Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model
This work addresses portfolio optimization for financial analysts in the Indian stock market, but it is incremental as it applies an existing LSTM method to new sector-specific data.
The paper tackled portfolio optimization for five thematic sectors in the Indian stock market by designing optimum risk and eigen portfolios using ten stocks per sector and predicting future returns with an LSTM model, achieving very high accuracy when comparing predicted and actual returns over seven months.
Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model.