Long Short Term Memory based Multi Period Price Prediction for Portfolio Management
In this paper, we present a novel, machine learning based approach for price prediction to portfolio construction in the context of multi period trading. We use a combination of recurrent neural network (RNN) and a long short term memory (LSTM) network for predicting the future prices and to perform constrained optimization on the portfolio update. Evaluation using a series of back test on a number of datasets obtained from Shanghai Stock Exchange (SSE) and National Association of Securities Deal Automated Quotations (NASDAQ) sources show that our proposed approach outperforms the most common conventional portfolio management technique, namely Robust Median Reversion strategy, on a number of different metrics. In back test experiment, our proposed method offers an average of 148% returns over 360 trading periods with 100 stocks, compared to 124% returns using conventional technique, over the same period and number of stocks.
MLDM