Loading...
D. Rameshkumar is an Assistant Professor in the Department of Commerce with CA at Sri Ramakrishna College of Arts and Science, Coimbatore, India. His academic interests include commerce, accounting, finance, and related business studies.
The stock market represents a highly dynamic and intricate financial system, wherein investment choices are influenced by a multitude of economic, technical, political, and behavioural elements. Conventional forecasting techniques reliant on statistical evaluation and past patterns frequently fail to account for the nonlinear and erratic characteristics of financial markets. The advent of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Internet of Things (IoT), Cloud Computing, and Explainable Artificial Intelligence (XAI) has greatly enhanced the capacity to forecast stock market movements, assess financial risks, and facilitate informed investment choices. AI-powered predictive models evaluate past stock prices, trade volumes, technical metrics, macroeconomic factors, financial news, and investor sentiment to produce precise market forecasts. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues related to AI-driven stock market trend forecasting and financial prediction.