Trading on Sentiment: Leveraging Generative Artificial Intelligence for Financial Market Predictions

Abstract

The advent of Generative Artificial Intelligence (AI) has revolutionized the field of financial analysis, offering new methodologies for deriving actionable insights from vast and unstructured datasets. This study explores the application of Generative AI to sentiment analysis within the S&P 500, with the goal of identifying profitable trading opportunities. First, our model analyzes news articles to extract sentiment related to publicly traded companies. Second, the sentiment data is integrated into a trading algorithm by determining buy and sell signals for various stocks. Finally, we evaluate the effectiveness of the trading strategy through backtesting. Key performance metrics, e.g., average return per trade of 11.50%, demonstrate the profitability and risk profile of the strategy.

Keywords: Generative Artificial Intelligence, Financial Analysis, Stock Market, S&P 500, Backtesting

DOI: 10.54941/ahfe1006401