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

Account and Financial Management Journal e-ISSN: 2456-3374 Volume 10 Issue 10 October 2025, Page No.-3807-3817 DOI: 10.47191/afmj/v10i10.10, Impact Factor: 8.167 © 2025, AFMJ 3807 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index Anwar Husain1, Tejas Trikha2, Adam Husain3, Azeem Khalifa4, Nathan Leung5 1,2,3,4University of Toronto 5Western University – Canada ORCID: https://orcid.org/0000-0002-1226-3532, https://orcid.org/0009-0006-9904-9286, https://orcid.org/0009-0009-3146-1735, https://orcid.org/0009-0002-0896-3955, https://orcid.org/0009-0007-8553-5379 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. KEYWORDS: Dow Jones Industrial Average; Index rebalancing; Passive investing; Risk-adjusted returns; Survivorship bias. 1.0 INTRODUCTION The Dow Jones Industrial Average (DJIA) is one of the most widely recognized stock indices in the world. Created in 1896, it has historically been regarded as a barometer of U.S. economic performance and a benchmark for blue-chip investing. Unlike broader indices such as the S&P 500, the DJIA is composed of only 30 large-cap companies selected by a committee to represent key sectors of the U.S. economy. Importantly, the index is periodically updated: firms that become less representative or underperform are removed, while emerging leaders are added to maintain relevance (S&P Dow Jones Indices, 2020). This process of reconstitution raises a fundamental question: Does actively updating the index through constituent changes meaningfully improve long-term returns compared to a purely passive strategy that holds the original stocks constant? In other words, would an investor who froze the DJIA constituents at the start of a period achieve better or worse results than one who tracked the updated index? Prior research provides mixed evidence. Studies of index effects suggest that removed firms may later rebound while additions sometimes underperform, questioning the benefit of rebalancing (Cai & Houge, 2007; Chen, Noronha & Singal, 2004). Other research highlights the importance of dividends and weighting methodology in explaining the performance of the DJIA (Lin, Selden, Shoven, & Sialm, 2021). These findings tie into broader financial debates. The Efficient Market Hypothesis (Fama, 1970) argues that such changes should not systematically add value, while passive investing advocates contend that turnover introduces hidden costs and tax burdens (Bogle, 1999). Conversely, proponents of active index design suggest that rebalancing reduces exposure to failing firms and keeps portfolios aligned with structural economic shifts. The purpose of this study is to evaluate these competing perspectives through an empirical comparison of DJIA investment strategies. Specifically, the objectives of this research are threefold: 1. To compare returns of constant (Static) portfolios versus dynamically updated (Dynamic) portfolios of DJIA constituents across three decades (1990–1999, 2000–2009, 2010–2019). 2. To analyze risk, including volatility, Sharpe ratios, and drawdowns, in order to assess whether active rebalancing provides superior risk-adjusted performance. “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3808 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 3. To assess the implications for index investing, identifying which investors benefit more from Static holding versus Dynamic rebalancing, and under what market conditions. By systematically addressing these objectives, this study contributes to ongoing debates regarding the relative merits of passive investing and active index rebalancing (Fama & French, 1993; Bogle, 1999). It also provides practical insights for bluechip investors and portfolio managers, offering evidence on the long-term consequences of index construction and reconstitution strategie 2.0 LITERATURE REVIEW 2.1 Index Construction and Methodology The Dow Jones Industrial Average (DJIA) is distinctive among major stock indices because it is price-weighted rather than market capitalization-weighted. Its composition is determined by an editorial committee at S&P Dow Jones Indices, which periodically updates the index by replacing constituents to ensure sectoral representation and ongoing relevance (S&P Dow Jones Indices, 2020). This discretionary rebalancing mechanism differentiates it from indices such as the S&P 500, where quantitative rules guide inclusion decisions. Prior studies have highlighted that the index weighting methodology and design have a significant influence on long-term performance outcomes (Lin, Selden, Shoven, & Sialm, 2021). In the case of the DJIA, price-weighting amplifies the influence of higherpriced stocks, potentially creating distortions relative to marketcap-weighted indices. 2.2 Survivorship Bias and Historical Constituents A major challenge in index performance evaluation is survivorship bias. Many historical analyses only consider firms that remain in the index, thereby ignoring the impact of companies that were deleted. This can overstate long-term performance by excluding underperformers. Research on the Russell 2000 and S&P 500 has shown that deleted firms may often outperform additions in subsequent years (Cai & Houge, 2007). For the DJIA, analyzing “frozen” portfolios of original constituents avoids survivorship bias and provides a direct comparison of as-selected portfolios versus as-updated indices. 2.3 Static vs. Dynamic Index Performance Direct comparisons between Static and Dynamic DJIA portfolios are rare, though related work on the S&P 500 and Russell 2000 offers insight. Studies suggest that passive buyand-hold strategies often perform comparably to, or better than, dynamically rebalanced indices, once transaction costs and tax effects are considered (Cai & Houge, 2007). Lin et al. (2021) demonstrate that dividends and weighting structures account for much of the DJIA’s historical return, implying that committeeled rebalancing decisions may have less influence during extended bull markets. This raises questions about whether reconstitution provides a meaningful edge in long-term wealth accumulation. Jeremy Siegel’s research on the S&P 500 over a 50-year horizon similarly found that long-term investors who reinvested dividends outperformed most active strategies, and that the bulk of returns came from dividend compounding rather than shortterm index changes (Siegel, Stocks for the Long Run; Siegel, “The Long-Run Equity Return”). His findings reinforce the idea that stability and reinvestment, rather than turnover, explain much of large-cap index performance, raising questions about whether reconstitution alone provides a meaningful edge in long-term wealth accumulation. However, Siegel’s study has notable limitations that this paper addresses. First, his analysis ends in 2007 and therefore omits the global financial crisis, a period in which rebalancing effects are especially pronounced. Second, Siegel presents an overall long-horizon view rather than breaking performance into discrete decades, which limits insight into how results vary across different market regimes. Third, because his study covers only one long time span, it does not differentiate between outcomes in expansionary versus contractionary decades, whereas this paper highlights such contrasts explicitly. 2.4 Index Rebalancing Effects The index effect, short-term price movements following additions or deletions, has been extensively studied. Chen, Noronha, and Singal (2004) find asymmetric outcomes: stocks added to the S&P 500 tend to experience short-term price increases that often reverse, while deletions suffer initial losses but later outperform. This evidence challenges the assumption that rebalancing decisions automatically enhance long-term returns. In the DJIA context, rebalancing aims to align the index with evolving economic conditions, but may also result in premature deletions of firms that later recover or undervaluation of new entrants. 2.5 Implications for Passive Investors From a broader perspective, passive investing theory and the Efficient Market Hypothesis (EMH) (Fama, 1970; Fama & French, 1993) argue that systematic outperformance through rebalancing is unlikely, since markets should already incorporate available information into prices. Instead, turnover imposes costs, both explicit (transaction fees) and implicit (tax drag). “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3809 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 Bogle (1999) emphasizes that long-term wealth accumulation is better served by “staying the course” through passive investing, minimizing unnecessary turnover. For blue-chip investors, the choice between Static and Dynamic strategies is therefore not only about returns but also about risk exposure, drawdown resilience, sector representation, and compounding efficiency. 3.0 DATA AND METHODOLOGY 3.1 Data Sources This study draws on publicly available historical market data to construct both Static and Dynamic DJIA portfolios. ● Google Finance and Yahoo Finance were used to collect stock price and total return data for DJIA constituents, adjusted for splits and dividends. ● Company-specific sources (e.g., Honeywell, IBM, Microsoft) were consulted to confirm accuracy and capture corporate actions such as spinoffs, mergers, and delistings. ● DJIA index-level data was retrieved from Yahoo Finance to represent the Dynamic portfolio. To account for dividends, a 3% dividend reinvestment assumption was applied across all portfolios to approximate the long-run average yield of Dow bluechip stocks. 3.2 Timeframe The analysis covers 30 years, divided into three distinct decades to capture contrasting market regimes: 1. 1990–1999: A prolonged expansionary bull market. 2. 2000–2009: A drawdown-heavy decade, marked by the dot-com crash and Global Financial Crisis. 3. 2010–2019: A sustained recovery and bull market. This division allows for comparisons of performance across different economic environments. 3.3 Portfolio Construction Static Portfolios: ● Constituents were frozen at the start of each decade (January 1, 1990; January 1, 2000; January 1, 2010). ● Returns for each stock were calculated using price appreciation plus the 3% dividend reinvestment assumption. ● Decade-level performance was summarized through compound annual growth rate (CAGR), volatility, and Sharpe ratios. Dynamic Portfolios: ● The Dynamic Dow was proxied by the official DJIA total return index, which incorporates periodic rebalancing decisions made by S&P Dow Indices. Below is a table of companies added/removed over the decades. Effective Date Added Removed 1991 Caterpillar (CAT), J.P. Morgan (JPM), Walt Disney (DIS) American Can, Navistar, U.S. Steel 1997 Travelers (TRV), HewlettPackard (HPQ), Johnson & Johnson (JNJ), Walmart (WMT) Westinghouse Electric, Texaco, Bethlehem Steel, Woolworth 1999 Microsoft (MSFT), Intel (INTC), SBC Communications (later AT&T/T), Home Depot (HD) Goodyear, Sears Roebuck, Union Carbide, Chevron 2004 AIG, Pfizer (PFE), Verizon (VZ) AT&T (old), Eastman Kodak, International Paper 2008 Bank of America (BAC), Chevron (CVX), Kraft Foods (KFT) Altria, Honeywell (HON), AIG “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3810 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 2009 Cisco (CSCO), Travelers (TRV) General Motors (GM), Citigroup (C) 2012 UnitedHealth Group (UNH) Kraft Foods (KFT) 2013 Goldman Sachs (GS), Visa (V), Nike (NKE) Bank of America (BAC), Hewlett-Packard (HPQ), Alcoa (AA) 2015 Apple (AAPL) AT&T (T) 2018 Walgreens Boots Alliance (WBA) General Electric (GE) 2020 Salesforce (CRM), Amgen (AMGN), Honeywell (HON, readded) ExxonMobil (XOM), Pfizer (PFE), Raytheon Technologies (RTX) Changes in DJIA Constituents, 1990–2020 (S&P Dow Jones Indices 2020; “Historical Components of the Dow Jones Industrial Average”) 3.4 Performance Metrics Performance was evaluated using the following measures: ● Compound Annual Growth Rate (CAGR): To account for dividends, a 3% reinvestment assumption was applied across all portfolios, reflecting the long-run average dividend yield of Dow blue-chip stocks (Siegel; “Dow Jones Industrial Average Dividend Yield”; Fama and French). ● Volatility: Annualized standard deviation of annual returns. For Static portfolios, volatility was calculated stock-by-stock, averaged, and then aggregated at the portfolio level at a decade level. For Dynamic, indexlevel volatility was used. ● Sharpe Ratio: where Rp = arithmetic mean return, Rf = average annualized 3-month Treasury rate for each decade, and σp = portfolio volatility. All of these were averages for each decade. ● Maximum Drawdown (MDD): Largest peak-totrough decline within each decade. ● Maximum Run-up (MRU): Largest percentage gain from a trough to the following peak within each decade. 3.5 Statistical Tests Two complementary approaches were used to test the robustness of results: ● Welch’s Two-Sample t-Test (unequal variances assumed): Applied to mean annual returns of Static vs. Dynamic portfolios, with the null hypothesis: ● Regression Analysis: Static returns regressed on Dynamic returns using the model: where α represents systematic out/underperformance, β captures sensitivity, and R² measures co-movement. Both tests were conducted using Excel’s Analysis ToolPak. Ttests provided direct comparisons of mean returns, while regressions revealed decade-specific alphas, betas, and explanatory power. “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3811 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 4.0 RESULTS 4.1 Portfolio Performance Summary Table 1 presents a summary of Static versus Dynamic portfolio performance across the three decades, including average CAGR, volatility, Sharpe ratio, maximum drawdown, and maximum run-up. Values below computed through Yahoo/Google Finance as cited in references. Static Portfolio Summary Decade Avg CAGR Avg Volatility Avg Sharpe Ratio Max Drawdown Max Run-Up 1990-99 15.80% 29.20% 0.375 -10.31% 277.48% 2000-09 -4.90% 34.98% -0.217 -32.40% 76.05% 2010-19 10.99% 15.80% 0.474 -4.63% 149.37% Dynamic Portfolio: Decade Avg CAGR Avg Volatility Avg Sharpe Ratio Max Drawdown Max Run-Up 1990-99 14.99% 18.93% 0.535 -4.34% 336.49% 2000-09 -1.20% 22.09% -0.176 -33.84% 59.02% 2010-19 10.87% 17.10% 0.603 -5.63% 173.70% This table highlights the broad similarities in average decadal CAGR across the two strategies, with differences emerging more clearly in volatility and risk-adjusted returns. 4.2 Risk-Adjusted Performance The Dynamic portfolio consistently achieved superior riskadjusted outcomes. Across all decades, its Sharpe ratios (0.535, –0.176, 0.603) exceeded those of the Static portfolio (0.375, – 0.217, 0.474). Similarly, it exhibited lower volatility (18.93%, 22.09%, 17.10%) compared to Static (29.20%, 34.98%, 21.97%). These findings reflect the effect of rebalancing, where underperforming or highly volatile constituents are replaced, smoothing the overall risk-return profile. The line graphs demonstrate how the two strategies had similar annualized returns across the three decades, closely following each other, yet different volatilities gave the Dynamic portfolio investors a higher return per unit of risk. “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3812 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 4.3 Long-Run Growth and Downturns CAGRs were broadly similar in growth decades, with Static slightly outperforming in the 1990s (15.80% vs. 14.99%) and both strategies nearly identical in the 2010s (10.99% vs. 10.87%). However, in the 2000s downturn, the Dynamic Dow substantially reduced losses (–1.20% vs. –4.90%), highlighting the benefit of reconstitution in avoiding bankruptcies and laggards. 4.4 Drawdowns and Runups Drawdown and runup analysis provide additional insight into risk path dependence. Maximum drawdowns for Static and Dynamic portfolios were (–10.31%, –4.34%) in the 1990s, (–32.40%, –33.84%) in the 2000s, and (–4.63%, –5.63%) in the 2010s. Dynamic generally limited downside losses but both strategies suffered in the 2000s. Maximum runups highlight the opposite effect, with Dynamic capturing stronger upside in expansionary decades (336.49% vs. 277.48% in the 1990s; 173.70% vs. 149.37% in the 2010s). “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3813 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 4.5 T-Test Results Welch’s two-sample t-tests were used to test for differences in mean annual returns between Static and Dynamic portfolios. P-values were 0.726 (1990s), 0.737 (2000s), and 0.815 (2010s), all above the 5% significance level. These results confirm that while Dynamic produced better riskadjusted outcomes, the average annualized returns were statistically indistinguishable. “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3814 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 4.6 Regression Results Regression analysis was conducted to examine systematic differences between Static and Dynamic portfolios. Results indicated: ● 1990s: α = –0.040 (not significant), β = 1.119, R² = 0.862 → close tracking with no systematic bias. ● 2000s: α = 0.0277 (significant at 5% significance), β = 1.049, R² = 0.969 → Static outperformed but closely tracked Dynamic. 2010s: α = 0.0031 (not significant), β = 0.873, R² = 0.938 → Static less sensitive but strongly correlated. “Comparing the Returns of Holding Stocks in the Dow Jones Index Constant vs. Investing in the Actively Updated Dow Index” 3815 Anwar Husain1, AFMJ Volume 10 Issue 10 October 2025 Scatter plots of Static versus Dynamic returns further illustrate the regression fit and co-movement across decades. 4.7 Interpretation for Investors For investors, the results indicate that while both Static and Dynamic strategies generate similar long-term average returns, the Dynamic Dow is the superior choice for passive investing. By removing failing companies, incorporating new entrants, and reflecting sectoral shifts, the Dynamic Dow consistently provides: ● Higher Sharpe ratios and lower volatility, ● Better downside resilience during downturns, and ● Stronger participation in market recoveries and sectoral rotations. These results suggest that the value of index reconstitution lies not in boosting raw returns but in enhancing efficiency, resilience, and adaptability; characteristics that are especially important for long-term passive investors.