The Hidden Costs of Passive Investing

Academic finance has uncovered powerful insights, from the efficient-market hypothesis to the Fama-French five-factor model. Unlike proprietary trading algorithms, these breakthroughs are published openly and accessible to anyone willing to dive into the research. But, as David Booth, chairman of Dimensional Fund Advisors, has emphasized: Insights aren’t enough. The real advantage comes from turning research into investable, low-cost strategies that can withstand real-world frictions. In other words, the hard part isn’t the theory—it’s the implementation.

Most investors are aware of the implementation costs that create hurdles for active managers versus passive investing, including higher management fees and greater turnover that results in higher transaction costs. Those higher costs, plus the efficiency of markets, have led to the tremendous growth in index funds and other systematic strategies (such as factor-based strategies offered by fund families such as Avantis, BlackRock, Dimensional, and Vanguard). While these strategies offer some compelling features (low fees, broad diversification, and simplicity), beneath their appealing surface lies an inconvenient reality that most investors never see and are, thus, likely unaware of.

Index funds face larger and more obvious implementation costs than other systematic strategies. Empirical research has found that mutual funds and exchange-traded funds designed to replicate indexes face significant adverse selection costs as they respond to changes in stock market composition: rebalancing due to IPOs, delistings, additions, deletions, new seasoned issuance, and buybacks. While the replication approach successfully tracks the index, it creates a systematic pattern of buying high and selling low, adversely affecting fund performance in ways that expense ratios never capture. By prioritizing minimizing tracking error rather than minimizing implementation costs, index replication strategies create material implementation drag.

The Research: Quantifying the Invisible Costs of Index Replication

In her June 2025 paper, “On the Hidden Costs of Passive Investing,” Iro Tasitsiomi examined the execution practices of index-tracking funds during reconstitution events. When index providers announce changes, passive managers—seeking to minimize tracking error—typically wait until the final moments of rebalancing day to complete their trades. The result is that they demand a lot of liquidity in a handful of names over a short period of time.

This approach keeps tracking error near zero. However, it also creates a clear opportunity for sophisticated traders to step in ahead of them, supplying liquidity on unfavorable terms. Tasitsiomi quantified this drag and highlighted a troubling pattern: Passive investors often end up paying far more in trading costs than the headline management fee (typically just a few basis points) suggests.

The Hidden Costs of Indexing in Practice

Tasitsiomi identified four hidden costs of passive investing:

1) Significant but Invisible Drag

Tasitsiomi’s analysis showed that waiting until the market close on reconstitution day can add trading costs hundreds of basis points above what strategic execution would require. This means that even funds advertising rock-bottom expense ratios (0–0.1%) may cost investors far more in reality once hidden trading frictions are included. These costs aren’t steady either—they spike during turbulent markets or when major index changes occur.

2) Predictable Trades, Profitable Opponents

Because index funds telegraph their trades, sophisticated market participants can take advantage. Traders can “pre-position” after announcements, then profit by providing liquidity at less favorable prices when passive funds are forced to transact—passive investors are subsidizing professional arbitrageurs who exploit this predictable calendar.

3) The Zero-Tracking-Error Trap

Why do funds accept these costs? Because staying perfectly aligned with the benchmark is treated as their main objective. Yet, Tasitsiomi found that even a modest relaxation—gradually building positions over time instead of waiting until the final closing print—can preserve tracking accuracy while dramatically reducing transaction costs.

4) The ETF Effect: Crowding and Pressure

With ETFs multiplying and scale ballooning, demand around rebalancing days has intensified. Securities with heavy ETF ownership experience especially sharp dislocations. Tasitsiomi documented short-term price spikes of 6%–10% and surges in trading volume during reconstitution, an echo chamber effect where the very success of passive investing amplifies its frictions.

Supporting Evidence from Additional Research

Marco Sammon and John Shim, in their January 2025 study “Index Rebalancing and Stock Market Composition,” concluded, “Index funds mechanically trade in response to IPOs and net issuance in a way that exactly exposes them to adverse selection by firms who buy back when prices are low and IPO/issue when prices are high. And even though this trading is small relative to the total value of index funds’ portfolios (less than 10% of AUM a year), these trades predict significant underperformance.”

They added, “Index funds’ returns could generally be increased if they rebalanced less frequently or reactively.”

Specifically, they found that a simple adjustment to the way stock indexes rebalance—going from quarterly rebalancing to annual rebalancing—would yield an additional 25 basis points per year to index fund investors.

Kaitlin Hendrix, Jerry Liu, and Trey Roberts reached similar conclusions in their September 2024 study, “Measuring the Costs of Index Reconstitution: A 10-Year Perspective.” They found: “With respect to transaction costs, adhering to an index reconstitution schedule can result in relatively poor execution prices—buying higher and selling lower—which are in turn reflected in investors’ returns.”

For example, they found that over the period 2019-23, the price for additions on average went up by 9 basis points, relative to nonrebalanced stocks, in the roughly 10 seconds between 4 p.m. on reconstitution day and market close and then reversed by a relative negative 13 basis points by market open the next morning. They also found that the greatest volume pressure occurred for the Russell 2000 Index, with 120 times volume on rebalance day compared with the previous month.

Robert Arnott, Christopher Brightman, Vitali Kalesnik, and Lillian Wu, authors of the study “Earning Alpha by Avoiding the Index Rebalancing Crowd,” explored alternative index rules to help investors mitigate the negatives of pure index replication strategies. They examined how index rebalancing creates hidden costs for cap-weighted index investors, focusing on S&P 500 changes from 1970 to 2021 (emphasizing post-1989 when changes were preannounced). The following is a summary of their key findings:

• Buy high, sell low problem: Stocks added to the S&P 500 trade at steep valuation premiums (≈92% more expensive than the market), while deletions are deep discounts (≈55% cheaper).
• Performance trends: In the year before addition, New entrants beat the S&P 500 by 41.5%; deletions lagged by 29.1% (gap of 70%). In the year after, Deletions outperformed additions by 22%. Around announcement: Additions gained around 5%, while deletions lost around 7%, creating a 12%–16% swing largely stemming index fund trading pressure.
• Long-term changes: Index effects weakened after 2005, possibly owing to greater market efficiency or diminished value premium.
• Trading strategies: Delaying or front-running index trades could add 23 basis points annually.

The findings of Arnott et al. led them to conclude that the major inefficiency of index investing is the systematic buy high/sell low cycle created by index committee decisions and forced fund flows. Smarter trading or fundamentals-based index construction can add meaningful alpha, revealing that index fund investing conceals real rebalancing costs.

The Passive Paradox

What emerges from this body of work is a paradox. Passive investing has been celebrated as the antidote to high-cost active management. And indeed, relative to the fee-heavy world of stock-pickers, index funds are a significant improvement. But just because expenses look minuscule on paper doesn’t mean total costs are. Hidden implementation frictions eat into returns, and as passive strategies scale, these costs only intensify. This raises an obvious question: If plain-vanilla indexing embeds hidden costs, what about the next evolution of “passive” investing: systematic factor strategies?

Beyond Indexing: The Rise of Factor Strategies

Factor investing, often called “smart-beta” or “systematic” investing, aims to do better by relying on academic evidence to tilt portfolios toward well-documented sources of return such as value, momentum, quality, low volatility, and profitability. Like indexing, these strategies are rules-based and implemented in a systematic, transparent, and replicable manner, while using algorithmic trading strategies to minimize transaction costs.

Firms like AQR, Avantis, BlackRock, Bridgeway, Dimensional, and Vanguard have championed this approach. By shifting the question from “Which stocks?” to “Which factors?” investors hope to harvest persistent return premiums while avoiding the pitfalls of active management. However, as you will see, scale and rules-based execution bring their own challenges. As these strategies attract billions in assets, they too run into implementation bottlenecks: crowded trades, liquidity constraints, and transaction costs that aren’t obvious to the end investor. Once again, implementation is key.

The Hidden Drag in Systematic Factor Strategies

A constrained approach can’t be better than an unconstrained one.

It would be a mistake to believe that implementation costs plague traditional index funds only. Large firms that run systematic, factor-based strategies also encounter meaningful frictions as their scale grows; they become victims of their own success. While they may emphasize trading flexibility to avoid the most obvious index-reconstitution effects, their size introduces different challenges.

The Three Trading Pillars

Every trade must balance three elements: the price paid, the quantity traded, and the time required. Investors cannot buy the amount they want exactly when they want, at the price they want. Trade-offs across these pillars are unavoidable, with each trade-off imposing a distinct cost on investors:

1) Slippage (Relaxing Price)

As discussed above, index replication funds are the classic case of slippage. They effectively ignore price by prioritizing quantity and time to minimize tracking error. As a result, they pay higher prices when buying and accept lower prices when selling. These costs show up as slippage.

2) Latency (Relaxing Time)

Large systematic managers attempt to reduce slippage by remaining flexible and limiting their participation in daily trading volume, typically to 1%–3% of average daily volume. For smaller managers, or highly liquid large-cap stocks, this constraint is immaterial. However, for mega-firms trading less-liquid small-value securities, it creates a latency problem. Positions may take quarters, or even years, to build or unwind.

Consider a firm with $50 billion in US small-value strategies: trading 2% of ADV every single day, it would take over a year to reach market weights for a fourth of its eligible universe. That delay creates significant opportunity costs. Factor premiums such as size, value, and profitability tend to persist for four to five years. The result is that a large share of a premium may be missed if it takes over a year just to establish a position. Latency also makes it nearly impossible to capture shorter-lived return drivers, such as momentum; short-term reversals; or cash mergers; which operate over days, weeks, or months rather than years. Mega-firms are forced to treat these shorter-term drivers as trading filters rather than full premiums because they are unable to fully capture them, given their size. And known poor performers, such as small-growth firms with high investment and low profitability, may take months to divest from rather than days, creating a drag on performance.

3) Dilution (Relaxing Quantity)

To reduce both slippage and latency, large firms often spread trades across more names. For example, instead of concentrating in the 100 securities with the highest expected returns, they may expand to 500 or 1,000. While these names may still have positive expected returns, each step down the ladder dilutes exposures. Thus, the portfolio drifts closer to the market and away from the factor premiums investors seek.

Dilution also arises across factors. For example, momentum, a premium with a three- to six-month horizon, requires turnover that mega-firms cannot realistically execute without incurring severe transaction costs. Rather than actively pursue this premium, they may use it as a reason not to trade. Unable to focus their buys on higher expected return “up momentum” names owing to their size, they factor in momentum by avoiding buying “down momentum” securities. While this half-measure is better than nothing, it often isn’t even executed. Mega-firms frequently are forced to relax this constraint and buy “down momentum” names to get capital invested or maintain portfolio characteristics. There are times when large asset managers have 50% of their buys in “down momentum” securities. Their size actively works against them, resulting in neutral or negative momentum loadings.

Firm-Level Constraints, Not Just Fund-Level

Importantly, these costs must be assessed at the firm level, not just the fund level. Market liquidity is a finite resource. When a mega-firm runs dozens of strategies, each competes internally for the same small share of daily trading volume. A small-value fund, a small-cap fund, a core total market fund, and a separately managed account all compete for the same limited small-value liquidity. No matter how trades are prioritized, some portfolios will systematically get preference over others. Investors must recognize that these firm-level bottlenecks affect their fund, as the small-value strategy might always get preferential treatment over their total market core fund (or vice versa).

Quantifying the magnitude of the impact of latency and dilution on returns is complicated and depends on the firm’s size. However, we can say that the larger the firm, the larger the impact. Best estimates for systematic firms with over $20 billion in AUM in small-value stocks alone are about 10–20 basis points per year in implementation drag.

Why Nimbleness Matters

For investors, the lesson is clear: Scale comes with hidden costs. Mega-firms, whether index funds or systematic managers, inevitably suffer from slippage, latency, and dilution. While big firms must choose how to balance these trade-offs, investors don’t have to choose to be big. Investing alongside smaller, more nimble managers can avoid these constraints. Smaller managers can pursue both long-term sources of return (size, value, profitability) and shorter-term drivers (momentum, asset growth, mergers, securities lending) without the drag of bloated scale.

While large firms may deliver lower published expense ratios, investors should remember that true implementation costs lie beneath the surface, and those costs compound silently over time. Thus, expense ratios, while important, should not be the only consideration when deciding which funds to use when making allocation decisions. Specifically, all else equal, investors should favor the fund with lower AUM.

Let’s look at an example of a fund that can be used to minimize implementation costs while still efficiently accessing factor premiums based on empirical academic research.

How to Minimize Implementation Costs

Longview Advantage ETF EBI, founded by a former portfolio manager at Dimensional Fund Advisors, was built with these implementation lessons in mind. EBI incorporates all the well-documented drivers of return into its strategy but avoids the hidden frictions of the mega-firms through careful trading design. Its lower AUM ($538 million as of Aug. 25, 2025) and lack of competing funds (from the same family) drawing on the same limited liquidity allow the fund to remain nimble and capture the intended premiums with minimal latency or dilution.

Having seen the limitations of scale from the inside, EBI’s team created this fund for like-minded advisors who want to avoid the bloat that increases implementation costs. The result is an evidence-based portfolio that reflects the academic insights and the implementation discipline needed to deliver more of the returns markets make available. Their nimble approach also allows them to take advantage of additional academic insights, such as modestly tilting more toward value when valuation spreads are historically wide and building a material momentum tilt within the ETF.

Key Takeaways for Investors

1) Question the “Low-Cost” Narrative

While passive funds advertise low expense ratios, investors should recognize that the total cost of ownership includes these hidden implementation costs. For example, a fund with even a 0.04% expense ratio might actually cost 0.4% or more when including trading impact.

2) Consider More Sophisticated “Indexing” Strategies

Some fund managers are adopting more sophisticated approaches that accept minimal tracking error in exchange for substantially lower implementation costs. Such strategies may deliver better net returns despite slightly higher expense ratios.

3) Evaluate Fund Implementation Methods

When selecting systematic funds, consider the AUM across all the funds in a family of funds and how they prioritize their limited liquidity budget. To minimize implementation costs, understand how they balance the trade-offs between slippage, dilution, and latency. In general, the smaller the AUM of the firm, the smaller the implementation drag.

When selecting passive funds, consider how the fund handles index reconstitution events, whether the manager uses predictive trading strategies, the fund’s historical tracking error versus implementation costs, and the size and liquidity of the fund’s underlying holdings.

Beyond Reconstitution: Other Hidden Weaknesses

The forced trading costs are just one weakness of index-replicating funds. Other issues that result from the desire to minimize tracking error include:

• Unintentional style drift: Because indexes typically reconstitute annually, they lose exposure to their asset class over time as stocks migrate across categories. According to a 2024 Dimensional study, roughly 25% of the Russell 2000 Index was composed of the largest 1,000 stocks in the Russell 3000 Index on average from 2010 through June 2023. Nonindex, but systematic, portfolios (like those of firms such as AQR, Avantis, Bridgeway, and Dimensional) typically reconstitute monthly, allowing them to maintain more consistent exposure to their asset class. That allows them to capture a greater percentage of the risk premiums in the asset classes they invest in.
• Inclusion of poor performers: Research shows that very low-priced stocks, stocks in bankruptcy, extreme small growth stocks with high investment and low profitability, and IPOs have poor risk-adjusted returns. Systematic portfolios can exclude such stocks through simple filters.
• Limited tax efficiency: Index funds have limited ability to pursue tax-saving strategies, including avoiding short-term gains and offsetting capital gains with losses.
• Hidden cash drag: Equity index funds will hold all securities in the indexes they track, even if that security does not deliver equity returns. Companies acquired for cash tend to trade like cash for the months between deal announcement and deal closure. Index funds continue to hold these names and incur hidden cash drag, while systematic strategies can remain flexible and sell out of these names to increase exposure to the equity premium and other factors.
• Post-IPO Underperformance: Capital markets research shows that mechanical buying of stocks immediately post-IPO by index funds leads to underperformance.

Be a provider, instead of a demander, of liquidity by trading patiently.

Summary

While media, academic, and advisor scrutiny of index funds has focused on expense ratios and management fees to investors, the empirical research we have reviewed shows that the hidden costs of passive investing represent a significant, yet underappreciated, drag on investor returns. While passive investing remains a valuable tool for most investors, understanding these costs is crucial for making informed decisions about fund selection and portfolio construction.

Investors should look beyond expense ratios to consider total implementation costs, seek out managers who employ more sophisticated trading strategies, and remain vigilant about the true cost of their “low-cost” investments. As the research demonstrates, what appears to be a simple, low-cost investment strategy may actually be transferring substantial wealth from ordinary investors to sophisticated market participants.

The key is not to abandon passive investing but to demand better implementation from systematic strategies and remain aware of the true costs involved in seemingly simple investment strategies. Mega-firms do not have an incentive to discuss their liquidity limitations and the subsequent impact on performance. Flexible trading can only help so much. Eventually, the three pillars of trading will have an impact on returns. Remaining small and nimble is the best way to continue to fully capture the returns markets offer.

Larry Swedroe is the author or co-author of 18 books on investing, including his latest, Enrich Your Future. He is also a consultant to RIAs as an educator on investment strategies.