A Forecasting Entropy Portfolio Selection Model

• Main Authors: Kai-Chen Yu, 于凱丞
• Other Authors: Jing-Rung Yu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/39994369998055177410

碩士 === 國立暨南國際大學 === 資訊管理學系 === 103 === In this research we present a forecasting entropy portfolio selection model, which have efficient diversification and consider uncertainty of future return. On the development of portfolio selection, Entropy is used as the measurement of risk to replace the variance in conventional Mean-Variance portfolio selection model to deal with the issue of non-diversification. Leung et al. (2001) illustrates the combined forecasts in portfolio optimization let the portfolio have better performance in the high volatility market. We consider the entropy model proposed by Yager (1995) and combine forecasts method proposed by Ustun and Kasimbeyli (2012) to construct a multi-objective Yager_Forecasting model (Yager_F) to deal with risk diversified and uncertainty of future return. To reflect the real transactions in the capital market, the proposed method allows short selling and considers transaction cost. In addition, we use two data sets: the constituent stocks in S&P 500 index (2007 to 2014) and Exchange Traded Funds (2007 to 2014) to compare with the 1/N model, MV model, Yager model and MV_Forecasting model (MV_F). The performance measures such as market value, realized return, Sharpe ratio, Omega ratio etc. are employed to evaluate each model under the floating required return and rebalancing mechanism. The results show that when the market is bearish like during Subprime mortgage crisis or long-term investment, Yager_F model can measure the uncertain returns and timely reflect to market volatility when Business Cycle converted, it make the market value of the portfolio can be outperform than other models.