Abstract
Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study.
Highlights
Stock market prediction aims to determine the future movement of the stock value of a financial exchange
Artificial neural networks are a subset of deep learning technology that falls within the large artificial intelligence domain, and it mimics the human brain and its nervous system work
The best result was obtained by using the neural network prediction technique, which indicates that the neural networks (NN) algorithm is profitable for any initial investment
Summary
Stock market prediction aims to determine the future movement of the stock value of a financial exchange. The accurate prediction of share price movement will lead to more profit investors can make. The prediction of shares offers huge chances for profit and is a major motivation for research in this area; knowledge of stock movements by a fraction of a second can lead to high profits [1]. Since stock investment is a major financial market activity, a lack of accurate knowledge and detailed information would lead to an inevitable loss of investment. The prediction of the stock market is a difficult task as market movements are always subject to uncertainties [2]. Stock market prediction methods are divided into two
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