主题｜Topic：Factors Affecting the Returns of Long/Short Equity Hedge Fund Categories
时间｜Time：10月26号（周五）｜Oct. 26th (Friday), 5：00 - 6：30PM
地点｜Venue：文波楼205教室｜Lecture Room 205，WENBO
主讲｜Speaker
(LEE) MICK SWARTZ 现为南加州大学Clinical Finance 系教授，主要教授EMBA及MBA课程。于2017年5月获得南加州大学研究生教学奖。他主要研究投资学，公司金融，衍生物，对冲基金等方向，并在风险管理和法律事务方面为对冲基金和投资者提供咨询。已在Journal of Accounting and Finance，Journal of Business Economics and Finance，Computational Economics等期刊发表文章。
研究领域｜Research Interests
Investments, Corporate Finance, Derivatives, Hedge Funds, CFA classes, CAIA,
投资学，公司金融，衍生物，对冲基金，CFA课程，特许另类投资分析师
摘要｜Abstract
This paper has four major contributions to the literature. First, it analyzes the risk characteristics for ten HFRX Equity Hedge individual strategies monthly returns which have never appeared in the previous hedge fund literature. Second, this paper introduces three new families of factors, the D family, L family, and the R family to analyze equity hedge fund returns and use these models to forecast returns; that when combined with the CAPM or Fama-French models are better risk measurements for hedge fund returns than conventional variables. These new factors assume investors use historical data from each hedge fund category to assess the risk in each category. This historical information, when included with asset pricing models such as the CAPM and Fama-French CAPM, is more powerful in terms of explaining hedge fund returns than the previous hedge fund models in the literature. Third, these strategy-specific models are more parsimonious and efficient than the models from the previous literature on hedge funds in all ten strategies using the Schwarz information criterion (SIC). All the generated models in this paper are corrected for possible time-series assumptions violations and Heteroskedasticity, while ten out of ten and eight out of ten factor models from the previous literature suffer from Conditional Heteroskedasticity and Serial Correlation, respectively. Fourth, contrary to previous studies, most strategies do not mimic Put-writing schemes.
