PREDICTIVE ANALYTICS IN FINANCE: A MACHINE LEARNING APPROACH TO STOCK MARKET FORECASTING

Abstract

The endeavor to forecast stock values is a formidable challenge in the financial world, characterized by its complexity and the influence of multifaceted variables. In this paper, we embark on an extensive exploration of how machine learning algorithms can be effectively employed to tackle this challenge. Our primary objective is to elucidate the methodologies and strategies that can lead to more accurate stock market predictions. We delve into the intricate art of stock value prediction and analyze the various machine learning algorithms that have been proposed and utilized for this purpose. Through a critical examination of these algorithms, we aim to offer insights into their respective strengths and weaknesses, ultimately guiding the reader toward an informed choice of the most suitable algorithm for their specific forecasting needs. In addition to algorithm selection and attribute analysis, our review extends to the examination of external factors that can exert a significant influence on stock values. These factors encompass a wide array of variables, such as economic conditions, geopolitical events, corporate news, and market sentiment. Understanding the interplay between these external elements and stock market dynamics is crucial for the development of more robust and reliable prediction models.