A STUDY ON ARTIFICIAL INTELLIGENCE FOR STOCK MARKET PREDICTION

Abstract

Shares of publicly traded corporations can be purchased and sold on the stock market. All
sellers and buyers attempt to forecast changes in the stock market price in order to maximise
gains and minimise losses. We will discuss and introduce a potential method for making
highly accurate stock movement predictions in this model. In order to solve the issue of stock
market prediction, artificial intelligence (AI) methodologies are used in this study. Technical
and fundamental analyses are the two main types of analysis that can be used to estimate
stock market predictions.
Regression machine learning (ML) techniques are used in the technical analysis approach to
forecast the trend of the stock price at the conclusion of the business day by using historical
price data. On the other hand, the fundamental analysis uses machine learning algorithms for
classification to categorise public opinion based on news and social media. Accurate share
price forecasting can be a valuable resource for stock market companies and offer practical
answers to the problems encountered by individual stock market investors. The aim of this
study was to model and forecast a stock market index's future price using artificial
intelligence (AI) approaches. Based on past price data, three artificial intelligence
techniques—neural networks (NN), support vector machines (SVMs), and neuro-fuzzy
systems—are used to anticipate the future price of a stock market index. Techniques from
artificial intelligence are employed as financial time series forecasting tools because they can
account for the intricacies of the financial system.