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投资人情绪对低波动度异常现象重要吗?

【中文摘要】:在一个有效率的股票市场中,投资人唯有承担高于平均水準的风险才能获得高于平均水準的报酬,隐含风险与报酬之间呈现正向关係。然而,Ang, Hodrick, Xing, and Zhang (2006, 2009)的实证研究发现,风险与报酬之间呈现负向关係,亦即高风险的股票会有较低的报酬。由于此现象违反了高风险高报酬的预期心理,被定义为「低波动度异常现象(low volatility anomaly)」。本研究以1965年1月至2010年12月间标準普尔500指数中的成分股为研究样本,验证在不同风险因子模型下的风险调整后报酬(risk-adjusted return),是否仍然存在低波动度异常现象。此外,本研究将投资人情绪(investor sentiment)纳入Fama-French-Carhart四因子迴归模型中,进一步探讨投资人情绪对于低波动度异常现象的影响。
本研究做多低波动度投资组合,并且放空高波动度投资组合,形成零交易成本的多空操作策略。实证结果显示(1)经过三因子与四因子的风险调整后报酬,仍然存在低波动度效应(low volatility effect)。(2)加入投资人情绪作为风险因子后,投资人情绪对于低波动度效应具有正向的预测力。(3)只有在景气扩张时期下,投资人情绪能正向预测低波动度效应;而在景气衰退时期下,并未发现投资人情绪能预测低波动度效应的证据。此外,本研究考量到分组后样本数太少和波动度丛聚(volatility clustering)的问题,透过改变投资组合的建构方式以测试上述的结果是否具有稳健性,实证结果发现改变投资组合的建构方式后结果仍然相同。
【英文摘要】:In an efficient market, investors earn above-average returns when they take above-average risks. This indicates that there is a positive relationship between risk and return. However, Ang, Hodrick, Xing, and Zhang (2006, 2009) suggested that stocks with higher risk tend to have lower returns. This phenomenon fights against the psychological expectations of high risk and high return. Hence, the phenomenon is defined as “low volatility anomaly”. We use constituent stocks of the S&P 500 index from January 1965 to December 2010 to examine whether the low volatility anomaly exists under different risk-factor models. Moreover, we add investor sentiment variable into the Fama-French-Carhart four-factor model, to further explore the impact of investor sentiment on the low volatility anomaly.
We form a long-short strategy through longing low volatility portfolio and shorting high volatility portfolio. The results show the following findings. First, the low volatility effect exists under the three-factor and four-factor models. Second, after we add investor sentiment as a risk factor into regression, results imply that investor sentiment positively predicts the low volatility effect. Third, only in periods of economic expansion, investor sentiment could positively and significantly predict the low volatility effect. Finally, there are some problems to overcome. One problem is, if sample stocks are divided into five groups according to volatilities, each group’s size will be too small. The other problem is that the possible existence of volatility clustering phenomenon might affect our empirical findings. Therefore, we conducted robustness testing through changing portfolios’ compositions, the results are the same as our main findings.
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  • 来源:中山大学;作者:吕睿璿
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