Bibliographic record
Abstract
Unbeknownst to the public, most investment funds actually underperform the broader market. Yet, millions of individual investors fare even worse, barely treading water. Algorithmic trading now accounts for over 80% of all trades and is the domain of professionals. Can it also help the small investor? In a previous paper, we laid the foundations of a simple algorithmic market timing approach based on the moving average crossover concept which can indeed both outperform the broader market and reduce drawdowns, in a way that even the retail investor can benefit. In this paper, we extend our work to study the recent volatile time period, 2020–2022, and especially the unexpected market roller coaster of 2022, to see how our ideas hold up. While our methods overall would not have made gains in 2022, they would have suffered lessor drawdowns than the market, and made consistent gains over longer periods, including the volatile 2020–2022 period. In addition, at least one of our algorithms would have made handsome profits even in 2022, and can generally negotiate black swans well.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".