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주식시장 수익률과 주식형 펀드 성과

  • 하연정 부산대학교 경영대학 학술연구교수
본 연구는 주식형 펀드의 시장 초과 수익률을 더 많은 정보(다요인 모형, 횡단면 수익률 분포)를 포함하여 재평가하였다. 이를 통해 분석 기간 동안 평균적으로 시장 수익률을 초과한 투자 스타일을 찾고, 극단적인 성과가 펀드 매니저의 능력에 의한 것인지 알아보았다. 분석 결과는 다음과 같다. 첫째, 우리나라 주식형 펀드는 보수 차감 후 수익률 및 보수 차감 전 수익률 모두 비정상 수익률은 평균이 0인 정규분포에 근접 하였다. 둘째, 시장 초과 수익률 분포에서 보수 차감 후 수익률(보수 차감 전 수익률)은 상위 1%(상위 4%) 펀드만이 유의적인 성과를 가져, 펀드 수익률은 시장 수익률에 의해 거의 설명되었고 극단적인 성과는 비체계적 위험에 의한 것임을 확인하였다. 셋째, 극단적으로 높은 시장 초과 수익률 또한 3요인 모형에 의해 모두 설명되었다. 넷째, 횡단면 알파 분포에 대한 정보를 포함하는 베이지안 알파의 성과 순위 변화를 검증하여, 비체계적 위험이 높은 펀드와 비체계적 위험이 높았던 시장 호황기에 성과 순위가 크게 변화하였다. 이는 극단적인 성과는 실제 펀드 매니저의 능력에 의한 것이 아님을 말해주는 것이다. 본 연구는 2001년부터 2014년까지 우리나라 주식형 펀드 수익률은 시장 수익률을 초과하지 못하였고, 펀드 보수는 소수 펀드의 유의적인 성과를 비유의적으로 변화시킴을 보였다. 현재 ETF(Exchange Traded Funds) 시장을 중심으로 펀드 시장은 크게 성장하고 있다. 낮은 보수에 시장을 추종하면서 추가적인 투자 스타일을 추구하는 ETF 시장의 발전을 기대한다.
주식시장 수익률; 펀드 횡단면 수익률 분포; 펀드 보수 차감 후 수익률; 펀드 보수 차감전 수익률; 베이지안 알파;Market Returns; Cross-sectional Distribution of Fund Returns; Fund Net Returns; Fund Gross Returns; Bayesian Alpha

Market Returns and Equity Fund Performance

  • Yeonjeong Ha
Starting with the capital asset pricing model (CAPM), a variety of systematic risk factors have been suggested. The performance of equity funds investing primarily in equities is evaluated using these risk factors and serves as the basis for judging a fund manager’s ability. Jensen (1968) evaluates fund performance using the CAPM; Carhart (1997) relies on a four-factor model that includes Fama and French’s (1993) three factors and Jegadeesh and Titman’s (1993) momentum factor; Pastor and Stambaugh (2002a, 2002b) include industry factors; and Barber, Huang, and Odean (2016) include Fama and French’s (2015) profitability and investment factors. As more systematic risk factors are considered, fund performance can be evaluated more precisely, and the systematic risk factor explaining the fund’s abnormal returns can be found. However, Barber, Huang, and Odean (2016) demonstrate that fund investors are most sensitive to CAPM alpha and that more sophisticated investors use more systematic risk factors to assess fund performance. In other words, sophisticated investors may be aware that equity funds are not likely to have abnormal returns in excess of their benchmark returns. Based on Barber, Huang, and Odean (2016), this study reassesses equity fund performance using more information about the fund’s market excess returns, which are most easily accessible to fund investors. This approach enables us to indirectly evaluate fund performance from the perspective of sophisticated investors and identify risk factors that explain the market excess returns of equity funds. Fama and French (2010) argue that the cross-sectional average alpha of U.S. mutual funds is close to zero, while only very extreme performance groups have a significant alpha. Thus, extreme performance is due to luck, not the fund manager’s skill. This study attempts to identify the systematic risk factors that explain extreme performance using market excess return distribution. It examines whether the significance of fund performance changes when more systematic risk factors are used. When equity funds do not earn higher returns than the benchmark returns, performance changes insignificantly and idiosyncratic risk explains the extreme performance when more systematic risk factors are considered. For example, when Fama and French’s (1993) three factors completely explain the performance of a fund with significant market excess returns during the analysis period, a fund investor who evaluates performance using the three-factor model judges the fund managers as having no skill. In addition, fund managers use a widely known strategy in the market rather than adopting various investment strategies. In other words, style investing strategies that do not rely on the three factors do not exceed the market returns on average. This study uses the CAPM, Fama and French’s (1993) three-factor model, and Fama and French’s (2015) five-factor model. This study investigates the effects of idiosyncratic risk on fund performance. Highperforming funds with high idiosyncratic risk are overestimated when only return data are used. Therefore, this study reevaluates the performance using Bayesian inference, which considers the idiosyncratic risk. The Bayesian inference is measured by the weighted average of the actual return data and the prior distribution. The effect of the prior distribution is stronger when the idiosyncratic risk of the actual returns is high. I examine the Bayesian alpha rank of the market excess return groups to see whether a change in performance rank occurs. The absolute value of the Bayesian alpha decreases compared with the OLS alpha due to the effect of prior distribution, but the performance rank does not change if the fund alpha is due to the fund manager’s ability, as all of the funds have the same prior distribution. However, funds with a high idiosyncratic risk have a significant effect on prior distribution, leading to a significant change in performance rank. I use Jones and Shanken’s (2005) Bayesian inference method, which includes the information that the cross-sectional average alpha of the fund is zero. This study identifies several results. First, the average abnormal returns of both the net and gross returns are zero, and the distribution of the returns is close to normal. Second, although performance is ranked according to the market excess returns, only the top 1% (top 4%) of the funds in net returns (gross returns) indicates significant performance. This result indicates that during the analysis period, fund returns are mostly explained by market returns, and extreme returns are due to idiosyncratic risk. Third, the three-factor model completely explains the extremely high returns. Thus, fund managers increase systematic risks (three factors), which are well known in the market. In other words, style investing strategies other than the three factors do not exceed market returns on average during the analysis period. Fourth, the performance rank of the high-performing funds with high idiosyncratic risk changes drastically when Bayesian inference is used. In particular, the effect of Bayesian inference is stronger in the boom market periods, which have a high idiosyncratic risk. Thus, a fund manager’s skill does not result in extreme performance. These findings are empirical and based on fund managers, fund investors, and the development of the fund industry. Although investors’ interest in equity funds weakened after the global financial crisis, the exchange traded fund (ETF) market is growing rapidly. The ETF market is expected to pursue various investment styles while following the market returns with low fund fees.