Incomplete Information, Heterogeneity, and Asset Pricing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We consider a pure exchange economy where the drift of aggregate consumption is unobservable. Agents with heterogeneous beliefs and preferences act competitively on financial and goods markets. We discuss how equilibrium market prices of risk differ across agents, and in particular we discuss the properties of the market price of risk under the physical (objective) probability measure. We propose a number of specifications of risk aversions and beliefs where the market price of risk is much higher, and the riskless rate of return lower, than in the equivalent full information economy (homogeneous and heterogeneous preferences) and thus can provide an(other) answer to the equity premium and risk-free rate puzzles. We also derive a representation of the equilibrium volatility and numerically assess the role of heterogeneity in beliefs. We show that a high level of stock volatility can be obtained with a low level of aggregate consumption volatility when beliefs are heterogeneous. Finally, we discuss how incomplete information may explain the apparent predictability in stock returns and show that in-sample predictability cannot be exploited by the agents, as it is in fact a result of their learning processes. Copyright 2006, Oxford University Press.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it