A Review of Prediction Techniques used in the Stock Market

Praveen Sadasivan; Ravinder Singh

doi:10.4108/eetsis.7535

Authors

Praveen Sadasivan Victoria University
Ravinder Singh Victoria University

DOI:

https://doi.org/10.4108/eetsis.7535

Keywords:

Stock Market, Machine Learning, Sentiment Analysis, ARIMA, GARCH, SVM, LSTM, Neural Network

Abstract

The prediction of stock market movements is a critical task for investors, financial analysts, and researchers. In recent years, significant advancements have been made in the field of stock prediction, driven by the integration of machine learning and data analysis techniques. Though stock market predictions are highly desired, there are many factors contributing towards volatility of the market. There is a need for extensive study and concentration on various predictive techniques to investigate different scenarios triggering such volatility. This paper reviews the latest methodologies employed for predicting stock prices, with a particular focus on the Australian stock market. Key techniques such as time series analysis like ARIMA & GARCH, machine learning models like SVM, LSTM & Neural Network, and sentiment analysis are discussed, highlighting their applications, key strengths, and some limitations.

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