Transforming Financial Decision-Making with Artificial Intelligence: A Comprehensive Study on AI-Driven Algorithms for Investment, Trading, and Portfolio Management

Abstract

The integration of artificial intelligence (AI) into financial decision-making is revolutionizing the industry, offering new methods for investment, trading, and portfolio management. This study explores the effectiveness of AI-driven algorithms, including machine learning models like Random Forest, Gradient Boosting Machines, Support Vector Machines, and advanced deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). By analyzing real-time and historical market data, the research evaluates these models across various performance metrics, including accuracy, precision, Sharpe ratio, and execution speed. The results demonstrate that while deep learning models, particularly CNNs and RNNs, excel in optimizing returns and minimizing risks, they present challenges in interpretability and require significant computational resources. In contrast, simpler models like Random Forests offer greater transparency but with slightly lower performance. This study also addresses the ethical considerations of AI in finance, particularly the "black box" problem, and the need for explainable AI (XAI) approaches to ensure responsible deployment. The findings underscore the transformative potential of AI in finance, highlighting the importance of selecting appropriate models based on specific use cases, balancing performance with interpretability, and considering the ethical implications of AI-driven decision-making.