QUANTILE-DRIVEN PERFORMANCE ANALYSIS OF MUTUAL FUNDS: LEVERAGING MORNINGSTAR STYLE CLASSIFICATIONS

ABSTRACT

This research undertakes a quantile-based analysis of U.S. mutual fund performance categorized by Morningstar Style Box classifications. By examining fund returns at the 25th, 50th (median), and 75th percentiles, the study identifies investment styles that consistently outperform relevant benchmarks such as the S&P 500 and Russell 2000. Using data from 1,710 mutual funds spanning 2010 to 2023, the study calculates various performance metrics including returns, betas, alphas, Sharpe ratios, and Omega ratios across different styles and quantiles. This approach aims to offer insights into optimal style allocations for constructing diversified portfolios that align with investors’ risk-return preferences. The findings highlight the advantages of style diversification, the role of market capitalization in risk mitigation, and the significance of fund selection within top-performing styles, contributing valuable knowledge to mutual fund analysis literature and practical investment strategies.

АННОТАЦИЯ

Это исследование проводит анализ эффективности взаимных фондов США, основанный на квантилях, классифицированных по типам категорий Morningstar. Изучая доходность фондов на 25-м, 50-м (медианном) и 75-м процентилях, исследование выявляет инвестиционные стили, которые стабильно превосходят соответствующие индексы, такие как S&P 500 и Russell 2000. Используя данные о 1710 взаимных фондах за период с 2010 по 2023 годы, исследование вычисляет различные показатели эффективности, включая доходность, бета-коэффициент, альфа Дженсена, коэффициент Шарпа и коэффициент Омега по различным стилям и квантилям. Этот подход направлен на предоставление информации об оптимальных распределениях стилей для построения диверсифицированного портфеля, которые соответствуют предпочтениям инвесторов по соотношению риска и доходности. Результаты подчеркивают преимущества диверсификации стилей, роль рыночной капитализации в снижении риска и значимость выбора фондов в рамках наиболее эффективных стилей, что вносит ценный вклад по анализу взаимных фондов и практические инвестиционные стратегии.

Keywords: mutual funds, Morningstar categories, quantile-based analysis, performance evaluation, portfolio optimization, diversification, investment style.

Ключевые слова: паевые фонды, категории Morningstar, анализ квантилей, оценка эффективности, оптимизация портфеля, диверсификация, стиль инвестирования.

INTRODUCTION

This study investigates the average returns of U.S. mutual funds classified by Morningstar Style Boxes, focusing on three quantiles: the 25th percentile, the median, and the 75th percentile. By assessing fund performance within each style category at these quantile levels, the research aims to pinpoint investment styles that deliver robust returns compared to benchmarks like the S&P 500 for large-cap funds and the Russell 2000 for mid-cap and small-cap funds.

Understanding how different investment styles perform across varying return quantiles is essential for investors and portfolio managers. This analysis provides insights into styles that maintain resilience and the potential for outperformance, even under challenging market conditions.

The motivation for this analysis arises from the need to identify investment styles balancing risk and return, adhering to the principles of diversification and portfolio construction. Consistent performance relative to benchmarks enhances portfolio stability and mitigates potential drawdowns during market downturns.

LITERATURE REVIEW

Evaluating mutual fund performance is a well-explored area in finance, with numerous studies analyzing various factors influencing returns and risk-adjusted performance. Sharpe (1966) [1] introduced the Sharpe ratio, a widely used measure of mutual fund risk-adjusted performance. Subsequent research, such as Fama and French (1993) [2], identified value and size effects in stock returns, laying the groundwork for analyzing mutual fund performance across different styles. Carhart (1997) [3] attributed persistent mutual fund performance primarily to investment costs and transaction costs, rather than stock-picking abilities.

Wermers (2003) [4] found that growth-oriented mutual funds, especially those investing in small-cap stocks, show significant performance persistence. Bassett and Chen (2001) [5] employed quantile regression to assess equity mutual fund performance, revealing that fund characteristics like size and turnover impact performance differently across quantiles.

Further research by Kosowski (2011) [6] indicated that mutual funds with concentrated portfolios and small-cap or value stock focus outperform during market recoveries, suggesting these styles offer higher returns during recovery phases. Studies have also examined industry concentration's impact on actively managed equity mutual funds (Kacperczyk et al. 2005) [7], the need to control for fixed income exposure in hybrid mutual funds (Comer et al. 2009) [8], and the effect of risk-shifting behavior on fund performance (Huang et al. 2011) [9].

The Morningstar Style Box classification system, categorizing funds by market capitalization and value-growth orientation, has become a standard framework for analyzing mutual fund performance [10]. Despite valuable insights from existing literature, there remains a need for comprehensive analyses examining average fund returns across Morningstar style categories at different return quantiles. This study aims to fill this gap, offering practical implications for constructing well-diversified portfolios tailored to investors’ risk-return preferences.

METHODOLOGY

This study employs a quantile-based framework to analyze the performance of mutual funds across various Morningstar style categories. By focusing on three key quantiles ‒ the 25th percentile, the median (50th percentile), and the 75th percentile ‒ we capture a comprehensive range of performance outcomes. For each quantile and Morningstar category, we calculate a set of performance indicators, including average annual return, beta coefficient, Jensen's alpha, Sharpe ratio, Treynor ratio, Omega ratio, and maximum drawdown.

To provide a visual representation of the data, we constructed three graphs for each quantile, corresponding to each company size (large-cap, mid-cap, and small-cap). Each graph visualizes the cumulative returns of the style categories compared to their respective benchmarks. Specifically, the large-cap fund graphs compare cumulative returns to the S&P 500, while the mid-cap and small-cap fund graphs compare to the Russell 2000.

The analysis aimed to identify which Morningstar categories perform best across different quantiles. We determined which categories showed the highest returns at the 75th quantile, offered the best balance of risk and return at the median quantile, and exhibited the lowest losses at the 25th quantile.

DATA OVERVIEW

The dataset used in this study comprises monthly return data for 1,710 U.S. mutual funds spanning the period from January 2010 to December 2023. The funds were classified according to the Morningstar Style Box framework, which categorizes funds based on their investment styles defined by market capitalization (size) and value-growth orientation.

The dataset includes funds from nine distinct Morningstar style categories: Large-Cap Growth (306 funds), Large-Cap Blend (291 funds), Large-Cap Value (281 funds), Mid-Cap Growth (147 funds), Mid-Cap Blend (123 funds), Mid-Cap Value (103 funds), Small-Cap Growth (167 funds), Small-Cap Blend (156 funds), Small-Cap Value (136 funds).

To evaluate fund performance relative to benchmarks, the study used the S&P 500 Index for large-cap funds and the Russell 2000 Index for mid-cap and small-cap funds.

RESULTS

The analysis revealed insights consistent with broader market trends. Large-cap funds, benchmarked against the S&P 500, generally outperformed mid-cap and small-cap funds, benchmarked against the Russell 2000. This influenced return profiles across different Morningstar style categories. This disparity in benchmark performance directly influenced the return profiles of funds within different Morningstar style categories.

Table 1.

Quantile-Based Performance Metrics Across Morningstar Styles

At the median (0.50) quantile, large-cap fund categories exhibited higher returns compared to their mid-cap and small-cap counterparts. This observation can be attributed to the superior performance of the S&P 500 Index, which serves as the benchmark for large-cap funds. The median returns of Large-Cap Blend and Large-Cap Growth categories were particularly noteworthy, outpacing the returns of smaller-cap fund categories.

Across all Morningstar styles, funds in the 75th percentile quantile demonstrated impressive outperformance relative to their respective benchmark indices. Notably, growth-oriented funds, irrespective of market capitalization, exhibited exceptionally strong returns at this quantile level. This finding can be attributed to the higher risk profiles of growth funds, which enabled them to capitalize on favorable market conditions and exposure to high-growth sectors or companies.

At the 25th percentile quantile, representing the lower end of the performance distribution, most fund categories underperformed their benchmark indices. However, the Large-Cap Blend category emerged as a notable exception, managing to avoid losses at this quantile. This resilience can be attributed to the balanced risk-return profile of large-cap blend funds, coupled with their exposure to well-established, large-cap companies, which typically exhibit lower volatility.

Figure 1. Median Quantile Cumulative Returns Across Market Caps and Benchmark

Figure 2. 75th Quantile Cumulative Returns Across Market Caps and Benchmark

Figure 3. 25th Quantile Cumulative Returns Across Market Caps and Benchmark

The observed patterns highlight the importance of considering risk profiles and investment objectives when constructing a diversified portfolio. While growth-oriented funds may offer opportunities for higher returns, as evidenced by their strong performance at the 75th quantile, they also carry higher risk, which can manifest in larger drawdowns at the lower quantiles.

Conversely, the defensive characteristics of large-cap blend funds, demonstrated by their ability to avoid losses at the 25th quantile, underscore their potential value in mitigating downside risk and enhancing overall portfolio stability.

CONCLUSION

The findings from this comprehensive analysis of mutual fund performance across Morningstar styles and quantiles offer valuable insights into constructing an optimal portfolio strategy. While the Large-Cap Growth category demonstrated exceptional returns at the 75th quantile, capitalizing on favorable market conditions and exposure to high-growth sectors, the Large-Cap Blend style exhibited defensive characteristics, avoiding losses at the 25th quantile and mitigating downside risk. This complementary blend of growth potential and risk management underscores the importance of a balanced approach to portfolio construction.

However, it is crucial to acknowledge that within each Morningstar style category, there exists a wide dispersion of fund performance, as evidenced by the quantile analysis. The findings underscore the importance of thorough due diligence and the need to identify skilled fund managers who can consistently generate above-average returns while effectively managing risk. By combining exposure to the Large-Cap Growth and Large-Cap Blend styles with a rigorous fund selection process focused on identifying top-performing funds within these categories, investors can potentially construct well-diversified portfolios that offer the potential for superior risk-adjusted returns over the long term.

References:

1. Sharpe, W. F. Mutual fund performance // The Journal of Business. 1966. Vol. 39. № 1. P.119-138.
2. Fama, E. F., French, K. R. Common risk factors in the returns on stocks and bonds // Journal of Financial Economics. 1993. Vol. 33. № 1. P.3-56.
3. Carhart, M. M. On persistence in mutual fund performance // The Journal of Finance. 1997. Vol. 52. № 1. P.57-82.
4. Wermers, R. Is money really smart? New evidence on the relation between mutual fund holdings and future stock returns // Working Paper. 2003.
5. Bassett, G. W., Chen, H. L. Portfolio style: Return-based attribution using quantile regression // Empirical Economics. 2001. Vol. 26. № 1. P.293-305.
6. Kosowski, R. Do mutual funds perform when it matters most to investors? US mutual fund performance and risk in recessions and expansions // Quarterly Journal of Finance. 2011. Vol. 1. № 3. P.607-664.
7. Kacperczyk, M., Sialm, C., Zheng, L. On the industry concentration of actively managed equity mutual funds // The Journal of Finance. 2005. Vol. 60. № 4. P.1983-2011.
8. Comer, G., Larrymore, N., Rodriguez, J. Controlling for fixed income exposure in portfolio evaluation: Evidence from hybrid mutual funds // The Review of Financial Studies. 2009. Vol. 22. № 2. P.481-507.
9. Huang, J., Sialm, C., Zhang, H. Risk shifting and mutual fund performance // The Review of Financial Studies. 2011. Vol. 24. № 8. P.2575-2616.
10.  Morningstar. Morningstar Style Box Methodology. Morningstar Methodology Paper. / [Electronic source] — Available at. — URL:  https://www.morningstar.com/ (Accessed on 14.06.2024).