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Information Characteristics and Errors in Expectations: Experimental Evidence

Published online by Cambridge University Press:  08 March 2017

Constantinos Antoniou,
Glenn W. Harrison,
Morten I. Lau and
Daniel Read
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Abstract

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We design an experiment to test the hypothesis that, in violation of Bayes’ rule, some people respond more forcefully to the strength of information than to its weight. We provide incentives to motivate effort, use naturally occurring information, and control for risk attitude. We find that the strength–weight bias affects expectations but that its magnitude is significantly lower than originally reported. Controls for nonlinear utility further reduce the bias. Our results suggest that incentive compatibility and controls for risk attitude considerably affect inferences on errors in expectations.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 52 , Issue 2 , April 2017, pp. 737 - 750
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2017 

Footnotes

1

We thank Elena Asparouhova (the referee), Hendrik Bessembinder (the editor), and conference and seminar participants at the 2012 Academy of Behavioral Finance and Economics (NYU-Poly), the 2010 Foundations and Applications of Utility, Risk and Decision Theory (Newcastle, U.K.), and Warwick Business School.

References

Andersen, S.; Fountain, J.; Harrison, G. W.; and Rutström, E. E.. “Estimating Subjective Probabilities.” Journal of Risk and Uncertainty, 48 (2014), 207–229.Google Scholar
Andersen, S.; Harrison, G. W.; Lau, M. I.; and Rutström, E. E.. “Discounting Behavior and the Magnitude Effect: Evidence from a Field Experiment in Denmark.” Economica, 80 (2013), 670–697.Google Scholar
Antoniou, C.; Harrison, G. W.; Lau, M. I.; and Read, D.. “Subjective Bayesian Beliefs.” Journal of Risk and Uncertainty, 50 (2015), 35–59.Google Scholar
Asparouhova, E.; Hertzel, M.; and Lemmon, M.. “Inference from Streaks in Random Outcomes: Experimental Evidence on Beliefs in Regime Shifting and the Law of Small Numbers.” Management Science, 55 (2009), 1766–1782.Google Scholar
Barberis, N.; Shleifer, A.; and Vishny, R.. “A Model of Investor Sentiment.” Journal of Financial Economics, 49 (1998), 307–343.Google Scholar
Bernard, V. L., and Thomas, J. K.. “Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?” Journal of Accounting Research, 27 (1989), 1–36.Google Scholar
Bloomfield, R., and Hales, J.. “Predicting the Next Step of a Random Walk: Experimental Evidence of Regime-Shifting Beliefs.” Journal of Financial Economics, 65 (2002), 397–414.Google Scholar
Bondarenko, O., and Bossaerts, P.. “Expectations and Learning in Iowa.” Journal of Banking and Finance, 24 (2000), 1535–1555.Google Scholar
Cason, T. N., and Plott, C. R.. “Misconceptions and Game Form Recognition of the BDM Method: Challenges to Theories of Revealed Preference and Framing.” Journal of Political Economy, 122 (2014), 1235–1270.Google Scholar
Charness, G., and Levin, D.. “When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect.” American Economic Review, 95 (2005), 1300–1309.Google Scholar
Charness, G.; Karni, E.; and Levin, D.. “On the Conjunction Fallacy in Probability Judgment: New Experimental Evidence Regarding Linda.” Games and Economic Behavior, 68 (2010), 551–556.Google Scholar
Conlisk, J.“Three Variants on the Allais Example.” American Economic Review, 79 (1989), 392–407.Google Scholar
Costa-Gomes, M. A., and Weizsäcker, G.. “Stated Beliefs and Play in Normal-Form Games.” Review of Economic Studies, 75 (2008), 729–762.Google Scholar
Daniel, K., and Titman, S.. “Market Reactions to Tangible and Intangible Information.” Journal of Finance, 61 (2006), 1605–1643.Google Scholar
de Dreu, J., and Bikker, J. A.. “Investor Sophistication and Risk Taking.” Journal of Banking and Finance, 36 (2012), 2145–2156.Google Scholar
Edwards, W.“Conservatism in Human Information Processing.” In Formal Representation of Human Judgment, Kleinmutz, B., ed. New York, NY: Wiley (1968), 17–52.Google Scholar
Fiore, S. M.; Harrison, G. W.; Hughes, C. E.; and Rutström, E. E.. “Virtual Experiments and Environmental Policy.” Journal of Environmental Economics and Management, 57 (2009), 65–86.Google Scholar
Gilboa, I.; Postlewaite, A. W.; and Schmeidler, D.. “Probability and Uncertainty in Economic Modeling.” Journal of Economic Perspectives, 22 (2008), 173–188.Google Scholar
Grether, D. M.“Bayes’ Rule as a Descriptive Model: The Representativeness Heuristic.” Quarterly Journal of Economics, 95 (1980), 537–557.Google Scholar
Griffin, D., and Tversky, A.. “The Weighing of Evidence and the Determinants of Confidence.” Cognitive Psychology, 24 (1992), 411–435.Google Scholar
Grinblatt, M.; Keloharju, M.; and Linnainmaa, J.. “IQ, Trading Behavior, and Performance.” Journal of Financial Economics, 104 (2012), 339–362.Google Scholar
Gupta-Mukherjee, S.“Investing in the ‘New Economy’: Mutual Fund Performance and the Nature of the Firm.” Journal of Financial and Quantitative Analysis, 49 (2013), 1–42.Google Scholar
Hackbarth, D.“Determinants of Corporate Borrowing: A Behavioral Perspective.” Journal of Corporate Finance, 15 (2009), 389–411.Google Scholar
Halevy, Y.“Ellsberg Revisited: An Experimental Study.” Econometrica, 75 (2007), 503–536.Google Scholar
Harrison, G. W.; Johnson, E.; McInnes, M. M.; and Rutström, E. E.. “Risk Aversion and Incentive Effects: Comment.” American Economic Review, 95 (2005), 897–901.Google Scholar
Harrison, G. W., and Rutström, E. E.. “Risk Aversion in the Laboratory.” In Risk Aversion in Experiments, Cox, J. C. and Harrison, G. W., eds. Bingley, U.K.: Emerald (2008), 41–196.Google Scholar
Hey, J. D., and Orme, C.. “Investigating Generalizations of Expected Utility Theory Using Experimental Data.” Econometrica, 62 (1994), 1291–1326.Google Scholar
Kadane, J. B., and Winkler, R. L.. “Separating Probability Elicitation from Utilities.” Journal of the American Statistical Association, 83 (1988), 357–363.Google Scholar
Kraemer, C., and Weber, M.. “How Do People Take into Account Weight, Strength and Quality of Segregated vs. Aggregated Data? Experimental Evidence.” Journal of Risk and Uncertainty, 29 (2004), 113–142.Google Scholar
Kuhnen, C. M.“Asymmetric Learning from Financial Information.” Journal of Finance, 70 (2015), 2020–2062.Google Scholar
Kuhnen, C. M., and Knutson, B.. “The Influence of Affect on Beliefs, Preferences, and Financial Decisions.” Journal of Financial and Quantitative Analysis, 46 (2011), 605–626.Google Scholar
Laury, S. K.; McInnes, M. M.; and Swarthout, J. T.. “Insurance Decisions for Low-Probability Losses.” Journal of Risk and Uncertainty, 39 (2009), 17–44.Google Scholar
Liang, L.“Post-Earnings Announcement Drift and Market Participants’ Information Processing Biases.” Review of Accounting Studies, 8 (2003), 321–345.Google Scholar
Loomes, G.; Starmer, C.; and Sugden, R.. “Do Anomalies Disappear in Repeated Markets?” Economic Journal, 113 (2003), 153–166.Google Scholar
Michaely, R.; Thaler, R. H.; and Womack, K. L.. “Price Reactions to Dividend Initiations and Omissions: Overreaction or Drift?” Journal of Finance, 50 (1995), 573–608.Google Scholar
Plott, C. R., and Zeiler, K.. “The Willingness to Pay–Willingness to Accept Gap.” American Economic Review, 95 (2005), 530–545.Google Scholar
Puetz, A., and Ruenzi, S.. “Overconfidence among Professional Investors: Evidence from Mutual Fund Managers.” Journal of Business Finance and Accounting, 38 (2011), 684–712.Google Scholar
Ramsey, F. P. Truth and Probability. The Foundations of Mathematics and Other Logical Essays. London, U.K.: Routledge and Kegan Paul (1931).Google Scholar
Rutström, E. E., and Wilcox, N. T.. “Stated Beliefs versus Inferred Beliefs: A Methodological Inquiry and Experimental Test.” Games and Economic Behavior, 67 (2009), 616–632.Google Scholar
Savage, L. J. The Foundations of Statistics. New York, NY: Wiley (1954).Google Scholar
Savage, L. J.“Elicitation of Personal Probabilities and Expectations.” Journal of the American Statistical Association, 66 (1971), 783–801.Google Scholar
Seru, A.; Shumway, T.; and Stoffman, N.. “Learning by Trading.” Review of Financial Studies, 23 (2010), 705–739.Google Scholar
Smith, V. L.“Microeconomic Systems as an Experimental Science.” American Economic Review, 72 (1982), 923–955.Google Scholar
Sorescu, S., and Subrahmanyam, A.. “The Cross Section of Analyst Recommendations.” Journal of Financial and Quantitative Analysis, 41 (2006), 139–168.Google Scholar
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