Chinese securities investment funds: the role of luck in performance

IF 3.6 Q1 BUSINESS, FINANCE Review of Accounting and Finance Pub Date : 2021-10-28 DOI:10.1108/raf-07-2020-0182
Jun Gao, Niall O’Sullivan, Meadhbh Sherman
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引用次数: 45

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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中国证券投资基金:运气在业绩中的作用
中国基金市场近年来发展迅速。然而,尽管已经有一系列研究对发达行业的基金绩效进行了评估,但中国快速发展的基金行业却很少受到关注。本研究旨在考察2003年5月至2020年9月期间投资于中国境内股票的开放式证券投资基金的绩效。具体来说,运用基金业绩文献中的非参数引导方法,作者研究了技能与运气在这个快速发展的投资基金行业中的作用。设计/方法/方法本研究使用自举方法来评估2003-2020年中国股票证券投资基金的业绩,以区分业绩中的技能和运气。作者考虑了无条件和条件性能模型。自举方法在基金回报的特殊风险中纳入了非正态性,这是“传统”业绩统计的一个主要缺陷。证据并不支持“真正的”熟练基金经理的存在。此外,这表明业绩不佳主要归因于糟糕的选股技能。研究发现,排名靠前的基金出现正异常表现的主要原因是“运气好”而非“技能好”,而排名靠后的基金出现负异常表现的主要原因是“技能差”。因此,对于大多数中国股票投资者来说,明智的建议是反对试图在中国证券投资基金中“挑选赢家基金”,但建议避免持有“输家”。目前,投资者应考虑其他类型的基金,例如交易量较低的指数/追踪基金。此外,风险厌恶程度较低的投资者可能会考虑中国对冲基金[Zhao(2012)]或交易所交易基金[Han(2012)]。这篇论文对文献有几处贡献。首先,作者考察了范围广泛(超过50种)的风险调整绩效模型,这些模型考虑了无条件和条件风险因素。作者还控制了Fama和French(2015)的盈利能力和投资风险。其次,作者在所有风险调整模型中选择“最佳拟合”模型,并从三个类别中选择一个单一的“最佳拟合”模型。因此,主要基于所选择的最优拟合模型的自举分析更为精确和稳健。第三,作者减少了研究结果可能是特定于样本期或可能是幸存者(向上)偏差的可能性。第四,作者考虑基于子周期的进一步分析,比较基金在不同市场条件下的表现,为投资者和从业者提供更多的启示。第五,作者进行了广泛的稳健性检查,并表明研究结果在不同的最小基金历史和序列相关和异方差调整方面是稳健性的。第六,采用频率较高的周数据改进统计估计。
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来源期刊
CiteScore
4.30
自引率
0.00%
发文量
18
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