A Portfolio Optimization Model Based on Information Entropy and Fuzzy Time Series

In this paper, a forecasting-mean-correlation-entropy portfolio optimization model (FMCE) is developed by using the fuzzy time series techniques to predict securities´ future returns distribution and employing entropy as risk measurement. Traditional portfolio models such as MV model have stringent conditions to returns distribution, while entropy as a new risk measurement is free from these restrictions. Besides, traditional models assume securities´ future returns distribution the same with their historical distribution which may be not suitable for the complex market. Therefore, a forecasting method is applied in FMCE. Based on the historical data of the Shanghai Stock Exchange and Shenzhen Stock Exchange in China, a comparison among the proposed model, hybrid models and traditional ones which are the mean-variance model, absolute deviation model and maximum absolute deviation model is made. The empirical results show that the proposed FMCE model is an effective tool in portfolio selection.