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Quantitative Strategies Based on FinGPT’s Analysis of the Federal Reserve’s Attitude
- 1 University of Minnesota, 100 Church Street SE, Minneapolis, MN 55455, Twin City, USA
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Abstract
This paper explores the impact of the Federal Reserve (Fed) on financial markets, specifically through the sentiment scoring of Fed meeting minutes by the AI analysis model FinGPT and its correlation with market performance. The article first introduces the method of sentiment analysis using FinGPT and conducts an in-depth analysis of the meeting minutes from 1990 to 2020 using this method. Furthermore, key market data, such as the S&P 500 Index, real estate, and financial sector indices, are used to assess the actual impact of the Fed’s attitude on market dynamics. Through quantitative trading and backtesting analysis, this study verifies the performance and effectiveness of a single-factor strategy in different markets. The results show that while the Fed’s sentiment scores have high predictive validity and market adaptability in the financial sector, their performance in the real estate market and the broader stock market index is relatively weaker. The findings indicate that the market applicability of the strategy is significantly influenced by market type and economic environment. Future strategy optimization needs to consider these factors to enhance adaptability and efficiency across different markets.
Keywords
Federal Reserve, AI Analysis, FinGPT, Quantitative Trading Analysis
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Cite this article
Chen,S. (2024). Quantitative Strategies Based on FinGPT’s Analysis of the Federal Reserve’s Attitude. Advances in Economics, Management and Political Sciences,90,82-90.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis
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