Understanding the Performance of Artificial Intelligence Tools Accuracy in Stock Market

Abstract

The study investigates the efficacy of artificial intelligence (AI) tools in predicting stock market movements, aiming to assess the accuracy, limitations, and implications of employing AI-driven models in financial forecasting. Leveraging diverse datasets encompassing historical stock prices, technical indicators, and sentiment analysis, various machine learning and deep learning algorithms are evaluated for their predictive capabilities across different timeframes and market conditions. Findings reveal a spectrum of model performances, with certain AI models demonstrating promising accuracy in short-term predictions, while others exhibit robustness in capturing long-term trends. The impact of data quality, feature engineering techniques, and alternative data sources on model performance is examined, emphasizing the significance of incorporating diverse information for enhanced predictions. In conclusion, while AI tools offer promising capabilities in predicting stock market movements, their practical deployment faces challenges pertaining to interpretability, ethical considerations, and adaptability to changing market conditions. Addressing these challenges is imperative to harness the full potential of AI in financial forecasting while ensuring responsible and transparent applications in real-world scenarios.