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Journal of Systems Science and Complexity

, Volume 31, Issue 6, pp 1603–1617 | Cite as

Biased Learning Creates Overconfidence

  • Xuanming Ni
  • Chen Wu
  • Huimin Zhao
Article
  • 3 Downloads

Abstract

The aim of this paper is to develop a multi-period economic model to interpret how the people become overconfident by a biased learning that people tend to attribute the success to their abilities and failures to other factors. The authors suppose that the informed trader does not know the distribution of the precision of his private signal and updates his belief on the distribution of the precision of his knowledge by Bayer’s rule. The informed trader can eventually recognize the value of the precision of his knowledge after an enough long time biased learning, but the value is overestimated which leads him to be overconfident. Furthermore, based on the definition on the luckier trader who succeeds the same times but has the larger variance of the knowledge, the authors find that the luckier the informed trader is, the more overconfident he will be; the smaller the biased learning factor is, the more overconfident the informed trader is. The authors also obtain a linear equilibrium which can explain some anomalies in financial markets, such as the high observed trading volume and excess volatility.

Keywords

Bayes’ rule biased learning overconfidence 

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References

  1. [1]
    Harrison M and Kreps D, Speculative investor behavior in a stock market with heterogeneous expectations, Quarterly Journal of Economics, 1978, 92: 323–336.CrossRefzbMATHGoogle Scholar
  2. [2]
    Kyle A S, Continuous auctions and insider trading, Economerica, 1985, 53: 1315–1336.CrossRefzbMATHGoogle Scholar
  3. [3]
    Kyle A S and Wang A, Speculation duopoly with agreement to disgree: Can overconfidence survive the market test?, Journal of Finance, 1997, 52: 2073–2090.CrossRefGoogle Scholar
  4. [4]
    Odean T, Volume, volatility, price and profit when all traders are above average, The Journal of Finance, 1998, 53: 1887–1934.CrossRefGoogle Scholar
  5. [5]
    Daniel K, Hirshleifer D, and Subrahmanyam A, Investor psychology and security market underand overreaction, Journal of Finance, 1998, 53: 1839–1885.CrossRefGoogle Scholar
  6. [6]
    Gervais S and Odean T, Learing to be overconfident, The Review of Financial Studies, 2001, 14: 1–27.CrossRefGoogle Scholar
  7. [7]
    Kyle A S and Lin T, Continuous speculation with overconfident competitors, Unpublished working paper, Duke University, 2001.Google Scholar
  8. [8]
    Lin T, Under- and over-reaction form relative and aggregate overconfidence, Working paper, Duke University, 2003.Google Scholar
  9. [9]
    Scheinkman J and Xiong W, Overconfidence and speculative bubbles, The Journal of Political Economics, 2003, 111(6): 1183–1219.CrossRefGoogle Scholar
  10. [10]
    Benabou R and Tirole J, Self-confidence and personal motivation, The Quarterly Journal of Economics, 2002, 117(3): 871–915.CrossRefzbMATHGoogle Scholar
  11. [11]
    Steen E V, Overconfidence by bayesian-rational agents, Management Science, 2011, 57: 884–896.CrossRefzbMATHGoogle Scholar
  12. [12]
    Libby R and Rennekamp K, Self-Serving attribution bias, overconfidence, and the issuance of management forecasts, Journal of Accounting Research, 2012, 50: 197–231.CrossRefGoogle Scholar
  13. [13]
    Han B and Hirshleifer D, Investor overconfidence and the forward premium puzzle, Review of Economic Studies, 2011, 78: 523–558.MathSciNetCrossRefzbMATHGoogle Scholar
  14. [14]
    McCarthy S, Oliver B, and Song S, Corporate social responsibility and CEO confidence, Journal of Banking and Finance, 2017, 75: 280–291.CrossRefGoogle Scholar
  15. [15]
    Malmendier U and Taylor T, On the verges of overconfidence, Journal of Economic Perspectives, 2015, 29: 3–8.CrossRefGoogle Scholar
  16. [16]
    Shefrin H, Beyond Greed and Fear: Understanding Behavioral Finance and the Psychology of Investing, Oxford University Press, New York, 2000.Google Scholar
  17. [17]
    Statman M, Thorley S, and Vorkink K, Investor overconfidence and trading volume, The Review of Financial Studies, 2006, 19(4): 1531–1565.CrossRefGoogle Scholar
  18. [18]
    Daniel K and Hirshleifer D, Overconfident investors, predictable returns, and excessive trading, Journal of Economic Perspectives, 2015, 29: 61–87.CrossRefGoogle Scholar

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina
  2. 2.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Business SchoolSun Yat-Sen UniversityGuangzhouChina

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