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Discovering Bayesian Market Views for Intelligent Asset Allocation

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  • Frank Z. Xing
  • Erik Cambria
  • Lorenzo Malandri
  • Carlo Vercellis

Abstract

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.

Suggested Citation

  • Frank Z. Xing & Erik Cambria & Lorenzo Malandri & Carlo Vercellis, 2018. "Discovering Bayesian Market Views for Intelligent Asset Allocation," Papers 1802.09911, arXiv.org, revised Jun 2018.
    • Handle: RePEc:arx:papers:1802.09911
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    1. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    2. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    5. S Satchell & A Scowcroft, 2000. "A demystification of the Black–Litterman model: Managing quantitative and traditional portfolio construction," Journal of Asset Management, Palgrave Macmillan, vol. 1(2), pages 138-150, September.
    6. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
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    Cited by:

    1. Akhilesh Prasad & Arumugam Seetharaman, 2021. "Importance of Machine Learning in Making Investment Decision in Stock Market," Vikalpa: The Journal for Decision Makers, , vol. 46(4), pages 209-222, December.
    2. Sang Il Lee & Seong Joon Yoo, 2019. "Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets," Papers 1903.06478, arXiv.org, revised Sep 2019.

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