A METHODICAL REVIEW OF SECURITY MARKETS USING STATISTICAL AND MACHINE LEARNING TECHNIQUES
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Abstract
Stock market pattern predictions are considered to be an important and most effective
activity. Therefore, stock prices will yield lucrative gains, if they make informed decisions.
Stock market-related forecasts are a major challenge for investors due to stagnant and noisy
data. Therefore, forecasting the stock market is a big challenge for investors to invest their
money for more profit. Stock market predictions use mathematical strategies and learning
tools. This study provides a comprehensive overview ofout of 30 research papers
recommending methods, including computational methods, machine learning algorithms,
performance parameters, and selected publications. Studies are selected based on research
questions. Therefore, these selected studies help to find the ML techniques along with their
data set for stock market forecasting. Most ANN and NN techniques are used to get accurate
stock market forecasts. Although a lot of work has been done, the latest stock market-related
prediction methodology has many limitations. In this study, it can be assumed that the stock
market forecast is an integrated process and the characteristic parameters for the stock market
forecast should be examined more closely.
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