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Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

Author

Listed:
  • Javier Orlando Pantoja Robayo

    (Universidad EAFIT)

  • Julián Alberto Alemán Muñoz

    (Universidad EAFIT)

  • Diego F. Tellez-Falla

    (Universidad EAFIT)

Abstract

We suggest using deep learning networks to create expert opinions as part of an iterative active portfolio management process. These opinions would be based on posts from the X platform and the fundamentals of stocks listed in the S&P 500 index. Expert views are integral to active portfolio management, as proposed by Black–Litterman. The method we propose addresses the original subjectivity of the opinions by incorporating innovation and accuracy to generate views using analytical techniques. We utilize daily data from 2010 to 2022 for stocks from the S&P 500 and daily posts from Twitter API v2, collected under a research account license spanning the same period. We found that incorporating sentiment factors with machine learning techniques into the view generation process of the Black–Litterman model improves optimal portfolio allocation. Empirically, our results notably outperform the S&P 500 market when considering the annualized alpha.

Suggested Citation

  • Javier Orlando Pantoja Robayo & Julián Alberto Alemán Muñoz & Diego F. Tellez-Falla, 2025. "Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 301-322, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10702-5
    DOI: 10.1007/s10614-024-10702-5
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    References listed on IDEAS

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    1. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    2. Deniz Can Yıldırım & Ismail Hakkı Toroslu & Ugo Fiore, 2021. "Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-36, December.
    3. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    4. Giulio Palomba, 2008. "Multivariate GARCH models and the Black-Litterman approach for tracking error constrained portfolios: an empirical analysis," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 10(4), pages 379-413.
    5. Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
    6. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    7. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    8. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    9. Albarrak, Mohammed S. & Elnahass, Marwa & Papagiannidis, Savvas & Salama, Aly, 2020. "The effect of twitter dissemination on cost of equity: A big data approach," International Journal of Information Management, Elsevier, vol. 50(C), pages 1-16.
    10. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    11. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    12. Amir Amel†Zadeh, 2011. "The Return of the Size Anomaly: Evidence from the German Stock Market," European Financial Management, European Financial Management Association, vol. 17(1), pages 145-182, January.
    13. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    14. Henrik Lumholdt, 2018. "Strategic and Tactical Asset Allocation," Springer Books, Springer, number 978-3-319-89554-3, January.
    15. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    16. Pesaran, M.H., 2010. "Predictability of Asset Returns and the Efficient Market Hypothesis," Cambridge Working Papers in Economics 1033, Faculty of Economics, University of Cambridge.
    17. Haiqi Li & Yu Guo & Sung Y. Park, 2017. "Asymmetric Relationship between Investors' Sentiment and Stock Returns: Evidence from a Quantile Non†causality Test," International Review of Finance, International Review of Finance Ltd., vol. 17(4), pages 617-626, December.
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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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