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Record W4415609290 · doi:10.47191/afmj/v10i10.10

Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index

2025· article· W4415609290 on OpenAlexaff
Anwar Husain, Tejas Trikha, Nathan Leung

Bibliographic record

VenueAccount and Financial Management Journal · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsSharpe ratioPortfolioIndex (typography)Downside riskConstant (computer programming)RecessionIndex fund

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.233
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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