IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2408.10255.html
   My bibliography  Save this paper

Large Investment Model

Author

Listed:
  • Jian Guo
  • Heung-Yeung Shum

Abstract

Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs. To overcome these challenges, we introduce the Large Investment Model (LIM), a novel research paradigm designed to enhance both performance and efficiency at scale. LIM employs end-to-end learning and universal modeling to create an upstream foundation model capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges, instruments, and frequencies. These "global patterns" are subsequently transferred to downstream strategy modeling, optimizing performance for specific tasks. We detail the system architecture design of LIM, address the technical challenges inherent in this approach, and outline potential directions for future research. The advantages of LIM are demonstrated through a series of numerical experiments on cross-instrument prediction for commodity futures trading, leveraging insights from stock markets.

Suggested Citation

  • Jian Guo & Heung-Yeung Shum, 2024. "Large Investment Model," Papers 2408.10255, arXiv.org, revised Aug 2024.
    • Handle: RePEc:arx:papers:2408.10255
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2408.10255
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Andrea Coletta & Joseph Jerome & Rahul Savani & Svitlana Vyetrenko, 2023. "Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness," Papers 2306.12806, arXiv.org.
    3. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Papers 2102.08811, arXiv.org, revised Jul 2021.
    4. Cao, Sean Shun & Jiang, Wei & Lei, Lijun (Gillian) & Zhou, Qing (Clara), 2024. "Applied AI for finance and accounting: Alternative data and opportunities," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    5. Feng Zhang & Ruite Guo & Honggao Cao, 2020. "Information Coefficient as a Performance Measure of Stock Selection Models," Papers 2010.08601, arXiv.org.
    6. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Applied Mathematical Finance, Taylor & Francis Journals, vol. 28(1), pages 79-95, January.
    7. Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
    8. Molei Qin & Shuo Sun & Wentao Zhang & Haochong Xia & Xinrun Wang & Bo An, 2023. "EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading," Papers 2309.12891, arXiv.org.
    9. Matthew Dixon, 2022. "The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1427-1428, August.
    10. Christopher Krauss, 2017. "Statistical Arbitrage Pairs Trading Strategies: Review And Outlook," Journal of Economic Surveys, Wiley Blackwell, vol. 31(2), pages 513-545, April.
    11. Engelberg, Joseph & McLean, R. David & Pontiff, Jeffrey & Ringgenberg, Matthew C., 2023. "Do Cross-Sectional Predictors Contain Systematic Information?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 58(3), pages 1172-1201, May.
    12. Zikai Wei & Bo Dai & Dahua Lin, 2023. "E2EAI: End-to-End Deep Learning Framework for Active Investing," Papers 2305.16364, arXiv.org.
    13. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    14. Rene Carmona & Kevin Webster, 2012. "High Frequency Market Making," Papers 1210.5781, 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. Antonio Briola & Silvia Bartolucci & Tomaso Aste, 2024. "HLOB -- Information Persistence and Structure in Limit Order Books," Papers 2405.18938, arXiv.org, revised Jun 2024.
    2. Mengying Zhu & Xiaolin Zheng & Yan Wang & Yuyuan Li & Qianqiao Liang, 2019. "Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling," Papers 1911.05309, arXiv.org, revised Nov 2019.
    3. Ilia Zaznov & Julian Kunkel & Alfonso Dufour & Atta Badii, 2022. "Predicting Stock Price Changes Based on the Limit Order Book: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-33, April.
    4. Peer Nagy & Jan-Peter Calliess & Stefan Zohren, 2023. "Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets," Papers 2301.08688, arXiv.org, revised Sep 2023.
    5. Michael Senescall & Rand Kwong Yew Low, 2024. "Quantitative Portfolio Management: Review and Outlook," Mathematics, MDPI, vol. 12(18), pages 1-25, September.
    6. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    7. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    8. Konark Jain & Nick Firoozye & Jonathan Kochems & Philip Treleaven, 2024. "Limit Order Book Simulations: A Review," Papers 2402.17359, arXiv.org, revised Mar 2024.
    9. Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
    10. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.
    11. Christiane Goodfellow & Dirk Schiereck & Steffen Wippler, 2013. "Are behavioural finance equity funds a superior investment? A note on fund performance and market efficiency," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 111-119, April.
    12. Harrison Hong & Terence Lim & Jeremy C. Stein, 2000. "Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies," Journal of Finance, American Finance Association, vol. 55(1), pages 265-295, February.
    13. Weiguang Han & Boyi Zhang & Qianqian Xie & Min Peng & Yanzhao Lai & Jimin Huang, 2023. "Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning," Papers 2301.10724, arXiv.org, revised Feb 2023.
    14. Frederico Belo & Chen Xue & Lu Zhang, 2010. "Cross-sectional Tobin's Q," NBER Working Papers 16336, National Bureau of Economic Research, Inc.
    15. Croce, M.M. & Nguyen, Thien T. & Raymond, S. & Schmid, L., 2019. "Government debt and the returns to innovation," Journal of Financial Economics, Elsevier, vol. 132(3), pages 205-225.
    16. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    17. David Hirshleifer & Danling Jiang, 2010. "A Financing-Based Misvaluation Factor and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3401-3436.
    18. Lepori, Gabriele M., 2015. "Investor mood and demand for stocks: Evidence from popular TV series finales," Journal of Economic Psychology, Elsevier, vol. 48(C), pages 33-47.
    19. David J. Moore & David McMillan, 2016. "A look at the actual cost of capital of US firms," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1233628-123, December.
    20. Ho, Ron Yiu-wah & Strange, Roger & Piesse, Jenifer, 2006. "On the conditional pricing effects of beta, size, and book-to-market equity in the Hong Kong market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(3), pages 199-214, July.

    More about this item

    Statistics

    Access and download statistics

    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:arx:papers:2408.10255. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.