Abstract
With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.
Highlights
An advancement in the fundamental aspects of information technology over the last few decades has altered the route of businesses
The results demonstrated that the Multilayer perceptron (MLP) outperformed DAN2 and GARCH-MLP
The results revealed that Nonlinear Auto-Regressive with exogenous inputs (NARX) made accurate predictions for the short term but failed in long-term predictions
Summary
An advancement in the fundamental aspects of information technology over the last few decades has altered the route of businesses. As one of the most captivating inventions, financial markets have a pointed effect on the nation’s economy [1]. The World Bank reported in 2018 that the stock market capitalization worldwide has surpassed 68.654 trillion US$ [2]. Over the last few years, stock trading has become a center of attention, which can largely be attributed to technological advances. Investors search for tools and techniques that would increase profit and reduce the risk [3]. Stock Market Prediction (SMP) is not a simple task due to its non-linear, dynamic, stochastic, and unreliable nature [4]. SMP is an example of time-series forecasting that promptly
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