Size matters! How position sizing determines risk and return of technical timing strategies
Bibliographic record
Abstract
The application of a technical trading rule, which just provides long and short signals, requires the investor to decide upon the exposure to stake in each trade. Although this position sizing (or money management) crucially affects the risk and return characteristics, recent academic literature has largely ignored this effect, leaving reported results incomparable. This work systematically analyzes the impact of position sizing on timing strategies and clarifies the relation to the Kelly criterion, which proposes to bet relative fractions from the remaining gambling budget. Both erratic as well as different relative positions, i.e. fixed proportions of the remaining portfolio value, are compared for simple moving average trading rules. The simulation of parametrized return series allows systematically varying those asset price properties, which are most in uential on timing results: drift, volatility, and autocorrelation. The study reveals that the introduction of relative position sizing has a severe impact on trading results compared to erratic positions. In contrast to a standard Kelly framework, however, an optimal position size does not exist. Interestingly, smaller trading fractions deliver the highest risk-adjusted returns in most scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".