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The Predictive Value of Data from Virtual Investment Communities

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  • Abdel-Karim, Benjamin M.
  • Benlian, Alexander
  • Hinz, Oliver

Abstract

Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.

Suggested Citation

  • Abdel-Karim, Benjamin M. & Benlian, Alexander & Hinz, Oliver, 2023. "The Predictive Value of Data from Virtual Investment Communities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 141359, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:141359
    DOI: 10.3390/make3010001
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/141359/
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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Mohammad Ali Kashefi, 2016. "Effect of salvage market on strategic technology choice and capacity investment decision of firm under demand uncertainty," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 17(1), pages 140-155, February.
    3. Nofer, Michael & Hinz, Oliver, 2014. "Are Crowds on the Internet Wiser than Experts? The Case of a Stock Prediction Community," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69935, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Meng Ma & Ritu Agarwal, 2007. "Through a Glass Darkly: Information Technology Design, Identity Verification, and Knowledge Contribution in Online Communities," Information Systems Research, INFORMS, vol. 18(1), pages 42-67, March.
    5. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    6. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    7. repec:pri:cepsud:91malkiel is not listed on IDEAS
    8. Essendorfer, Stephan & Diaz-Rainey, Ivan & Falta, Michael, 2015. "Creative destruction in Wall Street's technological arms race: Evidence from patent data," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 300-316.
    9. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    10. Gottschlich, Jörg & Hinz, Oliver, 2014. "A Decision Support System for Stock Investment Recommendations Using Collective Wisdom," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Neely, Christopher J. & Weller, Paul A. & Ulrich, Joshua M., 2009. "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 467-488, April.
    12. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    13. Brad Barber & Reuven Lehavy & Maureen McNichols & Brett Trueman, 2001. "Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns," Journal of Finance, American Finance Association, vol. 56(2), pages 531-563, April.
    14. JaeHong Park & Prabhudev Konana & Bin Gu & Alok Kumar & Rajagopal Raghunathan, 2013. "Information Valuation and Confirmation Bias in Virtual Communities: Evidence from Stock Message Boards," Information Systems Research, INFORMS, vol. 24(4), pages 1050-1067, December.
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