SPIVA Scorecard Results: How Active Managers Performed

Last updated: February 2026 | Data as of: December 31, 2024

The most striking number in the 2024 SPIVA Scorecard is not the one most people cite. Over a 15-year period ending December 2024, not a single U.S. equity fund category had a majority of active managers outperforming their benchmarks. Zero categories out of 22. That result holds after accounting for funds that merged or liquidated during the period, a methodological choice that makes the SPIVA data considerably more reliable than studies that only track surviving funds.

The SPIVA U.S. Scorecard, published semi-annually by S&P Dow Jones Indices, is the most widely cited measurement of active manager performance. It compares actively managed U.S. mutual funds against their appropriate S&P benchmarks across equity and fixed-income categories, with results spanning one-year through 20-year horizons. The table below summarizes the percentage of active managers who underperformed their benchmarks across the major equity categories.

Active Manager Underperformance by Category

Figures represent the percentage of active funds in each category that underperformed their benchmark over the stated period ending December 31, 2024. Data includes funds that merged or were liquidated during the period. Source: S&P Dow Jones Indices SPIVA U.S. Scorecard, Year-End 2024.

What the Data Tells Us

The pattern that emerges from the SPIVA data is consistent and predictable once you understand the underlying forces. At shorter time horizons, active manager results are noisy. In any given year, the split between outperformers and underperformers varies considerably by category, and some categories routinely show majority outperformance at the one-year mark. Seven of 22 equity categories showed majority active outperformance at the one-year horizon in the 2024 report. This variation gives active managers enough favorable data points to populate marketing materials.

As time horizons extend, the noise fades and a persistent signal emerges. Costs compound. A fund charging 80 basis points more than an index fund must beat the index by 0.80% every year just to break even, and that hurdle accumulates relentlessly. Over five years, the cumulative cost drag on a fund with a 1% expense ratio versus a 0.05% index fund exceeds 4.75% before compounding effects. Over 15 years, the arithmetic becomes punishing.

The data also reveals meaningful differences across market segments. Large-cap U.S. equity, the most competitive investment arena in the world, shows the worst results for active managers over long periods. Thousands of analysts scrutinize the same companies, information flows instantly, and trading costs are minimal. The market leaves little room for consistent informational advantages.

Smaller and less efficient markets tell a slightly different story. While the majority of active managers still underperform in every category over 15 years, the degree of underperformance is less severe in small-cap equities, international markets, and high-yield bonds. Over 15-year periods, approximately 85% of active small-cap managers underperform, compared to roughly 90% in large-cap categories. The gap is modest, but it reflects a real structural difference: less analyst coverage, more information asymmetry, and more room for original research to create advantages.

The fixed-income results deserve separate attention because many advisors assume that bond management requires active expertise. The data does not support that assumption in most categories. Investment-grade bond returns are driven primarily by duration and credit quality decisions, both of which are efficiently replicated by index funds at low cost. High-yield bonds are the exception, where issuer-specific credit analysis and default prediction create genuine opportunities for differentiation. The split between these two bond segments mirrors the equity pattern: active management struggles most in the categories where information is widely available and least in the categories where specialized research creates an edge.

What Advisors Should Know

The SPIVA data answers one question with unusual clarity: should an advisor default to active or passive management in a given market segment? For U.S. large-cap equity, the answer is unambiguous. Fewer than one in ten active managers beat the S&P 500 over 15 years, and identifying which ones will do so in advance is a separate (and equally difficult) problem.

The more useful question for practicing advisors is where the data creates room for active management to work. The category-by-category breakdown reveals a spectrum. At one end, large-cap domestic equity and investment-grade bonds offer almost no structural opportunity for active management. The cost hurdle is too high and the competition too fierce. At the other end, emerging markets, high-yield bonds, and certain specialty strategies operate in less efficient environments where information advantages and credit analysis can generate returns above the benchmark, at least for some managers.

This is where the SPIVA data becomes a decision-making tool rather than just a scorecard. An advisor constructing a portfolio can use the category-level data to guide the active-versus-passive allocation for each sleeve of the portfolio: index the efficient segments, consider active management selectively in less efficient segments, and accept that even in favorable categories the majority of active managers will still underperform.

One pattern that catches many advisors off guard is the survival bias adjustment. The SPIVA methodology accounts for funds that merged or liquidated during the measurement period. Many performance databases do not, which means they systematically exclude the worst performers and overstate the industry's track record. The gap between survivorship-biased results and the SPIVA-adjusted results is substantial, particularly over longer periods. An advisor relying on standard database queries without adjusting for dead funds is working with incomplete information.

The Time Horizon Effect

The relationship between time horizon and active manager underperformance is not linear, but the direction is consistent: longer periods produce worse results for the active management industry as a whole. Understanding why requires separating two effects.

The first is cost compounding. Expense ratios, trading costs, and cash drag operate continuously. A 1% annual cost difference between an active fund and its index benchmark compounds to a 10.5% cumulative drag over ten years and a 16.1% drag over fifteen years. This is not a theoretical concern. It is a mathematical certainty that every active fund must overcome before it can claim to have added value.

The second effect is mean reversion. Managers who outperform in one period often owe some portion of their results to favorable conditions for their particular style or sector. Value managers look brilliant during value rallies and terrible during growth cycles. Small-cap specialists thrive when small-caps lead and struggle when they lag. Over short periods, these style effects can dwarf any manager skill. Over longer periods, the style effects wash out and what remains is the manager's true ability to add value after costs.

The SPIVA Persistence Scorecard, a companion report, tracks whether top-performing funds maintain their rankings over time. The results are stark: top-quartile funds in one period have little better than random odds of remaining in the top quartile during the next period. The persistence that clients and advisors hope to find in manager selection is largely absent from the data.

This creates a practical challenge for client conversations. Clients often arrive with a specific fund or manager they read about in a financial publication, typically one that outperformed dramatically over the past one or three years. The SPIVA data provides the framework for a productive response: acknowledge the strong recent performance, explain that short-term results reflect a mix of skill, style, and luck, and show how the percentage of outperformers declines as the horizon extends. The data table itself becomes a visual aid in that conversation, grounding the discussion in evidence rather than opinion.

The Advisor's Edge

The SPIVA Scorecard data is publicly available. Any investor can download the report from S&P Dow Jones Indices and review the category-by-category results. What separates informed professional advice from the raw data is the ability to translate these numbers into portfolio construction decisions: knowing which market segments justify the cost of active management and which do not, evaluating individual managers within the categories where active management has structural opportunity, and constructing portfolios that blend active and passive strategies based on the evidence for each segment rather than ideology for either approach. These are the analytical competencies that the Certified Fund Specialist (CFS) designation develops across its curriculum. For advisors interested in the research on whether skilled active managers can be identified in advance, the Can You Identify Superior Active Fund Managers? article explores the persistence and manager selection evidence in depth.

Sources and Notes: Performance data from S&P Dow Jones Indices SPIVA U.S. Scorecard reports and SPIVA Persistence Scorecard reports. The SPIVA methodology uses the CRSP Survivor-Bias-Free U.S. Mutual Fund Database, which includes funds that have merged or liquidated, providing a more complete picture than databases that track only surviving funds. Category assignments follow S&P Dow Jones Indices classification methodology. Data as of December 31, 2024. This article is refreshed annually with year-end data, typically in January or February.