Role of Big Data Analysis in Predicting Financial Market

Abstract

In the ever-changing world of financial markets, the infusion of big data analytics has become a game-changer, reshaping how we traditionally analyze and forecast market trends. Big data is the term used to characterize the vast amounts of data that are gathered, saved, and examined in order to obtain knowledge and improve decision-making. A variety of sources, including social media, online analytics, user behaviour, and machine-generated data, are frequently used to gather big data. In the field of finance, big data refers to vast, varied, complex (both organized and unstructured) data sets that can be leveraged to address persistent business problems faced by global banking and financial services organizations. The phrase is increasingly understood to be a business necessity rather than only being used in the context of technology. Financial services businesses are utilizing it more and more to revolutionize their organizations, workflows, and sectors as a whole. This study offers a comprehensive examination of big data finance, including how it functions and whether predictive trends are present.

The Association Rule Mining Algorithm, Linear Regression Algorithms, Logistic Regression Algorithms, and Support Vector Machine (SVM) are the approaches used to predict the big data in this article.