Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 304

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Prospect Theory: Much Ado About Nothing?

Levy, Moshe, Levy, Haim·2002·Management Science

Abstract

Prospect theory is a paradigm challenging the expected utility paradigm. One of the fundamental components of prospect theory is the S-shaped value function. The value function is mainly justified by experimental investigation of the certainty equivalents of prospects confined either to the negative or to the positive domain, but not of mixed prospects, which characterize most actual investments. We conduct an experimental study with mixed prospects, using, for the first time, recently developed investment criteria called Prospect Stochastic Dominance (PSD) and Markowitz Stochastic Dominance (MSD). We reject the S-shaped value function, showing that at least 62%–76% of the subjects cannot be characterized by such preferences. We find support for the Markowitz utility function, which is a reversed S-shaped function—exactly the opposite of the prospect theory value function. It is possible that the previous results supporting the S-shaped value function are distorted because the prospects had only positive or only negative outcomes, presenting hypothetical situations which individuals do not usually face, and which are certainly not common in financial markets.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1287/mnsc.48.10.1334.276

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

See all 304

Metadata

Authors
Levy, M., & Levy, H.
Venue
Management Science
Published
2002-10
Volume
48
Issue
10
Pages
1334-1349
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
OpenAlex
W2149893228
Cited by
304
References
3
FWCI
8.76
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Prospect Theory: Much Ado About Nothing? https://app.cochat.ai/papers/10.1287/mnsc.48.10.1334.276
The paper
Levy, M., & Levy, H. (2002). Prospect Theory: Much Ado About Nothing? Management Science. https://doi.org/10.1287/mnsc.48.10.1334.276

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.