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Prediction-Based Portfolio Optimization Models Using Deep Neural Networks
IEEE Access ( IF 3.4 ) Pub Date : 2020-06-19 , DOI: 10.1109/access.2020.3003819 Yilin Ma , Ruizhu Han , Weizhong Wang
IEEE Access ( IF 3.4 ) Pub Date : 2020-06-19 , DOI: 10.1109/access.2020.3003819 Yilin Ma , Ruizhu Han , Weizhong Wang
Portfolio optimization is a hot research topic, which has attracted many researchers in recent decades. Better portfolio optimization model can help investors earn more stable profits. This paper uses three deep neural networks (DNNs), i.e., deep multilayer perceptron (DMLP), long short memory (LSTM) neural network and convolutional neural network (CNN) to build prediction-based portfolio optimization models which own the advantages of both deep learning technology and modern portfolio theory. These models first use DNNs to predict each stock's future return. Then, predictive errors of DNNs are applied to measure the risk of each stock. Next, the portfolio optimization models are built by integrating the predictive returns and semi-absolute deviation of predictive errors. These models are compared with three equal weighted portfolios, where their stocks are selected by DMLP, LSTM neural network and CNN respectively. Also, two prediction-based portfolio models built with support vector regression are used as benchmarks. This paper applies component stocks of China securities 100 index in Chinese stock market as experimental data. Experimental results present that the prediction-based portfolio model based on DMLP performs the best among these models under different desired portfolio returns, and high desired portfolio return can further improve the performance of this model. This paper presents the promising performance of DNNs in building prediction-based portfolio models.
更新日期：2020-06-19
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