Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index
Bibliographic record
Abstract
This study compares the performance of a frozen Static portfolio of Dow Jones Industrial Average (DJIA) constituents with the actively rebalanced Dynamic Dow across three decades: 1990–1999, 2000–2009, and 2010–2019. The objective is to evaluate whether a passive buy-and-hold strategy can match or exceed the returns of the updated index, and to analyze differences in risk, volatility, drawdowns, and sectoral shifts. Performance was assessed using compound annual growth rate (CAGR), volatility, Sharpe ratios, maximum drawdowns, and maximum runups, supplemented by t-tests and regressions for statistical significance. Results show that while average returns were not statistically different, the Dynamic Dow consistently achieved higher Sharpe ratios and lower volatility. It materially reduced losses during the downturn of 2000–2009 and captured stronger runups in bull markets, reflecting the benefits of constituent replacement. Overall, findings suggest that index reconstitution enhances efficiency, reduces downside risk, and better aligns portfolios with structural economic change.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".