IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2203.05673.html
   My bibliography  Save this paper

Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2203.05673
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Vernon L. Smith, 2003. "Constructivist and Ecological Rationality in Economics," American Economic Review, American Economic Association, vol. 93(3), pages 465-508, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    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. Bo Qian & Khaled Rasheed, 2010. "Foreign exchange market prediction with multiple classifiers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 271-284.
    2. Narayana Darapaneni & Anwesh Reddy Paduri & Himank Sharma & Milind Manjrekar & Nutan Hindlekar & Pranali Bhagat & Usha Aiyer & Yogesh Agarwal, 2022. "Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets," Papers 2204.05783, arXiv.org.
    3. Mamdouh Abdulaziz Saleh Al-Faryan & Everton Dockery, 2021. "Testing for efficiency in the Saudi stock market: does corporate governance change matter?," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 61-90, July.
    4. Li-Chen Cheng & Yu-Hsiang Huang & Ming-Hua Hsieh & Mu-En Wu, 2021. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    5. repec:ebl:ecbull:v:4:y:2006:i:33:p:1-7 is not listed on IDEAS
    6. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    7. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    8. 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.
    9. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    10. Arne Heise, 2017. "Whither economic complexity? A new heterodox economic paradigm or just another variation within the mainstream?," International Journal of Pluralism and Economics Education, Inderscience Enterprises Ltd, vol. 8(2), pages 115-129.
    11. Ehmke, Mariah & Lusk, Jayson & Tyner, Wallace, 2010. "Multidimensional tests for economic behavior differences across cultures," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 39(1), pages 37-45, January.
    12. Ratner, Mitchell, 1996. "Investigating the behavior and characteristics of the Madrid Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 20(1), pages 135-149, January.
    13. Khalil, Elias L., 2010. "The Bayesian fallacy: Distinguishing internal motivations and religious beliefs from other beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 75(2), pages 268-280, August.
    14. Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    15. Wang, Jianzhou & Lv, Mengzheng & Wang, Shuai & Gao, Jialu & Zhao, Yang & Wang, Qiangqiang, 2024. "Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    16. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    17. Sina Montazeri & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024. "CNN-DRL for Scalable Actions in Finance," Papers 2401.06179, arXiv.org.
    18. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    19. Ehmke, Mariah D. & Warziniack, Travis & Schroeter, Christiane & Morgan, Kari, 2008. "Applying Experimental Economics to Obesity in the Family Household," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(2), pages 539-549, August.
    20. Bartosz Bieganowski & Robert 'Slepaczuk, 2024. "Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data," Papers 2411.12753, arXiv.org, revised Nov 2024.
    21. Marcus Shera & Kacey Reeves West, 2024. "Two worlds collide: A review essay of Humanomics: moral sentiments and the wealth of nations for the twenty-first century," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 37(3), pages 333-349, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:2203.05673. 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.