Yucheng Liang
Assistant Professor
Carnegie Mellon University
Bio
I am an Assistant Professor at Tepper School of Business, Carnegie Mellon University, with a courtesy appointment at the Department of Social and Decision Sciences. I am also an Associated Member of IZA.
I use theoretical and empirical methods to study the foundations and applications of behavioral economics.
I teach undergraduate-level Introduction to Financial Accounting and PhD-level Behavioral Information Economics.
Curriculum Vitae (Updated October 2024)
Email: ycliang@cmu.edu
Publications
Learning from Unknown Information Sources
Management Science, Forthcoming
When an agent receives information from a source whose accuracy might be either high or low, standard theory dictates that she update as if the source has medium accuracy. In a lab experiment, subjects deviate from this benchmark by reacting less to uncertain sources, especially when the sources release good news. This pattern is validated using observational data on stock price reactions to analyst earnings forecasts, where analysts with no forecast records are classified as uncertain sources. A theory of belief updating where agents are insensitive and averse to uncertainty in information accuracy can explain these results.
[Experimental Instructions] [Old Version (2020)]
Selected as the Jaffray Lecture at the Risk, Uncertainty and Decision (RUD) 2020 Conference
Working Papers
Why Exclude Test Scores from Admission Criteria?
with Wenzhuo Xu
Current version: October 2024
Test-optional and test-blind college admission policies are often justified on the grounds that standardized test scores are invalid measures of applicants' abilities and biased against disadvantaged students. This justification contradicts standard economic reasoning, which holds that even imperfect information has non-negative value. In an experiment, we find that participants making admission decisions frequently exclude invalid or biased test scores from admission criteria. The exclusion is driven primarily by concerns about procedural fairness and doubts about the scores' usefulness. However, as participants gain experience in admission decisions, exclusion becomes less prevalent, suggesting that experience helps them better appreciate the scores' value.
The Inference-Forecast Gap in Belief Updating
with Tony Q. Fan and Cameron Peng
Current version: August 2024
Evidence from experiments, surveys, and the field has uncovered both underreaction and overreaction to new information. We provide new experimental evidence on the underlying mechanisms of under- and overreaction by comparing how people make inferences and revise forecasts in the same information environment. Participants underreact to signals when inferring about underlying states, but overreact to the same signals when revising forecasts about future outcomes---a phenomenon we term "the inference-forecast gap." We show that this gap is largely driven by different simplifying heuristics used in the two tasks.
Boundedly Rational Information Demand
Current version: July 2023
Acquiring information about available options before making a decision is useful because it allows decision makers to switch to a superior alternative if the default option is deemed inferior. Therefore, information demand should depend on the distribution of the options’ values. In an experiment, I show that information demand increases as the default worsens, while, on average, it remains insensitive to the prior value of the alternative. These patterns reflect bounded rationality in information valuation, which stems from the difficulty of foreseeing future choices and integrating their payoffs.
Risk Revisited
with Xindi He
Current version: November 2024
This study explores how the historical price path of a stock shapes investors’ perceptions of risk. In a series of experiments, we present participants with real and fabricated stock price charts and elicit their risk perceptions. We document that a parsimonious set of three conceptually independent features, i.e., recency, clustering, and sign, can explain large proportions of the variation in risk perceptions. We find that the effects of these three features are partially mediated by their impact on perceived volatility, which is not fully explained by Bayesian inference. In addition, a significant portion of the features’ effects on risk perceptions occurs independently of perceived volatility. Using U.S. real stock and mutual fund data, we show that these three features help explain cross-sectional variations in returns, trading volume, and future volatility of individual stocks, and predict mutual fund negative flows.
Social Comparison and the Value of Performance Trajectory Information: A Field Experiment in the Workplace
with Hugh Xiaolong Wu and Shannon X. Liu
Current version: February 2024
New workers often compare themselves to their high-achieving senior coworkers, but they often do so without knowing how senior workers performed in the early stages of their careers. This upward social comparison under incomplete information can have adverse effects on new workers’ well-being and employee turnover. We study whether providing performance trajectory information to new workers mitigates the negative consequences of performance comparison. In a large-scale randomized control trial at a leading multinational spa chain in China, we sent workers twice-weekly messages on the performance trajectories of their high-performing senior coworkers. This information treatment reduces the attrition rate of new workers by 12%, and the effect is most pronounced for the more productive workers. The lower attrition rate is mostly driven by an improvement in new workers' stress levels and mental health due to the lowering of their beliefs about senior coworkers' past performance. Overall, this study demonstrates that showing junior workers the "Curricula Vitae" of senior workers mitigates social comparison costs within firms.
Information-Dependent Expected Utility
Current version: February 2017
In decision problems under uncertainty, the subjective evaluation of an outcome can depend on the information content of its realization. To accommodate this dependence, we introduce and axiomatize a model of information-dependent expected utility by allowing the utility of an outcome to flexibly depend on its information content in an (Anscombe-Aumann) act. Subjective beliefs are identified in a special class of our model where the utility of an outcome can be decomposed as the sum of consumption utility and information utility. Our model allows for both information seeking and information averse preferences, as well as a comparative theory of information preferences. For information seeking preferences, we introduce a Hidden Acts representation where the value of information is as if induced from the expected utility of the optimal choice in a fictitious future decision problem given that information.