IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2503.09647.html
   My bibliography  Save this paper

Leveraging LLMS for Top-Down Sector Allocation In Automated Trading

Author

Listed:
  • Ryan Quek Wei Heng
  • Edoardo Vittori
  • Keane Ong
  • Rui Mao
  • Erik Cambria
  • Gianmarco Mengaldo

Abstract

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level.

Suggested Citation

  • Ryan Quek Wei Heng & Edoardo Vittori & Keane Ong & Rui Mao & Erik Cambria & Gianmarco Mengaldo, 2025. "Leveraging LLMS for Top-Down Sector Allocation In Automated Trading," Papers 2503.09647, arXiv.org, revised Apr 2025.
    • Handle: RePEc:arx:papers:2503.09647
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2503.09647
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abeyratna Gunasekarage & Anirut Pisedtasalasai & David M. Power, 2004. "Macroeconomic Influence on the Stock Market: Evidence from an Emerging Market in South Asia," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 3(3), pages 285-304, December.
    2. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Building Cross-Sectional Systematic Strategies By Learning to Rank," Papers 2012.07149, arXiv.org.
    3. Ayesha Jabeen & Muhammad Yasir & Yasmeen Ansari & Sadaf Yasmin & Jihoon Moon & Seungmin Rho & Gang Jin Wang, 2022. "An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning," Complexity, Hindawi, vol. 2022, pages 1-18, August.
    4. Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
    5. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    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. 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.
    2. Berg, Joyce E. & Rietz, Thomas A., 2019. "Longshots, overconfidence and efficiency on the Iowa Electronic Market," International Journal of Forecasting, Elsevier, vol. 35(1), pages 271-287.
    3. Rojahn, Joachim & Röhl, Christian W. & Frère, Eric, 2010. "Optimum Portfolio ETF Indices: Benchmarking für multidimensional diversifizierte Wertpapierportfolios," Berichte aus der Forschung der FOM 75202, FOM Hochschule für Oekonomie & Management.
    4. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    5. David A. Volkman, 1999. "Market Volatility And Perverse Timing Performance Of Mutual Fund Managers," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(4), pages 449-470, December.
    6. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    7. Klaus Grobys & James W. Kolari & Jere Rutanen, 2022. "Factor momentum, option-implied volatility scaling, and investor sentiment," Journal of Asset Management, Palgrave Macmillan, vol. 23(2), pages 138-155, March.
    8. Constantinos Antoniou & John A. Doukas & Avanidhar Subrahmanyam, 2016. "Investor Sentiment, Beta, and the Cost of Equity Capital," Management Science, INFORMS, vol. 62(2), pages 347-367, February.
    9. Agarwal, Vikas & Gay, Gerald D. & Ling, Leng, 2011. "Window dressing in mutual funds," CFR Working Papers 11-07, University of Cologne, Centre for Financial Research (CFR).
    10. Siddiqi, Hammad, 2015. "Anchoring and Adjustment Heuristic: A Unified Explanation for Equity Puzzles," MPRA Paper 68729, University Library of Munich, Germany.
    11. Philip A. Stork, 2011. "The intertemporal mechanics of European stock price momentum," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 28(3), pages 217-232, August.
    12. Onishchenko, Olena & Zhao, Jing & Kongahawatte, Sampath & Kuruppuarachchi, Duminda, 2024. "Investor heterogeneity and anchoring-induced momentum," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    13. Brad M. Barber & Yi‐Tsung Lee & Yu‐Jane Liu & Terrance Odean, 2007. "Is the Aggregate Investor Reluctant to Realise Losses? Evidence from Taiwan," European Financial Management, European Financial Management Association, vol. 13(3), pages 423-447, June.
    14. Dimitrios D. Thomakos & Michail S. Koubouros, 2011. "The Role of Realised Volatility in the Athens Stock Exchange," Multinational Finance Journal, Multinational Finance Journal, vol. 15(1-2), pages 87-124, March - J.
    15. AltInkIlIç, Oya & Hansen, Robert S., 2009. "On the information role of stock recommendation revisions," Journal of Accounting and Economics, Elsevier, vol. 48(1), pages 17-36, October.
    16. Tobias J. Moskowitz & Mark Grinblatt, 2002. "What Do We Really Know About the Cross-Sectional Relation Between Past and Expected Returns?," Yale School of Management Working Papers ysm259, Yale School of Management.
    17. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    18. John H. Cochrane, 1999. "New facts in finance," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 23(Q III), pages 36-58.
    19. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    20. David Peón & Anxo Calvo, 2012. "Using Behavioral Economics to Analyze Credit Policies in the Banking Industry," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 145-160.

    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:2503.09647. 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.