  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.
