Fund Alpha Research Articles

Performance of active portfolio managers when the benchmark is not observable

We present a specialized methodology designed for evaluating the performance of active portfolio managers in situations where the common benchmark portfolio cannot be directly observed or determined by the analyzing agent. This method assesses performance by examining the excess alpha of an optimal active fund with respect to a combination of such funds that efficiently minimizes residual risk. After establishing the theoretical underpinnings of this approach and deriving the necessary statistical tests, we then showcase its practical application in assessing the historical performance of pension fund administrators operating within the Peruvian Private Pension System.

Unsmoothing Returns of Illiquid Funds

Abstract Funds investing in illiquid assets report returns with spurious autocorrelation. Consequently, investors need to unsmooth these funds’ returns when evaluating their risk exposures. We show that funds with similar investments share a common source of spurious autocorrelation not fully resolved by traditional unsmoothing methods and thereby leading to underestimation of systematic risk. Thus, we propose a generalized unsmoothing technique and apply it to hedge funds and private commercial real estate funds. Our method significantly improves the measurement of funds’ risk exposures and risk-adjusted performance, especially for highly illiquid funds. Overall, the average illiquid fund alpha is lower than previously thought. (JEL G11, G12, G23)

Persistent Performance of Alpha in Evaluating and Predicting the Returns of Chinese Stock Mutual Funds

Alpha has been examined to be a significant performance indicator for mutual funds. It was developed in 1968 by Jensen and was widely applied as a measure in the evaluation of mutual funds managers. It quantitatively show the gap between the model-based expected returns and the actual fund returns. In my research, I wonder whether the persistence of alpha for different portfolios exist. I apply three mutual fund performance evaluation models in this paper and calculate the alpha of mutual funds under each model. Then I rank the alpha of each fund and group them into four portfolios. I keep tracking the alpha and performance of each portfolio from January 2008 to July 2020, and find that the portfolio with the highest historical alpha keeps beating the other three portfolios while having the highest alpha. Although there are still some parts of the experiment that could be improved, the empirical results of my research show that the alpha seems to be a persistent indicator of the performance of Chinese mutual funds.

Expense ratios and net alphas of large cap funds

Global equity markets witnessed a tumultuous time-period of decline followed by growth, starting from the early part of 2000 to the beginning of the current period. Investment in actively managed mutual funds also experienced a decent growth over the same period though there were times when investors especially retail investors sat on the sidelines and seemed reluctant to invest in equity funds. This study analyzes the performance of large cap equity funds between January 2000 and December 2013. The main objective is to assess the performance as reflected in the alpha of funds conditioned on expenses. We apply both OLS regression and ranked portfolio approaches to estimate the abnormal performance. Results of this study show that large cap funds underperform against benchmarks after incorporating expenses. Results also suggest that expenses are not the only reason behind their underperformance.

Assessing the Performance and Risk-Adjusted Returns of Financial Mutual Funds

In this study, we provide a comprehensive examination of the performance of financial (specialty sector financial) mutual funds over a 23-year period, a much longer time frame than what has been analyzed in previous literature. To fully understand the performance of these mutual funds, we consider multiple factors, including risk-adjusted performance, both unconditional and conditional multifactor analysis, and market timing and selectivity. Financial mutual funds have higher risk-adjusted performance than the overall market and financial sector benchmarks. However, fund alphas are not different from zero, and managers do not exhibit market timing or security selection abilities. Our analysis not only includes the overall performance of these mutual funds, but we also delve into sub-samples before and after the 2008 financial crisis and during the recent Coronavirus pandemic.

Market crises and benchmark-adjusted fund alphas in a small market context

Most mutual fund performance evaluation studies interpret fund alphas as the incremental performance of managers in relation to passive benchmark indices, which should exhibit statistically insignificant alphas. However, if these indices present significant non-zero alphas, standard (non-adjusted) fund alphas are biased. This paper investigates the impact of using benchmark-adjusted alphas to assess the performance of Portuguese-based mutual funds, investing in domestic and European equities. For the period 2000-2020, our results show that fund benchmarks exhibit significantly negative alphas, which lead to an underestimation of mutual fund performance when employing standard models. As a result, benchmark-adjusted alphas are significantly higher than unadjusted alphas for both fund categories, though the differences are larger for domestic than for European funds. We have also found that the impact of the benchmark-adjustment procedure depends on the state of markets. The domestic (European) benchmark exhibits considerably lower (higher) alphas during crisis than during non-crisis periods. During market crises, the differences between pre- and post-adjustment alphas are statistically significant only for domestic funds, whereas during non-crisis periods, both fund categories exhibit significant performance improvements. Our findings suggest that the benchmark-adjustment procedure has a higher impact when benchmark indices exhibit higher concentration.

Managerial characteristics and performance of eurozone mutual funds

AbstractWe investigate the relation between observable managerial characteristics (i.e., gender, age, tenure, professional qualifications, and advanced education) and performance in diversified equity mutual funds domiciled in the eurozone. We find that differences in the fund alphas are statistically significant only in groups based on age, tenure, and professional qualifications (i.e., chartered financial analyst [CFA]). We also find a significant positive relation for age and CFA certification with a fund's risk‐adjusted performance and a significant negative relation for tenure. However, we find no significant effect for gender and advanced education (i.e., master of business administration [MBA]). The differences in risk taking are significantly related only with age and tenure; the former has a negative and the latter a positive relation with risk taking.

De facto time‐varying indices‐based benchmarks for mutual fund returns

AbstractWe question time‐invariant indices as fund benchmarks and propose a regime‐switching methodology to identify time‐varying de facto benchmarks from a pool of market‐based indices, with or without a risk‐free asset. To ameliorate the benchmark mismatch issue, we highlight the importance of using time‐varying indices‐based benchmarks for fund performance evaluation. Our de facto benchmark captures fund styles better than other benchmark choices, substantially improves the identification of significant fund alphas, and provides better out‐of‐sample forecasts. We uncover several new findings in terms of fund performance evaluation using our de facto benchmarks.

Exploring the zoo of predictors for mutual fund performance in China

This study conducts a comprehensive and systematic empirical examination of the variables that can predict the cross-sectional alphas of the mutual funds in the Chinese market. From 47 predictors considered, it is found that about half show significant predictive ability for ex-post fund alphas of one, three, and six months. This finding is based on alpha spreads from quantile sorts or the slopes from cross-sectional regressions. Further, arbitrage activity may impede the predictability. Finally, the predictive characteristics identified in this study have not yet been fully learned by investors, whereas investors make their investment decisions based largely on past one-year returns. This finding implies the presence of capital misallocation in mutual fund investment in the Chinese market. Our results should be highly informative to fund rating agencies and professional fund investors.

Improving Equity Fund Alpha Estimates with a Second Size Factor

Practitioners and researchers seek to accurately estimate the value added by active equity fund managers. The authors hypothesize that the asset pricing models used to study equity funds may better capture nonlinearity in stock returns across market capitalizations by replacing the commonly used single size factor with two new size factors. This extension is important for explaining equity mutual fund returns because active fund holdings are weighted toward mid- and small-cap stocks to a greater extent than holdings of cap-weighted market indexes. In tests designed to minimize data mining issues, two size factors explain equity fund returns better than do the Fama–French single-size and style factors. Augmented Fama–French models explain over 25% of unexplained variance and yield superior adjusted R-squares for more than 75% of equity funds in the CRSP mutual fund database. Applied tests supplement these broad statistical analyses and confirm the technique’s value for practice. Also of benefit, the two proposed factor return series are readily available on the Internet to researchers and practitioners alike.

On the performance of cryptocurrency funds

We investigate the performance of funds that specialise in cryptocurrency markets. In doing so, we contribute to a growing literature that aims to understand the value of digital assets as investments. The main empirical results support the argument that cryptocurrency funds generate significantly positive alphas compared to passive benchmarks or conventional risk factors. To understand whether the fund managers have sufficient skills to more than cover their costs, we compare the actual fund alphas against the simulated values from a panel semi-parametric bootstrap approach. The analysis shows that the extreme outperformance is unlikely to be explained by the luck of fund managers. However, the significance of the alphas becomes statistically weaker after considering the cross-sectional correlation in fund returns.

Style Drift and Alphas: A Case Study in International Retail Funds

This paper examines style drift and alphas for a sample of 110 international retail funds offered to individual investors. We show that when fund managers “deviate” from their stated categories, alphas are biased upward. While previous studies in the international stock arena typically employ theoretical constructs to benchmark fund performance, we employ an actual investable vehicle (tradeable ETFs) in the same categories as the funds. For the period 2002-2020, we show empirically that managers do indeed deviate from their stated fund categories with subsequent upward bias to their fund alphas. For over half of the funds in our sample, we find significant drift to emerging markets and to the US equity market. We observe that alpha is biased upward an average of 86 basis points for the retail funds examined in this study.

The Information Content of Aggregate Mutual Fund Alpha

The Information Content of Aggregate Mutual Fund Alpha

Hedge fund alpha and beta corrected for stale pricing

Hedge fund alpha and beta corrected for stale pricing

Active Technological Similarity and Mutual Fund Performance

AbstractWe examine whether superior understanding of technological innovation is a source of mutual fund managers’ ability to garner positive abnormal returns. Consistent with our hypothesis, the inter-quintile annual net Carhart alpha spread for mutual funds sorted on changes in the technological similarity (TS) of their portfolio holdings is 282 basis points. Moreover, because changes in TS are largely orthogonal to other predictors of mutual fund success (e.g., industry concentration, active share, fund R2, and lag fund alpha), changes in TS can be combined with other measures to help identify the best performing funds.

Top-bottom investing skill and the fund alpha in Indian mutual fund industry: an empirical investigation

PurposeMutual funds are the second most preferred investment option in India and have garnered considerable research interest. The focus of Indian studies thus far has been restricted to the bottom-up approach of investing which rewards a fund manager for picking winner stocks and generates superior returns. While changing portfolio allocation as per varying macro-trends has been instrumental in generating superior returns, it has not been given the desired attention. This study addresses this important research gap.Design/methodology/approachThe authors analyze the industry selection ability of the fund manager on a robust sample by decomposing alpha into alpha due to industry selection and alpha attributable to stock selection. Alpha estimates are computed on a robust sample of 34 open-ended Indian equity mutual funds for a 10-year duration 2011–2020 using three base models of asset pricing – single-factor, four-factor and five-factor alpha under panel data methodology.FindingsThe study leads us to four major findings. One, industry selection explains more than two-fifth of the alpha both in cross-section and time series of returns; two, industry selection exhibits persistence for more than four quarters across asset pricing model; third, younger funds have level playing when alpha from picking right industries is concerned; four, broad industry allocation continues to explain superior returns as sector allocation undergoes consolidation during ongoing COVID-19 pandemic and funds increase exposure to defensive stocks, consistent with folio allocations as per macroeconomic conditions.Research limitations/implicationsThe authors find strong evidence of persistence in the case of alpha attributable to the industry selection component, and the findings are consistent with the persistence results reported in the empirical literature. While some funds excel in stock-picking skills and others excel in picking the right industries, both skills together make for winner funds that attract larger investor flows as investors chase superior performance. The authors also find no evidence of diseconomies of scale in the case of industry allocation alpha generated by the fund managers.Practical implicationsThe results suggest a fresh approach for investors while making mutual fund investment decisions; the investors can achieve superior returns by assessing industry selection skills as it tends to provide a more holistic picture concerning a perennial question – why some funds outperform and continue to contribute to investor's wealth?Social implicationsMutual funds have become a favored investment option for Indian investors more so as a disciplined investment option owing to dismal financial literacy rates. The study throws light on a relatively unaddressed dimension of choosing winner funds. The significance of right sector allocation assumed even more significance with the onset of the pandemic which lends further credence to the findings of the study.Originality/valueResearch has been conducted on secondary data extracted from a well-cited database for Indian mutual funds. Empirical analysis and conclusion drawn are based on authentic statistical analysis and adds to the existing literature.

Hedge Fund Alpha: What about Drawdowns?

The author reviews the relative risks of hedge fund investing using various commonly used measures, including market betas, correlations, and portfolio drawdowns. Historically, the data show that hedge funds have hedged a fair degree of systematic market risk, especially in the early years, offering meaningful diversification benefits to traditional stock/bond portfolios. However, the diversification benefits for investors in hedge funds have since declined, though they have not altogether been eliminated. Most recently, during the 2020 pandemic, modest drawdown benefits bore out for hedge fund investors, although again much less so than in the earlier years. <b>Key Findings</b> ▪ This article extends “Hedge Fund Alpha: Cycle or Sunset” (Sullivan 2021) with a focus on the relative risk of hedge funds versus a stock-only as well as more diversified portfolios that seek to blend hedge funds, stocks, and bonds. ▪ The hedging away of systematic risk by hedge funds appears to be diminishing in recent years, in part due to rising correlations between stocks and hedge funds, now approaching 90%. ▪ Drawdowns for various hedge fund composite portfolios during the turbulent periods of 2008 and 2020 have been more in line with those seen by a traditional 50/50 stock/bond portfolio. ▪ However, within the universe of hedge funds, some strategies have delivered more meaningful diversification benefits.

Towards a dead end? EMU bond market exposure and manager performance

Using factor models we empirically investigate the performance of European Monetary Union (EMU) bond managers. We find that (1) alpha is time varying, (2) bond managers exhibit alpha in the short run as opposed to long-term prior to the Global Financial Crisis (GFC), and (3) bond fund alpha is associated with government bond funds and funds with heavy exposure to government bonds. Our factor models do not detect alpha in corporate and high-yield bond funds on average. We observe that alpha was much higher in the period prior to the GFC that began in 2008. The number of funds in our sample generating alpha dropped significantly after that crisis and remained very low from 2008 to 2017 as EMU markets recovered. We apply three models to evaluate EMU bond fund manager alpha. We use the two bond-market related Fama-French factors – term and default. In addition, we use the three equity market-related Fama-French factors – size, value, and market – together with the momentum factor. We find evidence that the bond market-related Fama-French factors are significant for the EMU bond funds in our sample, but the equity market-related Fama-French factors are not. By applying factor analysis to the returns of EMU bond funds, we (1) identify the economic drivers of EMU bond market returns and (2) show the performance of bond portfolio managers regarding alpha prior to the GFC and explain the possible lack of alpha after the GFC.

Active Technological Similarity and Mutual Fund Performance

We examine whether superior understanding of technological innovation is a source of mutual fund managers’ ability to garner positive abnormal returns. Consistent with our hypothesis, the inter-quintile annual net Carhart alpha spread for mutual funds sorted on changes in the technological similarity of their portfolio holdings is 282 basis points. Moreover, because changes in technological similarity are largely orthogonal to other predictors of mutual fund success (e.g., industry concentration, active share, fund R2, and lag fund alpha), changes in technological similarity can be combined with other measures to help identify the best performing funds.

Improved inference for fund alphas using high-dimensional cross-sectional tests

Abstract The traditional fund-by-fund alpha inference suffers from various econometric problems (e.g., cross-sectional independence assumption, lack of power, time-invariant coefficient assumption, multiple-hypothesis-testing). Recognizing the panel nature of fund industries, we tailor four high-dimensional cross-sectional tests to shed light into both the zero-alpha hypothesis and ratio of non-zero alphas. Particularly, we augment Gagliardini et al. (2016) with a time-varying alpha estimator. Our results reject the zero-alpha joint hypothesis as the statistical significance of alphas is too high to be explained by luck. After controlling for luck, our empirical studies show that the power enhancement helps to identify a large portion of significant fund alphas, which cannot be achieved using the usual Wald test. Meanwhile, the time-varying approach shows that fund alphas diverge during the late 2000s Global Financial Crisis, which cannot be observed using the time-invariant model. Overall, relative to the literature, we draw a more accurate and complete picture, and provide several powerful tools for future research.