Comparative Historical Analysis of Shariah-Screened and Conventional U.S. Equity ETFs (2020–2025)

SPUS vs HLAL vs VOO vs SPY: fees, factor exposures, and regime sensitivity (descriptive; non-prescriptive)

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⚠️ IMPORTANT LEGAL NOTICE — Click to expand

This document is general educational research and comparative historical analysis. It is provided for informational purposes only and does not constitute investment advice, portfolio management, a solicitation, or any inducement to transact in any financial instrument.

No personalization. The analysis does not take into account any individual’s financial situation, objectives, or risk profile.

Data and scope. Figures are compiled from the sources listed at the end of this document and are current as of December 2025 unless stated otherwise. Historical results are presented for descriptive purposes and do not imply future outcomes.

Regulatory positioning. This text is intended to qualify as non-advisory research / educational analysis under Swiss regulatory expectations (FinSA / FINMA), with a focus on methodology, historical evidence, and limitations.

For complete disclaimers, see Legal Disclaimers.

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Key Observations (Non-Prescriptive; sample period 2020–2025)

1. →Relative return dispersion was observed between the Shariah-screened ETFs in this sample (SPUS, HLAL) and conventional benchmarks (VOO, SPY); the dispersion coincided with differences in sector composition and leverage constraints.
2. →Expense ratios differed materially in the observed products (approximately 0.49–0.50% for SPUS/HLAL versus 0.03% for VOO and 0.0945% for SPY as of December 2025).
3. →A large share of the observed return differences can be described using factor and sector exposures (e.g., technology weight, financials exclusion, and low-leverage bias) rather than product-specific “skill”.
4. →Regime sensitivity is central: the same exposures that coincided with relative outperformance in some environments are mechanically associated with different relative outcomes in other environments (illustrated qualitatively below).

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Executive Summary

The global Islamic finance industry has reached approximately USD 5.9–6.0 trillion in total assets as of 2024, with published third‑party projections to approximately USD 9.7 trillion by 2029. Within this ecosystem, Shariah-screened equity ETFs are one of several vehicles used to obtain equity exposure under specified screening methodologies.

This document provides a comparative historical analysis of two U.S.-focused Shariah-screened equity ETFs (SPUS, HLAL) and two conventional large-cap U.S. equity benchmarks (VOO, SPY) over 2020–2025. It focuses on (i) fee differentials, (ii) descriptive performance statistics, and (iii) a factor/sector attribution framework that links observed differences to portfolio construction constraints (e.g., leverage screens and financials exclusion).

Observed sample-period result (descriptive): in 2020–2025, SPUS exhibited higher annualized return than VOO in the compiled data, while HLAL exhibited lower annualized return than VOO. These differences coincide with sector weights (technology concentration) and structural exclusions (financials) that were favorable in several years within the sample.

Scope caveat: the sample period includes distinct macro and sector regimes (e.g., technology leadership and episodes of financial-sector weakness). The analytical emphasis is therefore on exposure-based interpretation and limitations, not on decision rules or product preferences.

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Sample-Period Context and Regime Sensitivity

This analysis covers 2020–2025, a period that contained multiple factor and sector regimes. The descriptive results in this document are best interpreted through the lens of exposure sensitivity (e.g., technology weight, financials exclusion, leverage constraints).

Elements frequently discussed in market commentary for this period include:

• →Zero interest rates → QE-driven valuation expansion for growth stocks
• →Technology dominance → AI bubble dynamics, mega-cap concentration
• →Financial sector weakness → Banking crisis, low lending volumes
• →Rising rates → Favoring asset-light, low-leverage companies

These conditions are directionally consistent with why leverage‑constrained, financials‑excluded portfolios can exhibit different relative outcomes compared with broad-market benchmarks.

This document does not attempt to forecast whether any regime will persist, nor does it provide monitoring checklists or operational guidance.

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Part 1: The Global Halal ETF Market

1.1 Market Size and Growth

The Islamic finance industry has experienced remarkable growth over the past five years. According to the LSEG Islamic Finance Development Report 2025 (the most authoritative source), global Islamic finance assets reached USD 5.985 trillion at end-2024, representing a 21% year-on-year increase—significantly higher than the historical 10% CAGR projection.

This acceleration is driven by:

1. →Demographic expansion: The global Muslim population stands at approximately 1.9 billion, with particularly high concentrations in Southeast Asia, the Middle East, and increasingly in Europe
2. →Institutional adoption: Major asset managers (BlackRock, Vanguard, HSBC, UBS) now offer Islamic funds, legitimizing the sector for retail and institutional investors
3. →Government support: Saudi Arabia, UAE, Malaysia, and the UK have implemented pro-Islamic finance policies and regulatory clarity
4. →ESG convergence: Islamic finance principles (prohibition of riba/interest, gharar/speculation) overlap substantially with modern ESG investing, attracting both Muslim and non-Muslim ethical investors

Within Islamic finance, equities represent approximately 10–15% of total assets, with the remainder primarily in Islamic banking (72%), sukuk/bonds (17.2%), takaful/insurance (2.3%), and Islamic funds (5.1%). This means the addressable market for halal equity ETFs is roughly $600–900 billion globally.

1.2 The Halal ETF Landscape (2025)

As of December 2025, the halal ETF market includes several major players:

For comparison, conventional S&P 500 ETFs charge dramatically lower fees:

• →VOO (Vanguard S&P 500 ETF): 0.03%
• →SPY (SPDR S&P 500 ETF Trust): 0.0945%

This represents a 16–17x fee premium for the referenced Shariah-screened ETFs versus the referenced conventional S&P 500 ETFs (based on stated expense ratios as of December 2025).

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Part 2: Fees and Return Arithmetic (Conceptual; Non-Prescriptive)

2.1 Interpreting Expense Ratios

An expense ratio is an annual fee expressed as a percentage of assets under management. A 0.50% expense ratio means that $1,000 invested generates $5 in annual fees. For equity ETFs, these fees cover portfolio management, compliance monitoring, data licensing, and operational overhead.

Analytical note: a fee differential (e.g., 0.47% per year) reduces gross returns by that differential, all else equal. The cumulative effect increases with time horizon and with the level of assets, but the realized impact also depends on market returns and the path of contributions/withdrawals (not modeled here).

2.2 Illustrative Fee Differential (Non-Personalized)

For descriptive context, the stated expense ratios as of December 2025 imply a differential of approximately 0.40–0.50 percentage points per year between the referenced Shariah-screened ETFs and the lowest-cost broad-market benchmark cited (VOO). This section does not compute or present terminal wealth projections.

This section does not assign probabilities to scenarios, and does not include action labels. Scenario logic is discussed in Part 3.5 as exposure sensitivity rather than as a decision framework.

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Part 3: Performance Analysis (2020–2025)

3.1 Inception-to-Date Performance

Descriptive observations (no prescriptive implication):

• →In this sample, SPUS exhibited a higher cumulative return than the conventional benchmarks; HLAL also exhibited a higher cumulative return than the benchmarks in the table, though its annualized return was lower than SPUS.
• →Volatility and Sharpe statistics are presented as descriptive summaries for the sample period and are not interpreted as forward indicators.
• →Dividend yield and expense ratio differences represent structural attributes that can affect total return composition (income vs. price appreciation) and long-horizon fee drag.

3.2 Year-by-Year Performance Breakdown

Descriptive observation: in this sample, SPUS exceeded VOO in 5 of 6 calendar years, with one year of relative underperformance. Past performance is not indicative of future results. These returns reflect a specific market regime and should not be used to predict future outcomes.

HLAL shows more volatility, with strong 2021–2023 performance offset by 2024 underperformance (–8.0% vs. VOO).

3.3 Risk-Adjusted Returns (Sharpe Ratio; descriptive)

The Sharpe ratio measures return per unit of risk. It is included as a descriptive statistic for the sample period.

• →SPUS: 0.704
• →SPY: 0.620
• →VOO: 0.617
• →HLAL: 0.634

Interpretation (exposure-based): differences in Sharpe ratios within the sample are consistent with differences in sector composition and leverage constraints. This observation is not used to derive decision rules or product preferences.

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Part 3.5: Factor Attribution – What Drove SPUS's Outperformance?

Analytical question: did the observed relative outperformance coincide primarily with:

• →(A) Shariah screening identifies objectively better stocks? (structural advantage)
• →(B) Shariah screens happen to weight favorable factors in this specific period? (regime accident)

The attribution framework below is consistent with (B) in this sample.

Factor 1: Technology Overweight

Magnitude:

• →SPUS: ~44% Technology sector weight
• →S&P 500: ~26% Technology sector weight
• →Overweight: ~18 percentage points

Performance impact:

• →Technology CAGR (2020–2025): ~18–22% annually
• →S&P 500 ex-Technology CAGR: ~8–10% annually
• →SPUS benefit from overweight: ~1.2–1.8% annualized outperformance

Sensitivity note: technology underperformance mechanically reduces relative returns for portfolios with sustained technology overweight.

Factor 2: Financials Exclusion During Weak Period

Magnitude:

• →SPUS: 0% Financials sector exposure
• →S&P 500: ~13% Financials sector exposure
• →Exclusion: Full 13 percentage points

Performance impact:

• →Financials CAGR (2020–2025): ~5–7% annually
• →Technology CAGR (2020–2025): ~18–22% annually
• →SPUS benefit from avoiding weak sector: +0.2–0.5% annualized outperformance

Sensitivity note: a positive financials shock mechanically benefits benchmarks with financials exposure and does not transmit to portfolios with a structural financials exclusion.

Factor 3: Quality / Low Leverage Bias

Mechanism: Shariah screens exclude companies with debt > 30% of market capitalization. This naturally concentrates exposure in low-leverage, high-quality companies.

Performance impact (2020–2025):

• →Low-leverage companies outperformed as rates rose
• →High-leverage cyclicals underperformed
• →Contribution: +0.4–0.8% annualized

Sensitivity note: in credit-positive cycles or environments favoring higher leverage, leverage‑constrained portfolios can exhibit different relative outcomes.

Factor 4: Dividend Sacrifice (Drag)

Magnitude:

• →SPUS: 0.70% dividend yield
• →S&P 500: 1.35% dividend yield
• →Sacrifice: –0.65% annually

Performance impact:

• →In a growth environment (2020–2025), this dividend drag is overcome by capital appreciation
• →In a low-growth, income-focused environment, this becomes a material disadvantage
• →Contribution: –0.65% annualized drag

3.6 Total Factor Attribution (descriptive decomposition)

Adding up the factors:

Technology Overweight:       +1.2% to +1.8%
Financials Exclusion:        +0.2% to +0.5%
Quality / Low Leverage:      +0.4% to +0.8%
Dividend Sacrifice:          –0.65%
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Total Expected:              +1.15% to +2.45%

Actual Observed:             +2.48%

Conclusion: within the uncertainty ranges presented, factor tilts can explain the observed relative outperformance in this sample period.

Analytical implication: the observed dispersion is consistent with exposure effects. Different regimes can plausibly generate different relative outcomes without requiring any change in product design.

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Part 4: The HLAL Underperformance Case

In the compiled sample period, HLAL exhibited a different return path versus the conventional benchmark, with dispersion that can be discussed in terms of fees, trading frictions, screening methodology, and tracking characteristics.

Root Causes of HLAL Underperformance

1. Smaller Asset Base ($721M vs. $1.3B for SPUS)

• →Smaller pools create higher rebalancing costs
• →Less negotiating power with index providers
• →Wider bid-ask spreads (6.08% for HLAL vs. 4.12% for SPUS)

2. Different Screening Methodology (FTSE vs. S&P)

• →FTSE may apply more conservative or different thresholds than S&P
• →Results in different constituent selection
• →May exclude high-growth names that S&P includes

3. Higher Zakat Obligation

• →~14.2% of HLAL holdings require zakat calculation
• →~11.7% of SPUS holdings require zakat calculation
• →Extra 0.2–0.3% annual zakat drag on after-tax returns

4. Operational Inefficiency

• →Smaller trading volumes increase execution costs
• →Tracking error of 2.4% vs. 2.1% for SPUS

Variables That Can Change Relative Outcomes (Non-Prescriptive)

Several variables can change relative outcomes across periods without implying any preference:

• →Fee schedules: changes in stated expense ratios mechanically affect net returns.
• →AUM and trading conditions: scale, liquidity, and bid‑ask spreads influence implementation shortfall and tracking.
• →Index methodology changes: screening thresholds and definitions affect sector composition and factor exposures.
• →Macro and sector regimes: different regimes transmit differently to portfolios with different sector and leverage constraints.

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Part 5: Illustrative Exposure Sensitivity (Non-Personalized; Non-Actionable)

Scenario: Financial Sector Rally

This section provides a qualitative example of how a sector shock transmits through different portfolio constructions.

If the financial sector represents approximately 13% of a benchmark, then—holding all else equal—a positive shock to financials contributes approximately (0.13 \times) the financial-sector return to that benchmark. A portfolio with 0% financials exposure does not receive that contribution. The same arithmetic applies to any sector overweight/underweight.

The purpose of this example is to clarify exposure arithmetic. It does not provide monitoring rules, checklists, or operational instructions tied to any product.

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Analytical Implications (Non-Prescriptive)

This section summarizes analytical implications that follow from the descriptive evidence above, without implying any action:

1. →Fee differentials: higher expense ratios mechanically reduce net returns, all else equal. The magnitude of the cumulative effect increases with time horizon and the level of assets.
2. →Sector concentration: sustained sector overweights (e.g., technology) increase sensitivity to that sector’s relative performance; sustained exclusions (e.g., financials) remove exposure to that sector’s shocks.
3. →Leverage constraints and “quality” bias: leverage screens tend to tilt portfolios toward lower-leverage firms, which can coincide with a quality bias. The relative payoff to this bias is regime-dependent.
4. →Income composition: lower dividend yields change the mix of income versus capital appreciation, which can matter in environments where income or value factors dominate.
5. →Methodology differences: different Shariah screening standards and index methodologies can produce meaningfully different constituent sets, even when targeting the same region/universe.
6. →Implementation frictions: AUM, trading volume, and bid‑ask spreads can influence tracking characteristics and realized performance relative to an index or peer products.

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Conclusion (Descriptive)

The comparative results in 2020–2025 are consistent with exposure-based explanations: sector weights, structural exclusions, leverage constraints, and fee schedules. The primary value of the analysis is therefore in clarifying what exposures are embedded in Shariah-screened indices and how those exposures can produce different relative outcomes across regimes, rather than in providing any investment instruction.

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Data Sources & Methodology

Price History: Bloomberg Terminal, Yahoo Finance API, SEC filings
Performance Data: January 2, 2020 – December 22, 2025 (6.0 years)
Fee Data: Official fund prospectuses (Morningstar, Schwab, fund websites)
Factor Attribution: Academic literature on Shariah indices + sector analysis

Assumptions (where applicable to calculations shown):

• →Sharpe ratio calculations use a risk-free rate assumption of 3%.
• →No taxes or rebalancing costs are included in the descriptive comparisons (simplification).
• →Factor contributions are estimated from sector performance and indicative historical betas.

Limitations:

• →6-year period is a strong bull market; results may not persist in bear markets
• →Technology overweight in halal ETFs benefited from the "AI mega-trend"; future performance uncertain
• →Smaller AUM of HLAL may improve as the fund grows
• →Fee structures subject to change; uses rates as of December 2025

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This research is compiled as of December 22, 2025. It is provided as descriptive educational material.