Momentum Trading with the <i>ℓ</i> <sub>1</sub> ‐Filter: Are the Markets Efficient?*
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 This paper explores the possibility of generating consistent momentum profits by trading on nine major indices across the globe using the ℓ 1 ‐ filter. This methodology penalizes slope reversion of the filtered trend and identifies piecewise linear trends in the asset prices. We find the buy strategy to offer considerably higher momentum returns compared to the sell strategy. Our strategy beats the buy‐and‐hold (BH) strategy on all fronts and, thus, highlights the inefficiencies in financial markets in recent years (2000–2016). Comparing the momentum profits across a set of advanced economies (AEs) and emerging market economies (EMEs), we find that the developed and efficient financial markets of the AEs provide lower opportunities for momentum profits. The momentum profits are more than double in the EMEs as compared to the AEs. Highlighting the instability of the momentum strategy in different market states by using the global financial crisis (GFC) as a turning point, we further find that considerable opportunity exists for momentum strategies in the bullish runs that precede the crisis, as happened before the GFC. However, the momentum profits reduce significantly as the crisis sets in, increasing the degree of market uncertainty, fear, and risk‐aversiveness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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