Search results

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.

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.

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.

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)].

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.

Chance models – mechanisms that explain empirical regularities through unsystematic variance – have a long tradition in the sciences but have been historically marginalized in management scholarship, relative to an agentic worldview about the role of managers and organizations. An exception is the work of James G. March and his coauthors, who proposed a variety of chance models that explain important management phenomena, including the careers of top executives, managerial risk taking, and organizational anarchy, learning, and adaptation. This paper serves as a tribute to the beauty of these “little ideas” and demonstrates how they can be recombined to generate novel implications. In particular, we focus on the example of an inverted V-shaped performance association centering around the year when executives were featured in a prominent listing, Barron’s annual list of Top 30 chief executive officers. Our recombination of several chance models developed by March and his coauthors provides a novel explanation for why many of the executives’ exceptional performances did not persist. In contrast to the common accounts of complacency, hubris, and statistical regression, the results show that declines from high performance may result from the way luck interacts with these executives’ slow adaptation, incompetence, and self-reinforced risk taking. We conclude by elaborating on the normative implications of chance models, which address many current management and societal challenges. We further encourage the continued development of chance models to help explain performance differences, shifting from accounts that favor heroic stories of corporate leaders toward accounts that favor their changing fortunes.

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.

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.

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.

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.

This is the first study to shed light on the presence of skill in mutual fund returns in CEE.

This study examined Indian institutional investors' holding data to understand their investment strategy (Portfolio Concentration/Diversification) and explored whether their skills were associated with their portfolio strategy and performance. The study introduced a new proxy to identify skilled investors by forecasting abnormal returns. Moreover, the study also highlighted where skilled Indian investors put their money for long-term investment.

This study measures portfolio concentration based on the number of holdings, the Hirschman–Herfindahl index (HHI) and benchmarks adjusted industry concentration. The study introduced a new proxy to identify skilled investors. We measured Investors' performance with the help of Carhart's four factors model and examined the relationship between variables through various regression models.

The study concluded a negative relationship between portfolio concentration and performance. However, skilled Indian investors get rewards from portfolio concentration decisions. It was found that skilled investors with few stocks and an industry concentration in their portfolio show a positive association between concentration and fund performance. Additionally, this study found Indian investors showing their faith in the financial sector for long-term investment.

This study examined Indian institutional investors' portfolio concentration strategy and introduced a new proxy to measure investors' skills.

Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.

The purpose of this paper is to examine managerial skill of US equity mutual funds in the context of both abnormal return and risk.

The authors evaluate manager skill based on the outperforming probability and cumulative distribution function of the actual funds and the bootstrapped funds. And the authors recognize the role of fund life cycle and use different evaluation horizons to control for fund age and the overall state of the market.

The authors find that a small percentage of equity funds can beat the market, and the percentage is overall higher than what the control group would predict. The authors find no evidence of persistence. The authors also document that the chance of underperformance is much higher than what the authors had expect from the control group. Taking the risk-return tradeoff into account, any performance advantage of actual funds over bootstrapped funds is correlated with tail risk, and a robustness check confirms this finding.

The authors find that the outperforming probability itself is not enough to confirm the existence of manager skill. The complete story of mutual fund alpha, should it exist, would not be complete without incorporating both risk and luck.

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.

The purpose of this paper is to utilize a constrained random portfolio-based framework for measuring the skill of a cross-section of Indian mutual fund managers. Specifically, the authors test whether the observed performance implies superior investment skill on the part of mutual fund managers. Additionally, the authors investigate the suitability of mutual fund investments under diverse investor expectations.

The authors use a new skill measurement methodology based on a cross-section of constrained random portfolios (Burns, 2007).

The authors find no evidence of superior investment skill in the sample of Indian equity mutual funds. Using a series of statistical tests, the authors conclude that the mutual funds fail to outperform the random portfolios. Furthermore, mutual funds show no persistence in their performance over time. These results are robust to choice of performance measure and the investment horizon. However, mutual funds provide lower downside risks and may be suitable for investors with high degree of risk aversion.

The authors extend Burns’ (2007) methodology in several aspects, especially by using a much wider range of performance and downside risk measures to address diverse investor expectations. To the best of the authors’ knowledge, this is first study to apply the constrained random portfolios-based skill tests in an emerging market.

The study assess the selection ability and market timing skills of mutual fund and unit trust managers in Ghana.

The study uses an improved survivorship bias-free dataset of yearly after-fee returns of all mutual funds and unit trusts operating in Ghana from January 2011 to December 2019, cumulating in nine years of quantitative fund data. The authors assess Mutual funds and Unit trusts that ever existed, “alive” or “dead,” over the sample period in the study. The authors construct factor loadings to enable the application of multifactor models in the analysis. The authors apply the unconditional versions of the Jensen alpha, Fama-French three-factor, and Carhart four-factor models to determine the selection ability and market timing skills of 32 mutual funds and 17 unit trusts. The authors deploy HAC-consistent robust standard errors to the OLS estimations to subdue the effect of heterogeneity and autocorrelation.

The results indicate that, on average, mutual funds and unit trust managers possess market timing skills but no selection ability. When the results are decomposed into fund types, fixed-income and balanced mutual fund managers possess selection ability and market timing skills.

To the authors' best knowledge, this study is the earliest to examine the selection ability and market timing skills of both mutual fund and unit trust managers in Sub-Saharan Africa (SSA). It is also the earliest to construct factor loadings for the Ghana stock market.