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1 – 10 of over 12000Kozo Omori and Tomoki Kitamura
Mutual fund investors assess a fund manager’s skills when allocating their capital. To identify the rationale behind retail investors’ decisions, this study aims to examine the…
Abstract
Purpose
Mutual fund investors assess a fund manager’s skills when allocating their capital. To identify the rationale behind retail investors’ decisions, this study aims to examine the relation between mutual fund flows and abnormal returns (alpha), as well as the various risk factors in the Japanese mutual fund market, which has distinctive characteristics regarding investors and distributors.
Design/methodology/approach
Six standard asset pricing models are used to investigate how investors assess mutual fund managers’ skills: the market-adjusted return, the capital asset pricing model and the Fama–French three-factor model and its augmented versions.
Findings
Contrary to the literature, this study finds that investors in Japan mainly rely on alpha to assess mutual funds. In particular, investors respond to alpha for fund inflows and their evaluations depend on the market environment and their mutual fund search costs.
Originality/value
This study measures the response of investors to the skills of mutual fund managers in the Japanese market – especially for funds purchased through bank-related distributors that have aimed to capture inexperienced retail investors since deregulation in the 1990s – and reveals their high response to alpha.
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This study investigates the performance distribution of passive funds in the Korean market and compares it with the performance distribution of active funds. The key findings are…
Abstract
This study investigates the performance distribution of passive funds in the Korean market and compares it with the performance distribution of active funds. The key findings are as follows, first, the performance distribution of passive funds has a thicker tail compared to that of active funds. There are passive funds that achieve outstanding performance, and both the false discovery rate (FDR) analysis and simulation analysis suggest that their outperformance is driven by managerial skill rather than luck. Second, passive fund performance is more persistent compared to active fund performance. Third, investors are less responsive to passive fund performance compared to active fund performance. The fund flow-performance relationship is significantly positive for active funds but not for passive funds. This implies that investors may not recognize the managerial skills of passive funds.
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Laleh Samarbakhsh and Meet Shah
This research aims to examine hedge funds’ performance, risk and flow before and after the implementation of the Stop Trading on Congressional Knowledge (STOCK) Act.
Abstract
Purpose
This research aims to examine hedge funds’ performance, risk and flow before and after the implementation of the Stop Trading on Congressional Knowledge (STOCK) Act.
Design/methodology/approach
This paper includes the use of different factor models to highlight the performance and risk of hedge funds before and after the implementation of the STOCK Act. Hedge fund holdings are retrieved from Thomson Reuters Lipper Hedge Fund Database (TASS).
Findings
This study finds significant differences before and after the implementation of the STOCK Act. The results for the entire sample period indicate that hedge funds suffered lower-alpha, standard deviation and idiosyncratic risk after the implementation of the STOCK Act.
Originality/value
The paper’s originality and value lie in addressing the relationship gap between the STOCK Act and hedge fund performance.
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This study aims to examine whether mutual funds can earn daily alpha and time daily market return.
Abstract
Purpose
This study aims to examine whether mutual funds can earn daily alpha and time daily market return.
Design/methodology/approach
Based on the Treynor and Mazuy (1966) model and the Henriksson and Merton (1981) model, the author tests the daily market-timing ability of actual mutual funds and bootstrapped mutual funds.
Findings
The author finds that daily alpha and daily market-timing ability can come from pure luck. In addition, the relation between fund alpha and market-timing ability is at best minimal.
Originality/value
Using bootstrapped funds as the benchmark, this study shows that daily fund market is overall efficient.
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Priya Malhotra and Pankaj Sinha
Mutual 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…
Abstract
Purpose
Mutual 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/approach
The 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.
Findings
The 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/implications
The 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 implications
The 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 implications
Mutual 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/value
Research 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.
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The standard market models assume that all investors are rational with the same level of risk aversion, whereas investors in the real world are neither rational nor homogeneous…
Abstract
Purpose
The standard market models assume that all investors are rational with the same level of risk aversion, whereas investors in the real world are neither rational nor homogeneous. This contrast makes these models inappropriate for evaluating manager skill. The purpose of this paper is to attempt to bridge the gap between model assumption and fund investment practice.
Design/methodology/approach
This study proposes a series of modified models using the excess return of peer funds to estimate fund alpha. In these models, the market excess return in the standard market models is replaced with the average excess return of bootstrapped funds. In addition, the author examines the reasons for the difference between the modified models and the standard models.
Findings
The modified models better explain the variation of fund returns, and they exhibit that a considerably higher percentage of funds can earn positive alpha, thus the skill of fund managers is underestimated based on the standard market models.
Originality/value
The proposed models provide a more reliable method for investors to identify skilled fund managers, and they can also serve as an objective benchmark in evaluating fund performance and in designing manager compensation packages.
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Jun Gao, Niall O’Sullivan and Meadhbh Sherman
The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed…
Abstract
Purpose
The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.
Design/methodology/approach
This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.
Findings
The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.
Practical implications
The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].
Originality/value
The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.
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Iuliia Naidenova, Petr Parshakov, Marina Zavertiaeva and Eduardo Tomé
– This paper aims to explore whether individual intellectual capital of a fund manager allows mutual fund to outperform market.
Abstract
Purpose
This paper aims to explore whether individual intellectual capital of a fund manager allows mutual fund to outperform market.
Design/methodology/approach
The sample includes 85 Russian equity funds for the period of 2013. First, Jensen’s alpha for each fund has been calculated, and then cross-sectional regression analysis has been used. While only a part of fund managers publish biographic sketches, the authors use the Heckman procedure to control for self-selection issues.
Findings
The results support the idea that the individual characteristics indicate the possibility to earn abnormal alpha. Managers with economic education and with Moscow education perform better than others. Relationship between both fund performance measures and manager’s experience has inverted U-shape. Jensen’s alpha reaches its highest level at the point of 9 years, whereas beta – at 10 years of manager’s experience.
Research limitations/implications
Investigation can be improved by including more variables that influence the disclosure of managers’ personal information, for example, by conducting surveys. Additionally, cross-sectional data restrict the analysis.
Practical implications
The discovered characteristics of managers’ intellectual capital can be used as additional screening tool for the investor who is deciding on mutual fund choice in Russia. While individual intellectual capital is observable and more persistent in time in comparison with the past fund performance, such tool allows better decision-making.
Originality/value
This is the first paper that explores which characteristics of Russian fund managers are connected with higher abnormal return (measured by Jensen’s alpha) and risk (beta) of mutual funds.
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The authors examine the performance of individual global equity funds in Central and Eastern Europe (CEE) and separate the skill of their fund managers from luck.
Abstract
Purpose
The authors examine the performance of individual global equity funds in Central and Eastern Europe (CEE) and separate the skill of their fund managers from luck.
Design/methodology/approach
The authors use cross-sectional bootstrap simulations to study the monthly net and gross returns of 175 funds over the period September 2005 to December 2019. Simulations are applied to three, four, and five-factor asset pricing models, and to regressions run on fund-specific benchmark indexes. The authors also examine the value added by all funds and by fund size groups.
Findings
Using multifactor models, a majority of the individual funds fail to deliver alpha, both net and gross of fees; whereas, most of the negative alphas appear due to poor skills, not bad luck. Relative to benchmark indexes, about 5% of the sample shows skill only gross of fees, indicating that fund management fees absorb this skill. As a whole, global equity funds in CEE add more economic value than they destroy, gross of fees, which is largely driven by large funds.
Practical implications
Market-tracking passive indexes are the most reliable choice for investors who want to maximise their risk-adjusted returns at the lowest possible cost. However, investors with a high level of risk appetite might prefer small actively managed funds in CEE when market conditions are stable or growing. Investors who are less risk tolerant might prefer large actively managed funds.
Originality/value
This is the first study to shed light on the presence of skill in mutual fund returns in CEE.
Wayne Ferson, Darren Kisgen and Tyler Henry
We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for…
Abstract
We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for discrete returns generates new empirical “factors,” and these factors contribute significant explanatory power to the models. We provide a conditional performance evaluation for US fixed income mutual funds, conditioning on a variety of discrete ex-ante characterizations of the states of the economy. During 1985–1999 we find that fixed income funds return less on average than passive benchmarks that do not pay expenses, but not in all economic states. Fixed income funds typically do poorly when short-term interest rates or industrial capacity utilization rates are high, and offer higher returns when quality-related credit spreads are high. We find more heterogeneity across fund styles than across characteristics-based fund groups. Mortgage funds underperform a GNMA index in all economic states. These excess returns are reduced, and typically become insignificant, when we adjust for risk using the models.