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Revolutionizing Finance with LLMs: An Overview of Applications and Insights

Paper · 2024 · Huaqin Zhao, Zhengliang Liu, Zihao Wu et al.

Summary

This survey examines how large language models can be applied to finance, a field where data is highly specialized and errors are costly. The authors, mainly from the University of Georgia, organize the literature around four task areas: engineering applications in finance, prediction of financial outcomes, the management of risk in finance, and answering financial questions in real time. They also cover the underlying technology, including Transformer self-attention, BPE tokenization, decoding strategies, and NER.

The empirical part tests GPT-4 on 11 financial tasks using natural-language instructions. Where public benchmarks exist, GPT-4 is scored on them. Where they do not, the authors present illustrative case studies. GPT-4 follows task prompts reliably and does well on text-centred work such as sentiment analysis and entity extraction. It is weaker on numeric reasoning and market-movement prediction.

The authors argue that LLMs currently serve as helpers rather than standalone tools for computational finance, including optimization and quantitative trading. They recommend hybrid systems that pair LLM text analysis with quantitative models. For practitioners, the survey offers a map of use cases and a realistic sense of current limits.

Key points

  • GPT-4 benchmark scores: FPB 0.78 Acc, FiQA-SA 0.79 Acc, NER 0.81 EntityF1, FinQA 0.64 EmAcc, ConvFinQA 0.73 EmAcc, BigData22 (stock movement) 0.53 Acc.
  • In a fraud-detection case study on the PaySim simulated mobile-money dataset, GPT-4 classified all 5 sample transactions correctly.
  • The survey's taxonomy groups LLM finance applications into four areas: financial engineering, forecasting, risk management, and real-time QA.
  • The authors identify a main limitation: LLMs cannot perform quantitative computation directly. Their sentiment outputs are better used as inputs to existing models.
  • Proposed future directions include LLM-plus-quant hybrid systems, more interpretable and reliable outputs, and trend prediction that combines historical data with news.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of GeorgiaAthens · United States17 works · 26,331 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksDecision Sciences · 209 worksManagement Science and Operations Research · 108 works

Cite

Cite as Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Hanqi Jiang, Yi Pan, Junhao Chen, Yifan Zhou, Zheyuan Zhang, Zeyu Zhang, Ruitong Sun, Gengchen Mai, Ninghao Liu and Tianming Liu. 2024. Revolutionizing Finance with LLMs: An Overview of Applications and Insights. arXiv:2401.11641.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2401.11641 · arXiv 2401.11641

@misc{zhao2024revolutionizing,
  title = {{Revolutionizing Finance with LLMs: An Overview of Applications and Insights}},
  author = {Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Hanqi Jiang, Yi Pan, Junhao Chen, Yifan Zhou, Zheyuan Zhang, Zeyu Zhang, Ruitong Sun, Gengchen Mai, Ninghao Liu and Tianming Liu},
  year = {2024},
  doi = {10.48550/arxiv.2401.11641},
  eprint = {2401.11641},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2401.11641}
}

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