The Cross-section of Expected Stock Returns
Bibliographic record
Abstract
The primary objective of the study is to examine the cross-sectional anomalies of stock returns in Nepali capital market. Measures of common stock returns are taken as capital gain yield, dividend yield and total yield. Explanatory variables are stock BETA, total assets growth, firm specific fundamental variables, and specific macroeconomic variables. The research design is descriptive and causal-comparative to investigate the direction, magnitude and nature of relationship between dependent and independent variables following a panel data of 576 (48 listed firms) observations of 2010/11-2021/22. The primary tools of analysis are the portfolio analysis and Ordinary Least Squares (OLS) regression. The findings suggest that the market risk as well as the asset growth have a strong positive impact on cross-section of stock returns in the Nepali capital market. Moreover, firm specific fundamentals variables, and macroeconomic variables are also a major determinant of common stock returns.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".