Analysis of Sectoral Profitability of the Indian Stock Market Using an
LSTM Regression Model
Abstract
Predictive model design for accurately predicting future stock prices
has always been considered an interesting and challenging research
problem. The task becomes complex due to the volatile and stochastic
nature of the stock prices in the real world which is affected by
numerous controllable and uncontrollable variables. This paper presents
an optimized predictive model built on long-and-short-term memory (LSTM)
architecture for automatically extracting past stock prices from the web
over a specified time interval and predicting their future prices for a
specified forecast horizon, and forecasts the future stock prices. The
model is deployed for making buy and sell transactions based on its
predicted results for 70 important stocks from seven different sectors
listed in the National Stock Exchange (NSE) of India. The profitability
of each sector is derived based on the total profit yielded by the
stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021.
The sectors are compared based on their profitability values. The
prediction accuracy of the model is also evaluated for each sector. The
results indicate that the model is highly accurate in predicting future
stock prices.