Stock Portfolio Optimization Using a Deep Learning LSTM Model
This addresses portfolio optimization for investors in the Indian stock market, but it is incremental as it applies an existing LSTM method to new data.
The authors tackled stock portfolio optimization by predicting future prices using an LSTM model on historical data from Indian stocks, finding that predicted and actual returns were high, indicating high precision.
Predicting future stock prices and their movement patterns is a complex problem. Hence, building a portfolio of capital assets using the predicted prices to achieve the optimization between its return and risk is an even more difficult task. This work has carried out an analysis of the time series of the historical prices of the top five stocks from the nine different sectors of the Indian stock market from January 1, 2016, to December 31, 2020. Optimum portfolios are built for each of these sectors. For predicting future stock prices, a long-and-short-term memory (LSTM) model is also designed and fine-tuned. After five months of the portfolio construction, the actual and the predicted returns and risks of each portfolio are computed. The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.