Empirical models of analyst forecasts
Abstract/Contents
- Abstract
- This dissertation is comprised of two studies on analyst forecasts. The first study provides empirical evidence about the objective function underlying analysts' choice of forecasts. Assumptions about sell-side analysts' objective function are critical to empirical researchers' understanding of their incentives and resulting behavior. In contrast to approaches used in previous papers which rely exclusively on statistical properties of forecasts, I compare theoretical models with alternate objective functions based on their ability to explain observed forecasts. A linear loss objective function which incorporates the effect future analysts' actions on analysts' deviation from peer forecasts is best rationalized by the data. I find that assumptions about the objective function have a substantial impact on the conclusions from empirical tests about analysts' incentives and behavior. The second study provides empirical estimates of uncertainty and disagreement about future earnings that underly analyst forecast dispersion. A parsimonious model which assumes that analysts' payoffs are jointly determined by forecast error and deviation from consensus reproduces many of the descriptive facts observed about forecast dispersion in the data. The strategic behavior that arises from the model distorts both the levels of forecast dispersion and the sensitivity of the measure with respect to cross-sectional variation in uncertainty. The estimated parameters perform better at predicting forecast dispersion out-of-sample than approaches based solely on regressions that use firm characteristics. Counterfactual simulations indicate that analysts' strategic incentives, together with the sequential forecast setting, plays a first-order role in determining forecast dispersion relative to the firm's information environment. The model-implied estimates of earnings uncertainty exhibit a substantially less negative association with future returns relative to the association generated by forecast dispersion. This finding partially reconciles the findings from previous studies with theories about the asset pricing implications of uncertainty and disagreement.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Xiao, Youfei |
|---|---|
| Associated with | Stanford University, Graduate School of Business. |
| Primary advisor | Larcker, David F |
| Primary advisor | Reiss, Peter C. (Peter Clemens) |
| Thesis advisor | Larcker, David F |
| Thesis advisor | Reiss, Peter C. (Peter Clemens) |
| Thesis advisor | Barth, Mary E |
| Advisor | Barth, Mary E |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Youfei Xiao. |
|---|---|
| Note | Submitted to the Graduate School of Business. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Youfei Xiao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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