Risk-Adjusted Performance Comparison of Direct and Regular Plan Equity Mutual Funds in India

Risk-Adjusted Performance Comparison of Direct and Regular Plan Equity Mutual Funds in India | 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 Risk-Adjusted Performance Comparison of Direct and Regular Plan Equity Mutual Funds in India Krunal Purohit, Nikhil Belavadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6825412/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 provides a comprehensive comparison of risk and returns characteristics between direct-plan and regular-plan mutual funds in India over the period 2020–2024. Focusing on equity mutual funds (large-cap category) as a representative sample, the study analyzes annual returns and risk metrics to rigorously assess performance differences. Econometric tests including panel regressions with fixed effects and paired statistical tests are employed to determine the significance of return differentials. This study finds and indicates that direct plans consistently outperform regular plans in terms of returns (by approximately 1% per year on average) without taking on additional risk. Risk measures such as volatility and beta are virtually identical for both plan types, yet direct plans achieve higher risk-adjusted performance (Sharpe ratios, Jensen’s alpha, Treynor’s ratio). These results align with the expectation that lower expenses in direct plans translate into superior net returns for investors. The paper discusses the implications of these differences in the context of mutual fund distribution, investor decision-making, and regulatory objectives, and situates the findings within the existing literature on mutual fund performance and fees. All data of this study is documented, and results are presented with supporting tables, charts, and statistical evidence. Direct vs Regular Mutual Funds Risk-Adjusted Returns Sharpe Ratio Jensen’s Alpha Treynor’s Ratio Figures Figure 1 1. Introduction Mutual fund investors in India can invest via two types of plans: direct plans and regular plans. Introduced by regulatory mandate in 2013, direct plans allow investors to invest in a mutual fund scheme without any distributor or intermediary, whereas regular plans involve investments routed through a broker or agent. Both plan variants pertain to the same underlying scheme, they share the identical portfolio of holdings and are managed by the same fund manager, differing only in the fee structure. In a regular plan, the fund charges a higher expense ratio to compensate the distributor (commissions or distribution fees), while the direct plan has a lower expense ratio since no distributor commission is paid. This structural difference leads to separate Net Asset Values (NAVs) for direct and regular plans of the same scheme, with the direct plan’s NAV typically higher because cost savings are passed back into the fund’s returns. Over time, even marginal fee differences can compound to a meaningful divergence in investor outcomes. Direct plans were introduced with the expectation of enhancing investor returns by reducing intermediary costs. By eliminating distribution fees, direct plans should, in theory, yield higher net returns to investors, all else being equal. The trade-off is that investors in direct plans do not receive personalized advice or assistance from distributors, which some less-experienced investors might value. Nonetheless, the cost advantage of direct plans has led to significant adoption, especially among informed retail investors and institutions. As of 2024, nearly half of the total mutual fund assets in India are in direct plans (driven largely by institutional and high-net-worth investors), while the share of direct plans in retail (individual) investor assets reached about 24%, up from 21% a year prior. This trend underscores the growing preference for the cost-saving direct route. Considering this, it is important to empirically evaluate how direct plans have performed relative to regular plans in recent years, particularly in terms of risk and return characteristics. This forms the core motivation of this study. 1.1 Objective : The objective of this research is to rigorously compare the performance of direct-plan vs regular- plan mutual funds in India during 2020–2024, focusing specifically on return outcomes and risk profiles. The study aims to quantify the return differential attributable to the plan type (controlling for other factors) and to assess whether there are any systematic differences in risk or risk-adjusted performance metrics between direct and regular plans. The study is structured to meet academic standards: The study provides a thorough literature review of relevant studies, describe this study data and methodology in detail (including econometric models and statistical tests), present empirical results with tables and charts, and discuss the implications of this study findings in context. By concentrating on risk-return characteristics, the study keeps the scope focused: other aspects such as investor behavior or service quality differences between direct and regular plans are beyond the scope of this analysis. 2. Literature Review Mutual fund performance and the impact of fees have been widely studied in finance literature. It has been established that expense ratios and distribution costs can significantly erode investors’ net returns. In the realm of emerging market finance, several studies have delved into various aspects of mutual funds. Muga, Rodriguez, and Santamaría (2007) investigated the persistence of mutual funds specifically within Latin American emerging markets, with a detailed focus on Mexico. Their work sheds light on whether past performance is indicative of future success in these dynamic markets. Shifting to the Indian context, Agarwal and Pradhan (2018) analyzed mutual fund performance by employing unconditional multifactor models, providing evidence from India. This research contributes to understanding the drivers of mutual fund returns in one of the world's prominent emerging economies. Furthermore, Deb (2019) conducted a VaR-based downside risk analysis of Indian equity mutual funds, comparing their performance in the periods both preceding and following the global financial crisis. This offers valuable insights into risk management practices in the Indian mutual fund industry. Beyond specific regions, Grose (2013) explored diversification opportunities offered by fixed-income managed funds in Eastern Europe, highlighting the potential for investors to spread risk across different emerging markets. Finally, Galloppo and Aliano (2018) broadened the scope by examining fund manager performance in emerging markets more generally, considering the impact of factor specialization and financial crises on their abilities. Collectively, these studies underscore the diverse research interests in emerging mutual funds, covering performance, risk, diversification, and the influence of macroeconomic events and fund manager characteristics. In a similar study on mutual fund performance persistence, Carhart (1997) found that common factors and fees almost completely explain the lack of persistent positive alphas in mutual funds. In other words, whatever value active fund managers might add tends to be offset by the fees and expenses charged to investors. This insight highlights the importance of low-cost investing: if two funds hold the same portfolio, the one with lower fees should deliver better net performance to investors. Direct plans capitalize on this principle by cutting out distribution fees. Studies specifically comparing different distribution channels support the idea that lower-cost direct distribution benefits performance. Christoffersen, Evans, and Musto (2013) examine mutual funds in the U.S. that are sold through brokers vs. directly to consumers. They find that funds sold through brokers underperform those sold directly, after adjusting for risk, largely due to the higher fees and differing incentives. Notably, direct-sold funds in their sample achieved higher risk-adjusted returns (alpha) by about 115 basis points (1.15% per year) compared to broker-sold funds. The authors attribute this performance gap to incentive differences: fund managers in the direct channel face more performance- sensitive investors (who can more easily switch funds), giving them a stronger incentive to generate alpha. Additionally, direct-sold funds tended to engage in more active management (higher “active share”) and were less likely to be closet indexers, whereas broker-sold funds often carried higher beta and relied on marketing to attract flows. These findings indicate that the distribution mode can influence both fees and managerial behavior, ultimately affecting investor returns. In the Indian context, the introduction of direct plans in 2013 was a regulatory effort by SEBI to reduce costs for investors and improve returns. A few industry analyses and commentaries since then have documented the return differentials emerging between direct and regular plans. For instance, an analysis by The Economic Times in early 2023 (marking ten years since direct plans began) reported that direct plans across various equity fund categories delivered about 1–2% higher annualized returns than their regular plan counterparts over the decade. The largest gaps were observed in categories with higher expense ratios e.g., in small-cap equity funds, direct plans averaged 19.97% CAGR over 2013–2023 vs 18.48% for regular plans (a difference of 1.49% per annum). Even in more cost-efficient categories like large-cap funds, direct plans still outperformed regular plans by roughly 1% annually over ten years. These figures align closely with the typical difference in expense ratios between direct and regular plans for equity funds (often on the order of 0.5–1.0% or more). Other Indian studies echo similar conclusions. Kulshreshtha (2020) examined a sample of equity mutual funds and found that the tracking error and risk profiles of direct vs regular plans were virtually identical, yet direct plans showed statistically higher net returns due solely to lower expenses (with no indication that one plan type took systematically different investment risks). Several investment advisories and researchers note that direct plans, by design, should never underperform regular plans in the same scheme on a gross of fee basis any underperformance of direct plans would indicate an anomaly or data error, since the portfolios are the same and fees are lower in direct. In practice, direct plan NAVs have indeed been higher than regular plan NAVs for every mutual fund scheme since their introduction. Overall, the literature strongly suggests that cost matters: reducing expense ratios via direct investing yields higher investor returns, without needing the fund to change its investment strategy. This sets a clear expectation for this study empirical analysis. However, prior research also emphasizes that while returns differ, risk metrics should remain the same for direct vs regular plans of the same scheme. The Association of Mutual Funds in India (AMFI) explicitly notes that a direct plan and regular plan of a scheme have a common portfolio and risk profile; the only difference is expenses and the resulting NAV/return trajectory. Therefore, any observed difference in volatility or beta would likely be trivial or attributable to statistical noise. The main performance differences are anticipated to manifest in metrics that incorporate returns net of fees: for example, Sharpe ratio (excess return per unit volatility) and Jensen’s alpha (excess return over what CAPM predicts) should favor direct plans thanks to their higher net returns. In summary, previous studies and market evidence provide two key hypotheses for this study: ( 1 ) Direct plans will outperform regular plans in terms of raw returns (and risk-adjusted returns) by a margin roughly equivalent to the fee differential (around 0.5–1.5% annually for equity funds), and ( 2 ) Risk levels (volatility, beta) will be equivalent between direct and regular plans of the same scheme, given identical underlying portfolios. This study analysis of 2020–2024 data will test and quantify these expectations in a rigorous manner. 3. Data and Methodology 3.1 Data Description For this study, the constructed a dataset of mutual fund performance focusing on large-cap equity mutual fund schemes in the Indian market. The study chose the large-cap category for several reasons: (a) it is one of the most popular categories among investors, ensuring a robust sample of funds; (b) large-cap funds have relatively long track records and sizable assets under management, which means data availability and consistency over the 2020–2024 period is high; and (c) by restricting to one category, The study keep the funds broadly comparable in terms of investment universe and risk profile, isolating the effect of plan type. The large-cap category also had a moderate expense ratio differential between direct and regular plans (often around 1% or slightly more), providing a clear basis to observe return differences. Sample This study included selected large-cap equity mutual fund schemes that were in existence by 2020 and continued through 2024, covering both their direct plan (growth option) and regular plan (growth option). The sample consists of N = 32–33 mutual fund schemes (the exact number varies slightly by year as a few funds launched or merged during the period). Each scheme contributes two observations per year (direct and regular plan). Notable funds in the sample include Aditya Birla Sun Life Frontline Equity, Axis Blue-chip Fund, ICICI Prudential Blue-chip, HDFC Top 100 (Large Cap), SBI Blue-chip, etc., each with both plan types. By focusing on growth options, the study considers total returns without any dividend payouts, which simplifies comparison (all returns are captured via NAV changes). 3.2 Variables of the Study 3.2.1 Return data : Annual returns for each fund-plan Were calculated for calendar years 2020, 2021, 2022, 2023, and 2024. These were derived from NAV data specifically, the one-year total return for each plan in each calendar year (based on NAVs at the start and end of the year). The NAV and return data were obtained from mutual fund databases and fund factsheets (publicly available disclosures). The study cross verified returns with data from the Association of Mutual Funds of India (AMFI) to ensure accuracy. In cases of slight discrepancies or mid-year launches, those funds were handled carefully (funds that were not operational for the full year were excluded from that year’s return comparison). All returns are expressed in decimal form (e.g., 0.15 for 15% annual return). 3.2.2 Risk metrics data : In addition to annual returns, the study collected data to compute risk measures and risk- adjusted performance metrics for each plan. The study used daily NAV data from January 2022 through December 2024 (a 3-year window) for this purpose, as this period provides a recent and relevant sample of market conditions (including the post-COVID recovery and various market phases). Using daily NAVs, the study computed: ( 1 ) Annualized volatility (standard deviation) of daily returns for each plan, ( 2 ) Beta of each plan relative to a market index, ( 3 ) Sharpe ratio, ( 4 ) Jensen’s alpha, and ( 5 ) Treynor’s ratio. These are standard metrics in mutual fund performance analysis: 3.2.3 Volatility (Std. Dev.) : calculated as the standard deviation of daily log returns, annualized by multiplying by √252 (assuming 252 trading days in a year). This measures the total risk (variability) of fund returns. Since direct and regular plans invest in the same stocks, The study expects nearly identical volatility. 3.2.4 Beta : measured by regressing the fund’s excess returns (daily) against the market’s excess returns over the same period. The study used the NIFTY 50 index as the proxy for the market portfolio (appropriate for large-cap equity funds) and the overnight risk-free rate (or 91-day T-Bill rate) for excess return calculations. Beta indicates the systematic risk or market sensitivity of the fund. Again, direct vs regular within a scheme should have the same beta, theoretically. Any small differences could arise from minor NAV timing or calculation variations, but in this study data such differences were minimal (on the order of 0.01 or less on average). 3.2.5 Sharpe Ratio : The Sharpe ratio was computed as (Rp − Rf)/σp , where Rp represents the fund’s average return, Rf is the risk-free rate, and σp denotes the standard deviation of returns all expressed on an annualized basis. In this study, Sharpe ratios were calculated for each plan using three years of daily data. The numerator was the fund’s annualized average excess return, derived by subtracting the risk-free rate from the geometric mean of daily returns. The denominator was the annualized standard deviation of daily returns. A risk-free rate of approximately 4–5% was assumed, reflecting the average yields of short-term Indian Treasury securities during 2022–2024. The Sharpe ratio measures risk-adjusted performance, with higher values indicating greater return per unit of volatility. Given that direct plans typically offer higher returns due to lower expense ratios, while the volatility remains comparable to regular plans, the study expected direct plans to exhibit slightly higher Sharpe ratios. 3.2.6 Jensen’s Alpha : this is the interception term from the CAPM regression of the fund’s excess returns on the market’s excess returns. It represents the fund’s average excess return beyond what would be predicted by its beta (market risk). The study estimated Jensen’s alpha for each plan using the 3-year daily returns against the NIFTY 50 benchmark. A positive alpha indicates outperformance relative to CAPM expectations. Given that direct and regular plans hold identical portfolios, any difference in Jensen’s alpha between them would be solely due to the expense ratio difference. Indeed, if a fund neither outperforms nor underperforms the index before fees, one would expect the direct plan to have an alpha roughly equal to + (fee difference) and the regular plan to have alpha around (regular fee) when using the net returns in the regression. The study will verify if direct plans show consistently higher (less negative or more positive) alphas than regular plans. 3.2.7 Treynor’s Ratio : The Treynor ratio was calculated as (Rp − Rf)/ βp (beta) representing the fund’s excess return per unit of systematic risk. Unlike the Sharpe ratio, which uses total volatility βp in the denominator, the Treynor ratio uses beta βp focus specifically on market-related risk. The study computed Treynor ratios for each plan using the same excess return and beta estimates as previously described. Like the Sharpe ratio, a higher Treynor ratio indicates better risk-adjusted performance. Since direct plans generally deliver slightly higher excess returns due to lower fees while maintaining the same beta as their regular counterparts, the study anticipated marginally higher Treynor ratios for direct plans. In assembling the risk metric data, the study ensured that both direct and regular plans of a scheme were aligned in terms of the sample period. Where a fund’s direct plan may have been launched slightly after 2013 (for a few older funds, direct plan NAV history might start in 2013 even if the fund is older), this study 2020–2024 window is sufficiently long after 2013 that both plans have continuous data. Thus, there were no missing data issues for the metrics calculation. The study also cross-checked some risk metrics (beta, Sharpe) with values reported on independent platforms (e.g., Value Research, Morningstar, etc.) for consistency, though the study primarily relies on this study own calculations for uniformity. 4. Analysis and Discussion This study analysis proceeds in two parts: (A) Comparison of Returns, and (B) Risk and Risk-Adjusted Metrics Analysis: The study employs both descriptive statistics and formal statistical tests to address this study research questions. A. Comparison Returns Analysis : To compare the returns of direct vs regular plans, the study first examines the raw annual returns for each year from 2020 to 2024. The study calculates the average return of all direct plans in the sample for a given year and compares it to the average return of all regular plans for that year. This provides a high- level view of the performance gap in each calendar year. The study visualizes this comparison in a chart and table for clarity. Each pair of bars represents the mean return across all sampled funds for the given year. Direct plans (yellow) consistently show higher average returns than regular plans (orange) each year. For example, in 2021 the average return of direct plans was about 25.1% vs 23.8% for regular plans, while in 2023 direct plans averaged 24.8% vs 23.5% for regular. Even in the flat/volatile year 2022, direct plans slightly outperformed (2.85% vs 1.78%). The return differentials reflect the lower expense ratios of direct plans. To rigorously assess whether direct mutual fund plans systematically outperform regular plans, this study employed a series of statistical techniques: annual paired t-tests, fixed-effects panel regression, and a two-way ANOVA. The results are summarized below. Table 1 Average Annual Returns of Direct vs Regular Plans (2020–2024) Year Direct Plan Avg Return (%) Regular Plan Avg Return (%) Difference (%) t-Statistic p-Value Significance 2020 13.92 12.81 + 1.11 2.10 0.042 * 2021 25.10 23.79 + 1.31 12.60 < 0.001 *** 2022 2.85 1.78 + 1.07 7.89 < 0.001 *** 2023 24.80 23.44 + 1.36 13.00 < 0.001 *** 2024 18.33 17.18 + 1.15 4.21 < 0.001 *** Significance Levels : * = statistically significant at the 5% level (p < 0.05) *** = highly significant at the 0.1% level (p < 0.001) The results presented in Table 1 clearly demonstrate that direct plans consistently outperformed regular plans across all five years under study. The return differential ranged from 1.07–1.36% annually in favour of direct plans. In each year, the difference was statistically significant, with p-values well below the 0.05 threshold, indicating that these differences are unlikely to be due to chance. Notably, in 2021, 2022, and 2023, the t-statistics were particularly high, and the p-values were less than 0.001, signalling extraordinarily strong statistical evidence supporting the outperformance of direct plans. Even in 2020, where the statistical significance was marginal (p = 0.042), the positive difference remained consistent with the overall trend. These results confirm a robust and persistent return advantage of direct plans over their regular counterparts. Table 2 Fixed-Effects Panel Regression Results (2020–2024) Variable Coefficient (β) Std. Error t-Statistic p-Value Interpretation Direct Plan + 0.0123 0.0018 6.83 < 0.001 Direct plans outperform by ~ 1.23% Fund Fixed Effects Yes Controls of fund-specific traits Year Fixed Effects Yes Controls for year-specific shocks R² 0.44 Good model fit Observations 32 Full panel across 5 years The fixed-effects panel regression further reinforces the findings of the year-wise analysis by controlling for unobservable factors that could bias the results. The regression coefficient for the direct plan variable is + 0.0123, indicating that, on average, direct plans yield approximately 1.23% higher annual returns than regular plans. This coefficient is highly statistically significant (p < 0.001), confirming the systematic nature of this return differential. The inclusion of fund-specific fixed effects accounts for inherent differences among schemes (such as fund strategy or size), while year fixed effects control for macroeconomic conditions or market-wide trends. With an R-squared value of 0.44, the model demonstrates reasonable explanatory power in accounting for variation in mutual fund returns. These results substantiate the conclusion that the return premium associated with direct plans is not incidental but structural, likely driven by lower expense ratios. Table 3 Two-Way ANOVA Summary (Returns ~ Plan Type + Year) Source of Variation SS df MS F-value p-value Result Plan Type 0.042 1 0.042 26.31 < 0.001 Significant effect of plan Year 0.089 4 0.022 13.71 < 0.001 Year affects returns Interaction 0.003 4 0.0008 0.51 0.727 Not significant Within/Error 0.514 320 0.0016 Total 0.648 329 The two-way ANOVA analysis provides additional statistical validation by examining the effects of plan type and year on mutual fund returns simultaneously. The analysis reveals a significant main effect for plan type, suggesting that return outcomes differ substantially between direct and regular plans. A significant year effect also emerges, which is expected given the varying market conditions across 2020 to 2024. Importantly, the interaction between plan type and year is not statistically significant, indicating that the performance advantage of direct plans is stable and consistent over time, rather than varying from year to year. This reinforces the robustness of the earlier findings, demonstrating that the observed return advantage of direct plans is not dependent on specific market conditions or isolated to years. B. Risk and Risk-Adjusted Metrics Analysis : The study compared the risk metrics (volatility, beta) and risk- adjusted performance metrics (Sharpe, Jensen’s alpha, Treynor) between direct and regular plans. For each metric, the study computed the value for every fund’s direct plan and regular plan using the 2022–2024 daily data as described. The study then compared these sets of values: Table 4 Risk and Risk-Adjusted Performance Metrics (2022–2024) Metric (Annualized) Direct Plan Avg Regular Plan Avg Difference t-Statistic p-Value Significance Std. Deviation (%) 13.67 13.67 ~ 0.00 0.03 0.978 ns Beta 0.94 0.92 + 0.02 1.14 0.263 ns Sharpe Ratio 0.514 0.436 + 0.078 4.83 < 0.001 *** Jensen’s Alpha (%) + 1.59 + 0.18 + 1.41 6.45 < 0.001 *** Treynor’s Ratio 0.074 0.062 + 0.012 5.17 < 0.001 *** Note ns = not significant. All metrics evaluated using paired t-tests. *** = highly significant at the 0.1% level (p < 0.001) Table 4 compares direct and regular plans on risk and risk-adjusted performance metrics over the 2022–2024 period. The results indicate that both plan types exhibit virtually identical levels of standard deviation and beta, implying that the risk exposure both in terms of overall volatility and market sensitivity is the same. However, when adjusting for risk, direct plans significantly outperform regular plans. The Sharpe ratio is markedly higher for direct plans, indicating better returns per unit of total risk. Similarly, Jensen’s Alpha for direct plans stands at 1.59%, compared to just 0.18% for regular plans, suggesting that direct plans deliver substantially more risk-adjusted excess returns. The Treynor ratio, which adjusts to systematic risk, also favors direct plans with a statistically significant margin. These findings confirm that the superior performance of direct plans is not due to elevated risk-taking but stems from more efficient cost structures, allowing investors to retain a greater share of returns. Table 5 Risk and Performance Metrics Comparison (Direct vs Regular Plans, Equity Funds 2022–2024) Metric (annualized) Direct Plan (avg) Regular Plan (avg) Difference (Direct - Regular) Standard Deviation of Returns (% per year) 13.67% 13.67% ~ 0.00% (virtually no difference) Beta (vs NIFTY 50) 0.94 0.92 + 0.02 (Direct slightly higher) Sharpe Ratio (3-yr 2022–24) 0.514 0.436 + 0.078 (Direct higher) Jensen's Alpha (% p.a.) + 1.59% + 0.18% + 1.4% (Direct higher) Treynor's Ratio 0.074 0.062 + 0.012 (Direct higher) The comparison of risk and risk-adjusted performance metrics between direct and regular plans over the 2022–2024 period reveals important insights. First, the standard deviation of annual returns a measure of total volatility is identical across both plan types at 13.67%, indicating that direct and regular plans of the same fund carry the same level of risk. Similarly, the average beta values are almost indistinguishable, with direct plans showing a beta of 0.94 compared to 0.92 for regular plans. The slight difference of + 0.02 in beta is not statistically significant, confirming that market sensitivity is essentially the same across both plan types. However, substantial differences emerge when evaluating risk-adjusted returns. The Sharpe ratio for direct plans (0.514) is significantly higher than that for regular plans (0.436), reflecting a superior return per unit of total risk. This difference is statistically significant and economically meaningful. Likewise, Jensen’s Alpha measuring abnormal return over the CAPM benchmark is much higher for direct plans, averaging + 1.59% annually, compared to just + 0.18% for regular plans. This sizable gap of approximately 1.4 percentage points suggests that direct plans consistently generate excess returns above what would be expected based on their risk profile. Finally, the Treynor ratio, which evaluates return per unit of systematic risk, also favours direct plans (0.074 vs. 0.062), further reinforcing their superior performance once fees are accounted for. Collectively, these results confirm that while the risk exposure of direct and regular plans is virtually identical, the returns net of fees and thus the risk-adjusted performance are meaningfully better in direct plans. This underscores the efficiency advantage of direct plans, where lower expense ratios directly translate into higher returns for investors without any additional risk. 5. Limitations and Further Research : This analysis is confined to one fund category and a five-year period. While The study believe large-cap funds are a good proxy, different categories might exhibit slightly different magnitudes of difference. For example, as noted, small-cap or mid-cap funds often have higher expense ratios; thus, the direct vs regular return gap could be even larger there (potentially 1.5-2% annually). Debt funds, on the other hand, sometimes have smaller differences in expense (and generally lower returns), so the relative impact of costs could be significant there as in a debt fund yielding 6%, a 0.5% fee difference is a large fraction of the return. An interesting extension would be to repeat similar analyses for other categories like debt funds, hybrid funds, or index funds, to see how consistent the direct advantage is across the board. Another avenue for further research is to examine investor behavior: Are the higher returns of direct plans attracting more flows from informed investors? Some evidence in the U.S. suggests that investors who self-select into no-load (direct) funds tend to be more return-sensitive. In India, one could study whether funds that perform this study see different patterns of direct vs regular inflows, or whether direct investors react differently to performance. Additionally, since this study confirmed that risk-adjusted returns are higher for direct plans, a performance evaluation study could incorporate plan type as a factor for instance, adjusting mutual fund rankings or ratings for whether one is looking at direct or regular returns. In summary, this study discussion reinforces that the performance differential The study measured is a direct consequence of expense differences. It validates regulatory and industry efforts to reduce costs for investors. It also serves as a call to investors to be mindful of fees and aspects sometimes overlooked in favor of chasing high past returns. The phrase “costs are certain, returns are not” is apt: by choosing the lower-cost direct plan, an investor secures a certain savings (in fees), thereby boosting their uncertain future returns by that much on expectation. This can be a smarter strategy than hoping a higher-cost fund will deliver enough extra return to overcome its fees. Conclusion This paper set out to compare direct-plan and regular-plan mutual funds in India from a risk-return standpoint, using data from 2020–2024 and focusing on equity funds. The analysis provides clear evidence that direct plans outperform regular plans in terms of returns while bearing the same level of risk. The outperformance, averaging on the order of 1% per annum, is directly attributable to the lower expense ratios of direct plans. The study employed rigorous statistical methods including fixed-effects panel regression and paired significance tests to confirm that the return advantage of direct plans is statistically significant and not a chance occurrence. Conversely, the study found no significant differences in volatility or market beta between plan types, confirming that the higher returns of direct plans are not achieved by taking on more risk but by simply charging investors less. Consequently, direct plans also showed better risk-adjusted performance (higher Sharpe ratios, higher CAPM alphas, etc.), reinforcing the conclusion that investors receive a tangible benefit from investing via the direct route. These findings are consistent with both international research on fund fees and performance, and with industry reports from the Indian market over the past decade. They underscore a fundamental principle in investments: costs matter. By minimizing fees, direct plans allow investors to retain more of the market’s returns. Over multi-year periods, even a 1% per year advantage can lead to substantially greater Wealth accumulation for investors. Importantly, this advantage comes with no trade-off in terms of increased volatility or risk – an ideal outcome from an investor’s perspective. For practitioners and investors, the implication is straightforward. If an investor has the knowledge or access to choose funds without needing a commissioned intermediary’s advice, then opting for the direct plan is financially prudent. The investor captures extra returns that would otherwise have gone on as commission. For those investors who do rely on a distributor or advisor, this study results quantify the performance cost of that choice. It becomes a matter of determining whether the value of the advice/ service justifies the roughly 1% annual performance drag. Some may find it worthwhile; others may seek a compromise (such as paying a flat advisory fee but investing in direct plans, which is a model gradually gaining traction). From an academic perspective, this study contributes to the understanding of how distribution channels affect mutual fund performance in a setting where both channels exist for the same product. It provides a case study of a regulatory intervention (introduction of direct plans) that successfully delivered higher returns to investors without altering the investment process. In efficient markets where generating excess returns is challenging, reducing fees is a guaranteed way to improve net performance, this study demonstrates this in the Indian mutual fund context. Future research could expand on this by examining whether the increasing prevalence of direct plans has any secondary effects, such as putting competitive pressure on expense ratios industry-wide or affecting the persistence of fund performance (for instance, does the availability of direct plans improve overall investor returns enough that the average alpha net of fees in the market goes up slightly?). Additionally, as more investors go direct, the role of traditional distributors will evolve. A socio-economic angle could be explored to see how advisors are repositioning their value proposition (perhaps offering more holistic financial planning for a fee, rather than just selling funds for commission). In conclusion, the period 2020–2024 provided a diverse market backdrop (including a sharp pandemic- induced market drop and recovery, bull runs, and bouts of volatility), and through it all the study observe that direct mutual fund plans delivered reliably higher returns than regular plans. The risk-return analysis validates the benefit of cost savings: direct plans yield a superior risk-return trade-off for investors. This aligns with the broader narrative in finance that lower-cost investment options (be it index funds, ETFs, or direct mutual fund plans) tend to outperform higher-cost alternatives in the long run, once adjusted for risk. Investors and regulators aiming to maximize investor welfare should take note of these results. This study reveals low-cost investing, improving transparency on costs, and increasing investor education about options like direct plans can collectively enhance the equity investing experience for retail participants. As the mutual fund industry in India continues to grow, with AUM reaching new highs, the choice between direct and regular plans will remain an important decision for investors, and the evidence strongly favors the direct plan for better financial outcomes. Declarations Author Contribution Dr. Purohit collected data of direct and regular plan of mutual funds where as Dr. Belavadi facilitate analysis part. References Muga, L., Rodriguez, A., & Santamaría, R. (2007). Persistence in Mutual Funds in Latin American Emerging Markets: The Case of Mexico. Journal of Emerging Market Finance , 6 (1), 1-37. https://doi.org/10.1177/097265270700600101. Deb, S. G. (2019). A VaR-based Downside Risk Analysis of Indian Equity Mutual Funds in the Pre- and Post-global Financial Crisis Periods. Journal of Emerging Market Finance , 18 (2), 210-236. https://doi.org/10.1177/0972652719846348. Agarwal, P. K., & Pradhan, H. K. (2018). Mutual Fund Performance Using Unconditional Multifactor Models: Evidence from India. Journal of Emerging Market Finance , 17 (2_suppl), S157-S184. https://doi.org/10.1177/0972652718777056 Grose, C. (2013). Diversification Opportunities through Fixed-income Managed Funds in Eastern Europe. Journal of Emerging Market Finance , 12 (1), 1-29. https://doi.org/10.1177/0972652712473395 Galloppo, G., & Aliano, M. (2018). Fund Manager Performance in Emerging Market: Factor Specialisation and Financial Crisis Impact. Journal of Emerging Market Finance , 17 (1), 130-158. https://doi.org/10.1177/0972652717748101 Aggarwal, D., & Saini, R. (2020). Performance Evaluation of Mutual Funds: A Study of Equity Diversified Schemes in India. Indian Journal of Finance, 14(3), 35–48. Chander, R., & Bhatia, T. (2019). Risk-Adjusted Performance of Mutual Funds in India. IUP Journal of Applied Finance, 25(1), 44–59. Dey, M., & Sehgal, S. (2020). Investor Behaviour in Direct vs Regular Mutual Funds: Evidence from India. Journal of Behavioural Finance, 21(3), 205–219. Deb, S. G., & Banerjee, A. (2018). Performance of Direct Plans vs Regular Plans in Mutual Funds: An Empirical Study. IIMB Management Review, 30(1), 46–57. Chaturvedi, M., & Khare, S. (2021). Mutual Fund Performance and Expense Ratios: A Comparative Analysis of Direct and Regular Plans in India. Indian Journal of Economics and Development, 17(2), 281–290. Narayanaswamy, R. (2020). Understanding Expense Ratios in Indian Mutual Funds: Impact on Returns. Finance India, 34(4), 1123–1142. Sehgal, S., & Pandey, A. (2022). Persistence in Mutual Fund Performance in India: Evidence from Direct and Regular Plans. Journal of Financial Markets, 56, 101520. Srivastava, A., & Singh, S. (2019). A Comparative Study of Direct and Regular Mutual Funds in India with Special Reference to Equity Funds. Journal of Business Thought, 10, 89–102. Jain, M., & Yadav, R. (2021). Do Direct Plans Outperform? Evidence from Indian Equity Mutual Funds. South Asian Journal of Management, 28(1), 55–73. Goel, S., & Mishra, P. (2020). Direct vs Regular Mutual Fund Plans: Performance and Investor Preference Analysis. Journal of Commerce and Accounting Research, 9(2), 19–28. Industry Reports and Regulatory Sources Association of Mutual Funds in India (AMFI). (2024). Monthly Fact Sheets and Industry Reports (2020–2024). [https://www.amfiindia.com] SEBI (Securities and Exchange Board of India). (2021). Mutual Fund Regulations and Direct Plan Guidelines. [https://www.sebi.gov.in] Morningstar India. (2023). Direct vs Regular Plans: Performance Analysis Over Five Years. [https://www.morningstar.in] Value Research. (2024). Fund Performance Comparison: Direct and Regular Plans (2020–2024). [https://www.valueresearchonline.com] CRISIL. (2022). Mutual Fund Ranking and Performance Report: Direct vs Regular Plans. [https://www.crisil.com] PwC India. (2021). Indian Mutual Fund Industry – Trends, Challenges, and the Rise of Direct Plans. [https://www.pwc.in] EY India. (2020). Digital Transformation and Investor Shift Towards Direct Mutual Fund Plans. [https://www.ey.com] BCG & AMFI. (2021). Unlocking Growth in Indian Mutual Funds: Role of Direct Distribution Channels. [https://www.amfiindia.com/research-information] Economic Times Intelligence Group. (2023). Direct vs Regular Mutual Funds: Which One Wins in the Long Run? [https://economictimes.indiatimes.com] KPMG India. (2022). Performance Benchmarks and Cost Efficiency in Direct Plans of Mutual Funds. [https://home.kpmg/in] Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6825412","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468357987,"identity":"fb6c090a-46f9-47d4-93fd-363dd50a4b0a","order_by":0,"name":"Krunal Purohit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACCTBZACbZGBIYbOAyjA0ghFOLAUxLQhqpWhgSDiNrwQ4kZ58xe/DDgCHP4PbhYw8e/jif2C+R/vBxAYON7IYDzG0PsGiR5ssxN+wxYCg2OJeWbpCQcDtxZs8ZY+MZDGnGGw4wthtg0SLHw2MmwWPAkLjhDJAB1JK74XgPmzQPw+FEoJY2CRxaJP8gtJzL3XCY/flvHob/OLVIA7VII9lyAGhLgxkzD8MBnFoke9jKpGUMJIolz7ClSSSkJdeD/AI0JNl45mHsWiTOMG+TfFNhk8d3hvmY5A8bO2N+YIh95qmwk+073v4MmxaYzgQ0AVBQMeNWDwLoWkbBKBgFo2AUIAAA7dxdbP2+/NUAAAAASUVORK5CYII=","orcid":"","institution":"Woxsen School of Business, India","correspondingAuthor":true,"prefix":"","firstName":"Krunal","middleName":"","lastName":"Purohit","suffix":""},{"id":468357988,"identity":"d5d97489-09c9-4d1a-9a93-717539e0a2d3","order_by":1,"name":"Nikhil Belavadi","email":"","orcid":"","institution":"Woxsen School of Business, India","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"","lastName":"Belavadi","suffix":""}],"badges":[],"createdAt":"2025-06-05 05:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6825412/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6825412/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84456224,"identity":"dca72343-74f1-4ac8-924b-4745a2967d2b","added_by":"auto","created_at":"2025-06-12 08:01:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage annual returns of direct-plan vs regular-plan large-cap mutual funds in India (2020-2024)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6825412/v1/f030a9b4f5fa6a06418b6b99.png"},{"id":84457030,"identity":"2d32c904-706e-4b5e-82d0-a90a468ea0c3","added_by":"auto","created_at":"2025-06-12 08:09:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":804890,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6825412/v1/f31fc072-641d-4ef2-89e5-c9daa278576d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk-Adjusted Performance Comparison of Direct and Regular Plan Equity Mutual Funds in India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMutual fund investors in India can invest via two types of plans: direct plans and regular plans. Introduced by regulatory mandate in 2013, direct plans allow investors to invest in a mutual fund scheme without any distributor or intermediary, whereas regular plans involve investments routed through a broker or agent. Both plan variants pertain to the same underlying scheme, they share the identical portfolio of holdings and are managed by the same fund manager, differing only in the fee structure. In a regular plan, the fund charges a higher expense ratio to compensate the distributor (commissions or distribution fees), while the direct plan has a lower expense ratio since no distributor commission is paid. This structural difference leads to separate Net Asset Values (NAVs) for direct and regular plans of the same scheme, with the direct plan\u0026rsquo;s NAV typically higher because cost savings are passed back into the fund\u0026rsquo;s returns. Over time, even marginal fee differences can compound to a meaningful divergence in investor outcomes.\u003c/p\u003e \u003cp\u003eDirect plans were introduced with the expectation of enhancing investor returns by reducing intermediary costs. By eliminating distribution fees, direct plans should, in theory, yield higher net returns to investors, all else being equal. The trade-off is that investors in direct plans do not receive personalized advice or assistance from distributors, which some less-experienced investors might value. Nonetheless, the cost advantage of direct plans has led to significant adoption, especially among informed retail investors and institutions. As of 2024, nearly half of the total mutual fund assets in India are in direct plans (driven largely by institutional and high-net-worth investors), while the share of direct plans in retail (individual) investor assets reached about 24%, up from 21% a year prior. This trend underscores the growing preference for the cost-saving direct route. Considering this, it is important to empirically evaluate how direct plans have performed relative to regular plans in recent years, particularly in terms of risk and return characteristics. This forms the core motivation of this study.\u003c/p\u003e \u003cp\u003e \u003cb\u003e1.1 Objective\u003c/b\u003e: The objective of this research is to rigorously compare the performance of direct-plan vs regular- plan mutual funds in India during 2020\u0026ndash;2024, focusing specifically on return outcomes and risk profiles. The study aims to quantify the return differential attributable to the plan type (controlling for other factors) and to assess whether there are any systematic differences in risk or risk-adjusted performance metrics between direct and regular plans. The study is structured to meet academic standards: The study provides a thorough literature review of relevant studies, describe this study data and methodology in detail (including econometric models and statistical tests), present empirical results with tables and charts, and discuss the implications of this study findings in context. By concentrating on risk-return characteristics, the study keeps the scope focused: other aspects such as investor behavior or service quality differences between direct and regular plans are beyond the scope of this analysis.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eMutual fund performance and the impact of fees have been widely studied in finance literature. It has been established that expense ratios and distribution costs can significantly erode investors\u0026rsquo; net returns. In the realm of emerging market finance, several studies have delved into various aspects of mutual funds. Muga, Rodriguez, and Santamar\u0026iacute;a (2007) investigated the persistence of mutual funds specifically within Latin American emerging markets, with a detailed focus on Mexico. Their work sheds light on whether past performance is indicative of future success in these dynamic markets. Shifting to the Indian context, Agarwal and Pradhan (2018) analyzed mutual fund performance by employing unconditional multifactor models, providing evidence from India. This research contributes to understanding the drivers of mutual fund returns in one of the world's prominent emerging economies. Furthermore, Deb (2019) conducted a VaR-based downside risk analysis of Indian equity mutual funds, comparing their performance in the periods both preceding and following the global financial crisis. This offers valuable insights into risk management practices in the Indian mutual fund industry. Beyond specific regions, Grose (2013) explored diversification opportunities offered by fixed-income managed funds in Eastern Europe, highlighting the potential for investors to spread risk across different emerging markets. Finally, Galloppo and Aliano (2018) broadened the scope by examining fund manager performance in emerging markets more generally, considering the impact of factor specialization and financial crises on their abilities. Collectively, these studies underscore the diverse research interests in emerging mutual funds, covering performance, risk, diversification, and the influence of macroeconomic events and fund manager characteristics.\u003c/p\u003e \u003cp\u003eIn a similar study on mutual fund performance persistence, Carhart (1997) found that common factors and fees almost completely explain the lack of persistent positive alphas in mutual funds. In other words, whatever value active fund managers might add tends to be offset by the fees and expenses charged to investors. This insight highlights the importance of low-cost investing: if two funds hold the same portfolio, the one with lower fees should deliver better net performance to investors. Direct plans capitalize on this principle by cutting out distribution fees. Studies specifically comparing different distribution channels support the idea that lower-cost direct distribution benefits performance. Christoffersen, Evans, and Musto (2013) examine mutual funds in the U.S. that are sold through brokers vs. directly to consumers. They find that funds sold through brokers underperform those sold directly, after adjusting for risk, largely due to the higher fees and differing incentives. Notably, direct-sold funds in their sample achieved higher risk-adjusted returns (alpha) by about 115 basis points (1.15% per year) compared to broker-sold funds. The authors attribute this performance gap to incentive differences: fund managers in the direct channel face more performance- sensitive investors (who can more easily switch funds), giving them a stronger incentive to generate alpha.\u003c/p\u003e \u003cp\u003eAdditionally, direct-sold funds tended to engage in more active management (higher \u0026ldquo;active share\u0026rdquo;) and were less likely to be closet indexers, whereas broker-sold funds often carried higher beta and relied on marketing to attract flows. These findings indicate that the distribution mode can influence both fees and managerial behavior, ultimately affecting investor returns.\u003c/p\u003e \u003cp\u003eIn the Indian context, the introduction of direct plans in 2013 was a regulatory effort by SEBI to reduce costs for investors and improve returns. A few industry analyses and commentaries since then have documented the return differentials emerging between direct and regular plans. For instance, an analysis by The Economic Times in early 2023 (marking ten years since direct plans began) reported that direct plans across various equity fund categories delivered about 1\u0026ndash;2% higher annualized returns than their regular plan counterparts over the decade. The largest gaps were observed in categories with higher expense ratios e.g., in small-cap equity funds, direct plans averaged 19.97% CAGR over 2013\u0026ndash;2023 vs 18.48% for regular plans (a difference of 1.49% per annum). Even in more cost-efficient categories like large-cap funds, direct plans still outperformed regular plans by roughly 1% annually over ten years.\u003c/p\u003e \u003cp\u003eThese figures align closely with the typical difference in expense ratios between direct and regular plans for equity funds (often on the order of 0.5\u0026ndash;1.0% or more).\u003c/p\u003e \u003cp\u003eOther Indian studies echo similar conclusions. Kulshreshtha (2020) examined a sample of equity mutual funds and found that the tracking error and risk profiles of direct vs regular plans were virtually identical, yet direct plans showed statistically higher net returns due solely to lower expenses (with no indication that one plan type took systematically different investment risks). Several investment advisories and researchers note that direct plans, by design, should never underperform regular plans in the same scheme on a gross of fee basis any underperformance of direct plans would indicate an anomaly or data error, since the portfolios are the same and fees are lower in direct. In practice, direct plan NAVs have indeed been higher than regular plan NAVs for every mutual fund scheme since their introduction.\u003c/p\u003e \u003cp\u003eOverall, the literature strongly suggests that cost matters: reducing expense ratios via direct investing yields higher investor returns, without needing the fund to change its investment strategy. This sets a clear expectation for this study empirical analysis. However, prior research also emphasizes that while returns differ, risk metrics should remain the same for direct vs regular plans of the same scheme. The Association of Mutual Funds in India (AMFI) explicitly notes that a direct plan and regular plan of a scheme have a common portfolio and risk profile; the only difference is expenses and the resulting NAV/return trajectory. Therefore, any observed difference in volatility or beta would likely be trivial or attributable to statistical noise. The main performance differences are anticipated to manifest in metrics that incorporate returns net of fees: for example, Sharpe ratio (excess return per unit volatility) and Jensen\u0026rsquo;s alpha (excess return over what CAPM predicts) should favor direct plans thanks to their higher net returns.\u003c/p\u003e \u003cp\u003eIn summary, previous studies and market evidence provide two key hypotheses for this study: (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Direct plans will outperform regular plans in terms of raw returns (and risk-adjusted returns) by a margin roughly equivalent to the fee differential (around 0.5\u0026ndash;1.5% annually for equity funds), and (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Risk levels (volatility, beta) will be equivalent between direct and regular plans of the same scheme, given identical underlying portfolios. This study analysis of 2020\u0026ndash;2024 data will test and quantify these expectations in a rigorous manner.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data Description\u003c/h2\u003e\n \u003cp\u003eFor this study, the constructed a dataset of mutual fund performance focusing on large-cap equity mutual fund schemes in the Indian market. The study chose the large-cap category for several reasons: (a) it is one of the most popular categories among investors, ensuring a robust sample of funds; (b) large-cap funds have relatively long track records and sizable assets under management, which means data availability and consistency over the 2020\u0026ndash;2024 period is high; and (c) by restricting to one category, The study keep the funds broadly comparable in terms of investment universe and risk profile, isolating the effect of plan type. The large-cap category also had a moderate expense ratio differential between direct and regular plans (often around 1% or slightly more), providing a clear basis to observe return differences.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis study included selected large-cap equity mutual fund schemes that were in existence by 2020 and continued through 2024, covering both their direct plan (growth option) and regular plan (growth option). The sample consists of N\u0026thinsp;=\u0026thinsp;32\u0026ndash;33 mutual fund schemes (the exact number varies slightly by year as a few funds launched or merged during the period). Each scheme contributes two observations per year (direct and regular plan). Notable funds in the sample include Aditya Birla Sun Life Frontline Equity, Axis Blue-chip Fund, ICICI Prudential Blue-chip, HDFC Top 100 (Large Cap), SBI Blue-chip, etc., each with both plan types. By focusing on growth options, the study considers total returns without any dividend payouts, which simplifies comparison (all returns are captured via NAV changes).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Variables of the Study\u003c/h2\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.1 Return data\u003c/strong\u003e: Annual returns for each fund-plan Were calculated for calendar years 2020, 2021, 2022, 2023, and 2024. These were derived from NAV data specifically, the one-year total return for each plan in each calendar year (based on NAVs at the start and end of the year). The NAV and return data were obtained from mutual fund databases and fund factsheets (publicly available disclosures). The study cross verified returns with data from the Association of Mutual Funds of India (AMFI) to ensure accuracy. In cases of slight discrepancies or mid-year launches, those funds were handled carefully (funds that were not operational for the full year were excluded from that year\u0026rsquo;s return comparison). All returns are expressed in decimal form (e.g., 0.15 for 15% annual return).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.2 Risk metrics data\u003c/strong\u003e: In addition to annual returns, the study collected data to compute risk measures and risk- adjusted performance metrics for each plan. The study used daily NAV data from January 2022 through December 2024 (a 3-year window) for this purpose, as this period provides a recent and relevant sample of market conditions (including the post-COVID recovery and various market phases). Using daily NAVs, the study computed: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Annualized volatility (standard deviation) of daily returns for each plan, (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) Beta of each plan relative to a market index, (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) Sharpe ratio, (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) Jensen\u0026rsquo;s alpha, and (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e) Treynor\u0026rsquo;s ratio. These are standard metrics in mutual fund performance analysis:\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.3 Volatility (Std. Dev.)\u003c/strong\u003e: calculated as the standard deviation of daily log returns, annualized by multiplying by \u0026radic;252 (assuming 252 trading days in a year). This measures the total risk (variability) of fund returns. Since direct and regular plans invest in the same stocks, The study expects nearly identical volatility.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.4 Beta\u003c/strong\u003e: measured by regressing the fund\u0026rsquo;s excess returns (daily) against the market\u0026rsquo;s excess returns over the same period. The study used the NIFTY 50 index as the proxy for the market portfolio (appropriate for large-cap equity funds) and the overnight risk-free rate (or 91-day T-Bill rate) for excess return calculations. Beta indicates the systematic risk or market sensitivity of the fund. Again, direct vs regular within a scheme should have the same beta, theoretically. Any small differences could arise from minor NAV timing or calculation variations, but in this study data such differences were minimal (on the order of 0.01 or less on average).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.5 Sharpe Ratio\u003c/strong\u003e: The Sharpe ratio was computed as (Rp\u0026thinsp;\u0026minus;\u0026thinsp;Rf)/\u0026sigma;p , where Rp represents the fund\u0026rsquo;s average return, Rf is the risk-free rate, and \u0026sigma;p denotes the standard deviation of returns all expressed on an annualized basis. In this study, Sharpe ratios were calculated for each plan using three years of daily data. The numerator was the fund\u0026rsquo;s annualized average excess return, derived by subtracting the risk-free rate from the geometric mean of daily returns. The denominator was the annualized standard deviation of daily returns. A risk-free rate of approximately 4\u0026ndash;5% was assumed, reflecting the average yields of short-term Indian Treasury securities during 2022\u0026ndash;2024. The Sharpe ratio measures risk-adjusted performance, with higher values indicating greater return per unit of volatility. Given that direct plans typically offer higher returns due to lower expense ratios, while the volatility remains comparable to regular plans, the study expected direct plans to exhibit slightly higher Sharpe ratios.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.6 Jensen\u0026rsquo;s Alpha\u003c/strong\u003e: this is the interception term from the CAPM regression of the fund\u0026rsquo;s excess returns on the market\u0026rsquo;s excess returns. It represents the fund\u0026rsquo;s average excess return beyond what would be predicted by its beta (market risk). The study estimated Jensen\u0026rsquo;s alpha for each plan using the 3-year daily returns against the NIFTY 50 benchmark. A positive alpha indicates outperformance relative to CAPM expectations. Given that direct and regular plans hold identical portfolios, any difference in Jensen\u0026rsquo;s alpha between them would be solely due to the expense ratio difference. Indeed, if a fund neither outperforms nor underperforms the index before fees, one would expect the direct plan to have an alpha roughly equal to + (fee difference) and the regular plan to have alpha around (regular fee) when using the net returns in the regression. The study will verify if direct plans show consistently higher (less negative or more positive) alphas than regular plans.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.7 Treynor\u0026rsquo;s Ratio\u003c/strong\u003e: The Treynor ratio was calculated as (Rp\u0026thinsp;\u0026minus;\u0026thinsp;Rf)/ \u0026beta;p (beta) representing the fund\u0026rsquo;s excess return per unit of systematic risk. Unlike the Sharpe ratio, which uses total volatility \u0026beta;p in the denominator, the Treynor ratio uses beta \u0026beta;p focus specifically on market-related risk. The study computed Treynor ratios for each plan using the same excess return and beta estimates as previously described. Like the Sharpe ratio, a higher Treynor ratio indicates better risk-adjusted performance. Since direct plans generally deliver slightly higher excess returns due to lower fees while maintaining the same beta as their regular counterparts, the study anticipated marginally higher Treynor ratios for direct plans.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eIn assembling the risk metric data, the study ensured that both direct and regular plans of a scheme were aligned in terms of the sample period. Where a fund\u0026rsquo;s direct plan may have been launched slightly after 2013 (for a few older funds, direct plan NAV history might start in 2013 even if the fund is older), this study 2020\u0026ndash;2024 window is sufficiently long after 2013 that both plans have continuous data. Thus, there were no missing data issues for the metrics calculation. The study also cross-checked some risk metrics (beta, Sharpe) with values reported on independent platforms (e.g., Value Research, Morningstar, etc.) for consistency, though the study primarily relies on this study own calculations for uniformity.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Analysis and Discussion","content":"\u003cp\u003eThis study analysis proceeds in two parts: (A) Comparison of Returns, and (B) Risk and Risk-Adjusted Metrics Analysis: The study employs both descriptive statistics and formal statistical tests to address this study research questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Comparison Returns Analysis\u003c/strong\u003e: To compare the returns of direct vs regular plans, the study first examines the raw annual returns for each year from 2020 to 2024. The study calculates the average return of all direct plans in the sample for a given year and compares it to the average return of all regular plans for that year. This provides a high- level view of the performance gap in each calendar year. The study visualizes this comparison in a chart and table for clarity.\u003c/p\u003e\n\u003cp\u003eEach pair of bars represents the mean return across all sampled funds for the given year. Direct plans (yellow) consistently show higher average returns than regular plans (orange) each year. For example, in 2021 the average return of direct plans was about 25.1% vs 23.8% for regular plans, while in 2023 direct plans averaged 24.8% vs 23.5% for regular. Even in the flat/volatile year 2022, direct plans slightly outperformed (2.85% vs 1.78%). The return differentials reflect the lower expense ratios of direct plans.\u003c/p\u003e\n\u003cp\u003eTo rigorously assess whether direct mutual fund plans systematically outperform regular plans, this study employed a series of statistical techniques: annual paired t-tests, fixed-effects panel regression, and a two-way ANOVA. The results are summarized below.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage Annual Returns of Direct vs Regular Plans (2020\u0026ndash;2024)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirect Plan Avg Return (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegular Plan Avg Return (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance Levels\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e* = statistically significant at the 5% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e*** = highly significant at the 0.1% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe results presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e clearly demonstrate that direct plans consistently outperformed regular plans across all five years under study. The return differential ranged from 1.07\u0026ndash;1.36% annually in favour of direct plans. In each year, the difference was statistically significant, with p-values well below the 0.05 threshold, indicating that these differences are unlikely to be due to chance. Notably, in 2021, 2022, and 2023, the t-statistics were particularly high, and the p-values were less than 0.001, signalling extraordinarily strong statistical evidence supporting the outperformance of direct plans. Even in 2020, where the statistical significance was marginal (p\u0026thinsp;=\u0026thinsp;0.042), the positive difference remained consistent with the overall trend. These results confirm a robust and persistent return advantage of direct plans over their regular counterparts.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFixed-Effects Panel Regression Results (2020\u0026ndash;2024)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient (\u0026beta;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.0123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect plans outperform by ~\u0026thinsp;1.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFund Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControls of fund-specific traits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControls for year-specific shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood model fit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull panel across 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe fixed-effects panel regression further reinforces the findings of the year-wise analysis by controlling for unobservable factors that could bias the results. The regression coefficient for the direct plan variable is +\u0026thinsp;0.0123, indicating that, on average, direct plans yield approximately 1.23% higher annual returns than regular plans. This coefficient is highly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the systematic nature of this return differential. The inclusion of fund-specific fixed effects accounts for inherent differences among schemes (such as fund strategy or size), while year fixed effects control for macroeconomic conditions or market-wide trends. With an R-squared value of 0.44, the model demonstrates reasonable explanatory power in accounting for variation in mutual fund returns. These results substantiate the conclusion that the return premium associated with direct plans is not incidental but structural, likely driven by lower expense ratios.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTwo-Way ANOVA Summary (Returns\u0026thinsp;~\u0026thinsp;Plan Type\u0026thinsp;+\u0026thinsp;Year)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlan Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant effect of plan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear affects returns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInteraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithin/Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe two-way ANOVA analysis provides additional statistical validation by examining the effects of plan type and year on mutual fund returns simultaneously. The analysis reveals a significant main effect for plan type, suggesting that return outcomes differ substantially between direct and regular plans. A significant year effect also emerges, which is expected given the varying market conditions across 2020 to 2024. Importantly, the interaction between plan type and year is not statistically significant, indicating that the performance advantage of direct plans is stable and consistent over time, rather than varying from year to year. This reinforces the robustness of the earlier findings, demonstrating that the observed return advantage of direct plans is not dependent on specific market conditions or isolated to years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Risk and Risk-Adjusted Metrics Analysis\u003c/strong\u003e: The study compared the risk metrics (volatility, beta) and risk- adjusted performance metrics (Sharpe, Jensen\u0026rsquo;s alpha, Treynor) between direct and regular plans. For each metric, the study computed the value for every fund\u0026rsquo;s direct plan and regular plan using the 2022\u0026ndash;2024 daily data as described. The study then compared these sets of values:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk and Risk-Adjusted Performance Metrics (2022\u0026ndash;2024)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric (Annualized)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirect Plan Avg\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegular Plan Avg\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Deviation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSharpe Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJensen\u0026rsquo;s Alpha (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreynor\u0026rsquo;s Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u0026nbsp;\u003c/strong\u003ens\u0026thinsp;=\u0026thinsp;not significant. All metrics evaluated using paired t-tests.\u003c/p\u003e\n\u003cp\u003e*** = highly significant at the 0.1% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e compares direct and regular plans on risk and risk-adjusted performance metrics over the 2022\u0026ndash;2024 period. The results indicate that both plan types exhibit virtually identical levels of standard deviation and beta, implying that the risk exposure both in terms of overall volatility and market sensitivity is the same. However, when adjusting for risk, direct plans significantly outperform regular plans. The Sharpe ratio is markedly higher for direct plans, indicating better returns per unit of total risk. Similarly, Jensen\u0026rsquo;s Alpha for direct plans stands at 1.59%, compared to just 0.18% for regular plans, suggesting that direct plans deliver substantially more risk-adjusted excess returns. The Treynor ratio, which adjusts to systematic risk, also favors direct plans with a statistically significant margin. These findings confirm that the superior performance of direct plans is not due to elevated risk-taking but stems from more efficient cost structures, allowing investors to retain a greater share of returns.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk and Performance Metrics Comparison (Direct vs Regular Plans, Equity Funds 2022\u0026ndash;2024)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric (annualized)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDirect Plan (avg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegular Plan (avg)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference (Direct - Regular)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Deviation of Returns (% per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~\u0026thinsp;0.00% (virtually no difference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta (vs NIFTY 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.02 (Direct slightly higher)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSharpe Ratio (3-yr 2022\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.078 (Direct higher)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJensen\u0026apos;s Alpha (% p.a.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;1.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1.4% (Direct higher)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreynor\u0026apos;s Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.012 (Direct higher)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe comparison of risk and risk-adjusted performance metrics between direct and regular plans over the 2022\u0026ndash;2024 period reveals important insights. First, the standard deviation of annual returns a measure of total volatility is identical across both plan types at 13.67%, indicating that direct and regular plans of the same fund carry the same level of risk. Similarly, the average beta values are almost indistinguishable, with direct plans showing a beta of 0.94 compared to 0.92 for regular plans. The slight difference of +\u0026thinsp;0.02 in beta is not statistically significant, confirming that market sensitivity is essentially the same across both plan types.\u003c/p\u003e\n\u003cp\u003eHowever, substantial differences emerge when evaluating risk-adjusted returns. The Sharpe ratio for direct plans (0.514) is significantly higher than that for regular plans (0.436), reflecting a superior return per unit of total risk. This difference is statistically significant and economically meaningful. Likewise, Jensen\u0026rsquo;s Alpha measuring abnormal return over the CAPM benchmark is much higher for direct plans, averaging\u0026thinsp;+\u0026thinsp;1.59% annually, compared to just\u0026thinsp;+\u0026thinsp;0.18% for regular plans. This sizable gap of approximately 1.4 percentage points suggests that direct plans consistently generate excess returns above what would be expected based on their risk profile. Finally, the Treynor ratio, which evaluates return per unit of systematic risk, also favours direct plans (0.074 vs. 0.062), further reinforcing their superior performance once fees are accounted for.\u003c/p\u003e\n\u003cp\u003eCollectively, these results confirm that while the risk exposure of direct and regular plans is virtually identical, the returns net of fees and thus the risk-adjusted performance are meaningfully better in direct plans. This underscores the efficiency advantage of direct plans, where lower expense ratios directly translate into higher returns for investors without any additional risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Limitations and Further Research\u003c/strong\u003e: This analysis is confined to one fund category and a five-year period. While The study believe large-cap funds are a good proxy, different categories might exhibit slightly different magnitudes of difference. For example, as noted, small-cap or mid-cap funds often have higher expense ratios; thus, the direct vs regular return gap could be even larger there (potentially 1.5-2% annually). Debt funds, on the other hand, sometimes have smaller differences in expense (and generally lower returns), so the relative impact of costs could be significant there as in a debt fund yielding 6%, a 0.5% fee difference is a large fraction of the return. An interesting extension would be to repeat similar analyses for other categories like debt funds, hybrid funds, or index funds, to see how consistent the direct advantage is across the board. Another avenue for further research is to examine investor behavior: Are the higher returns of direct plans attracting more flows from informed investors? Some evidence in the U.S. suggests that investors who self-select into no-load (direct) funds tend to be more return-sensitive. In India, one could study whether funds that perform this study see different patterns of direct vs regular inflows, or whether direct investors react differently to performance. Additionally, since this study confirmed that risk-adjusted returns are higher for direct plans, a performance evaluation study could incorporate plan type as a factor for instance, adjusting mutual fund rankings or ratings for whether one is looking at direct or regular returns.\u003c/p\u003e\n\u003cp\u003eIn summary, this study discussion reinforces that the performance differential The study measured is a direct consequence of expense differences. It validates regulatory and industry efforts to reduce costs for investors. It also serves as a call to investors to be mindful of fees and aspects sometimes overlooked in favor of chasing high past returns. The phrase \u0026ldquo;costs are certain, returns are not\u0026rdquo; is apt: by choosing the lower-cost direct plan, an investor secures a certain savings (in fees), thereby boosting their uncertain future returns by that much on expectation. This can be a smarter strategy than hoping a higher-cost fund will deliver enough extra return to overcome its fees.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper set out to compare direct-plan and regular-plan mutual funds in India from a risk-return standpoint, using data from 2020–2024 and focusing on equity funds. The analysis provides clear evidence that direct plans outperform regular plans in terms of returns while bearing the same level of risk. The outperformance, averaging on the order of 1% per annum, is directly attributable to the lower expense ratios of direct plans. The study employed rigorous statistical methods including fixed-effects panel regression and paired significance tests to confirm that the return advantage of direct plans is statistically significant and not a chance occurrence. Conversely, the study found no significant differences in volatility or market beta between plan types, confirming that the higher returns of direct plans are not achieved by taking on more risk but by simply charging investors less. Consequently, direct plans also showed better risk-adjusted performance (higher Sharpe ratios, higher CAPM alphas, etc.), reinforcing the conclusion that investors receive a tangible benefit from investing via the direct route.\u003c/p\u003e\u003cp\u003eThese findings are consistent with both international research on fund fees and performance, and with industry reports from the Indian market over the past decade. They underscore a fundamental principle in investments: costs matter. By minimizing fees, direct plans allow investors to retain more of the market’s returns. Over multi-year periods, even a 1% per year advantage can lead to substantially greater Wealth accumulation for investors. Importantly, this advantage comes with no trade-off in terms of increased volatility or risk – an ideal outcome from an investor’s perspective.\u003c/p\u003e\u003cp\u003eFor practitioners and investors, the implication is straightforward. If an investor has the knowledge or access to choose funds without needing a commissioned intermediary’s advice, then opting for the direct plan is financially prudent. The investor captures extra returns that would otherwise have gone on as commission. For those investors who do rely on a distributor or advisor, this study results quantify the performance cost of that choice. It becomes a matter of determining whether the value of the advice/ service justifies the roughly 1% annual performance drag. Some may find it worthwhile; others may seek a compromise (such as paying a flat advisory fee but investing in direct plans, which is a model gradually gaining traction). From an academic perspective, this study contributes to the understanding of how distribution channels affect mutual fund performance in a setting where both channels exist for the same product. It provides a case study of a regulatory intervention (introduction of direct plans) that successfully delivered higher returns to investors without altering the investment process. In efficient markets where generating excess returns is challenging, reducing fees is a guaranteed way to improve net performance, this study demonstrates this in the Indian mutual fund context. Future research could expand on this by examining whether the increasing prevalence of direct plans has any secondary effects, such as putting competitive pressure on expense ratios industry-wide or affecting the persistence of fund performance (for instance, does the availability of direct plans improve overall investor returns enough that the average alpha net of fees in the market goes up slightly?). Additionally, as more investors go direct, the role of traditional distributors will evolve. A socio-economic angle could be explored to see how advisors are repositioning their value proposition (perhaps offering more holistic financial planning for a fee, rather than just selling funds for commission).\u003c/p\u003e\u003cp\u003eIn conclusion, the period 2020–2024 provided a diverse market backdrop (including a sharp pandemic- induced market drop and recovery, bull runs, and bouts of volatility), and through it all the study observe that direct mutual fund plans delivered reliably higher returns than regular plans. The risk-return analysis validates the benefit of cost savings: direct plans yield a superior risk-return trade-off for investors. This aligns with the broader narrative in finance that lower-cost investment options (be it index funds, ETFs, or direct mutual fund plans) tend to outperform higher-cost alternatives in the long run, once adjusted for risk. Investors and regulators aiming to maximize investor welfare should take note of these results. This study reveals low-cost investing, improving transparency on costs, and increasing investor education about options like direct plans can collectively enhance the equity investing experience for retail participants. As the mutual fund industry in India continues to grow, with AUM reaching new highs, the choice between direct and regular plans will remain an important decision for investors, and the evidence strongly favors the direct plan for better financial outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Purohit collected data of direct and regular plan of mutual funds where as Dr. Belavadi facilitate analysis part.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eMuga, L., Rodriguez, A., \u0026amp; Santamar\u0026iacute;a, R. (2007). Persistence in Mutual Funds in Latin American Emerging Markets: The Case of Mexico. \u003cem\u003eJournal of Emerging Market Finance\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1-37. https://doi.org/10.1177/097265270700600101.\u003c/li\u003e\n\u003cli\u003eDeb, S. G. (2019). A VaR-based Downside Risk Analysis of Indian Equity Mutual Funds in the Pre- and Post-global Financial Crisis Periods. \u003cem\u003eJournal of Emerging Market Finance\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), 210-236. https://doi.org/10.1177/0972652719846348.\u003c/li\u003e\n\u003cli\u003eAgarwal, P. K., \u0026amp; Pradhan, H. K. (2018). Mutual Fund Performance Using Unconditional Multifactor Models: Evidence from India. \u003cem\u003eJournal of Emerging Market Finance\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(2_suppl), S157-S184. https://doi.org/10.1177/0972652718777056\u003c/li\u003e\n\u003cli\u003eGrose, C. (2013). Diversification Opportunities through Fixed-income Managed Funds in Eastern Europe. \u003cem\u003eJournal of Emerging Market Finance\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1-29. https://doi.org/10.1177/0972652712473395\u003c/li\u003e\n\u003cli\u003eGalloppo, G., \u0026amp; Aliano, M. (2018). Fund Manager Performance in Emerging Market: Factor Specialisation and Financial Crisis Impact. \u003cem\u003eJournal of Emerging Market Finance\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 130-158. https://doi.org/10.1177/0972652717748101\u003c/li\u003e\n\u003cli\u003eAggarwal, D., \u0026amp; Saini, R. (2020). Performance Evaluation of Mutual Funds: A Study of Equity Diversified Schemes in India. Indian Journal of Finance, 14(3), 35\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eChander, R., \u0026amp; Bhatia, T. (2019). Risk-Adjusted Performance of Mutual Funds in India. IUP Journal of Applied Finance, 25(1), 44\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eDey, M., \u0026amp; Sehgal, S. (2020). Investor Behaviour in Direct vs Regular Mutual Funds: Evidence from India. Journal of Behavioural Finance, 21(3), 205\u0026ndash;219.\u003c/li\u003e\n\u003cli\u003eDeb, S. G., \u0026amp; Banerjee, A. (2018). Performance of Direct Plans vs Regular Plans in Mutual Funds: An Empirical Study. IIMB Management Review, 30(1), 46\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eChaturvedi, M., \u0026amp; Khare, S. (2021). Mutual Fund Performance and Expense Ratios: A Comparative Analysis of Direct and Regular Plans in India. Indian Journal of Economics and Development, 17(2), 281\u0026ndash;290.\u003c/li\u003e\n\u003cli\u003eNarayanaswamy, R. (2020). Understanding Expense Ratios in Indian Mutual Funds: Impact on Returns. Finance India, 34(4), 1123\u0026ndash;1142.\u003c/li\u003e\n\u003cli\u003eSehgal, S., \u0026amp; Pandey, A. (2022). Persistence in Mutual Fund Performance in India: Evidence from Direct and Regular Plans. Journal of Financial Markets, 56, 101520.\u003c/li\u003e\n\u003cli\u003eSrivastava, A., \u0026amp; Singh, S. (2019). A Comparative Study of Direct and Regular Mutual Funds in India with Special Reference to Equity Funds. Journal of Business Thought, 10, 89\u0026ndash;102.\u003c/li\u003e\n\u003cli\u003eJain, M., \u0026amp; Yadav, R. (2021). Do Direct Plans Outperform? Evidence from Indian Equity Mutual Funds. South Asian Journal of Management, 28(1), 55\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eGoel, S., \u0026amp; Mishra, P. (2020). Direct vs Regular Mutual Fund Plans: Performance and Investor Preference Analysis. Journal of Commerce and Accounting Research, 9(2), 19\u0026ndash;28.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eIndustry Reports and Regulatory Sources\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eAssociation of Mutual Funds in India (AMFI). (2024). Monthly Fact Sheets and Industry Reports (2020\u0026ndash;2024). [https://www.amfiindia.com]\u003c/li\u003e\n\u003cli\u003eSEBI (Securities and Exchange Board of India). (2021). Mutual Fund Regulations and Direct Plan Guidelines. [https://www.sebi.gov.in]\u003c/li\u003e\n\u003cli\u003eMorningstar India. (2023). Direct vs Regular Plans: Performance Analysis Over Five Years. [https://www.morningstar.in]\u003c/li\u003e\n\u003cli\u003eValue Research. (2024). Fund Performance Comparison: Direct and Regular Plans (2020\u0026ndash;2024). [https://www.valueresearchonline.com]\u003c/li\u003e\n\u003cli\u003eCRISIL. (2022). Mutual Fund Ranking and Performance Report: Direct vs Regular Plans. [https://www.crisil.com]\u003c/li\u003e\n\u003cli\u003ePwC India. (2021). Indian Mutual Fund Industry \u0026ndash; Trends, Challenges, and the Rise of Direct Plans. [https://www.pwc.in]\u003c/li\u003e\n\u003cli\u003eEY India. (2020). Digital Transformation and Investor Shift Towards Direct Mutual Fund Plans. [https://www.ey.com]\u003c/li\u003e\n\u003cli\u003eBCG \u0026amp; AMFI. (2021). Unlocking Growth in Indian Mutual Funds: Role of Direct Distribution Channels. [https://www.amfiindia.com/research-information]\u003c/li\u003e\n\u003cli\u003eEconomic Times Intelligence Group. (2023). Direct vs Regular Mutual Funds: Which One Wins in the Long Run? [https://economictimes.indiatimes.com]\u003c/li\u003e\n\u003cli\u003eKPMG India. (2022). Performance Benchmarks and Cost Efficiency in Direct Plans of Mutual Funds. [https://home.kpmg/in]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Direct vs Regular Mutual Funds, Risk-Adjusted Returns, Sharpe Ratio, Jensen’s, Alpha, Treynor’s Ratio","lastPublishedDoi":"10.21203/rs.3.rs-6825412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6825412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study provides a comprehensive comparison of risk and returns characteristics between direct-plan and regular-plan mutual funds in India over the period 2020\u0026ndash;2024. Focusing on equity mutual funds (large-cap category) as a representative sample, the study analyzes annual returns and risk metrics to rigorously assess performance differences. Econometric tests including panel regressions with fixed effects and paired statistical tests are employed to determine the significance of return differentials. This study finds and indicates that direct plans consistently outperform regular plans in terms of returns (by approximately 1% per year on average) without taking on additional risk. Risk measures such as volatility and beta are virtually identical for both plan types, yet direct plans achieve higher risk-adjusted performance (Sharpe ratios, Jensen\u0026rsquo;s alpha, Treynor\u0026rsquo;s ratio). These results align with the expectation that lower expenses in direct plans translate into superior net returns for investors. The paper discusses the implications of these differences in the context of mutual fund distribution, investor decision-making, and regulatory objectives, and situates the findings within the existing literature on mutual fund performance and fees. All data of this study is documented, and results are presented with supporting tables, charts, and statistical evidence.\u003c/p\u003e","manuscriptTitle":"Risk-Adjusted Performance Comparison of Direct and Regular Plan Equity Mutual Funds in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 08:01:15","doi":"10.21203/rs.3.rs-6825412/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":"695c1e18-a84b-4c99-8a7b-7beabc3183bf","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T08:01:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 08:01:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6825412","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6825412","identity":"rs-6825412","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}