GPT's idea of stock factors

Abstract

We amalgamate the capabilities of the GPT-4 computational model with the avant-garde methodology of autonomous factor generation, culminating in the synthesis of high-return factors within the equity investment milieu. Empirical outcomes elucidate that the factors conceptualized by ChatGPT attain a commendable Sharpe ratio peaking at 4.49, accompanied by an annualized return reaching 66.16%. Notably, the superlative excess returns garnered remain unaccounted for by the quintessential five-factor model. Through the implementation of an unembellished model averaging paradigm, the ensemble of 35 factors, conceived by ChatGPT, manifests an apex long-short annualized return of 88% and a Sharpe ratio registering at 2.46. In stark contrast to conventional data mining techniques, the temporal expenditure requisite for GPT's factor generation is minuscule. It relies on knowledge inference without the need for data input, and it can provide a thorough economic explanation for its factors.

Keywords:

• LLM
• Asset pricing
• Automated factor generating
• ChatGPT
• Knowledge inference

JEL Classifications:

• G11
• G12
• G17

Acknowledgements

Special thanks to Hui Chen for his kindly advice. Thanks to Yuming Zeng for his contributions as a research assistant. Ke Tang acknowledges financial support from the Guoqiang Institute, Tsinghua University (2020GQG1018).

Disclosure statement

No potential conflict of interest was reported by the author(s).