The Role of Expectations in Value and Glamour Stock Returns
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
Abstract What happens when value and glamour stocks miss earnings expectation targets? Although, as expected, prices for glamour stocks have historically fallen, prices for value stocks have gone up—even when business fundamentals deteriorated based on results found in this study of global equities. These results suggest the superior returns delivered by value stocks may not be a result of positive developments relative to expectations but instead are more likely due to a gradual and corrective reversal of earlier overreaction and mispricing. This augments research by select scholars and provides fresh evidence explaining why value investing historically has been a successful long-term strategy. Keywords: Behavioral financeExpectationsValue investingOverreactionOveroptimismEarnings surprise Notes 1. CitationFama and French [1998] also found evidence of a value premium in more than 30 emerging markets between 1987 and 1995. They tempered conviction for the findings, noting the short period of study, the volatility of emerging market returns, and the return patterns, which were “quite leptokurtic and right skewed.” 2. Though results reported in this study represent a global universe of stocks during an essentially nonoverlapping period of study, when the study was narrowed to U.S.-based companies (as La Porta et al. did), there was still no evidence that value stocks had systematically higher earnings surprises. 3. Includes developed markets as defined by MSCI. As of June 30, 2009, developed nations by region included Asia Pacific: Australia, Hong Kong, Japan, New Zealand, and Singapore; Europe: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom; and North America: Canada and the United States. 4. This does not represent the traditional earnings surprise of comparing estimated EPS to actual results on the day financials are reported. Rather expectations were recorded each June 30 and measured relative to what a company reported on its subsequent annual filing. Cross-sectionally, therefore, expectations are as of the same date, reflecting all information available on that date. 5. Averages calculated by putting a cap on earnings surprises at 100% and floor at −100% to mitigate outliers. Earnings surprise is defined as (Actual-Expected)/abs(Expected). 6. Represents simple price return, not total return. 7. Increasing or decreasing leverage can be a positive and negative development for equity holders. This of course depends on many factors including existing capital structure, industry, economic conditions, and business cycle. When evaluated in aggregate, however, it can be a useful gauge of balance sheet health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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