Role of Artificial Intelligence in Financial Market Analysis: An Empirical Investigation of Machine Learning Techniques for Forecasting Market Behavior

With the increasing complexity and volatility of financial markets, traditional financial analysis methods have become insufficient for generating accurate and reliable forecasts. In this context, Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) algorithms, has emerged as a powerful tool for analyzing complex financial data, predicting price trends, and optimizing investment decisions. This paper aims to investigate the practical applications of AI in financial market analysis, focusing on the performance of algorithms such as Long Short-Term Memory (LSTM) Neural Networks (NNs), XGBoost, and reinforcement learning in predicting market behavior. The input data includes daily stock prices, technical indicators, and sentiment data extracted from social media platforms. The results indicate that these AI-based models significantly improve forecasting accuracy, reduce investment risk, and enhance portfolio performance. Furthermore, integrating structured and unstructured data within hybrid models contributes substantially to their effectiveness. The findings highlight the potential of AI to play a transformative role in the future of financial analytics.

کلیدواژه ها:

Artificial Intelligence ، Machine Learning ، Deep Learning ، Financial Markets ، Price Prediction ، Sentiment Analysis ، Smart Trading ، XGBoost ، Neural Networks