Ambiguity, Information Quality, and Asset Pricing
Open accessAbstract
When ambiguity‐averse investors process news of uncertain quality, they act as if they take a worst‐case assessment of quality. As a result, they react more strongly to bad news than to good news. They also dislike assets for which information quality is poor, especially when the underlying fundamentals are volatile. These effects induce ambiguity premia that depend on idiosyncratic risk in fundamentals as well as skewness in returns. Moreover, shocks to information quality can have persistent negative effects on prices even if fundamentals do not change.
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.
Integrity signals
Registry
Cites
Statistics
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
When news quality is ambiguous, investors rationally react more to bad news than good news.
“When the news quality is ambiguous, as evidenced with our news data of which ChatGPT identifies more than half as neutral, investors rationally react more to bad news than good news (Epstein and Schneider, 2008).”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Epstein and Schneider, 2008
Epstein and Schneider (2008) posits that ambiguity-averse investors, when faced with news of uncertain quality, tend to adopt a worst-case perspective and respond more to negative news than positive news.
“This differentiation is consistent with the theoretical exposition by Epstein and Schneider (2008), which posits that ambiguity-averse investors, when faced with news of uncertain quality, tend to adopt a worst-case perspective regarding its veracity. Consequently, this predisposes them to exhibit more pronounced responses to negative news compared to positive news”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Epstein and Schneider (2008)
This finding exemplifies the asymmetric influence of news type on investor expectations, affirming the hypothesis set forth by Epstein and Schneider (2008).
“This finding exemplifies the asymmetric influence of news type on investor expectations, affirming the hypothesis set forth by Epstein and Schneider (2008).”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Epstein and Schneider (2008)
The analysis corroborates the heightened sensitivity of investors to negative news, aligning with the theoretical insights of Epstein and Schneider (2008).
“In essence, our analysis corroborates the heightened sensitivity of investors to negative news, aligning with the theoretical insights of Epstein and Schneider (2008).”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Epstein and Schneider (2008)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 28
- A. Abel, An exploration of the effects of pessimism and doubt on asset returns, J. Econ. Dynam. Control, 26 (2002), 7-8, 1075-1092.
- N.Barberis and M. Huang, Mental accounting, loss aversion and individual stock returns, J. Finance 56 (2001), 1247-1292.
- G. Bekaert and G. Wu, Asymmetric volatility and risk in equity markets, Rev. Finan. Studies 13, 1 (2002), 1-42.
- A. Brav and J. Heaton, Competing theories of financial anomalies, Rev. Finan. Studies 15 (2002), 575-606.
- M. J. Brennan and Y.Xia, Stock price volatility and the equity premium, J. Monet. Econ. 47 (2001), 249-283.
- M. Cagetti, L.P. Hansen, T. J. Sargent and N. Williams, Robustness and pricing with uncertain growth, Rev. Financ. Stud. 15 (2002), 363-404.
- J. Campbell, M. Lettau, B. Malkiel and Y. Xu, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, J. Finance, 56 (2001), 1-43.
- A. Caplin and J. Leahy, Psychological expected utility theory and anticipatory feel- ings, Quart. J. Econ. 116 (2001), 55-80.
- Z. Chen and L.G. Epstein, Ambiguity, risk and asset returns in continuous time, Econometrica 70 (2002), 1403-43.
- K. Daniel, D. Hirshleifer, and A. Subrahmanyam, Investor psychology and security market under-and overreactions, J. Finance, 53, 5 (1998), 1839-1886.
- L.G. Epstein and M. Schneider, Recursive multiple-priors, J. Econ. Theory 113 (2003), 1-31 (with Martin Schneider).
- L.G. Epstein and M. Schneider, IID: independently and indistinguishably distrib- uted, J. Econ. Theory 113 (2003), 32-50.
- L.G. Epstein and T. Wang, Intertemporal asset pricing under Knightian uncertainty, Econometrica 62 (1994), 283-322.
- M. Feldman, On the generic nonconvergence of Bayesian actions and beliefs, Econ. Theory 1 (1991), 301-321.
- I. Gilboa and D. Schmeidler, Maxmin expected utility with nonunique prior, J. Math. Econ. 18 (1989), 141-153.
- L.P. Hansen, T.J. Sargent and T. Tallarini, Robust permanent income and pricing, Rev. Ec. Stud. 66 (1999), 873-907.
- L.P. Hansen, T.J. Sargent and N. Wang, Robust permanent income and pricing with filtering, Macroeconomic Dynamics 6 (2002), 40-84.
- L.P. Hansen and K. Singleton, Generalized instrumental variable estiamtion of non- linear rational expectatioons models, Econometrica 50 (1982), 1269-1288.
- C. Harvey and A. Siddique, Conditional skewness in asset pricing tests, J. Finance 55, 3 (2000), 1263-1295.
- B. N. Lehmann, Residual risk revisited, J. Econometrics, 45 (1990), 71-97.
- J. Lewellen and J. Shanken, Learning, asset pricing tests and market efficiency, J. Finance, 58,3 (2002), 1113-1145.
- M. Marinacci, Limit laws for non-additive probabilities, and their frequentist inter- pretation, J. Econ. Theory 84 (1999), 145-195.
- S. Mukerji and J. Tallon, Ambiguity aversion and incompleteness of financial mar- kets, Rev. Econ. Stud. 68 (2001), 883-904.
- B. Routledge and S. Zin, Model uncertainty and liquidity, NBER #8683, 2001.
- A. Timmermann, How learning in financial markets generates excess volatility and predictability in stock returns, Quart. J. Economics 108 (1993), 1135-1145.
- P. Veronesi, Stock market overreaction to bad news in good times: a rational expec- tations equilibrium model, Rev. Finan. Stud. 12 (1999), 976-1007.
- P. Veronesi, How does information quality affect stock returns?, J. Finance 55, 2 (2000), 807-837.
- K. West, Dividend innovations and stock price volatility, Econometrica 56 (1988), 37-61.
Cited by
Sorted by citations
Axiomatic methods, dopamine and reward prediction error
Caplin, A., & Dean, M. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.07.007 · Cited by 88 · see citing works
ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?
Chen, J., Tang, G., Zhou, G., & Zhu, W. (2025) · arXiv · doi:10.48550/arxiv.2502.10008 · Cited by 6 · see citing works
- Authors
- EPSTEIN, L. G., & SCHNEIDER, M.
- Affiliations
- University of Rochester (US)
- Boston University (US)
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 2008-01-10
- Volume
- 63
- Issue
- 1
- Pages
- 197-228
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W2119094949
- Cited by
- 833
- References
- 0
- Citations by year
- 2026: 60 · 2025: 47 · 2024: 38 · 2023: 41 · 2022: 40 · 2021: 54 · 2020: 45 · 2019: 43 · 2018: 53 · 2017: 46 · 2016: 36 · 2015: 44 · 2014: 48 · 2013: 52 · 2012: 51
- FWCI
- 68.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Finance (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Finance › Stochastic processes and financial applications
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- ambiguity aversion, information quality, asset pricing, ambiguity premium, news processing, idiosyncratic risk, return skewness, price persistence
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
