May 2024 Buy-Sell Guide for Dow Jones 30 Stocks and Modified Omega Criterion
Bibliographic record
Abstract
We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stock-picking algorithms and their average ranks. We explain the reasons for ignoring the claim that the Sharpe ratio algorithm lacks monotonicity. Since the version of “omega” in the literature uses weights that distort the actual gain–pain ratio faced by investors, we propose new weights. We use data from 30 stocks using the past 474 months (39+ years) of monthly closing prices, ending in May 2024. Our buy-sell recommendations also use newer “pandemic-proof” out-of-sample portfolio performance comparisons from the R package ‘generalCorr’. We report twelve sets of ranks for both out-of- and in-sample versions of the six algorithms. Averaging the twelve sets yields the top and bottom k stocks. For example, k=2 suggests buying Visa Inc. and Johnson & Johnson while selling Coca-Cola and Procter & Gamble.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.147 |
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".