Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model
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.
Keywords
Cite
@article{arxiv.2104.06259,
title = {Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model},
author = {Jaydip Sen and Abhishek Dutta and Sidra Mehtab},
journal= {arXiv preprint arXiv:2104.06259},
year = {2021}
}
Comments
This is the accepted version of our paper in the Second IEEE International Conference on Emerging Technologies (IEEE INCET 2021) which will be organized in Belgaum, Karnataka, INDIA from May 21 to May 23, 2021. The paper is eight pages long, and has fifteen tables and fourteen figures. This is not the final version of the paper