IDEAS home Printed from https://ideas.repec.org/a/gam/jfinte/v3y2024i3p25-478d1481681.html
   My bibliography  Save this article

A Comprehensive Review of Generative AI in Finance

Author

Listed:
  • David Kuo Chuen Lee

    (School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore)

  • Chong Guan

    (SUSS Academy, Singapore University of Social Sciences, Singapore 408601, Singapore)

  • Yinghui Yu

    (School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore)

  • Qinxu Ding

    (School of Business, Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494, Singapore)

Abstract

The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.

Suggested Citation

  • David Kuo Chuen Lee & Chong Guan & Yinghui Yu & Qinxu Ding, 2024. "A Comprehensive Review of Generative AI in Finance," FinTech, MDPI, vol. 3(3), pages 1-19, September.
    • Handle: RePEc:gam:jfinte:v:3:y:2024:i:3:p:25-478:d:1481681
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2674-1032/3/3/25/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2674-1032/3/3/25/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    2. Chong Guan & Ding Ding & Jing Ren & Jiancang Guo, 2024. "Unveiling the aesthetic “wow factor”: The role of aesthetic incongruity and image quality in NFT art valuation with computer vision," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-16, December.
    3. Xinli Yu & Zheng Chen & Yuan Ling & Shujing Dong & Zongyi Liu & Yanbin Lu, 2023. "Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting," Papers 2306.11025, arXiv.org.
    4. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2020. "Stock returns and investor sentiment: textual analysis and social media," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(3), pages 458-485, July.
    5. Yi Yang & Yixuan Tang & Kar Yan Tam, 2023. "InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning," Papers 2309.13064, arXiv.org.
    6. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
    7. Yang Li & Yangyang Yu & Haohang Li & Zhi Chen & Khaldoun Khashanah, 2023. "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance," Papers 2309.03736, arXiv.org.
    8. Yuwei Yin & Yazheng Yang & Jian Yang & Qi Liu, 2023. "FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models," Papers 2308.00065, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuan Li & Bingqiao Luo & Qian Wang & Nuo Chen & Xu Liu & Bingsheng He, 2024. "A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading," Papers 2407.09546, arXiv.org.
    2. Haohang Li & Yupeng Cao & Yangyang Yu & Shashidhar Reddy Javaji & Zhiyang Deng & Yueru He & Yuechen Jiang & Zining Zhu & Koduvayur Subbalakshmi & Guojun Xiong & Jimin Huang & Lingfei Qian & Xueqing Pe, 2024. "INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent," Papers 2412.18174, arXiv.org.
    3. Raeid Saqur & Ken Kato & Nicholas Vinden & Frank Rudzicz, 2024. "NIFTY Financial News Headlines Dataset," Papers 2405.09747, arXiv.org.
    4. Monica Martinez-Blasco & Vanessa Serrano & Francesc Prior & Jordi Cuadros, 2023. "Analysis of an event study using the Fama–French five-factor model: teaching approaches including spreadsheets and the R programming language," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    5. Çelik, Gaffari & Talu, Muhammed Fatih, 2020. "Resizing and cleaning of histopathological images using generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 56-65.
    7. Deborah Miori & Constantin Petrov, 2023. "Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations," Papers 2311.14419, arXiv.org.
    8. Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
    9. Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
    10. Andrea Giuseppe Di Iura & Giulia Terenzi, 2021. "A Bayesian analysis of gain-loss asymmetry," Papers 2104.06044, arXiv.org.
    11. Raeid Saqur, 2024. "What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs," Papers 2406.15508, arXiv.org.
    12. Jun Lu & Danny Ding, 2022. "A Hybrid Approach on Conditional GAN for Portfolio Analysis," Papers 2208.07159, arXiv.org.
    13. Seyed Mohammad Sina Seyfi & Azin Sharifi & Hamidreza Arian, 2020. "Portfolio Risk Measurement Using a Mixture Simulation Approach," Papers 2011.07994, arXiv.org.
    14. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
    15. Ye-Sheen Lim & Denise Gorse, 2021. "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow," Papers 2109.13905, arXiv.org.
    16. Sohyeon Kwon & Yongjae Lee, 2024. "Can GANs Learn the Stylized Facts of Financial Time Series?," Papers 2410.09850, arXiv.org.
    17. Carvajal-Patiño, Daniel & Ramos-Pollán, Raul, 2022. "Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies," Research in International Business and Finance, Elsevier, vol. 62(C).
    18. Rizzato, Matteo & Wallart, Julien & Geissler, Christophe & Morizet, Nicolas & Boumlaik, Noureddine, 2023. "Generative Adversarial Networks applied to synthetic financial scenarios generation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    19. Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
    20. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jfinte:v:3:y:2024:i:3:p:25-478:d:1481681. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.