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Information Valuation and Confirmation Bias in Virtual Communities: Evidence from Stock Message Boards

Author

Listed:
  • JaeHong Park

    (School of Management, Kyung Hee University, Dongdaemun-gu, Seoul, South Korea 130-701)

  • Prabhudev Konana

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

  • Bin Gu

    (The W.P. Carey School of Business, Arizona State University, Tempe, Arizona 85281)

  • Alok Kumar

    (School of Business Administration, University of Miami, Coral Gables, Florida 33146)

  • Rajagopal Raghunathan

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

Abstract

Virtual communities continue to play a greater role in social, political, and economic interactions. However, how users value information from these communities and how that affects their behavior and future expectations is not fully understood. Stock message boards provide an excellent setting to analyze these issues given the large user base and market uncertainty. Using data from 502 investor responses from a field experiment on one of the largest message board operators in South Korea, our analyses revealed that investors exhibit confirmation bias, whereby they preferentially treat messages that support their prior beliefs. This behavior is more pronounced for investors with higher perceived knowledge about the market and higher strength of belief (i.e., sentiment) toward a particular stock. We also find a negative interaction effect between the perceived knowledge and the strength of prior belief on confirmation bias. Those exhibiting confirmation bias are also more overconfident; as a result, they trade more actively and expect higher market returns than is warranted. Collectively, these results suggest that participation in virtual communities may not necessarily lead to superior financial returns.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orisre:v:24:y:2013:i:4:p:1050-1067
    DOI: 10.1287/isre.2013.0492
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    2. Sepideh Bazazi & Jorina von Zimmermann & Bahador Bahrami & Daniel Richardson, 2019. "Self-serving incentives impair collective decisions by increasing conformity," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-12, November.
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    4. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.
    5. Hilmer Christiana & Hilmer Michael John, 2021. "Does confirmation bias exist in judged events at the Olympic Games?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(1), pages 1-10, March.
    6. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    7. Jie Li & Li Yu & Xiaofeng Mei & Xu Feng, 2022. "Do social media constrain or promote company violations?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(1), pages 31-70, March.
    8. Chu Xin Cheng, 2019. "Confirmation Bias in Investments," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(2), pages 50-55, February.
    9. Ting Li & Robert J. Kauffman & Eric van Heck & Peter Vervest & Benedict G. C. Dellaert, 2014. "Consumer Informedness and Firm Information Strategy," Information Systems Research, INFORMS, vol. 25(2), pages 345-363, June.
    10. Chi-Wen Chen & Marios Koufaris, 2015. "The impact of decision support system features on user overconfidence and risky behavior," European Journal of Information Systems, Taylor & Francis Journals, vol. 24(6), pages 607-623, November.
    11. Cafferata, Alessia & Tramontana, Fabio, 2019. "A financial market model with confirmation bias," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 252-259.
    12. Abdel-Karim, Benjamin M. & Benlian, Alexander & Hinz, Oliver, 2021. "The Predictive Value of Data from Virtual Investment Communities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124589, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Sifat, Imtiaz Mohammad & Thaker, Hassanudin Mohd Thas, 2020. "Predictive power of web search behavior in five ASEAN stock markets," Research in International Business and Finance, Elsevier, vol. 52(C).
    14. Geng, Yuedan & Ye, Qiang & Jin, Yu & Shi, Wen, 2022. "Crowd wisdom and internet searches: What happens when investors search for stocks?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    15. Patricia L. Moravec & Antino Kim & Alan R. Dennis, 2020. "Appealing to Sense and Sensibility: System 1 and System 2 Interventions for Fake News on Social Media," Information Systems Research, INFORMS, vol. 31(3), pages 987-1006, September.
    16. Jie Ren & Hang Dong & Balaji Padmanabhan & Jeffrey V. Nickerson, 2021. "How does social media sentiment impact mass media sentiment? A study of news in the financial markets," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(9), pages 1183-1197, September.
    17. Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2016. "Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth," Information Systems Research, INFORMS, vol. 27(1), pages 131-144, March.
    18. Patricia L. Moravec & Antino Kim & Alan R. Dennis & Randall K. Minas, 2022. "Do You Really Know if It’s True? How Asking Users to Rate Stories Affects Belief in Fake News on Social Media," Information Systems Research, INFORMS, vol. 33(3), pages 887-907, September.
    19. Shuyu Zhang & Xuanyu Zhou & Huifeng Pan & Junyi Jia, 2019. "Cryptocurrency, confirmatory bias and news readability – evidence from the largest Chinese cryptocurrency exchange," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1445-1468, March.
    20. Sinha, Ankur & Kedas, Satishwar & Kumar, Rishu & Malo, Pekka, 2019. "Buy, Sell or Hold: Entity-Aware Classification of Business News," IIMA Working Papers WP 2019-04-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    21. Haris Krijestorac & Rajiv Garg & Prabhudev Konana, 2021. "Decisions Under the Illusion of Objectivity: Digital Embeddedness and B2B Purchasing," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2232-2251, July.
    22. Shane Greenstein & Grace Gu & Feng Zhu, 2021. "Ideology and Composition Among an Online Crowd: Evidence from Wikipedians," Management Science, INFORMS, vol. 67(5), pages 3067-3086, May.
    23. Zhang, Zuochao & Goodell, John W. & Shen, Dehua & Lahmar, Oumaima, 2024. "Media opinion divergence and stock returns: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 93(C).
    24. Feng, Xunan & Johansson, Anders C., 2019. "Top executives on social media and information in the capital market: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 824-857.
    25. Jacobs, Heiko, 2020. "Hype or help? Journalists’ perceptions of mispriced stocks," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 550-565.

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