. The associations between stock prices, inflation rates, interest rates are still persistent: empirical evidence from stock duration model. J Econ Finance Administrative Sci. 2020;25(49):149–61. Fama EF. Efficient capital markets: a review of theory and empirical work. J Finance. 1970;25(2):383–417. Fama EF, Schwert GW. Asset returns and inflation. J Financ Econ. 1977;5(2):115–46. Feldstein M. (1979), Inflation and the stock market, NBER Working Paper, No. 276, National Bureau of Economic Research, Cambridge, MA, available at. https:// (accessed 1 February 2025). Feldstein M. The effects of fiscal policy on stock prices. Natl Tax J. 1997;50(4):485–97. Garza-Garcia JG, Yue Y. 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Introduction","content":"\u003cp\u003eThere has been considerable discussion regarding the effectiveness of stock exchanges due to their significant role in the global economies and financial markets. Stock markets assist the economy in expanding by influencing how people and businesses spend and save their money. They achieve these objectives by making sure people spend their money wisely and pushing them to make investments that will pay dividends. As a result, market performance has become a key measure of economic fundamentals that has an effect on the investment decisions of both domestic and international investors (Islam et al., 2015). Macroeconomic variables are often considered crucial factors influencing stock market trends since they might impact future corporate cash flows and the risk-adjusted discount rates used in valuation models (Islam et al., 2015; Eldomiaty et al., 2020). Stock exchanges are also beneficial for firms because they are easy to trade and have a lot of cash flow (Adjasi \u0026amp; Biekpe, 2006). For this beneficial role to work, the stock market and key macroeconomic data must be closely connected. Theorists agree that macroeconomic factors are important, but their nature, size, and direction are unclear. This position is especially true in Emerging Markets (EM), where institutional and structural factors are completely unique (Islam et al., 2014; Islam et al., 2015; Current Market Valuation, 2025). This variation shows how vital it is to look at each country separately. There have been several ups and downs at the Dhaka Stock Exchange (DSE) in Bangladesh over the years (Islam et al., 2023a). Therefore, investors, regulators, and politicians need to figure out what macroeconomic conditions affect its performance.\u003c/p\u003e"},{"header":"2. Background of the Study","content":"\u003cp\u003eStock markets are pivotal for economic growth; they function as efficient mechanisms for mobilising capital and channelling it toward productive investment. In emerging economies, their roles are even more evident, as they intensify domestic financial systems, enhance resource allocation, and support industrialisation and economic development (Islam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The increasing interconnectedness of global financial markets further accentuates the importance of understanding their complex dynamics (Islam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This study focuses on the DSE, Bangladesh\u0026rsquo;s primary stock exchange, within the broader context of the country\u0026rsquo;s strong economic growth recently (Islam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). National economic conditions closely tie the DSE's performance, serving as a barometer of basic fundamentals. However, the exchange is characterised by unique characteristics and risks, including frequent regulatory changes, a relatively small market size, low liquidity, and limited depth. Historically, the DSE has been prone to manipulation and sharp crashes, most notably in 1996 and 2011 (Gwala \u0026amp; Mashau, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). These features distinguish the DSE from developed markets and impact the transmission of macroeconomic shocks. Evidence indicates that the DSE is not weak-form efficient because its index follows a predictable pattern (Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Structural weaknesses, such as low market capitalisation and turnover, a narrow range of financial products, and a large proportion of inexperienced investors (Islam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e)\u0026mdash;undermine price discovery and slow the incorporation of information into stock prices. Consequently, macroeconomic announcements may exert a stronger or delayed effect compared to efficient markets. This inefficiency highlights the relevance of analysing macroeconomic-stock market linkages in Bangladesh. For investors, understanding these relationships provides valuable guidance for portfolio management. For policymakers, it sheds light on the interplay between financial markets and broader economic stability (Islam et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Theoretical Foundation and Literature Review","content":"\u003cp\u003eResearch indicates that macroeconomic factors contribute to approximately 30\u0026ndash;35% of the variations in stock prices (Chandra, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This study focuses on economic fundamentals, offering a thorough examination of fundamental analysis and illustrating the impact of root macroeconomic variables on stock price movements. Corporate earnings, a crucial determinant of stock prices, are affected by multiple factors, including economic growth, productivity, labour quality, and capital stock (Golob \u0026amp; Bishop, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, direct and indirect conditions such as interest rates, industrial output, inflation, exchange rates, and money supply also significantly affect stock market performance (Goswami \u0026amp; Jung, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Maysami et al. (2005), applying cointegration analysis to Singapore\u0026rsquo;s stock market, confirmed the importance of these macroeconomic variables. Similar findings have been reported by Aga \u0026amp; Kocaman (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Abeyratna et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Bilson et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), among others.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Theoretical Foundations of Macroeconomic Influences on Stock Markets\u003c/h2\u003e \u003cp\u003eThe interaction between macroeconomic conditions and stock market performance is pivotal to financial economics, grounded in several theoretical constructs. The \u003cem\u003eEfficient Market Hypothesis\u003c/em\u003e (EMH), articulated by Fama (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), posits that stock prices fully reflect market expectations of a company\u0026rsquo;s future performance, which is inherently tied to broader macroeconomic conditions (Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a truly efficient market, all available information, including macroeconomic data, is quickly and completely absorbed into prices. However, evidence indicates that the DSE does not conform to the weak form of EMH (Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), implying that macroeconomic variables may exert stronger and more exploitable effects due to information asymmetries and delayed adjustments. Ross\u0026rsquo;s (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) \u003cem\u003eArbitrage Pricing Theory\u003c/em\u003e (APT) further extends this view, proposing that asset returns are influenced by multiple macroeconomic risk factors, with investors earning risk premia in response. APT does not identify specific factors; however, it provides a comprehensive framework for the simultaneous analysis of multiple variables, in contrast to single-factor models like the Capital Asset Pricing Model (CAPM) (Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eInterest rates\u003c/em\u003e are widely regarded as a major determinant of stock prices. They fluctuate with time, default risk, inflation, and capital productivity (Chandra, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Shifts in interest rates influence investor behaviour, leading to reallocations between equity markets and fixed-income instruments and speculative activity. Central banks regulate interest rates through monetary policy (Kevin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), while informal financial markets remain unregulated. Theory predicts an inverse relationship between interest rates and stock prices. Smith (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) exhibited that U.S. stock prices often rose following interest rate cuts. In Korea, Goswami \u0026amp; Jung (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) found an inverse relationship between stock prices and long-term rates, but a positive correlation with short-term rates due to liquidity effects.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ecash-flow discounting model\u003c/em\u003e posits that elevated interest rates augment discount rates, consequently diminishing the present value of anticipated cash flows and, by extension, stock prices (Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). At the same time, higher borrowing costs reduce corporate profitability, suppressing valuations. Conversely, lower rates stimulate investment and economic activity, boosting stock returns. The \u003cem\u003eopportunity cost channel\u003c/em\u003e reinforces this relationship: when interest rates rise, fixed-income assets become more attractive relative to equities, encouraging investors to shift capital away from the stock market (Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given the inefficiency of the DSE, this channel likely has an amplified effect, as investors may quickly reallocate funds into bank deposits promising higher guaranteed returns. \u003cem\u003eInflation\u003c/em\u003e also plays a pivotal role. While Chandra (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) noticed its dual impact across industries, most studies indicate an inverse relationship between inflation and stock prices. Fama \u0026amp; Schwert (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) demonstrated that both expected and unexpected inflation reduce stock returns. Feldstein (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1979\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and Summers (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) shaded the distortive impact of inflation on taxation and accounting rules, including understated depreciation and taxation of nominal gains, which depress real returns. Amadi \u0026amp; Odubo (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) argued that mispricing often results from investors relying on nominal instead of real measures. Empirical evidence strongly supports the negative effect of inflation, which arises through purchasing power erosion, reduced profitability, tax distortions, and negative real returns (Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gopinath, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The \u003cem\u003eexchange rate\u0026ndash;stock price relationship\u003c/em\u003e is more intricate. Fluctuations directly affect import-dependent industries and corporate earnings. Maku \u0026amp; Atanda (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found a positive correlation between stock prices and a depreciating Naira in Nigeria. Exchange rates influence competitiveness, trade balances, and reserves, creating risks and opportunities (Osamwonyi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Channels include (i) international competitiveness and trade balance, where depreciation boosts exports (Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Phylaktis \u0026amp; Ravazzolo, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); (ii) transaction and translation exposure in trade and financial statements; and (iii) the economic activity channel, where depreciation can increase aggregate demand. Empirical findings are mixed\u0026mdash;negative (Mfugale \u0026amp; Olomi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), positive (Golder et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or even reverse causality (Ullah et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This variability underscores the need for country-specific analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Empirical Evidence in Emerging Economies\u003c/h2\u003e \u003cp\u003eA substantial frame of work highlights the impact of macroeconomic factors on emerging markets (EM) (Islam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Garza-Garcia \u0026amp; Yue (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrated U.S. influence on Chinese markets, while Sharma \u0026amp; Mahendru (2010) found similar dynamics in India. Asmy et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) noted differing pre- and post-crisis relationships among inflation, money supply, and exchange rates. Humpe \u0026amp; Macmillan (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found strong macro-market linkages in the U.S. and Japan. Qayyum \u0026amp; Anwar (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) showed that monetary policy drives volatility in Pakistan. More recent studies confirm that GDP, interest rates, inflation, exchange rates, money supply, and capital flows are key factors, though results often conflict in direction and significance (Mfugale \u0026amp; Olomi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This inconsistency demonstrates the importance of localised analysis, as causality and magnitude vary across contexts (Adrian et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gopinath, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Literature on the Dhaka Stock Exchange\u003c/h2\u003e \u003cp\u003eFor the DSE, multiple studies confirm a long-run cointegrating relationship between stock prices and macroeconomic variables (Islam et al., 2018). Inflation is consistently shown to negatively affect DSE returns (Gwala \u0026amp; Mashau, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Golder et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mfugale \u0026amp; Olomi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, interest rates negatively affect prices, aligning with opportunity cost and profitability theories (Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mfugale \u0026amp; Olomi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Exchange rate effects, however, are mixed: some studies demonstrate a positive link, others negative, and some indicate reverse causality, where stock prices Granger-cause exchange rates (Ullah et al., 2018). These contradictions highlight the DSE\u0026rsquo;s inefficiency, low liquidity, and heightened sensitivity to macro shocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Identification of the Research Gap\u003c/h2\u003e \u003cp\u003eWhile theory and empirical evidence suggest that macroeconomic factors influence stock markets, findings for emerging markets (EM) are inconsistent and context-dependent. In Bangladesh, the DSE\u0026rsquo;s inefficiency, volatility, and structural limitations suggest that macroeconomic shocks may exert stronger and delayed effects compared to developed markets. Empirical evidence on inflation and exchange rate impacts is particularly contradictory, revealing a need for context-specific verification. This study addresses these gaps by examining the effects of \u003cem\u003einterest rates, inflation, and exchange rates\u003c/em\u003e on the DSE using updated data (2015\u0026ndash;2024) and advanced econometric methods. It tests the applicability of established theories within Bangladesh\u0026rsquo;s unique market environment, which is characterised by import dependency and the opportunity cost of bank deposits. The findings aim to provide nuanced insights for investors, policymakers, and regulators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Research Questions\u003c/h2\u003e \u003cp\u003eThis study aims to address the following research questions:\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.5.1\u003c/b\u003e \u003cb\u003eTime-Series Properties\u003c/b\u003e: What are the stationarity characteristics and descriptive statistics of the DSE index, interest rate, inflation rate, and exchange rate of Bangladesh during 2015\u0026ndash;2024?\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.5.2\u003c/b\u003e \u003cb\u003eLong-Run Relationships\u003c/b\u003e: Are the DSE index, interest rate, inflation rate, and exchange rate cointegrated in the long run over the study period?\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5.3\u003c/b\u003e \u003cb\u003eShort-Run and Long-Run Effects\u003c/b\u003e: How do interest rates, inflation, and exchange rates influence the DSE index in both the short run and the long run?\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5.4\u003c/b\u003e \u003cb\u003eCausality Dynamics\u003c/b\u003e: Does Granger causality exist between the DSE index and the selected macroeconomic variables? If so, what is the direction of causality?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Research Hypotheses\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 H1: \u003cem\u003eTime-Series Properties\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eH1a: The DSE index, interest rate, inflation rate, and exchange rate are non-stationary at their levels.\u003c/p\u003e \u003cp\u003eH1b: These variables become stationary after first differencing, indicating integration of order one, I (1).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 H2: \u003cem\u003eLong-Run Relationships\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eH2a: The DSE index, interest rate, inflation rate, and exchange rate share at least one cointegrating relationship in the long run.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eH2b: Interest rates exert a significant negative long-run effect on the DSE index.\u003c/p\u003e \u003cp\u003eH2c: Inflation and exchange rates exert significant long-run effects on the DSE index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 H3: \u003cem\u003eShort-Run Dynamics\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eH3a: Interest rates have a significant short-run effect on the DSE index.\u003c/p\u003e \u003cp\u003eH3b: Inflation has a significant short-run effect on the DSE index.\u003c/p\u003e \u003cp\u003eH3c: Exchange rate fluctuations have a significant short-run effect on the DSE index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.6.4 H4: \u003cem\u003eGranger Causality\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eH4a: There exists bidirectional Granger causality between interest rates and the DSE index.\u003c/p\u003e \u003cp\u003eH4b: Inflation Granger-causes the DSE index and/or the exchange rate.\u003c/p\u003e \u003cp\u003eH4c: Exchange rate fluctuations Granger-cause the DSE index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Data and Research Methods","content":"\u003cp\u003e\u003cstrong\u003e4.1 Data Availability:\u003c/strong\u003e This study uses monthly time-series data for the DSE Index (as the dependent variable) and three key macroeconomic variables: the interest rate, inflation rate, and exchange rate (as independent variables). The primary data source for all variables is the website of Bangladesh Bank, ensuring consistency and direct applicability to the research query. The dataset covers a continuous period of 10 years, from January 2015 to December 2024, providing a total of 120 monthly observations for each variable. This length of data is generally considered sufficient for robust time-series econometric analysis.\u003c/p\u003e\n\u003cp\u003eAmong the four variables, the first, i.e., the DSE Index datasets collected from the Dhaka Stock Exchange depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables, recognised were collected from the Bangladesh Bank\u0026apos;s (BB) repository system (https://www.bb.org.bd/en/index.php). Both repositories are publicly available. The two data sources name and repositories are:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1 Name of data source:\u003c/strong\u003e\u0026nbsp; Dhaka Stock Exchange (DSE) and\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRepository:\u003c/strong\u003e https://www.dsebd.org/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2 Name of data source:\u0026nbsp;\u003c/strong\u003eBangladesh Bank (BB) and\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRepository:\u003c/strong\u003e https://www.bb.org.bd/en/index.php\u003c/p\u003e\n\u003cp\u003eThe variables are defined as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.1 DSE Index:\u003c/strong\u003e Represents the overall performance of the Dhaka Stock Exchange, serving as a proxy for the aggregate stock market return.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2 Interest Rate:\u003c/strong\u003e Reflects the general cost of borrowing in the economy and the opportunity cost of holding equity investments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.3 Inflation Rate:\u003c/strong\u003e Measured by the Consumer Price Index (CPI), indicating the rate of change in the general price level of goods and services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.4 Exchange Rate:\u003c/strong\u003e Represents the value of the Bangladeshi Taka against USD.\u003c/p\u003e\n\u003ch2\u003e4.2 Model Specification:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eINDEX =A*INTEREST_RATE + B*INFLATION_RATE +C*EXCHANGE_RATE + D + E\u003c/p\u003e\n\u003cp\u003eWhere A, B and C are coefficients, D and E consist of a parameter and disturbance term, respectively, and INDEX means DSE Index.\u003c/p\u003e\n\u003ch2\u003e4.3 General Tendency of Data\u003c/h2\u003e\n\u003cp\u003eThe study seeks to empirically investigate the long-term and short-term effects through time series analysis of three principal macroeconomic variables\u0026mdash;interest rate, inflation rate, and exchange rate\u0026mdash;on the influence of the Dhaka Stock Exchange (DSE). To do so, the data of each variable are depicted individually by Fig. 1. In the figure the study plotted each variable and got each frequency curve, which presents the general trend line about the study. Among these four quartiles of the figure, the inflation rate and exchange rate showed an upward trend over time (3rd and 4th quartiles). With little fluctuation, the interest rate also found an upward tendency (2nd quartile), but the dependent variable of the research, the DSE index, did not show any trend line (1st quartile). As a matter of fact, the DSE index did not respond equally to the progress of the three independent variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Time Series Properties:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea) Stationarity Tests:\u003c/strong\u003e A fundamental prerequisite for many time-series econometric models, particularly regression analyses, is that the variables are stationary. Stationarity implies that the statistical properties of a time series (mean, variance, and autocorrelation) remain constant over time. To formally test for the presence of a unit root (indicating non-stationarity) in each variable, the Augmented Dickey-Fuller (ADF) Test and the Phillips-Perron (PP) Test are typically employed. The ADF test is a widely used and robust test that extends the basic Dickey-Fuller test by incorporating lagged differenced terms to account for potential serial correlation in the error terms. The null hypothesis (H\u003csub\u003e0\u003c/sub\u003e) for the ADF test is the presence of a unit root (i.e., the series is non-stationary). The PP test is another powerful test for unit roots that addresses serial correlation and heteroskedasticity in the error terms through non-parametric corrections to the t-statistic. Similar to ADF, its null hypothesis (H\u003csub\u003e0\u003c/sub\u003e) is the presence of a unit root. Using both tests provides a more robust assessment of stationarity. Unit root test results directly determine the appropriate econometric model for subsequent analysis. If all variables are stationary at levels, a standard Vector Autoregression (VAR) model can be employed. The test results reveal whether the series exhibit trends or random walks, providing insights into their underlying economic dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Cointegration Analysis:\u003c/strong\u003e If the variables are found to be non-stationary but integrated of the same order (e.g., I (1)), cointegration analysis is performed to determine if there is a long-run equilibrium relationship among them (Corporate Finance Institute, 2025). The Johansen Cointegration Test is typically employed for this purpose. This method is generally preferred over the Engle-Granger two-step method when dealing with more than two variables, as it allows for the identification of multiple cointegrating relationships (Corporate Finance Institute, 2025). It is also considered more robust for larger sample sizes, which is relevant for the 10-year monthly data used in this study. Both the trace statistic and the maximum eigenvalue statistic are used to test the null hypothesis of no cointegration against the alternative of one or more cointegrating relationships (Corporate Finance Institute, 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec) Vector Error Correction Model (VECM) Specification:\u003c/strong\u003e The choice between a Vector Autoregression (VAR) model and a Vector Error Correction Model (VECM) is determined by the outcome of the cointegration tests. If no cointegration is found among the variables (or if variables are stationary at levels), a standard VAR model is applied to the appropriate (stationary) series (Buteikis, 2019). A VAR model captures the linear interdependencies among multiple time series, where each variable in the system is affected by its own past values (lags) and the past values of all other variables in the system. However, if cointegration is found, a VECM is used. A VECM is a restricted VAR model that explicitly incorporates the long-run cointegrating relationships through an error correction term while simultaneously modelling the short-run dynamics and adjustments towards the long-run equilibrium (Buteikis, 2019). The optimal lag length for the VAR/VECM is determined using various information criteria (e.g., Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan-Quinn Information Criterion (HQIC)) to ensure model parsimony and avoid overfitting while capturing sufficient dynamics. The selected VAR/VECM is then estimated using appropriate econometric software to obtain the coefficients, standard errors, and significance levels of the relationships between the DSE Index and the macroeconomic variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed) Granger Causality Analysis:\u003c/strong\u003e Granger causality analysis is conducted to investigate the predictive power between the DSE Index and the selected macroeconomic variables (interest rate, inflation rate, and exchange rate) (Buteikis, 2019). Granger causality tests determine whether past values of one variable can statistically help predict the current values of another variable, implying a causal relationship in a statistical, rather than philosophical, sense. Granger causality tests are conducted within the established VAR/VECM framework. The F-test is commonly employed to assess the joint significance of the lagged coefficients of the \u0026quot;causing\u0026quot; variable in the equation of the \u0026quot;effected\u0026quot; variable. A low p-value (typically below 0.05) indicates the rejection of the null hypothesis that the variable does not Granger-cause the other, suggesting a statistically significant causal link. The analysis explores all pairwise relationships (e.g., interest rate Granger-causes DSE, DSE Granger-causes interest rate, etc.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used two repository systems to collect the data and analyse the econometric model. Among the four variables: The datasets for all four variables\u0026mdash;1. DSE Index, 2. Interest Rate, 3. Inflation Rate, and 4. Exchange Rate\u0026mdash;covered a 10-year period from January 2015 to December 2024, consisting of 120 monthly point-to-point datasets for each variable. Among the four variables, the first, i.e., the DSE Index (independent variable) datasets collected from the Dhaka Stock Exchange (DSE) depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables, recognised as independent, were collected from the Bangladesh Bank\u0026apos;s (BB) repository system (https://www.bb.org.bd/en/index.php). The datasets used in this study are available in the following repositories:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea) Name:\u0026nbsp;\u003c/strong\u003eDhaka Stock Exchange (DSE),\u0026nbsp;\u003cstrong\u003ePersistent Web Link to Datasets:\u003c/strong\u003e https://www.dsebd.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Name:\u0026nbsp;\u003c/strong\u003eBangladesh Bank (BB),\u0026nbsp;\u003cstrong\u003ePersistent Web Link to Datasets:\u003c/strong\u003e https://www.bb.org.bd/en/index.php\u003c/p\u003e"},{"header":"5. Empirical Results and Discussions","content":"\u003cp\u003e \u003cb\u003e5.1 Descriptive Statistics of Variables\u003c/b\u003e: Initial statistical summaries of each variable are computed to understand their basic characteristics, central tendencies, and dispersion over the study period. This step is crucial for gaining preliminary insights into the behaviour of the data. Measures calculated for each variable include the mean, median, standard deviation (as a proxy for volatility), minimum value, and maximum value (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The table below presents the calculated descriptive statistics for the DSE Index, interest rate, inflation rate, and exchange rate from January 2015 to December 2024.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Variables (January 2015 - December 2024)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticulars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINDEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINTEREST_RATE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINFLATION_RATE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEXCHANGE_RATE\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\u003e5474.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5413.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.80\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\u003e7329.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120.00\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\u003e3989.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.80\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\u003e792.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.20\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\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\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\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.49\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\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\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\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eBangladesh Bank (January 2015 to December 2024), Study Approximations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe DSE Index, for instance, shows a mean of approximately 5474.59, with a standard deviation of 792.17, indicating notable volatility throughout the decade. The minimum index value was 3989.09, while the maximum reached 7329.04, highlighting significant market swings (2nd column, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interest rates in Bangladesh during this period averaged 6.70%, ranging from a low of 2.33% to a high of 12.31%. This wide range reflects dynamic monetary policy adjustments and economic conditions over the ten years (3rd column, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inflation rate, measured by CPI, had a mean of 6.55% and a standard deviation of 1.52, indicating a relatively stable but upward-trending price level, especially towards the latter part of the study period (4th column, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The exchange rate showed a mean of 88.86, with a significant range from 77.80 to 120.00, underscoring a substantial depreciation of the Bangladeshi Taka, particularly evident from mid-2022 onwards (5th column). This depreciation is a key trend that warrants further in-depth analysis regarding its impact on the DSE. Observing the raw trends in the data provides preliminary visual clues about potential correlations before formal econometric testing. For instance, the DSE Index experienced a notable bull run from late 2020 to late 2021, a period during which interest rates were comparatively low (Data File, 2015\u0026ndash;2024). Conversely, from mid-2022 to 2024, the DSE Index generally trended downwards, coinciding with rising interest rates and inflation and a sharp depreciation of the exchange rate (Data File, 2015\u0026ndash;2024). These observed co-movements, even prior to formal statistical validation, establish an intuitive basis for the expected relationships and aid in contextualising the subsequent econometric findings, thereby allowing for a richer interpretation of the results.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.2 Results of Unit Root Tests\u003c/b\u003e: Based on the results from the table, both the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests consistently indicate that all four variables \u0026ndash; the DSE Index, Interest Rate, Inflation Rate, and Exchange Rate \u0026ndash; are non-stationary at their levels. The p-values for the level series are generally above the conventional 0.05 significance level, leading to a failure to reject the null hypothesis of a unit root. This suggests that these series exhibit trends or random walk characteristics, meaning that shocks to these variables have permanent effects and they do not revert to a long-run mean.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit Root Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eTest Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADF Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOrder of Integration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDSE Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.887039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.822736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-stationary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.471213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.419641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStationary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInterest Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.364658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.992641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-stationary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.116200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.86208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStationary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInflation Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.920426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.759389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-stationary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.394659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.410458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStationary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExchange Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.297940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-stationary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.054045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.979780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStationary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eSource\u003c/b\u003e: Bangladesh Bank (January 2015 to December 2024), Study Approximations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, upon taking the first difference of each variable, both ADF and PP tests show highly significant p-values, leading to a strong rejection of the null hypothesis of a unit root (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This indicates that all variables become stationary after first differencing, classifying them as integrated of order one, or I (1) (8th column, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It suggests that shocks to the market have permanent effects, meaning the market does not automatically revert to a long-run mean after a disturbance. This is a critical characteristic for investors and risk managers, as it implies that market downturns or upturns might represent new, persistent levels rather than temporary deviations. Such a characteristic underscore the necessity of employing econometric models that can appropriately handle non-stationary data.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.3 Results of Cointegration Tests\u003c/b\u003e: Based on the Trace Statistic, the null hypothesis of no cointegrating relationships (r\u0026thinsp;=\u0026thinsp;0) is rejected at the 0.05 significance level (Trace Statistic\u0026thinsp;=\u0026thinsp;51.46, Critical Value\u0026thinsp;=\u0026thinsp;47.85, P-value\u0026thinsp;=\u0026thinsp;0.0220). This indicates the presence of at least one cointegrating vector. Similarly, the Maximum Eigenvalue Statistic also rejects the null hypothesis of r\u0026thinsp;=\u0026thinsp;0 (Max-Eigen Statistic\u0026thinsp;=\u0026thinsp;30.19, Critical Value\u0026thinsp;=\u0026thinsp;27.58, P-value\u0026thinsp;=\u0026thinsp;0.0226), further confirming at least one cointegrating relationship (2nd row, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJohansen Cointegration Test Results\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized No. of CE(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrace Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 Critical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax-Eigen Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05 Critical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.46347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.85613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.19204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.58434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.27143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.79707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.33580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.13162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.935626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.49471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.926842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.26460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.841465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.841465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9135\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\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eBangladesh Bank (January 2015 to December 2024), Study Approximations\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHowever, for the hypothesis of \"at most 1\" cointegrating relationship (r\u0026thinsp;\u0026le;\u0026thinsp;1), both the Trace Statistic (21.27) and Max-Eigen Statistic (14.34) are below their respective 0.05 critical values (29.80 and 21.13), with p-values less than 0.05 (3rd row, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that the null hypothesis of \"at most 1\" cointegrating relationship cannot be rejected. Therefore, the results indicate the existence of one cointegrating relationship among the variables. The finding that all variables are integrated of order one I (1)) and exhibit a single cointegrating relationship strongly supports the theoretical notion that macroeconomic fundamentals drive stock market performance in the long run, even in an emerging market like Bangladesh. This empirically validates the idea that the DSE is not merely subject to random fluctuations but is fundamentally tied to the broader economic environment, with an underlying equilibrium relationship that ensures long-term co-movement.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4 VAR/VECM Estimation Results\u003c/b\u003e: Given the confirmation of a single cointegrating relationship, a VECM is the appropriate choice for estimating the short-run and long-run impacts of the macroeconomic variables on the DSE Index. A summary of the results is shown in the following Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Output using EViews software is given in \u003cem\u003eAppendix A\u003c/em\u003e.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCointegrating Relationship (Long-run Equation)\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=\"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 \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\u003et-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance (at 5%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINTEREST_RATE (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;290.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINFLATION_RATE (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-271.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXCHANGE_RATE (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4623.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Bangladesh Bank (January 2015 to December 2024), Study Approximations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe cointegrating equation represents the long-run equilibrium relationship that the variables tend to revert to over time. The estimated long-run equation, normalised on INDEX (-1), is:\u003c/p\u003e\u003cp\u003eINDEX (-1)\u0026thinsp;+\u0026thinsp;290.7916 * INTEREST_RATE (-1)\u0026thinsp;\u0026minus;\u0026thinsp;271.7757 * INFLATION_RATE (-1)\u0026thinsp;\u0026minus;\u0026thinsp;11.49420 * EXCHANGE_RATE (-1)\u0026thinsp;\u0026minus;\u0026thinsp;4623.596\u0026thinsp;=\u0026thinsp;0 (from the 1st and 2nd column of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRearranging to express INDEX in terms of the other variables:\u003c/p\u003e \u003cp\u003eINDEX (-1) = -290.7916 * INTEREST_RATE (-1)\u0026thinsp;+\u0026thinsp;271.7757 * INFLATION_RATE (-1)\u0026thinsp;+\u0026thinsp;11.49420 * EXCHANGE_RATE (-1)\u0026thinsp;+\u0026thinsp;4623.596\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.1 Interest Rate\u003c/b\u003e: The coefficient for INTEREST_RATE (-1) (290.79) is statistically significant (t-statistic [4.58]) (2nd row, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests a strong long-run inverse relationship: in the long run, a one-unit increase in the interest rate is associated with a substantial decrease in the Index, holding other factors constant. This highlights the central and robust role of interest rates as the primary macroeconomic determinant of the DSE's long-term equilibrium path among the variables studied.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.2 Inflation Rate\u003c/b\u003e: The coefficient for INFLATION_RATE (-1) (-271.78) is not statistically significant (t-statistic [-1.38]) (3rd row, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This indicates that, within this specific long-run equilibrium, the inflation rate does not have a statistically significant long-term impact on the Index.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.3 Exchange Rate\u003c/b\u003e: The coefficient for EXCHANGE_RATE (-1) (-11.49) is also not statistically significant (t-statistic [-0.49]) (4th row, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similar to the inflation rate, the exchange rate does not appear to have a statistically significant long-term relationship with the index in this cointegrating vector. This could indicate that the long-run effects of inflation and exchange rates are more complex, possibly non-linear, or subject to regime shifts not captured by a single linear co-integrating vector.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e5.4.4 Error Correction Terms (ECT)\u003c/div\u003e \u003cp\u003eThe error correction term (COINTEQ1) in each differenced equation indicates how quickly each variable adjusts to correct deviations from the long-run equilibrium. A negative and statistically significant coefficient suggests that the variable adjusts back towards equilibrium (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\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\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eError Correction Terms (Speed of Adjustment)\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECT Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjustment to Long-run Equilibrium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(INDEX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-3.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant, negative (adjusts)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(INTEREST_RATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.00029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusts, but positive sign \u0026mdash; suggests divergence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(INFLATION_RATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.0000198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive, may not support equilibrium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EXCHANGE_RATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.000159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Bangladesh Bank (January 2015 to December 2024), Study Approximations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.4.1 D (INDEX)\u003c/b\u003e: The coefficient of the Error Correction Term (ECT) is -0.15 and is highly statistically significant (t-statistic [-3.99]) (2nd row, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The negative sign and statistical significance of the ECT confirm that the DSE Index adjusts towards its long-run equilibrium with the macroeconomic variables. The negative ECT confirms convergence, but the speed (15%) is unusually slow vs. mature markets (typically 20\u0026ndash;30%). This aligns with DSE\u0026rsquo;s documented inefficiency (Uddin, M. H., 2009).\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.4.2 D(INTEREST_RATE)\u003c/b\u003e: The coefficient for COINTEQ1 is 0.00029 (t-statistic [2.58]). This coefficient is statistically significant, but it is positive (3rd row, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Typically, a negative coefficient is expected for an error correction mechanism, indicating an adjustment towards equilibrium. A positive sign here suggests that if the system deviates from the long-run equilibrium, the interest rate might move in a direction that \u003cem\u003eincreases\u003c/em\u003e the disequilibrium, or it could imply that the interest rate is a driving force of the disequilibrium rather than an adjusting variable.\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.4.3 D(INFLATION_RATE)\u003c/b\u003e: The coefficient for COINTEQ1 is 1.98E-05 (t-statistic [2.83]). This coefficient is also statistically significant and positive, similar to the interest rate (4th row, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This suggests a similar anomaly where the inflation rate does not adjust towards the equilibrium in the expected manner.\u003c/p\u003e\u003cp\u003e \u003cb\u003e5.4.4.4 D(EXCHANGE_RATE)\u003c/b\u003e: The coefficient for COINTEQ1 is 0.000159 (t-statistic [1.30371]). This coefficient is not statistically significant 5th row, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.4.5 Short-Run Effects\u003c/b\u003e: The short-run dynamics capture the immediate impact of past changes in one variable on the current changes in another, after accounting for the long-run equilibrium. These are represented by the coefficients of the lagged differenced terms in each equation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEquation for D(INDEX) (Changes in Index)\u003c/strong\u003e \u003cp\u003eThis equation models how the current change in the Index is influenced by past changes in the other variables.\u003c/p\u003e \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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShort-Run Effects (Selected Lagged Differences)\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\u003ePredictor (Lag 1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔ(INDEX) Coef.\u003c/p\u003e \u003cp\u003e(t-stat)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔ(INTEREST_RATE) Coef. (t-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔ(INFLATION_RATE) Coef. (t-stat)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔ(EXCHANGE_RATE) Coef. (t-stat)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ(INDEX)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1501 (1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00031\u003c/p\u003e \u003cp\u003e(-1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000017\u003c/p\u003e \u003cp\u003e(-1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00029\u003c/p\u003e \u003cp\u003e(-1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ(INTEREST_RATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.4485 (0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2286\u003c/p\u003e \u003cp\u003e(\u003cb\u003e-2.26\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00049\u003c/p\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1660\u003c/p\u003e \u003cp\u003e(-1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ(INFLATION_RATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e607.7578 (\u003cb\u003e2.12\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9871\u003c/p\u003e \u003cp\u003e(-1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76199\u003c/p\u003e \u003cp\u003e(\u003cb\u003e14.51\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0302\u003c/p\u003e \u003cp\u003e(\u003cb\u003e2.22\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ(EXCHANGE_RATE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.9414\u003c/p\u003e \u003cp\u003e(-0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0990\u003c/p\u003e \u003cp\u003e(1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00880\u003c/p\u003e \u003cp\u003e(1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3437\u003c/p\u003e \u003cp\u003e(\u003cb\u003e3.98\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-12.8684 (-0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0335\u003c/p\u003e \u003cp\u003e(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00562\u003c/p\u003e \u003cp\u003e(1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1838\u003c/p\u003e \u003cp\u003e(\u003cb\u003e2.55\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Bangladesh Bank (January 2015 to December 2024), Study Approximations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e6.4.5.1 D (INFLATION_RATE (-1))\u003c/b\u003e: The coefficient is 607.7578, with a t-statistic of 2.12. This is statistically significant, indicating that a one-unit increase in the previous period's change in the inflation rate leads to a substantial positive change in the current period's index (4th row, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The result conflicts with Feldstein\u0026rsquo;s (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) tax-channel theory, which predicts negative effects.\u003c/p\u003e \u003cp\u003e \u003cb\u003e6.4.5.2\u003c/b\u003e No other lagged differenced variables (D (INDEX (-1)), D (INTEREST_RATE (-1)), D (EXCHANGE_RATE (-1))) or the constant term show a statistically significant impact on D(INDEX) in this equation (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the findings of VECM largely corroborate previous research on the DSE and other EM concerning the general influence of macroeconomic variables. The consistent negative impact of interest rates on the DSE Index is also in agreement with established literature for Bangladesh (Gwala \u0026amp; Mashau, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mfugale \u0026amp; Olomi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e) and another emerging market (Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The lack of long-term significance for inflation and exchange rate despite short-term movements implies these are transient or driven by other forces (e.g., policy, foreign investment sentiment).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Granger Causality Test Results\u003c/h2\u003e \u003cp\u003eThe Granger Causality Test results, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, reveal the direction of predictive power between the DSE Index and the selected macroeconomic variables.\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 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger Causality Test 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=\"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=\"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\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcluded Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCausality (at 5%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eINDEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINTEREST_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINFLATION_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXCHANGE_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJointly Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eINTEREST_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINDEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINFLATION_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXCHANGE_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJointly Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eINFLATION_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINDEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINTEREST_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXCHANGE_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJointly Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEXCHANGE_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINDEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINTEREST_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINFLATION_RATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJointly Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Bangladesh Bank (January 2015 to December 2024), Study Approximations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSummary of Causal Relationships are-\u003c/p\u003e \u003cp\u003ea) Interest Rate is a significant Granger cause of the Index.\u003c/p\u003e \u003cp\u003eb) Index is a significant Granger cause of the Interest Rate.\u003c/p\u003e \u003cp\u003ec) Inflation Rate is a significant Granger cause of the Exchange Rate.\u003c/p\u003e \u003cp\u003ed) There is marginal evidence (at 10% significance) that the Index, Interest Rate, and Exchange Rate individually Granger cause the Inflation Rate.\u003c/p\u003e\u003cp\u003eIn all cases, the \u003cem\u003ejoint test for Granger causality from all other variables is significant\u003c/em\u003e, indicating that collectively, the other variables provide predictive power for each dependent variable. These results highlight the complex short-run interdependencies among these macroeconomic variables, suggesting that changes in one variable can indeed help predict changes in others.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Summary of Principal Findings","content":"\u003cp\u003eThis empirical research of the DSE from January 2015 to December 2024 demonstrates substantial macroeconomic impacts on its performance. All analysed variables\u0026mdash;the DSE Index, interest rate, inflation rate, and exchange rate\u0026mdash;were determined to be non-stationary at their levels but achieved stationarity following initial differencing, signifying they are integrated into order one, I(1). A solitary cointegrating link was discerned among these variables, affirming a stable long-term equilibrium in which they exhibit a tendency to co-move over time. The estimation of the Vector Error Correction Model (VECM) indicated that the interest rate exerts a statistically significant negative long-term effect on the DSE Index, aligning with acknowledged economic theories. In contrast, the inflation rate demonstrated a statistically significant positive short-term effect on the DSE Index, corroborating the idea of speculative rallies in emerging markets. The error correction term was substantial, validating the DSE Index's adjustment towards its long-term equilibrium. Granger causality experiments indicated bidirectional links between the interest rate and the DSE Index. The minimal influence of the exchange rate on the DSEX may result from the Bangladesh Bank's intervention in regulating the currency rate.\u003c/p\u003e"},{"header":"7. Constraints of the Research","content":"\u003cp\u003eThis work offers significant insights; however, it is restricted by multiple constraints. \u003cem\u003eFirst\u003c/em\u003e, the scope of macroeconomic variables was limited to three: interest rate, inflation rate, and exchange rate. Other notable macroeconomic variables, including Gross Domestic Product (GDP) growth, industrial output, aggregate money supply, foreign direct investment (FDI), and global equity market indices, are considered to impact stock markets (Islam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gwala \u0026amp; Mashau, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Eldomiaty et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Islam et al., 2023b) but were excluded due to limitations in data availability or scope delineation. \u003cem\u003eSecond\u003c/em\u003e, the use of monthly data, although suitable for my dataset, may conceal higher-frequency dynamics or prompt immediate daily market responses to macroeconomic news. \u003cem\u003eThird\u003c/em\u003e, the VAR/VECM models employed presume linearity and stable parameters during the whole study duration. In actuality, structural fractures (e.g., resulting from significant policy alterations or economic crises) or non-linear interactions may be present, which these models might inadequately represent. \u003cem\u003eFinally\u003c/em\u003e, the results are exclusive to the Dhaka Stock Exchange and the economic setting of Bangladesh. They may not be directly applicable to other emerging or developed markets, which display unique characteristics and sensitivities. The DSE's recorded vulnerability to \"unethical and poorly objective-orientated conduct among market participants, insufficient due diligence, and manipulation\" (Gwala \u0026amp; Mashau, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) suggests that just macroeconomic models may not comprehensively encompass all factors influencing market performance. This indicates that non-fundamental, market-specific factors may significantly influence DSE fluctuations, highlighting a shortcoming in the current study's scope.\u003c/p\u003e"},{"header":"8. Policy Recommendations","content":"\u003cp\u003eIn light of the empirical findings, the following policy recommendations are proposed:\u003c/p\u003e\u003cp\u003e \u003cb\u003e8.1 Recommendations for the Bangladesh Bank (Central Bank)\u003c/b\u003e: In light of the considerable adverse effects of interest rates and inflation on the DSE, the Bangladesh Bank must meticulously evaluate the potential ramifications of its monetary policy actions on the stock market. Policies designed to provide price stability and regulate interest rates must be developed with consideration of their direct impact on the capital market, with the objective of promoting market expansion while regulating inflation.\u003c/p\u003e \u003cp\u003e \u003cb\u003e8.2. For the Bangladesh Securities and Exchange Commission (BSEC)\u003c/b\u003e: The BSEC must persist in its efforts to improve market efficiency, liquidity, and depth. It is essential to address concerns, including regulatory failures, market manipulation, and the influx of ignorant investors, as emphasised in the literature (Gwala \u0026amp; Mashau, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). A more resilient and transparent market would facilitate the efficient pricing of macroeconomic information, potentially mitigating excessive volatility caused by non-fundamental issues.\u003c/p\u003e\u003cp\u003e \u003cb\u003e8.3 Inter-Agency Coordination\u003c/b\u003e: The identified Granger causation from the DSE Index to the Exchange Rate indicates that the stock market is not solely a passive entity influenced by macroeconomic factors but can also serve as a precursor for currency fluctuations. This necessitates improved collaboration between the Bangladesh Bank and the BSEC. Policymakers ought to integrate DSE performance measurements into their comprehensive economic forecasts and policy development, especially with foreign currency management and capital movement oversight.\u003c/p\u003e \u003cp\u003e \u003cb\u003e8.4 Investor Education\u003c/b\u003e: Considering the DSE's attributes and the observed \"influx of numerous investors lacking pertinent knowledge and expertise\" (Islam et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), ongoing investor education initiatives are essential. These programs need to clarify the connections between macroeconomic issues and stock market performance, enabling investors to make more informed decisions and diminishing their vulnerability to market manipulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e9.1 Ethical Approval anda Consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants, human data, or human tissue. All the data used in this research was obtained from publicly available and anonymised secondary sources, including the official publications of the Bangladesh Bank (BB), the Dhaka Stock Exchange (DSE), the Bangladesh Security and Exchange Commission (BSEC), and the Bangladesh Bureau of Statistics (BBS). Formal ethical approval and individual consent to participate were unnecessary because this research did not involve primary data collection from individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9.2 Consent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person’s data in any form (including any individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9.3 Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding or financial assistance was received for conducting this study at any stage of the research. The research was carried out independently without any financial support from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA) Mallik Rowshan Alam is responsible for preparing the concept note and literature review, identifying research gaps, collecting data, organising the manuscript, and conducting the primary methodology and data analysis, as well as writing some parts of the conclusion.B)\u0026nbsp;Dr Aoulad Hosen has contributed to the research objective determination, model setting, part of data analyses, research outcomes, conclusion, and policy recommendation. In addition to that, the final review and corresponding work were done by Dr Hosen.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eTo complete the research work, we took support from different institutes, such as Bangladesh Bank, Dhaka Stock Exchange (DSE), Bangladesh Security and Exchange Commission (BSEC), Investment Corporation of Bangladesh (ICB), Institute of Capital Market, and the Ministry of Finance. We humbly acknowledge these institutes for their invaluable support. As we have done the estimation, we have included four variables: 1. DSE Index, 2. Interest Rate, 3. Inflation Rate and 4. Exchange Rate. All datasets had covered 10-year periods, from January 2015 to December 2024, and they had considered monthly datasets. It consisted of 120 (10\u0026times;12) point-to-point datasets for each variable. Among the four variables, the first, i.e., the DSE Index (independent variable) datasets collected from the Dhaka Stock Exchange depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables, recognised as independent, were collected from the Bangladesh Bank's (BB) repository system (https://www.bb.org.bd/en/index.php). Both repositories are publicly available, and the dataset is attached in the system as an Excel file.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability: This study uses monthly time-series data for the DSE Index (as the dependent variable) and three key macroeconomic variables: the interest rate, inflation rate, and exchange rate (as independent variables). The primary data source for all variables is the website of Bangladesh Bank, ensuring consistency and direct applicability to the research query. The dataset covers a continuous period of 10 years, from January 2015 to December 2024, providing a total of 120 monthly observations for each variable. This length of data is generally considered sufficient for robust time-series econometric analysis.Among the four variables, the first, i.e., the DSE Index datasets collected from the Dhaka Stock Exchange depository, refers to the datasets that track the performance of the Dhaka Stock Exchange, which is the main stock exchange in Bangladesh (https://www.dsebd.org/). The other three variables recognised were collected from the Bangladesh Bank's (BB) repository system (https://www.bb.org.bd/en/index.php). Both repositories are publicly available. The two data source names and repositories are:4.1.1 Name of data source: Dhaka Stock Exchange (DSE) and Repository: https://www.dsebd.org/4.1.2 Name of data source: Bangladesh Bank (BB) and Repository: https://www.bb.org.bd/en/index.php\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbeyratna G, Pisedtasalasai A, Brown D. Macroeconomic influence on the stock market: evidence from an emerging market in South Asia. J Emerg Market Finance. 2004;3(3):285\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdjasi CKD, Biekpe NB. Stock market development and economic growth: the case of selected African countries. Afr Dev Rev. 2006;18(1):144\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdrian T, Erceg C, Kolasa M, Lind\u0026eacute; J, McLeod R, Veyrune R, Zabczyk P. 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Asian Economic Financial Rev. 2017;7(8):770\u0026ndash;9.\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":"Dhaka Stock Exchange (DSE), Macroeconomic Variables, Stock Market Volatility, Time-Series Models","lastPublishedDoi":"10.21203/rs.3.rs-8926796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8926796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study is based on the performance of the Dhaka Stock Exchange (DSE) from January 2015 to December 2024 and empirically analyses the implications for key macroeconomic variables, including interest rates, inflation rates, and currency rates. The analysis uses monthly data from the Bangladesh Bank and employs time-series econometric methodologies, including unit root tests, Johansen cointegration, vector error correction models (VECM), and Granger causality tests. The results demonstrate that all variables are integrated in order one, with indications of a singular long-run cointegrating relationship. The results indicate that interest rates have a substantial negative long-term impact on the DSE index, aligning with theoretical predictions that increased borrowing and opportunity costs diminish equity prices. Conversely, inflation and exchange rates do not exhibit statistically significant long-term effects; nonetheless, short-term dynamics indicate that inflation positively influences the index, perhaps due to speculative market behaviour. Granger causality studies establish bidirectional causality between interest rates and the DSE index, while inflation is determined to affect exchange rates. These results underscore the DSE's inefficacy, wherein macroeconomic shocks may exert delayed or exacerbated impacts on pricing. The research offers pragmatic insights for investors, policymakers, and regulators, including the importance of monetary policy coordination and enhanced market efficiency in Bangladesh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes:\u003c/strong\u003e G12, E00, G28, E44, C32.\u003c/p\u003e","manuscriptTitle":"An Inquiry into the Influence of Key Macroeconomic Variables on the Dhaka Stock Exchange Equity Returns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 08:21:11","doi":"10.21203/rs.3.rs-8926796/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":"7cf6c786-9f6c-4698-8e21-cfb825c668ae","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-14T03:42:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:00:56+00:00","index":53,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T03:55:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 08:21:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8926796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8926796","identity":"rs-8926796","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}