Learning from unknown information sources
Abstract/Contents
- Abstract
- When an agent receives information generated by a source whose accuracy might either be high or low, standard economic theory dictates that she update as if the source has medium accuracy. In a lab experiment, I find that subjects' updating behaviors deviate from this benchmark. First, subjects under-react to information when the source is uncertain. Second, the under-reaction is more pronounced for good news than for bad news. These two patterns, under-reaction and pessimism, are consistent with a theory of belief updating where agents are insensitive and averse to compound uncertainty and ambiguity. I also find that subjects' reactions to information with uncertain accuracy are uncorrelated with their evaluations of bets with uncertain odds. This suggests that people have distinct attitudes toward uncertainty in information accuracy and uncertainty in economic fundamentals. The experimental results are validated using observational data on stock price reactions to analyst earnings forecasts, where analysts with no forecast records are classified as uncertain information sources
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Liang, Yucheng |
|---|---|
| Degree supervisor | Ostrovsky, Michael |
| Thesis advisor | Ostrovsky, Michael |
| Thesis advisor | Bernheim, B. Douglas |
| Thesis advisor | Niederle, Muriel |
| Degree committee member | Bernheim, B. Douglas |
| Degree committee member | Niederle, Muriel |
| Associated with | Stanford University, Graduate School of Business. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yucheng Liang |
|---|---|
| Note | Submitted to the Graduate School of Business |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Yucheng Liang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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