Summary of Aapm: Large Language Model Agent-based Asset Pricing Models, by Junyan Cheng et al.
AAPM: Large Language Model Agent-based Asset Pricing Models
by Junyan Cheng, Peter Chin
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach combines qualitative discretionary investment analysis from Large Language Model (LLM) agents with quantitative manual financial economic factors to predict excess asset returns. The results show that this fusion outperforms machine learning-based asset pricing baselines in portfolio optimization and asset pricing errors. Specifically, the Sharpe ratio and average absolute value of alpha for anomaly portfolios improved significantly by 9.6% and 10.8%, respectively. Ablation studies and data analysis reveal further insights into the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a new way to predict how well investments will do. It combines two kinds of information: what experts think about an investment (qualitative) and numbers from finance economics (quantitative). The results show that this combination works better than other methods for making smart investment decisions. The experts’ opinions made the predictions more accurate, with a big improvement in how well the investments did overall. |
Keywords
» Artificial intelligence » Large language model » Machine learning » Optimization
