Design and Analysis of Robust Deep Learning Models for Stock Price Prediction

Abstract

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. The Metastock tool is used to download the historical stock prices over a period of two years (2013- 2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.

arXiv commentThis is the pre-print of our chapter that has been accepted for publication in the forthcoming book entitled "Machine Learning: Algorithms, Models, and Applications". The book will be published by IntechOpen, London, UK, in an open access in the later part of the year 2021. The chapter is 29 pages long, and it has 20 figures and 21 tables. arXiv admin note: substantial text overlap with arXiv:2103.15096

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