Taiwan stock arbitrage strategy based on beta uncertainty
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
This study investigates an investment arbitrage strategy based on CAPM beta uncertainty, using monthly data from companies listed in Taiwan from 1993 to 2023. The in-sample analysis reveals that beta uncertainty has a stronger positive relationship with stock returns than beta itself, suggesting that beta uncertainty is a more effective risk factor than the traditional CAPM beta. Specifically, holding high-beta or high-beta-uncertainty stocks while shorting low-beta or low-beta-uncertainty stocks can generate significant investment returns. Stock portfolios are further segmented based on historical betas, market values, book-to-price ratios, and past cumulative returns to ensure the robustness of the findings. In the out-of-sample analysis, we observe that within the highest beta groups, a strategy involving a long position in the highest beta group and a short position in the lowest beta group exhibits the highest investment performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.001 |
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