The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Loss Aversion Under Prospect Theory: A Parameter-Free Measurement
Open accessAbstract
Agrowing body of qualitative evidence shows that loss aversion, a phenomenon formalized in prospect theory, can explain a variety of field and experimental data. Quantifications of loss aversion are, however, hindered by the absence of a general preference-based method to elicit the utility for gains and losses simultaneously. This paper proposes such a method and uses it to measure loss aversion in an experimental study without making any parametric assumptions. Thus, it is the first to obtain a parameter-free elicitation of prospect theory's utility function on the whole domain. Our method also provides an efficient way to elicit utility midpoints, which are important in axiomatizations of utility. Several definitions of loss aversion have been put forward in the literature. According to most definitions we find strong evidence of loss aversion, at both the aggregate and the individual level. The degree of loss aversion varies with the definition used, which underlines the need for a commonly accepted definition of loss aversion.
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Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D., & Tversky, A. (1988) · Decision, Probability and Utility · doi:10.1017/cbo9780511609220.014 · Cited by 32,474
Advances in prospect theory: Cumulative representation of uncertainty
Tversky, A., & Kahneman, D. (1992) · Journal of Risk and Uncertainty · doi:10.1007/bf00122574 · Cited by 15,467
Loss aversion in riskless choice: A reference-dependent model.
Tversky, A., & Kahneman, D. (1991) · The Quarterly Journal of Economics · doi:10.2307/2937956 · Cited by 6,432
Mehra, R., & Prescott, E. C. (1985) · Journal of Monetary Economics · doi:10.1016/0304-3932(85)90061-3 · Cited by 5,535
Experimental Tests of the Endowment Effect and the Coase Theorem
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990) · Journal of Political Economy · doi:10.1086/261737 · Cited by 3,194 · cited 3×
“Many empirical studies have found evidence of loss aversion (e.g. Kahneman et al. 1990; Tversky and Kahneman 1991; Barberis et al. 2001).”
InfluentialExperimental Tests of the Endowment Effect and the Coase Theorem
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (2000) · Behavioral Law and Economics · doi:10.1017/cbo9781139175197.009 · Cited by 2,873
Myopic Loss Aversion and the Equity Premium Puzzle
Benartzi, S., & Thaler, R. H. (1995) · The Quarterly Journal of Economics · doi:10.2307/2118511 · Cited by 2,487
The Handbook of Experimental Economics
(1995) · Princeton University Press eBooks · doi:10.1515/9780691213255 · Cited by 2,051
Foundations of Mathematics and other Logical Essays
Ramsey, F. P. (2013) · Routledge eBooks · doi:10.4324/9781315887814 · Cited by 1,994
Cited by
Chiu, C., Wang, E. T. G., Fang, Y., & Huang, H. (2014) · Information Systems Journal · doi:10.1111/j.1365-2575.2012.00407.x · Cited by 1,332
Individual Preferences, Monetary Gambles, and Stock Market Participation: A Case for Narrow Framing
Barberis, N., Huang, M., & Thaler, R. (2006) · The American Economic Review · doi:10.1257/000282806779468652 · Cited by 600
Thinking like a trader selectively reduces individuals' loss aversion
Sokol-Hessner, P., Hsu, M., Curley, N. G., Delgado, M. R., Camerer, C. F., & Phelps, E. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806761106 · Cited by 452
New paradoxes of risky decision making.
Birnbaum, M. H. (2008) · Psychological Review · doi:10.1037/0033-295x.115.2.463 · Cited by 386
Risk attitudes to low-probability climate change risks: WTP for flood insurance
Botzen, W. J. W., & van den Bergh, J. C. J. M. (2012) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2012.01.005 · Cited by 280
Metadata
- Authors
- Abdellaoui, M., Bleichrodt, H., & Paraschiv, C.
- Venue
- Management Science
- Published
- 2007-10
- Volume
- 53
- Issue
- 10
- Pages
- 1659-1674
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2119240992
- Cited by
- 783
- References
- 55
- FWCI
- 16.10
- Type
- journal article
- Language
- en
- Open access
- green
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