Reducing Friction and Improving Performance Through Direct Indexing

Written by: Paul Kenney | Syntax Data

Over the past three decades, the S&P 500 Equal Weight Index has demonstrated a compelling performance advantage, outpacing the cap-weighted S&P 500 by 54 basis points (10.79% vs. 10.22%)^[1] before factoring in expenses. In a recent WSJ article, “Your Investing Strategy Is Great, So Long as You Don’t Actually Trade Anything”, Jason Zweig explores the difficulty of translating this paper outperformance into realized returns. Specifically, he notes that the Invesco S&P 500 Equity Weight ETF (ticker: RSP) has trailed its cost-free benchmark by 45 basis points since its inception in 2003. As Zweig observes, the friction of fees and transaction costs can effectively neutralize a strategy's theoretical edge. While this assessment accurately reflects the constraints of traditional pooled vehicles, direct indexing offers a structural alternative designed to mitigate these execution costs while providing an additional source of returns through enhanced tax management for taxable investors. 

Reducing Fee Drag

The most immediate and predictable drag on performance is the internal expense ratio. While RSP carries a 20 basis-point fee, a direct indexing implementation can often be executed for approximately 10 basis points. By removing the fund 'wrapper' and owning the underlying securities directly in a separate account, investors effectively recoup a substantial amount of the expense ratio which immediately drops to the investors bottom line.

Mitigating Turnover and Execution Costs

Zweig correctly notes that RSP suffers from significantly higher turnover (approximately 22%) compared to the standard S&P 500 turnover of around 2%^[2]. Because the fund rebalances quarterly to maintain its equal-weight methodology, it is compelled to systematically sell winners and buy losers every three months. This mechanical rebalancing four times a year generates friction in the form of bid/ask spreads and commission costs.

Direct indexing offers three "levers" to reduce this friction without introducing significant tracking error:

• Less Frequent Rebalancing: While an ETF will follow its dictated schedule, a direct index can utilize buffer zones or ranges. For example, a position is only rebalanced if it drifts significantly beyond its target weight. This reduces portfolio churn for the sake of minor adjustments. As a simple example, we created our own version of an equally weighted large cap portfolio and decreased the rebalance frequency from quarterly to annually. In our test, we found turnover was reduced from 22% to approximately 12%; tracking error was less than 1%, and a performance difference of 6 basis points per year (in favor of the reduced turnover variant!). The test was over the past 7 years, a period reflecting the full COVID-driven market cycle.
• Lower Constituent Count: You do not need all 500 stocks to capture the essence of an equal-weight strategy. By optimizing for a representative sample of 50 or 100 names, an advisor can drastically reduce the number of small-ticket trades that drive up execution costs.
• Smarter Execution: Traditional ETFs are frequently compelled to trade on fixed schedules, regardless of market conditions or liquidity. Conversely, direct indexing platforms can employ advanced tools that monitor portfolio positions closely and execute trades opportunistically. This prioritizes execution quality over a predetermined calendar date. 

Capturing Tax Alpha

Perhaps the greatest weakness of the ETF structure is that tax-loss harvesting is restricted to the fund level. If the equal-weight index is up, the investor can realize no tax benefit, even if 200 of the underlying stocks are trading at a loss.

In a direct indexing separate account, the investor owns individual tax lots rather than a single fund ticker. This ownership transforms the rebalancing process, which Zweig correctly identifies as a cost for the ETF, into an opportunity to systematically harvest losses. By capturing these losses during the same rebalancing that creates friction for a pooled vehicle, advisors can generate 'tax alpha' that directly offsets trading costs. In a 2023 paper titled “Tax-Loss Harvesting: A Primer”, Harry Mamaysky of Columbia University and QuantStreet Capital estimated the average tax alpha in the 30-65 basis point range, depending upon model assumptions.^[3]

Conclusion: Theory into Better Practice

Jason Zweig is right: for most investors, the high turnover inherent in an equal-weighted strategy can prevent a fund like RSP from matching its paper index. While the theory itself necessitates frequent trading, the problem is compounded by the structural inefficiencies of the pooled vehicle, which lacks the flexibility and tax-management tools required to offset these costs.

By utilizing direct indexing, advisors can deliver the structural benefits of strategies like equal-weighting while reducing fees, mitigate turnover costs through intelligent rebalancing, and generate alpha through tax-loss harvesting that can more than offset other costs. As a result, direct indexing offers a pragmatic solution for managing the persistent trade-offs between strategy implementation and market friction.

Industry consultant Joel Bruckenstein, a former investment advisor who now dedicates the majority of his time serving as producer of the T3 Technology Tools for Today Conference, echoes the thoughts in this article, saying, “Direct indexing represents one of the most important structural innovations in portfolio management in decades because it allows advisors to implement sophisticated strategies while controlling costs and improving tax efficiency. Platforms like Syntax Direct give advisors the technology to deliver these benefits at scale – reducing friction, improving implementation, and ultimately helping clients capture more of the returns that investment theory promises but traditional vehicles often fail to deliver.”

Paul Kenney is SVP, Client Solutions at Syntax Data, a financial data and technology company that empowers investment managers, wealth advisors, and financial institutions with precise, transparent data solutions that optimize index development, portfolio customization, and investment analysis to drive better investment outcomes.

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