AI-Enabled Forecasting of Financial Markets: Opportunities and Challenges
Abstract
Accurately predicting financial markets remains a complex challenge due to high volatility, nonlinear patterns, and the influence of global events. This paper examines the application of artificial intelligence in financial market forecasting, focusing on deep learning, recurrent neural networks (RNNs), and natural language processing (NLP) for sentiment analysis of financial news and social media. Experimental results demonstrate that hybrid models combining time-series forecasting with sentiment-based signals outperform traditional statistical approaches such as ARIMA and GARCH. The paper also discusses the limitations of AI-based forecasting, including data quality issues, model overfitting, and susceptibility to black swan events. Ultimately, findings suggest that AI offers substantial advantages in capturing market dynamics but requires careful integration with human expertise and regulatory oversight for reliable and ethical financial forecasting.
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