EXPLORING MACHINE LEARNING-BASED APPROACHES FOR STOCK PRICE PREDICTION: A COMPREHENSIVE REVIEW
Abstract
Stock price prediction (SPP) is an important research problem in the area of finance, aiming to forecast the future movements of stock markets. The stock market mainly depends on different factors like socioeconomic issues, inflation, and currency fluctuations. These factors are the prominent drivers of stock price movements that make stock price forecasting a difficult task. In this paper, we perform a review of machine learning-based SPP techniques. Findings from the year 2011 to 2022 were studied after obtaining them from online digital libraries and databases. Next, several scientific developments in market analysis and forecasting come into prominence. We present classical approaches such as fundamental analysis, technical analysis, and other methods. To understand SPP a comprehensive study of different methods has been conducted. In this paper, we have mainly focused on the study of machine learning-based techniques for SPP. Some of the widely used machine learning techniques for SPP are artificial neural network (ANN), bayesian model (BM), linear classifier (LC), deep learning DL, genetic algorithms (GA), fuzzy algorithms (FA), and ensemble techniques (ET). In the study of methods developed in the past twelve years, found that ANN and FA are the most frequently used.
Keyword : Stock market prediction, Machine learning, Fundamental analysis, Technical analysis, Deep learning, Market movement.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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