Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model

TL;DR

This paper introduces an LSTM-based deep learning model that predicts stock prices and assesses sector profitability, aiding investment decisions in the Indian stock market.

Contribution

The paper presents a novel LSTM regression model that automatically scrapes data, forecasts stock prices, and evaluates sector profitability for improved investment strategies.

Findings

The model achieves high forecast accuracy across 75 stocks.

Predicted stock prices effectively inform profitable investment decisions.

Sector analysis reveals varying profitability levels for investors.

Abstract

Designing robust systems for precise prediction of future prices of stocks has always been considered a very challenging research problem. Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices. We present a deep learning-based regression model built on a long-and-short-term memory network (LSTM) network that automatically scraps the web and extracts historical stock prices based on a stock's ticker name for a specified pair of start and end dates, and forecasts the future stock prices. We deploy the model on 75 significant stocks chosen from 15 critical sectors of the Indian stock market. For each of the stocks, the model is evaluated for its forecast accuracy. Moreover, the predicted values of the stock prices are used as the basis for investment decisions, and the returns on the investments are computed. Extensive results are presented on the performance of the model. The analysis of the results demonstrates the efficacy and effectiveness of the system and enables us to compare the profitability of the sectors from the point of view of the investors in the stock market.