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Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization

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  • Mufhumudzi Muthivhi
  • Terence L. van Zyl

Abstract

The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem. To this end, the study uses a Semantic Attention Model to predict sentiment towards an asset. We select the optimal portfolio through a sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for price prediction and a mean-variance strategy. Our sentiment portfolio strategies achieved on average a significant increase in revenue above the non-sentiment aware models. However, the results show that our strategy does not outperform traditional portfolio allocation strategies from a stability perspective. We argue that an improved fusion of sentiment prediction with a combination of price prediction and portfolio optimization leads to an enhanced portfolio selection strategy.

Suggested Citation

  • Mufhumudzi Muthivhi & Terence L. van Zyl, 2022. "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers 2203.05673, arXiv.org.
    • Handle: RePEc:arx:papers:2203.05673
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    References listed on IDEAS

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    1. Manolis Kavussanos & Everton Dockery, 2001. "A multivariate test for stock market efficiency: the case of ASE," Applied Financial Economics, Taylor & Francis Journals, vol. 11(5), pages 573-579.
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    3. Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
    4. Butler, Kirt C. & Malaikah, S. J., 1992. "Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia," Journal of Banking & Finance, Elsevier, vol. 16(1), pages 197-210, February.
    5. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    Cited by:

    1. Taeisha Nundlall & Terence L Van Zyl, 2023. "Machine Learning for Socially Responsible Portfolio Optimisation," Papers 2305.12364, arXiv.org.

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