Inference for the Difference of Two Independent KS Sharpe Ratios under Lognormal Returns
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Bibliographic record
Abstract
A higher-order likelihood-based asymptotic method to obtain inference for the difference between two KS Sharpe ratios when gross returns of an investment are assumed to be lognormally distributed is proposed. Theoretically, our proposed method has <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mi>n</mi> </mrow> <mrow> <mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> <mo>/</mo> <mn>2</mn> </mrow> </mrow> </msup> </mrow> </mfenced> </math> distributional accuracy, whereas conventional methods for inference have <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mi>n</mi> </mrow> <mrow> <mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> <mo>/</mo> <mn>2</mn> </mrow> </mrow> </msup> </mrow> </mfenced> </math> distributional accuracy. Using an example, we show how discordant confidence interval results can be depending on the methodology used. We are able to demonstrate the accuracy of our proposed method through simulation studies.
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Full frame distilled prediction
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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) | 0.000 | 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