XLK Trades 1¢ Cheaper Than VGT: Fee Not Decisive

XLK and VGT are the two largest ETFs used for broad exposure to US technology stocks, and a recent note in Yahoo Finance (Apr 25, 2026) highlighted a headline difference: XLK charges one penny less than VGT per $100 invested. That $0.01 difference translates to 0.01 percentage points, or one basis point, on an annual expense comparison and is factually correct according to provider factsheets dated April 2026. But for institutional allocators the relevance of that tiny cost delta is limited when placed against drivers such as index construction, sector and sub-sector weights, turnover, tax treatment and tracking error. This report examines the data behind the headline, quantifies the real-world P&L implications over investment horizons, and assesses why expense ratios alone should not determine an ETF selection in the technology sector.

Context

The XLK ETF from State Street tracks the S&P Technology Select Sector Index, while Vanguard's VGT follows the MSCI US Investable Market Information Technology Index, creating material index exposure differences that are not captured by headline expense ratios. Source documents and issuer factsheets released in April 2026 show XLK with an expense ratio approximately 0.09% and VGT at roughly 0.10%, consistent with the one-penny-per-$100 characterization reported by Yahoo Finance on Apr 25, 2026. These indices use different inclusion rules and weighting schemes, which affect holdings concentration; for example, XLK tends to be more concentrated in larger-cap names within the S&P 500, whereas VGT includes a broader-cap segment of the US technology universe based on MSCI IMI rules. For a $10 million institutional allocation, that 1bp gap equates to $1,000 in annual fees, which is immaterial relative to potential tracking variation caused by sector microstructure and stock-level risk.

Investor behavior shows fees matter more at scale and over long horizons, but they are not the dominant driver for short- to medium-term returns. Historical comparisons over multi-year windows demonstrate that tracking error and style drift can produce active deviations in performance that frequently dwarf a 1bp annual fee gap, especially in a sector where a handful of mega-cap names can represent a large portion of returns. Source: Yahoo Finance (Apr 25, 2026), State Street and Vanguard factsheets (Apr 2026).

Data Deep Dive

Expense ratio is the simplest metric for cost, but a rigorous cost analysis must include total cost of ownership, which for ETFs includes implicit transaction costs, tracking difference, bid-ask spreads and tax efficiency. Using a hypothetical $100,000 investment over a ten-year period with a compounded annual return of 10%, a 1bp fee difference reduces terminal value by about $79 — a measurable amount but small relative to volatility in tech returns. By contrast, a 50bp tracking drift or a 200bp active divergence over the same period would change terminal wealth by tens of thousands of dollars, illustrating the scale mismatch between headline fee differences and realistic performance drivers.

Turnover and portfolio composition are measurable contributors to realized costs. VGT's broader MSCI IMI-based construction increases exposure to mid-cap software and hardware vendors outside the S&P 500, which can increase turnover and trading costs in rebalancing periods; XLK's S&P-based approach results in lower turnover historically but higher concentration in mega-cap names. For example, in 2024 and 2025 reconstitution windows, MSCI index rebalance activity led to incremental trade volumes that were visible in historical trading volumes for VGT classes, according to custodian and exchange data published in issuer notes. Those trading frictions show up as small performance drags that are separate from the published expense ratio.

AUM and liquidity are frequently overlooked when comparing ETFs. Larger AUM tends to lower bid-ask spreads in secondary markets and reduce market impact for sizeable institutional trades. As of issuer reports in April 2026, both funds report multi-billion-dollar AUM; differences in AUM relative to an institutional trade size can change execution costs materially and should be modeled. Institutional investors executing blocks of $50m or more will find the spread and market depth differences between XLK and VGT most relevant, not the one-penny annual expense gap.

Sector Implications

The composition differences between XLK and VGT create distinct exposures to sub-sectors such as semiconductors, software-as-a-service, and IT services, which have heterogeneous return drivers and macro sensitivity. For example, semiconductors are typically more cyclical and sensitive to capex cycles, while software revenues are more recurring and growth-oriented; a tilt of several percentage points toward either sub-sector can alter a portfolio's volatility profile. In risk-off windows, a fund with heavier semiconductor exposure may underperform software-heavy benchmarks by several hundred basis points in short windows; this structural divergence is far larger than a 1bp fee advantage.

Benchmark selection also affects performance attribution and manager evaluation. Allocators using XLK may get nearer-term tracking to S&P 500 sector dynamics and the sector's largest constituents, whereas VGT holders capture a broader investable universe that historically has had slightly different sector rotations. Comparing year-on-year (YoY) returns, there have been periods where VGT outperformed XLK by 200-400 basis points over rolling 12-month windows, and conversely periods where XLK led. Those swings are driven by idiosyncratic returns of mega-cap names and are not explained by the 1bp fee differential.

For institutions with liability-driven frameworks or factor overlays, the subtle index differences can impede integration. Risk teams must model exposures at the factor level — size, value/growth, momentum — rather than rely on headline fees. That modeling typically uncovers that portfolio construction and rebalancing frequency drive realized cost and risk far more than the published expense ratios.

Risk Assessment

Relying on expense ratio alone risks underestimating tracking error, concentration risk, and single-stock exposure. Both ETFs are highly correlated to the technology sector and to each other, but correlation does not eliminate divergence risk. During market dislocations, liquidity in underlying securities can evaporate, and the resulting premium or discount between ETF NAV and market price can cause execution slippage that far outstrips a 1bp cost advantage. Institutions should stress-test allocations under scenarios of widened spreads, halted trading in large-cap names, and index reconstitution events.

Counterparty and operational risks are also relevant for large ETF holdings. Creation and redemption mechanisms, authorized participant behavior, and intraday liquidity provision are operational factors that can affect how an ETF trades around market events. These are governed by issuer design and market structure rather than expense ratios, and they can impose implicit costs during episodes of flow stress, such as a 1% sell-off across mega-cap tech names. Historical episodes, like the March 2020 liquidity shock, show that spreads and market impact costs can spike by multiples that dwarf annual fee differentials.

Regulatory and tax considerations add another layer of risk. While ETFs are generally tax-efficient, differences in index replication methodology and portfolio churn can create different capital gains distributions profiles. For taxable institutional accounts or taxable clients, that variation can produce after-tax performance differences materially larger than the headline fee gap across similar horizons. Fund prospectuses and historical distribution records through April 2026 should be reviewed when estimating after-tax total return.

Fazen Markets Perspective

The one-penny headline is useful as a conversation starter, but our institutional analysis points to a different emphasis: marginal fee savings should be subordinated to portfolio-level questions about index fit, concentration tolerance, and execution strategy. A 1bp advantage is essentially noise for most institutional mandates when compared with potential tracking error of several hundred basis points in stressed periods. We believe allocators should prioritize liquidity-adjusted transaction cost analysis for their expected trade sizes, incorporate factor-level attribution models, and stress-test how each ETF would behave in reconstitution or liquidity-stress scenarios. For block trades, model the execution cost explicitly: in many cases the market impact and spread cost of a single block trade will exceed the cumulative savings from a decade of lower fees.

Contrarianly, smaller endowments or fee-sensitive retail channels that plan to dollar-cost average small amounts may value even minimal fee differentials. But even for those users, simplicity and clarity of index exposure should be the first-order selection criterion. In practice, many institutions adopt both products for different sleeves: one ETF for benchmark tracking and one for extended market exposure, rather than treating the fee delta as a tiebreaker. See our related coverage on ETF selection criteria and trade execution at topic and topic.

FAQ

Q: If fees are practically identical, can an allocator hold both XLK and VGT to diversify index risk?

A: Yes. Holding both ETFs can be a pragmatic way to blend S&P-based and MSCI-based exposures, smoothing idiosyncratic index reconstitution effects. Combining a 60/40 split, for instance, alters effective sector and cap tilts and reduces single-index concentration. Model the combined holdings across historical scenarios and measure factor exposures to ensure the blended sleeve meets the mandate.

Q: How should large block trades be executed to avoid implicit cost that dwarfs the fee difference?

A: Institutional traders should use algorithmic execution with liquidity-aware strategies, seek natural liquidity in futures and options wrappers if available, and consider step-outs or crossing networks. Pre-trade analysis should quantify expected implementation shortfall; in many cases, a disciplined VWAP or participation algorithm will minimize market impact relative to attempting a one-off block trade in the ETF itself.

Bottom Line

The 1¢ per $100 expense gap between XLK and VGT is real but economically marginal for most institutional mandates; index construction, trading costs, and tracking behaviour are far more consequential. Choose based on exposure fit, execution plan, and after-tax modeling rather than headline fees.

Disclaimer: This article is for informational purposes only and does not constitute investment advice.