The Role of Artificial Intelligence and Machine Learning in Quantitative Finance and Stock Market Forecasting

Abstract

This study investigates the application of artificial intelligence (AI) and machine learning (ML) in quantitative finance, financial technology, and stock market forecasting. Emphasizing their ability to manage large datasets and improve financial predictions, the research details the entire methodology, including data collection, preprocessing, model selection, simulation, and outcome analysis. Linear regression models, particularly suited for stock price prediction, are used alongside Python libraries such as Scikit-learn and TensorFlow to facilitate large-scale implementation. The study also evaluates other models, including Artificial Neural Networks (ANN), Support Vector Machines (SVMs), and Decision Trees, applied to tasks like stock price forecasting and credit risk classification. Results demonstrate that AI and ML significantly enhance financial forecasting accuracy and improve portfolio management. Furthermore, this research highlights the strengths and limitations of current techniques and suggests potential advancements through the integration of AI with technologies such as blockchain and quantum computing. Future directions include exploring these emerging technologies to further increase efficiency and innovation in the financial sector.