Surviving Black Swans: The Challenge of Market Timing Systems
Bibliographic record
Abstract
It is an open secret that most investment funds actually underperform the market. Yet, millions of individual investors fare even worse, barely treading water. Algorithmic trading is now so common, it accounts for over 80% of all trades and is the domain of professionals. Can it also help the small investor? Individual investors are advised to buy-and-hold an index fund or a balanced portfolio including stocks, bonds, and cash equivalents. That would ensure market performance. However, market indices also occasionally have deep drawdowns (such as the devastating market crash of 1929 and other so-called Black Swan events). In contrast to received wisdom, we argue with evidence from backtesting on major U.S. market indices, as well as some select stocks that simple ideas in rule-based market timing can in fact be useful. One can not only obtain good results, but outperform market indices, while, at the same time, reducing deep drawdowns, surviving Black Swan events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".