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Building predictive models for robust and accurate prediction of stock prices and
stock price movement is a challenging research problem to solve. The well-known
efficient market hypothesis believes in the impossibility of accurate prediction of
future stock prices in an efficient stock market as the stock prices are assumed to be
purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of
precision using sophisticated algorithms, model architectures, and the selection of
appropriate variables in the models. This chapter proposes a collection of predictive
regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National
Stock Exchange (NSE) of India.