A Review on Statistical Machine Learning Models for Stock Market Forecasting

A Review on Statistical Machine Learning Models for Stock Market Forecasting

Gaurav Mourya1, Prof. Preetish Kshirsagar2

Abstract: Machine learning is transforming stock market prediction by leveraging vast datasets, advanced algorithms, and computational power to provide more accurate forecasts. While challenges remain, continuous advancements in artificial intelligence and deep learning are improving predictive models, making them an essential tool for traders and investors. Stock market prediction extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. For real life applications utilizing stock market data, it is necessary to predict stock market data with low errors and high accuracy. This needs design of appropriate artificial intelligence (AI) and machine learning (ML) based techniques which can analyze large and complex data sets pertaining to stock markets and forecast future prices and trends in stock prices with relatively high accuracy. This paper presents a comprehensive review on the various techniques used in recent contemporary papers for stock market forecasting.

Keywords: Time Series Models, Stock Market Forecasting, Artificial Intelligence, Artificial Neural Networks, Forecasting accuracy.