Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-20T02:33:07.831Z Has data issue: false hasContentIssue false

Conservatisme, représentativité et ancrage dans un contexte dynamique : Une approche expérimentale

Published online by Cambridge University Press:  17 August 2016

Anne Corcos
Affiliation:
CRIISEA, Université de [email protected]
François Pannequin
Affiliation:
CES (Université de Paris 1) et [email protected] auteurs remercient les deux rapporteurs anonymes de la revue pour leurs remarques précieuses.
Get access

Résumé

Différentes heuristiques ont été avancées par les psychologues et les économistes afin de rendre compte des comportements sur les marchés financiers. Elles soulignent les biais cognitifs qui affectent les croyances individuelles, et s'efforcent d'expliquer dans une certaine mesure les anomalies constatées sur les marchés financiers. L'expérimentation menée vise à tester les heuristiques de conservatisme, de représentativité et d'ancrage-ajustement dans un contexte dynamique de quinze périodes : les sujets reçoivent, à chaque période, une information financière et révisent individuellement leurs croyances quant à la qualité d'une entreprise. Les croyances observées s'avèrent incompatibles avec l'hypothèse de révision bayésienne: les sujets ont tendance à surévaluer les petites probabilités et à sous-évaluer les fortes probabilités. L'heuristique de représentativité est, de la même manière, invalidée : le traitement économétrique montre que les sujets sous-pondèrent les signaux les plus intenses, preuve qu'ils ne tirent pas parti de leurs intensités informationnelles. Les hypothèses de conservatisme et d'ancrage-ajustement sont au contraire conjointement validées : les sujets sous-pondèrent l'information nouvelle quand ils révisent leurs croyances mais ce comportement de révision est pleinement conditionné au fait que les sujets s'écartent ou se rapprochent d'une valeur d'ancrage.

Summary

Summary

Several heuristics have been developed by economists and psychologists in order to explain economic behaviour on financial markets. They stress the cognitive bias that affect individual judgments and that partially could explain anomalies observed on financial markets. The aim of our experiment is to test the pertinence of one or the other of conservatism, representativeness and anchorage-adjustment heuristics in a financial context. Its specificity relies on its dynamical context. Fifteen periods along, subjects are given financial information on firm profitability. They are asked to formulate beliefs and to update them accordingly to new information. Econometric treatment of our experimental panel data refutes Bayesian updating: subjects underestimate high probabilities and overestimate low ones. Representativeness heuristic seems to be invalidated in the same way: subjects underweight the most intensive signals and thus, never exploit the whole information. On the contrary, conservatism and anchorage-adjustment are jointly accepted: subjects underweight new information when updating, but this behaviour becomes actually obvious when distinguishing situations in which subjects move away from the anchoring value, from those in which they move closer this value.

Type
Research Article
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 2008 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bibliographie

Amir, E. et Ganzach, Y. (1998). “Overreaction and underreaction in analysts’ forecasts”, Journal of Economie Behavior and Organization, 37, 333347.Google Scholar
Barberis, N. Shleifer, A. et Vishny, R. (1998). “A model of investor sentiment”, Journal of Financial Economics, 49, pp. 307343.Google Scholar
Barberis, N. et Thaler, T. (2003). “A survey of Behavioral Finance”, in: Constantinides, George Harris, Milton Stulz, Rene eds., Handbook of the Economics of Finance, (Amsterdam: North-Holland).Google Scholar
Camerer C, (1995). "Individual Decision Making", in J. Hagel et A. Roth (eds), Handbook of experimental economics, Princeton UP, pp.587-703.Google Scholar
Czaczkes, B., et Ganzach, Y. (1996). “The Natural Selection of Prediction Heuristics: Anchoring and Adjustment Versus Representativeness”, Journal of Behavioral Decision Making, 9, 125140.Google Scholar
Daniel, K.D., Hirshleifer, D. et Subrahmanyam, A. (1998). “Investor Psychology and Security Market Under- and Overreactions”, Journal of Finance, 53(6), pp.1839–86.Google Scholar
De Bondt, W. et Thaler, R. (1985). “Does the Stock Market Overreact?”, Journal of Finance, 40, pp. 793807.Google Scholar
De Bondt, W. Werner, F.M. et Thaler, R. (1987). “Further Evidence on Investor Overreaction and Stock Market Seasonality”, Journal of Finance, 42, pp.557581.Google Scholar
De Bondt, W. Werner, F.M. et Thaler, R. (1990). “Do Security Analysts Overreact?”, American Economic Review, 82, pp. 5257.Google Scholar
Edwards, W., (1961). “Probability learning in 1000 trials”, Journal of Experimental Psychology, 62, pp.385394.Google Scholar
Edwards, W., (1968). “Conservatism in Human Information Processing”, in Kleinmuntz, B. (ed.). Formal Representation of Human Judgment, New York: John Wiley, pp.1752.Google Scholar
Eiser, J.R. (1990). Social Judgement. Milton Keynes: Open University Press.Google Scholar
Fama, E. (1965). “The Behavior of Stock Market Prices”, Journal of Business, 38, pp.34105.Google Scholar
Fama, E., (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of Finance, 25, pp.34105.Google Scholar
Fama, E. et French, K. (1996). “Multifactor Explanations of Asset Pricing Anomalies”, Journal of Finance, 51, pp.5584 Google Scholar
Fama, E., (1998). “Market Efficiency, Long-Term Returns, and Behavioral Finance”, Journal of Financial Economics, 49, pp.283306.Google Scholar
Frankfurter, G. et McGoun, E. (2002). “Resistance is futile: the assimilation of behavioural finance”, Journal of Economic Behavior and Organisation, 48(4), pp.375389.Google Scholar
Grether, David M, (1980), “Bayes Rule as a Descriptive Model: The Representativeness Heuristic”, The Quarterly Journal of Economics, 95(3), pp. 537–57.Google Scholar
Hirshleifer, D. (2001). “Investor Psychology and Asset Pricing”, The Journal of Finance, 56,(4), pp.1533.Google Scholar
Hirshleifer, D., Welch, I. (2002). “An Economic Approach to the Psychology of Change: Amnesia, Inertia, and Impulsiveness”, Journal of Economics & Management Strategy, 11(3), pp. 379421.Google Scholar
Hong, H. et Stein, J. (1999). “A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets”, Journal of Finance, 54(6), pp 2143.Google Scholar
Kahneman et, Miller (1986). “Norm Theory: Comparing reality to its alternatives”, Psychological Review, 80, 136153.Google Scholar
Kahneman, D., Slovic, P. et Tversky, A. (1982). Judgement under uncertainty: Heuristics and biases, Cambridge UP.Google Scholar
Kahneman, D. et Tversky, A. (1973). “On the psychology of prediction”, Psychological Review, 80, pp.237251.Google Scholar
Kahneman, D. et Tversky, A. (1979). “Prospect theory: An analysis of decision under risk”, Econometrica, 47, pp. 313327.Google Scholar
Loughran, T. et Ritter, J. (2000). “Uniformly least powerful tests of market efficiency”, Journal of Financial Economics, 55, pp. 361389.Google Scholar
Murphy, A.H. et Winkler, R.L. (1970). “Scoring Rules in Probability Assessment and Evaluation”, Acta Psychologica, 34, pp. 273286.Google Scholar
Mussweiler, T. (2003). “Comparison processes in social judgment: Mechanisms and consequences”, Psychological Review, 110, 472489.Google Scholar
Mussweiler, T. et Strack, F. (2000). “Numeric judgements under uncertainty: the role of knowledge in anchoring”, Journal of experimental social psychology, 36, p. 495518.Google Scholar
Mussweiler, T. et Schneller, K. (2003). ‘“What goes up must come down’-How charts influence decisions to buy and sell stocks”, The Journal of behavioral finance, vol 4(3), 121130.Google Scholar
Odean, T. (1998a). “Are Investors Reluctant to Realize Their Losses?”, Journal of Finance, Vol. 53(5), pp. 17751798 Google Scholar
Odean, T. (1998b). “Volume, Volatility, Price, and Profit When All Traders Are Above Average”, Journal of Finance, 53(6), pp.18871934.Google Scholar
Rabin, M. (1998). “Psychology and Economics”, Journal of Economic Literature, Vol.36, March, 1146.Google Scholar
Rabin, M. et Schrag, J. (1999). “First Impressions Matter: A Model of Confirmatory Bias”, Quarterly Journal of Economics, 114, pp. 3782.Google Scholar
Ritter, J. (2003). “Behavioral Finance”, Pacific-Basin Finance Journal, 11(4), pp. 429437.Google Scholar
Shefrin, H. (2000). Beyond Greed and Fear: Understanding Behavioral Finance and the Psychology of Investing, Boston, Massachusetts: Harvard Business School Press.Google Scholar
Shiller, R. (2002). “From Efficient Market Theory to Behavioral Finance”, Cowles Foundation Discussion Paper No. 1385.Google Scholar
Shleifer, A., (1999). Inefficient Markets: An Introduction to Behavioral Finance, Oxford: Oxford University Press.Google Scholar
Slovic, P. et Lichtenstein, S. (1971). “Comparison of Bayesian and regression approaches to the study of information processing in judgment”, Organizational Behavior and Human Performance, 6, pp.649744.Google Scholar
Sonnemans, J. et Offerman, T. (2004). “What’s Causing Overreaction? An Experimental Investigation of Recency and the Hot Hand Effect”, Scandinavian Journal of Economics, 2004, vol. 106, issue 3, pages 533554.Google Scholar
Statman, M. (1999) “Behavioral Finance: Past Battles and Future Engagements”, Financial Analysts Journal, 55, pp. 1827.Google Scholar
Thaler, R. (1986). “The Psychology and Economics conference handbook: Comments on Simon, on Einhorn and Hogarth, and on Tversky and Kahneman”, Journal of Business, 59(4), pp. 95100.Google Scholar
Thaler, R. (1999). “The End of Behavioral Finance”, Financial Analysts Journal, 55, pp. 1217.Google Scholar
Thaler, R. (1991). The Winner’s Curse: Paradoxes and Anomalies of Economic Life, Free Press, 1991 (Princeton University Press paperback, 1993).Google Scholar
Tversky, A. et Kahneman, D. (1973). “Availability: A heuristic for judging frequency and probability”, Cognitive Psychology, 5, pp. 207232.Google Scholar
Tversky, A. et Kahneman, D. (1974). “Judgment under uncertainty: Heuristics and biaises”, Science, 185, pp. 11241131.Google Scholar
Tversky, A. et Kahneman, D. (1992). “Advances in prospect theory: Cumulative representation of uncertainty”, Journal of risk and Uncertainty, 5, pp. 297323 Google Scholar
Van der Sar, N. (2004). “Behavioral finance: How matters stand”, Journal of Economic Psychology, vol. 25(3), pp. 425444.Google Scholar