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How Does Fund Performance Affect Style Drift and Manager Turnover: Theory and Empirical Evidence from China
购买基金是缺乏专业技能投资者获取证券收益的重要渠道,基金投资风格本应帮助投资者匹配符合其目标和风险偏好的基金。然而实践中许多基金严重偏离其预先承诺的投资风格,基金常向“市场热点”靠拢,形成“风格漂移”。已有文献虽识别了许多风格漂移的成因,但缺乏统一分析框架解释其内在机制及后果。本文从理论与实证两方面考察基金业绩如何影响风格漂移倾向和基金经理更换。我们强调基金业绩的关键作用,因为良好业绩通常是基金公司和经理的核心目标。首先,假设基金经理能力以一阶随机占优方式提升基金业绩,但公司和经理本人均不确定其能力,因此公司需利用贝叶斯规则基于历史业绩等信息更新对经理能力的信念。我们构建两期理论模型并得到三个结果:(1)第二期风格漂移与第一期业绩呈U形关系;(2)第一期业绩不佳的基金经理有动机模仿前期业绩优异基金的投资风格;(3)若市场经历风格漂移,连续业绩不佳的基金经理在第二期更可能被解雇。为检验这些结果,我们收集2008—2017年中国1579只开放式基金的季度数据。以基金排名衡量业绩,依据Sharpe强模型刻画基金投资风格,用两期投资风格的“曼哈顿距离”测度风格漂移程度。实证分析有力支持理论结果:第一,基金排名与风格漂移存在显著U形关系;第二,格兰杰因果检验表明低排名基金确实倾向于模仿上期业绩优异基金的投资风格;第三,当市场最优风格变化时,基金公司更可能更换前期业绩较差的基金经理。为处理遗漏变量等潜在内生性问题,我们用滞后一期的基金规模变化作为基金排名的工具变量,IV估计表明结论不变。本文的潜在贡献:(1)据我们所知,首次通过两期模型中参与者的信念刻画基金业绩影响风格漂移与经理更换的微观基础;(2)用两期投资风格的“曼哈顿距离”测度风格漂移,比传统方法更适合分析高波动、风格轮动频繁的中国金融市场;(3)不同于已有文献简单划分赢家与输家并考察其经理风险态度和投资风格的组间差异,我们研究整个业绩区间上基金排名与风格漂移的U形关系。研究发现对投资决策具有启示:风险容忍度高的投资者可通过购买持续优秀业绩的基金追求高收益,而不必关注其风格漂移;风险容忍度低的投资者则宜关注排名居中且投资风格与自身相似的基金。
Abstract
Buying funds is an important channel for investors who lack the professional skills to obtain good returns on securities, and the investment styles of funds are expected to help in matching investors with the funds that are most appropriate in terms of goals and risk preferences. However, in practice many funds deviate significantly from their pre-committed investment styles. Funds often gravitate toward "market hotspots", resulting in so-called "style drift". Although the existing literature has empirically identified many contributing factors for style drift, no unified analytical framework has explained the underlying mechanisms and the ensuing effects of style drift. This paper presents a theoretical and empirical examination of how the performance of funds affects their tendency for style drift and for dismissal of fund managers. We emphasize the crucial role of fund performance, for good performance is usually the key target for both fund companies and managers. To start with, we assume that fund performance increases with the manager's ability in terms of first-order stochastic dominance. However, at any given time neither the company nor the manager himself/herself knows exactly what the manager's ability is. Therefore, the company needs to use the Bayes rule to update its belief of manager ability based on its available information, and especially its information on historical fund performance. We build a two-period theoretical model and derive three results: (1) There is a U-shaped relationship between fund style drift in the second period and fund performance in the first period. (2) Fund managers with poor first-period performances have incentives to mimic the investment styles of funds with excellent first-period performances. (3) Consecutively underperforming fund managers are more likely to be fired in the second period if the market has experienced a style drift. To test these results, we collect the quarterly data on 1,579 open-end funds in China from 2008 to 2017. We measure the funds' performances by fund ranking, characterize the fund investment styles according to Sharpe's strong model, and use the "Manhattan distance" of the investment styles of each fund over two periods to measure the degrees of fund style drift. Our empirical analysis strongly supports our theoretical results. First, we find a significant U-shaped relationship between fund ranking and style drift. Second, the Granger causality test shows that low-ranking funds do tend to imitate the investment styles of the funds with excellent performances in the last period. Third, fund companies are more likely to replace fund managers who have poor previous performance when the optimal style of the market changes. To tackle potential endogenous problems due to missing variables or other causes, we use a one-period-lagged fund size change as our instrument for fund ranking, and IV estimation shows that our conclusions remain unchanged. Our paper makes several potential contributions to the literature. (1) To our limited knowledge, we are the first to characterize the micro-foundation for how fund performance affects fund style drift and the dismissal of fund managers via beliefs of the players in a two-period model. (2) We measure fund style drift by using the "Manhattan distance" of investment styles between two periods. This approach is more suitable than traditional measures for analyzing China's financial market with its high volatility and frequent style rotations. (3) Unlike the existing related literature, which simply divides funds into winners and losers and then investigates the cross-group differences in their managers' attitudes towards risk and investment styles, we study the U-shaped relationship between fund ranking and style drift over the whole spectrum of performance. Our findings shed some light on investment decision-making. If investors have high tolerance for risk, they can seek high rates of return by buying funds with continuous outstanding performance without paying attention to those funds' style drifts. However, for investors with low risk tolerance, they'd better focus on funds which are ranked in the middle and share similar investment styles with them.
基金排名与风格漂移呈显著U形关系
以夏普强模型刻画风格、曼哈顿距离测度漂移,滞后一期基金排名一次项系数为-0.3056、二次项系数为0.2457,均在1%水平显著,表明业绩极好和极差的基金漂移更大,中等排名基金漂移最小。
低排名基金模仿上期优秀基金风格
格兰杰因果检验显示,基金当期排名对下一期风格领导-跟随转换有显著影响(系数0.6936,1%水平显著),说明前期业绩较差的基金倾向于模仿上期业绩优异基金的投资风格。
市场风格变化时低业绩经理更易被更换
Logit回归显示,市场最优风格变化变量系数为1.1481(1%水平显著),滞后一期基金排名系数为0.6225(1%水平显著),表明当市场风格发生漂移时,前期业绩较差的基金经理更可能被基金公司更换。
工具变量缓解内生性后结论稳健
以滞后一期基金规模变化作为基金排名的工具变量进行IV估计,弱工具变量检验显示约0.55,AR检验在1%水平显著,U形关系等核心结论保持不变。
业绩极好基金同样存在风格漂移
二次项显著为正意味着业绩排名最高端的基金风格漂移程度也较高,与低排名基金类似,中间排名基金风格最稳定,这与传统赢家-输家二分法的预期不同。
核心解释变量
基金业绩排名(滞后一期基金排名及其二次项),以基金收益率在同类基金中的排名衡量,并采用滞后一期基金规模变化作为工具变量。
被解释变量
基金风格漂移程度,以夏普强模型估计各期投资风格权重,采用相邻两期风格权重的曼哈顿距离(Fsds1、Fsds2)衡量;基金经理更换以Logit模型中的更换概率衡量。
样本与数据
2008—2017年中国1579只开放式基金,经筛选后基准回归样本为21624个基金-季度观测,基金经理更换检验样本为19003个观测。数据来自Wind数据库。
识别方法 / 模型设定
构建两期贝叶斯信念更新理论模型推导U形关系等命题;实证上采用面板固定效应回归,以滞后一期基金业绩排名及其二次项为核心解释变量,控制基金经理特征、基金特征和公司特征;基金经理更换采用Logit模型。
内生性及稳健性检验
采用滞后一期基金规模变化作为基金排名的工具变量进行IV估计,并报告AR弱识别检验结果;同时进行格兰杰因果检验区分领先与滞后关系,剔除低质量样本及替换风格漂移指标后结论依然稳健。
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