Investigating Long-Term Short Pairing Strategies for Leveraged Exchange-Traded Funds Using Machine Learning Techniques
Bibliographic record
Abstract
This article examines the profitability of short-selling strategies of different portfolios with varying combinations of bull and bear leveraged exchange-traded funds (LETFs). We find that although short-selling a combination of bull and bear LETFs does not yield significant positive returns compared to the market, short-selling a portfolio with only bear LETFs can significantly outperform the market, especially if the position is established after a period of heightened market volatility. Moreover, using machine learning techniques, we show that as the correlation of LETFs with their underlying index increases, the return from short-selling both bull and bear LETFs decreases. At the same time, an increase in the net asset value (NAV) of bull LETFs results in an increase in the return of short-sold bull LETFs and a decrease in the return of short-sold bear LETFs.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".