【中文摘要】:一直以来,如何选择出好的股票都是一个深具挑战性且重要的问题。要建构一个成功的投资组合必须仰赖可靠的选股策略,而随着财务工程与资料探勘技术的发展,使得这个问题能够更有效的被处理。本文以台湾股票市场为例,利用财务报表资讯及Zakamouline and Koekebakker (2009)所提出之经过偏态与峰态调整的Sharpe比率(Adjusted for skewness and kurtosis Sharpe ratio, ASKSR)来筛选股票,并以Copula-GARCH模型进行估计与蒙地卡罗模拟,最后利用预期效用函数最适化权重来建构投资组合,希望能建立一个系统化、数量化的选股模型,持续的击败大盘。
【英文摘要】:Stock selection always has been a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Thanks to recent advances in financial engineering and data mining, we can solve these problems more effectively. In this study, we use financial statement information and the ASKSR proposed by Zakamouline and Koekebakker(2009) for stock selection. Furthermore, we apply Copula-GARCH model on Monte Carlo method to generate dynamic optimal weights based on expected utility function. According to this process, we try to construct a quantitative stock selection model which can consistently beat the market.
We take all the listed companies in the Taiwan stock market over the period of 1998-2012 as our sample and examine the profitability of portfolios constructed by the combination of different length of in sample and out sample data. The empirical result shows that our portfolios can earn significantly higher return than the TAIEX Total Return Index, especially those that generate optimal weights by mean-variance utility function andγ=1 CRRA utility function. Moreover, after we applied market neutral strategy, we can significantly improve the stability of our portfolios and reduce the possibility of severe losses by the impact of financial crisis. Finally, a robustness test was built to validate if this method works well all the time. We divide our investment period into three shorter periods, and it turns out this method still have great performances on each of the shorter period. As a result, this portfolio construction strategy indeed can be applied consistently and effectively in Taiwan stock market.

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  • 来源:中山大学;作者:赖柏成


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