Discovering Bayesian Market Views for Intelligent Asset Allocation

  • Frank Z. XingEmail author
  • Erik Cambria
  • Lorenzo Malandri
  • Carlo Vercellis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants’ behavior is affected by public mood has been rarely discussed. Consequently, there has been little progress in leveraging public mood for the asset allocation problem, which is preferred in a trusted and interpretable way. In order to address the issue of incorporating public mood analyzed from social media, we propose to formalize public mood into market views, because market views can be integrated into the modern portfolio theory. In our framework, the optimal market views will maximize returns in each period with a Bayesian asset allocation model. We train two neural models to generate the market views, and benchmark the model performance on other popular asset allocation strategies. Our experimental results suggest that the formalization of market views significantly increases the profitability (\(5\%\) to \(10\%\) annually) of the simulated portfolio at a given risk level.


Market views Public mood Asset allocation 


  1. 1.
    Angeletos, G., La’O, J.: Sentiments. Econometrica 81(2), 739–779 (2013)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Antweiler, W., Frank, M.Z.: Is all that talk just noise? The information content of internet stock message boards. J. Finance 59(3), 1259–1294 (2004)Google Scholar
  3. 3.
    Black, F., Litterman, R.: Asset allocation: combining investor view with market equilibrium. J. Fixed Income 1, 7–18 (1991)Google Scholar
  4. 4.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)Google Scholar
  5. 5.
    Brandt, M.W.: Portfolio choice problems. In: Handbook of Financial Econometrics, vol. 1, chap. 5, pp. 269–336. Elsevier B.V., Oxford (2009)Google Scholar
  6. 6.
    Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)Google Scholar
  7. 7.
    Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A. (eds.): A Practical Guide to Sentiment Analysis. Springer International Publishing, Switzerland (2017). Scholar
  8. 8.
    Chan, S.W., Chong, M.W.: Sentiment analysis in financial texts. Decis. Support Syst. 94, 53–64 (2017)Google Scholar
  9. 9.
    Chaturvedi, I., Ragusa, E., Gastaldo, P., Zunino, R., Cambria, E.: Bayesian network based extreme learning machine for subjectivity detection. J. Frankl. Inst. 355(4), 1780–1797 (2018)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Fama, E.F., French, K.R.: Luck versus skill in the cross-section of mutual fund returns. J. Financ. 65(5), 1915–1947 (2010)Google Scholar
  11. 11.
    Gers, F.A., Eck, D., Schmidhuber, J.: Applying LSTM to time series predictable through time-window approaches. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 669–676. Springer, Heidelberg (2001). Scholar
  12. 12.
    Greff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)MathSciNetGoogle Scholar
  13. 13.
    He, G., Litterman, R.: The intuition behind black-litterman model portfolios. Goldman Sachs Working Paper (1999).
  14. 14.
    Hommes, C.: The New Palgrave Dictionary of Economics. Interacting Agents in Finance, 2nd edn. Palgrave Macmillan, Basingstoke (2008)Google Scholar
  15. 15.
    Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)Google Scholar
  16. 16.
    Kasabov, N.K., Song, Q.: Denfis: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10, 144–154 (2002)Google Scholar
  17. 17.
    Li, Q., Jiang, L., Li, P., Chen, H.: Tensor-based learning for predicting stock movements. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 1784–1790 (2015)Google Scholar
  18. 18.
    Markowitz, H.: Portfolio selection. J. Finance 7, 77–91 (1952)Google Scholar
  19. 19.
    Nguyen, T.H., Shirai, K.: Topic modeling based sentiment analysis on social media for stock market prediction. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 1354–1364 (2015)Google Scholar
  20. 20.
    Nofer, M., Hinz, O.: Using twitter to predict the stock market: where is the mood effect? Bus. Inf. Syst. Eng. 57(4), 229–242 (2015)Google Scholar
  21. 21.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 122–129 (2010)Google Scholar
  22. 22.
    Pant, P.N., Starbuck, W.H.: Innocents in the forest: forecasting and research methods. J. Manag. 16(2), 433–460 (1990)Google Scholar
  23. 23.
    Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., Mozetič, I.: The effects of twitter sentiment on stock price returns. PLoS One 10(9), 1–21 (2015)Google Scholar
  24. 24.
    Satchell, S., Scowcroft, A.: A demystification of the black-litterman model: managing quantitative and traditional portfolio construction. J. Asset Manag. 1(2), 138–150 (2000)Google Scholar
  25. 25.
    Shen, W., Wang, J.: Portfolio selection via subset resampling. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 1517–1523 (2017)Google Scholar
  26. 26.
    Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic based twitter sentiment for stock prediction. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 24–29 (2013)Google Scholar
  27. 27.
    Smailović, J., Grčar, M., Lavrač, N., Žnidaršič, M.: Predictive sentiment analysis of tweets: a stock market application. In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 77–88. Springer, Heidelberg (2013). Scholar
  28. 28.
    Sortino, F.A., Price, L.N.: Performance measurement in a downside risk framework. J. Invest. 3, 59–64 (1994)Google Scholar
  29. 29.
    Steinbach, M.C.: Markowitz revisited: mean-varian-ce models in financial portfolio analysis. SIAM Rev. 43(1), 31–85 (2001)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Tieleman, T., Hinton, G.E.: Lecture 6.5-RMSProp: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4, 26–31 (2012)Google Scholar
  31. 31.
    Xing, F.Z., Cambria, E., Welsch, R.E.: Natural language based financial forecasting: a survey. Artif. Intell. Rev. 50(1), 49–73 (2018)Google Scholar
  32. 32.
    Xing, F.Z., Cambria, E., Zou, X.: Predicting evolving chaotic time series with fuzzy neural networks. In: International Joint Conference on Neural Networks, pp. 3176–3183 (2017)Google Scholar
  33. 33.
    Yoshihara, A., Seki, K., Uehara, K.: Leveraging temporal properties of news events for stock market prediction. Artif. Intell. Res. 5(1), 103–110 (2016)Google Scholar
  34. 34.
    Zhang, W., Skiena, S.: Trading strategies to exploit blog and news sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 375–378 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Frank Z. Xing
    • 1
    Email author
  • Erik Cambria
    • 1
  • Lorenzo Malandri
    • 2
  • Carlo Vercellis
    • 2
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Data Mining and Optimization Research GroupPolitecnico di MilanoMilanItaly

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