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Predictive model design for accurately predicting future stock prices hasalways been considered an interesting and challenging research problem. Thetask becomes complex due to the volatile and stochastic nature of the stockprices in the real world which is affected by numerous controllable anduncontrollable variables. This paper presents an optimized predictive modelbuilt on long-and-short-term memory (LSTM) architecture for automaticallyextracting past stock prices from the web over a specified time interval andpredicting their future prices for a specified forecast horizon, and forecaststhe future stock prices. The model is deployed for making buy and selltransactions based on its predicted results for 70 important stocks from sevendifferent sectors listed in the National Stock Exchange (NSE) of India. Theprofitability of each sector is derived based on the total profit yielded bythe stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021. Thesectors are compared based on their profitability values. The predictionaccuracy of the model is also evaluated for each sector. The results indicatethat the model is highly accurate in predicting future stock prices.

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model