基于LSTM的两阶段均值-CVaR投资组合决策

A Two-stage Mean-CVaR Investment Strategy Based on LSTM

文章提出了一种基于长短期记忆网络(Long-short Term Memory network,LSTM)的两阶段均值-CVaR投资组合模型(LSTM+CVaR)。该模型在第一阶段采用LSTM预测股票收益并对股票进行选择;在第二阶段运用均值-CVaR模型来确定所选股票的投资比例。最后,以沪深300指数股为样本数据,在考虑交易成本和上界约束的情况下,比较LSTM+CVaR模型、LSTM预测选股的等比例模型、随机选股的CVaR模型、随机选股的等比例模型和沪深300指数的风险收益特征、累计收益率和夏普比率。实证结果表明:LSTM+CVaR模型能够实现比传统的投资组合模型更高的平均收益率、收益风险比、累计收益率和夏普比率;减少交易成本和放宽上界约束能提升投资组合模型的表现。

A novel dual-stage mean-CVaR investment strategy based on long-short term memory networks(LSTM)is proposed,namely,LSTM+CVaR model.Specifically,in the first stage,the LSTM is used to predict the return of stocks and select stocks.In the second stage,the mean-CVaR model is used to determine the weight of every stock.Finally,using China Securities 300 Index component stocks as sample,considering the influence of threshold constraints and transaction costs,the characteristics of risk and return,cumulative return and Sharpe Ratio of portfolio models are compared in this paper,and the portfolio models include LSTM+CVaR model,equal weight model based on LSTM prediction,CVaR model based on random stock selection,equal weight model based on random stock selection and China Securities 300 Index component stocks.The empirical results demonstrate that the LSTM+CVaR model achieves higher average returns,return-risk ratio,cumulative returns,Sharpe Ratio,and decreasing the transaction costs or increasing the threshold constraint can improve the performance of the above model.