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Scorecard Receives Poor Grade

Scorecard Receives Poor Grade
ESSAY

Scorecard Receives Poor Grade

Smart Thinking: A Skill Versus Luck Essay Series | Issue 10

Written by Michael A. Ervolini

8 min read

Scorecard Receives Poor Grade

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INTRODUCTION

Ambivalence aptly describes how many investors feel about actively managed funds – especially equities. The fact that 50% of managed equities are now in passive funds highlights this point. Passive equity products are clearly the right solution for achieving a number of overall portfolio objectives (e.g., diversification, convenient exposures, parking cash short term, minimizing fees). Frequently, however, what motivates passive allocations is not a financial objective: Rather it is plain old fear.

The power of fear in shaping allocation decisions is enormous. It stems in good part from the weak information available about manager skill.[1] The angst generated by this informational void is then hypercharge by the gloomy reporting on the overall performance of active funds. The net effect is to position active equities so marginally as to render them less and less desirable as an asset class.

The idea of a “fear factor” effecting allocations to active funds is bolstered by newly published research. This work describes how the SPIVA Scorecard (Scorecard) persistently overstates underperformance among actively managed mutual funds. By doing so the Scorecard is authoritatively (I’ve certainly cited this source many times) misleading investors into believing that active equity management outcomes are far worse than reality. This overstatement of underperformance suggests that the Scorecard may be doing a disservice to investors and the market.

THE TITLE SAYS IT ALL

In their paper “How the SPIVA U.S. Scorecard Understates the Performance of Actively Managed Mutual Funds” authors K.J. Martijn Cremers, Jon Fulkerson, and Timothy Riley consider the impacts of several Scorecard assumptions on the report’s results. [2] The three Scorecard assumptions they investigate, and the alternatives assumptions used in their comparative analysis, are:

·  Methods to eliminate survivorship bias. The Scorecard assigns any fund that ceases to exist during an analysis period as underperforming in all years of the analysis. Cremers et al used the actual results from a discontinued fund during the years it was in operation.

·  Weighting results. The Scorecard weighted its results by number of funds, effectively giving an equal weight to all funds regardless of size. Cremers et al weighted their results by assets under management, essentially dollar-weighting their results.

·   Benchmark selection. The Scorecard uses a relatively small number of broad indices or what the authors refer to as hypothetical benchmarks (i.e., they cannot be invested in directly). Cremers et al compared each actively managed fund to the most closely matched passive product (a truly investable passive alternative).

After computing results based on their alternative assumptions the authors compared their levels of underperformance to those calculated by the Scorecard. They found that the results were consistently more negative for each individual Scorecard assumption versus their alternative assumption. The combined effect from all three assumptions they observed frequently produce completely opposite findings.

MANAGING ONE BIAS BY CREATING ANOTHER

When funds that are terminated during a multi-year analysis are excluded from a cohort investigation the potential exists for what is referred to as survivorship bias. The reasoning goes something like this: Funds that cease to operate at any time during the analysis period do so, it is assumed, because they are underperforming. It is further assumed that the funds that survive throughout the full period generated returns on average at least higher than those funds which were closed (if not actually outperforming their benchmarks). If the defunct funds are then excluded from the analysis the results are believed to be skewed more positively than had the excluded funds been included (i.e., results are biased toward the higher performing survivor funds). Although such performance tilt is not found among the survivors in every fund

cohort, safeguarding against survivorship bias is, nevertheless, a common analytic practice. So far so good.

The approach that the Scorecard takes to guard against survivorship bias is – well let’s just say ingenious. For any fund operating at the beginning of an analysis period and which then ceases to exist before the period ends, the Scorecard assumes: a) that from the day the fund closes until the end of the analysis period such fund remains in its cohort and is deemed to be underperforming, and b) during the period the fund did exist it is also designated as underperforming regardless of its actual performance. In other words, if a fund drops out at any time it is automatically assumed to be an underperforming fund from the first day of the analysis period on through to the final day. This method of correcting survivorship bias has the perverse effect of biasing the results toward greater underperformance. Moreover, the effect this method has on results increases monotonically over time. Because fewer funds survive year after year more and more funds are branded as underperforming as the analysis horizon lengthens.

Cremers et al recomputed the Scorecard results using what seems a more straightforward method for managing survivorship bias. Their alternative method simply includes all funds during the years in which they are in operation. Each fund’s performance is based on its actual returns and, importantly, none are transmuted into zombie nonperformers once they cease to exist. Unsurprisingly, greater levels of underperformance are reported by the Scorecard than those computed by Cremers et al, especially over longer periods. For example, between 2004 and 2024 (20 years) the percent of all equity funds reported as underperforming by the Scorecard and Cremers et al were approximately 94% and 78%, respectively (a 16% difference). Within the U.S. large cap funds cohort, the Scorecard reported the percent of underperforming funds by slightly more than 18% greater than what is computed by the researchers. Even greater differences in results are found among several fixed income and hedge funds categories. These differences reflect comparisons based on changing only a single assumption.

REPLACING ALL THREE ASSUMPTIONS

Cremers et al evaluated the entire mutual fund universe, comparing the results of the Scorecard to outcomes they generated using their three alternative assumptions. The comparisons produced impressive results even over relatively short time horizons, as the researchers explain: “Each change has a significant impact on the results. For example, the Scorecard method indicates that, over the 3-year period of 2022 through 2024, 81% of large-cap core funds underperform. Not automatically counting exiting funds as underperformers decreases that percentage to 77%. Likewise, weighting by assets decreases the underperformance to 71% and comparing against equivalent passive funds decreases it to 73%. Making all three changes simultaneously decreases the percentage to 46%. That is, the results invert, from a supermajority of active funds underperforming to a small majority of assets outperforming.”

Cremers et al found even greater differences over longer periods. As the researcher’s report: “Averaging across the cap- valuation U. S. equity categories, the Scorecard method indicates that 92% of funds have underperformed over the last 20 years. Among those categories, the lowest percentage [in the Scorecard] is only 86% (large- cap value). After our changes, we find an average of 55%, with half of the categories having an underperformance rate for their assets below 50%. Thus, while underperformance among U. S. equities remains the most common outcome after our changes, the likelihood approaches a coin flip.” While a 50/50 chance of the equity fund you select going on to outperform may not seem heartening it is far better than the paltry 8% that the Scorecard would have you believe.

MORE FINDINGS

Cremers et al found even greater differences across fixed income and hedge fund products. One example they cite regarding fixed income is: “Using the 10-year horizon (i.e., the longest horizon with full coverage), the Scorecard method indicates an across-category average underperformance rate of 71%. After our changes, that rate falls to just 37%, meaning that nearly two out of three dollars invested in active fixed income funds outperformed equivalent passive funds over the last 10 years.” They go on to observe: “Among high yield funds, one of the largest fixed income categories, the 15-year horizon underperformance rate decreases from 74% to 12% after our changes. Thus, within the fixed income class, our changes fully reverse the Scorecard’s conclusion: the data support the finding that active fixed income funds tend to outperform.”

BELEAGUERED PERHAPS, BUT NOT TO BE FORSAKEN

Active equity funds pose a challenging allocation dilemma. Keen assessment is required to identify funds more likely than not to outperform going forward. Such analysis is, however, severely hampered by the current lack of widely available rigorous manager skill metrics. Eschewing active equities altogether is an option that sidesteps the emotional challenges of selecting an actively managed fund. Unfortunately, it requires eliminating the diversification and excess return potential of an entire asset class. This is the uncomfortable choice facing investors today.

Actively managed funds actually perform significantly better than suggested by the Scorecard according to new research conducted by Cremers et al. The improved outcomes they identified are the result of substituting what are arguably three reasonable assumptions for those used in producing the Scorecard. It is unclear just what impact these findings will have on investor behavior. A hopeful speculation is that it may cause some investors to rethink their allocations to this asset class. While not the focus of their work, the insights Cremers et al provide underscore both the shortcomings of fund outcomes as proxies for skill and the urgent need to begin integrating decision-based skill metrics into active equity assessment.

ENDNOTES

  1. Michael A. Ervolini, “Skill Versus Luck – Taking The Guessing Out Of Equity Fund Selection,” MIT Press, 2026.
  2. K.J. Martijn Cremers, Jon Fulkerson, and Timothy Riley, “How the SPIVA U.S. Scorecard Understates the Performance of Actively Managed Mutual Funds”, May 4, 2026. Available at SSRN: https://ssrn.com/
Michael Ervolini headshot

MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

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Essays

What Billy Beane Can Teach Us About Active Management

What Billy Beane Can Teach Us
ESSAY

What Billy Beane Can Teach Us About Active Management

Smart Thinking: A Skill Versus Luck Essay Series | Issue 9

Written by Michael A. Ervolini

3 min read

What Billy Beane Can Teach Us About Active Management

“The problem is not that baseball professionals are stupid; it is that they are human. Like most people, including experts, they tend to rely on simple rules of thumb, on traditions, on habits, on what other experts seem to believe.

– Sunstein and Thaler

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Even those who are not sports enthusiasts are likely familiar with the 2002 best seller Moneyball by Michael Lewis.[1] The book explains how Billy Beane, manager of the Oakland Athletics, used new and rigorous analytics to build a highly competitive baseball team despite limited financial resources. When Beane took over the Athletics salary total was half that of their division’s average and one-third of that spent by the New York Yankees. Beane’s innovation was to challenge the conventional thinking regarding the measurement of player skill. Rather than relying on well-established skill metrics, Beane advanced more effective measures of player ability referred to as sabermetrics. The newer metrics enabled Beane and his coaches to see skill that was not being recognized by other teams. This enabled them to pursue highly skilled players that were not in high demand and, therefore, more affordable. In short order the Athletics became the winningest team in their league while continuing to spend far less on their team salaries. The simple act of rethinking how skill should be assessed changed not just the Athletics fortunes and baseball overall but all sports, forever.

The parallels between sports back in 2003 and active equity management today are uncanny. Conventional portfolio analytics have been around for decades. They are widely used not just to assess past performance and risk but also to gauge manager skill. These analytics are excellent in explaining how a fund has performed and identifying the risk/return tradeoffs undertaken in achieving those outcomes. However, these same conventional analytics provide poor measures of manager skill. Their shortfall lies in the data used in their computation. Conventional analytics use as their inputs either a fund’s return series or its daily holdings. These data are themselves fund outcomes. They do not contain the information necessary to recognize and rigorously quantify manager skill.

Fortunately fund manager skill is identifiable. It is found by relating the decisions the manager makes with the outcomes those decisions generate. Doing so connects cause and effect. Such analytics are referred to as decision-based skill metrics.[2] They are the active management equivalent of Beane’s sabermetrics. Decision-based skill analytics provide asset owners and allocators the ability to identify skilled managers. These same metrics also illuminate skill consistency. This being essential for determining likely future outperformance and making effective allocation decisions.  

If decision-based skill analytics are so darn powerful why haven’t they been more widely adopted? In paraphrasing a review of Moneyball by Cass R. Sunstein and Richard H. Thaler [3] the answer is that: The active management industry has evolved into a “bad equilibrium.” Rather than identifying and computing manager skill directly, the industry continues to rely on the weak skill proxies afforded by outcome-based analytics. Change, as Sunstein and Thaler point out, is difficult even when clear evidence for its necessity is present. And it is made all the more difficult when attempting to introduce change into an industry already besieged with myriad challenges.[3] Ironically, the adoption of decision-based skill analytics might very well lessen the industry’s turmoil. For surely a great deal of the unrest across active management derives from the enormous uncertainty regarding who is highly skilled and who isn’t. Thus, rendering fund selection today more a guessing game than an analytic exercise.

Change is difficult. So are the effects of ineffective allocations, poor performance, and a rapidly shrinking number of active equity providers. This sure looks like the right time to change for the better.

ENDNOTES

  1. Michael Lewis, Moneyball: The Art of Winning an Unfair Game, W. W. Norton & Company, 2003.
  2. Michael A. Ervolini, Skill Versus Luck: Taking The Guessing Out Of Equity Fund Selection, MIT Press, February 2026.
  3. Cass R. Sunstein and Richard H. Thaler, “Who’s On First: Review of Moneyball by Michael Lewis,” The New Republic (September 1, 2003).
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MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

Categories
Essays

Size Really Does Matter

Smart Thinking Issue 8
ESSAY

Size Really Does Matter

Smart Thinking: A Skill Versus Luck Essay Series | Issue 8

Written by Michael A. Ervolini

5 min read

Size Really Does Matter

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INTRODUCTION

Position Sizing is a crucial fund manager skill. It determines if the alpha from great buys is effectively harvested and if questionable purchases are restricted in the damage they inflict. Alternatively, chronically underweighting the strongest positions and overweighting the weakest holdings is a headwind if not a surefire path to underperformance. While the asset management industry expends vast amounts of time and energy discussing this activity surprisingly little is known about which managers size positions effectively, which don’t, and how to tell them apart. Fortunately, this situation is changing for the better. The improvement is due to the growing number of firms providing decision-based skill analytics.[1]

POSITION SIZING

One such firm is Alpha Theory. They are laser focused on position sizing. This includes quantification of a manager’s sizing skill, helping managers improve their sizing processes for greater alpha capture, and supporting asset owners/investors in assessing a manager’s sizing acumen. Alpha Theory has analyzed well over 200 equity funds involving more than 14 years of historical data. Their research indicates far more sizing opportunity than success currently, as they relate in their 2025 Year In Review: “Active sizing, the activity managers devote enormous amounts of energy to, reduces returns on average in our dataset.” [2] Specifically, they have uncovered: “Across the past 14 years, the Optimal portfolio has outperformed Actual by an average of +3.9% annualized.” Alpha Theory attributes the lost opportunity to process shortfalls: “The result is a persistent gap between research conviction and capital allocation.”

The way Alpha Theory computes its results is to compare a fund’s actual returns to a counterfactual portfolio in which positions are sized optimally based on Alpha Theory’s proprietary analytics. The latter approach is referred to as “optimum position sizing.” In constructing the counterfactual portfolio Alpha Theory uses all of the manager’s actual decisions of when to buy (open a position) and sell (close a position). Alpha Theory then adjusts how positions are sized to take full advantage of all the information available to the manager. As they describe it: “The Optimal portfolio is not an outside model or black box optimizer. Instead, it’s the manager’s own price targets, probabilities, conviction levels, and risk constraints applied consistently and without the behavioral noise that creeps into day-to-day sizing decisions.” This method assures that the counterfactual constructed reflects realistic and achievable sizing levels bespoke to each fund.

The Alpha Theory research indicates that the results are persistent over time as well. Their report states: “On average, the Optimal portfolio has outperformed Actual in 13 of 14 years — a 93%-win rate.” Interestingly the research shows that higher success is mostly about doing better at the margins: “By Position: Optimal sizing wins 57% of the time — a modest edge per position, but powerful at scale.” This finding underscores the importance of both having an effective process and actually adhering to it.

UPSHOT

Achieving benchmark-beating results is difficult. Doing so requires that each skill is well understood, contributing positively to alpha, and is likely to continue doing so going forward. The work done by Alpha Theory enables asset owners and allocators to formulate a deeper understanding of how well the manager is capitalizing on their best buys. With 4% hanging in the wings this type of analysis is invaluable.

CONCLUSION

Position sizing can be either a source of incremental alpha or a risk factor. Developing and then relying upon a sound sizing process is what makes the difference, according to research from Alpha Theory. Their findings are confirmed by other decision-based analytics investigations.[3] Interestingly, Alpha Theory found that on average the funds studied would have performed better even if the positions were equally weighted. However, managers can do much better than this they encourage: “The solution is not to abandon sizing, but to structure it. In our dataset, when those same research insights are applied through a disciplined sizing framework, they outperform equal weight by an additional 2.2%. That is the real advantage: not better stock selection, but better alignment between capital and conviction.”

Decision-based analytics such as those provided by Alpha Theory are substantially improving the industry’s understanding of manager skill. These newer analytics can rigorously quantify individual skills like sizing and compute its consistency. This information supports stronger allocation decisions and enables fund managers to become more self-aware and improve.

ENDNOTES

  1. Michael A. Ervolini, Skill Versus Luck: Taking The Guessing Out Of Equity Fund Selection, MIT Press, February 2026, Chapter Three.
  2. Cameron Hight and Justin Olson, “Alpha Theory 2025 Year in Review: Position Sizing as a Persistent Edge.” The full text is available at: https://www.alphatheory.com/blog/alpha-theory-2025-year-in-review-position-sizing-as-a-persistent-edge
  3. Michael A. Ervolini, Skill Versus Luck: Taking The Guessing Out Of Equity Fund Selection, MIT Press, February 2026, Chapter Thirteen.
Michael Ervolini headshot

MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

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Essays

Going Beyond Hunches To Identify Skill

Cover Image Going Beyond Hunches To Identify Skill
ESSAY

Going Beyond Hunches To Identify Skill

Smart Thinking: A Skill Versus Luck Essay Series | Issue 7

Written by Michael A. Ervolini

8 min read

Going Beyond Hunches To Identify Skill

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INTRODUCTION

“I know it when I see it,” Supreme Court Justice Potter Stewart famously wrote when describing his method for determining if materials were pornographic or not.[1] Identifying a skilled equity manager frequently relies upon a surprisingly similar approach. Skill analysis remains a highly subjective and idiosyncratic process. It involves a concoction of conventional performance and risk metrics, peer comparisons, and manager interviews. Agitating this mixture are the tendency to overinterpret outputs from conventional metrics, unconscious wrestling matches between seeking pleasure and avoiding pain, and plain old wishful thinking.[2] It is no wonder that even within the same style and strategy cohorts, allocation teams frequently arrive at very different funds on their short lists. Each allocator may know skill when they see it but there is little agreement across the industry regarding just what “it” looks like. A generous interpretation of this inconsistency is that allocation teams are applying distinct alpha seeking processes and arriving at different solution sets. But don’t be fooled the differences are really about massive inefficiencies and confusion.

The absence of an industry-wide definition of skill is a serious problem. It makes the identification of skilled managers nearly impossible. It makes equity allocation decisions much riskier. It undermines the confidence of those working hard to identify top equity managers. It causes angst, fear, and avoidance that is unnecessary, which further fuels the reallocation of capital to passive equity products. Formulating meaningful ways to discuss skill is long overdue. So, let’s get started right now.

LET’S TALK SKILL

2026 is the year we change how skill is discussed.  A highly useful initial step is to define that set of attributes which compose skill. Surely skill is more than some mystical qualities which a manager either possesses or doesn’t. In order to be useful, a description of skill must reflect abilities that are generally recognizable and readily quantifiable. These abilities must be far less subjective and more analytically robust than what passes for skill metrics today. Here’s the challenge: The discussion of skill must evolve from “feeling it or not” to “having measured it or not.”

There does exist a description of skill that is working for a growing number of asset owners and allocators. It focuses on the qualities that the manager brings to fund management. It concerns elements over which the manager has full control and so can refine and improve over time. Most importantly, it leads to metrics that identify which of the manager’s decisions and actions are helping elevate fund results (positive skills) and which are undermining fund results (negative skills). The benefit being that information of this quality enables you to better distinguish skill from luck. Here it is:

Definition: Skill is the combined effects of expert judgment and investment process.

Let’s consider what this definition implies.

Judgment is, of course, critical to skillful fund management. How else might a fund manager arrive at a decision? Pretty much every decision associated with equity management has a probabilistic outcome. Said differently, uncertainty surrounds each investment choice. What psychology and neuroscience make clear is that after all the data analysis is complete, individuals then overcome uncertainty with the help of their judgment. When the analysis is strong and the judgment well considered the result can be what is commonly referred to as high conviction successfully applied. Alternatively, when either the analysis or judgment falls short the resulting decisions often reflect more bluster than well-reasoned conviction. Therefore, judgment is an essential element of skill and needs to be measured. 

It is pretty much a given that investment process is critical to successful equity management. What is less common is considering it a component of manager skill. One reason for this oversight is that little substance is ever discussed about investment process. Typically, the fund manager presents a slide or two which a) illustrate their process graphically or b) describe its equivalence in bullet form. This is frequently followed by one or more anecdotes. What is unclear is: Is this process actually being used? Is it used regularly? Does it reliably lead to more successful decisions than not? If the manager is responsible for constructing and implementing investment processes, then these processes represent elements of the manager’s skill. Otherwise, why bother with process? Therefore, skill measures must include analytically derived assessments of the investment process.  

The phrase combined effects underscores just how tightly judgment and process are intertwined within equity fund decision making. And while the goal is to identify and quantify skill components with as much distinction and granularity as possible the complete separation of judgment and process is not always achievable. Therefore, skill metrics should be computed as specifically as the data allows while it is through considering distinct metrics together that the greatest insights can be achieved.

GETTING TO THE NUMBERS

Expert judgment and investment process can be carefully measured using decision-based skill analytics.[3] Skills such as buying, selling, and sizing are readily quantified with these newer analytics. The enhancement to or detraction from fund level excess return from each skill can be fully assessed, as can be their persistence. Decision-based analytics also expose how decisions are being made over time. This analytic approach to investigating investment process highlights which set of attributes support alpha generating decisions and how consistently the process is being followed.

IT’S UP TO YOU

There are two things you can do right now to improve the quality of skill information flowing through the equity ecosystem.      

  • First, keep talking about skill. In particular the need to have ready access to metrics that rigorously measure skill – not merely reflect its possible presence or absence.
  • Second, you can demand better skill analytics from all of your equity managers. There are a number of firms that can help managers prepare this type of analysis. The costs are far from prohibitive. And if everyone demands these skill analytics the average cost to the manager per client approaches zero. So, think of it this way: Your demanding decision-based skill analytics from your equity managers will not only help you make better decisions it is providing a positive externality. By making this information available to our industry everyone benefits–even the asset managers. 

Doing this just might provide the strongest return on investment you realize this year.

ENDNOTES

  1. Potter Stewart, Supreme Court Justice, wrote in his June 22, 1964, consent opinion in the case of Jacobellis v. Ohio.
  2. David Tuckett and Richard Taffler, Fund Management: An Emotional Finance Perspective, CFA Monograph,2012.
  3. Michael A. Ervolini, Skill Versus Luck: Taking The Guessing Out Of Equity Fund Selection, MIT Press, February 2026.
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MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

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Essays

Where To Look For Skilled Equity Managers

Smart Thinking Issue 6
ESSAY

Where To Look For Skilled Equity Managers

Behavioral Matters: Insights from the application of Behavioral Finance | Issue 6

Written by Michael A. Ervolini

4 min read

Where To Look For Skilled Equity Managers

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INTRODUCTION

Skepticism regarding the existence of skilled equity fund managers is understandable. In most years the majority of actively managed equity funds underperform.[1] Among those that do outperform few manage to do so for 3, 5, or 10 years in a row.[2] Then there is the direct experience that most asset owners and allocators share: conducting a search for a new equity manager, performing rigorous due diligence, establishing a short list, selecting the final candidate, and making an allocation. Then watching with a mixture of surprise and dismay as the once high expectation fund delivers quarter after quarter of underperformance. It is no wonder why so many institutional investors have grown timid about allocations to actively managed equities.

However, not all asset owners and allocators are frustrated by their active equity programs. In fact, many capture meaningful alpha from their actively managed equity investments. They do it by more fully understanding the skill of their fund managers. This added knowledge enables them to sharpen their fund assessment processes and make better allocation decisions. This translates into the majority of their allocations outperforming in any given year. The managers of their funds are not perfect or super stars. They are what Inalytics LTD refers to as elite managers. Working with elite managers can tilt the odds in favor of earning alpha more frequently than not.

WHAT INALYTICS UNCOVERED

In their 2022 research paper Inalytics evaluated a group of 752 actively managed equity funds.[3] Among this group 630 or 84% outperformed their benchmarks for the prior three years. Whereas 122 or 16% underperformed. Clearly this group is not representative of the overall active equity industry. It consists of funds chosen to be analyzed by clients of Inalytics. Each fund had already gone through the gauntlet of traditional fund due diligence. Therefore, it is not surprising that the majority of these funds were generating excess returns. What is impressive is that the outperformers provided an average 397 basis points (bps) of after-cost alpha over the analysis period. Moreover, the Inalytics investigation showed that strong buying, or what they term the “research process”, drove fund results. Skilled buying , they computed, provided 319 bps of fund excess returns. This equates to 104 percent of the average excess returns for the outperforming group. Inalytics also observed that the sizing skill slightly impaired results, contributing (11) bps of alpha. Neither rebalancing position weights (adds and trims) or selling skills seemed to have a material impacts on fund returns according to their study.

It is worth pointing out that the skill or skills that most contributed to excess returns might vary when analyzed by the other decision-based analytics.[4] The potential for differing skill measures stems from the varying methods used in computing skills across the providers.[5]

CONCLUSION

The Inalytics research makes clear that quantifiable skills are responsible for fund excess returns in many instances. Subsequent analyses by Inalytics and other decision-based skill analytics providers confirms this observation. The analysis also underscores why a growing number of asset owners and allocators are incorporating decision-based analytics into their fund assessment processes.

By using these newer analytics investors know much more about the true skill of their equity managers (external and internal). They can also assess the consistency of skills that are most responsible for benchmark-beating results. Which means these investors have deeper insight into which managers are more likely to deliver positive alpha going forward. Now that sounds like smart investing.

ENDNOTES

  1. “SPIVA® Year-End 2025,” reports for U.S. and globally. https://www.spglobal.com
  2. “S. Persistence Scorecard Year-End 2024 – SPIVA. https://www.spglobal.com
  3. “Investment Skill: Does It Exist and What Does It Look Like?”, Inalytics LTD, Spring 2022.
  4. Michael A. Ervolini, Skill Versus Luck – Taking The Guessing Out Of Equity Fund Selection, MIT Press, February 2026.
  5. Ibid.

MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

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Essays

Assessing Manager Skill – Shifting from Guessing To Knowing

Smart Thinking Issue 5
ESSAY

Assessing Manager Skill – Shifting from Guessing To Knowing

Smart Thinking: A Skill Versus Luck Essay Series | Issue 5

Written by Michael A. Ervolini

8 min read

Assessing Manager Skill – Shifting From Guessing To Knowing

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INTRODUCTION

Sorting out which equity fund managers are skilled is arduous and expensive. Untold hours and huge budgets are expended each year in assessing manager skill across existing allocations as well as the vetting of potential new opportunities. Even more concerning, however, is that skill assessment is an extremely uncertain undertaking. Consequently, the risks inherent to fund assessment as it is currently conducted are likely underappreciated by the majority of decision-makers. Unwelcome results can include overconfidence and faulty allocation choices.

THE PROBLEM

Increasingly recognized as an industry shortcoming is that little is actually known about skill – what it looks like, how it should be measured, or who has it.1 Sure, there are mountains of academic and practitioner papers discussing skill. And every manager search or review includes some commentary about skill. These assessments rely upon well-established metrics such as relative return, style, active share, information ratio, batting average/slugging ratio, and multi-factor alpha. These and other conventional analytics deliver tremendous insights into a fund’s results and the risk/return tradeoffs used in generating those results. Unfortunately, conventional analytics offer scant information about skill itself. They hint at its presence or absence – pretty much stopping there. The underlying issue is that conventional analytics use as inputs either a fund’s return series or its history of daily holdings. These data are themselves fund outcomes. They do not contain the information necessary to identify and quantify specific skills. A frequently used simile is that attempting to identify manager skill using fund returns is like trying to gauge a tennis player’s ability to serve balls knowing only how many games the player won and lost. Intuition may nudge you toward formulating a judgment but it is based more on guessing and/or desire than analysis.

DECISION-BASED ANALYTICS

Today you can gain total control over assessing manager skill. It’s done by incorporating decision-based analytics into your review processes. Decision-based analytics relate the decisions made by the fund manager with the outcomes they generate. These analytics connect cause and effect. Using decision-based analytics in conjunction with conventional analytics you achieve a deeper understanding of precisely what’s driving fund results. You’ll observe:

  • How much incremental return results from buy, sell, and sizing decisions
  • The level of consistency for each skill year-by-year, rolling periods, and over time
  • The stability of skill levels across sectors, global regions, and factors
  • An orthogonal assessments of key fund risks such as (i) the tendency to hold losers far too long or (ii) selling winners well before their full alpha is captured.

These and other insights available from decision-based analytics elevate your ability to assess a fund’s desirability and to make more effective allocation decisions.

BRINGING IT HOME

You can learn precisely how to incorporate decision-based skill analytics into your processes today. It begins by reading my latest book: Skill Versus Luck – Taking The Guessing Out Of Equity Fund Selection (MIT Press). In just a few hours you’ll learn:

  • Why conventional analytics are insufficient for evaluating fund manager skill
  • How decision-based analytics are computed and provide rigorous measures of manager skill
  • What skill insights you are missing with conventional analytics and how easily they are obtained from decision-based analytics
  • Techniques currently used by the world’s most sophisticated asset owners in combining decision-based analytics with conventional analytics to achieve superior allocations.

To get started, order your copy on Amazon.com: Skill Versus Luck: Taking the Guessing Out of Equity Fund Selection, by Michael A. Ervolini.

ENDNOTES

  1. Based on scores of conversations involving sovereign wealth funds, pension funds, endowments, family offices, insurance companies, banks, and manager search consultancies.

MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

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Active Share Is No Indicator of Manager Skill

Smart Thinking Issue 4
ESSAY

Active Share Is No Indicator of Manager Skill

Behavioral Matters: Insights from the application of Behavioral Finance | Issue 4

Written by Michael A. Ervolini

5 min read

Active Share Is No Indicator of Manager Skill

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INTRODUCTION

Actively managed equities continue to receive significant allocations from all manner of investors. Among the group commonly referred to as institutional asset owners (e.g., sovereign wealth funds, pension funds, endowments, insurance companies, banks, family offices, and builders of multi-asset products), actively managed equities remain an important staple within their allocation line-ups. Yet, many of these asset owners find it extremely difficult to identify truly skilled managers from among the many competing for their capital. Relying exclusively on conventional portfolio analytics has done little to improve the fund selection process. Conventional analytics are very helpful in understanding a fund’s past results and how they were generated. However, they offer very little insight into manager skill.

Not long ago the concept of active share was introduced to enhance fund selections. It was believed that this analytic could help identify skilled equity managers. Over time it became clear that active share is a helpful metric in assessing how closely a fund’s position weights match those within its benchmark. On the other hand, its ability to identify skilled managers has proven to be no better than that of other conventional analytics.

THE SIREN SONG OF HOPE

In 2009 the asset management industry was abuzz with a paper by Martijn Cremers and Antti Petajisto describing a novel portfolio metric termed active share. The authors defined active share as one-half of the sum of the absolute values of the differences in weights between all positions held in an equity fund and their weights in the fund’s benchmark. (1) Which means active share quantifies how similarly or differently a fund’s capital is deployed relative to its benchmark. Greater active share indicates more benchmark divergence. Smaller active share indicates less benchmark divergence. The thinking being that the lower the active share (i.e., more similar a fund’s position weights match their benchmark weights) the harder it is to generate excess returns. Conversely, the larger the active share the more bets the manager has made and thus the greater the potential to outperform, at least conceptually.

In their paper Cremers and Petajisto reported that higher active share was correlated with stronger fund results. They went even further to say that active share provides a meaningful indicator of manager skill and can also help predict future fund outperformance. As explained by the authors: “Active Share predicts fund performance: funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence.” (2) The availability of a straightforward metric and its ability to identify skilled managers proved alluring.Many asset owners and allocation professionals quickly integrated active share into their equity fun assessment processes.

The results were far less than hoped. A number of highly active share funds did outperform for years and some did so for shorter periods. Asset owners lucky enough to have allocations into the outperforming high active share funds did well. However, many high active share funds delivered only benchmark level returns or less. Asset owners unlucky enough to be invested in these lower performing high active share funds earned lackluster returns or worse. What happened? Why didn’t more high active share funds outperform as expected?

OUTCOME VS. SKILL

The fundamental problem is that active share like many other conventional analytics is a measure of fund outcome not skill. This fact is readily evident by considering the types of data they use as inputs. These data consist of a fund’s return series and its history of daily holdings. Since these data are themselves fund outcomes, they do not contain the information required to compute manager skill. What these two data types support effectively is the computation of analytics that describe how a fund was managed. Examples include information ratio, which indicates how much risk the fund took for each increment of excess return; attribution analysis, which describes how much of a fund’s excess return came from stock selection versus sector allocation; and multi-factor regression analysis, which explains which factor exposures had the largest impact on results. It is one thing to explain how past results were generated. It is very different to identify which manager skills added to or detracted from the fund’s excess returns. The latter requires thinking anew about what constitutes skill and how it should be computed.

CAUSE AND EFFECT

Measuring skill requires relating manager decisions to the outcomes they generate. It involves linking cause and effect. In my latest book Skill Versus Luck, I describe how skill identification and quantification are being done using what are termed decision-based analytics. (3) Decision-based analytics isolate and group distinct types of decisions made by a fund manager. These include initiating new buys, selling out of positions, and adjusting position sizes. The impact of these decisions is then computed. It is done by reversing a single type of decision to create an adjusted fund history. Comparing the results of the actual fund to those of the adjusted history indicates the level of skill reflected in those decisions. If the actual results are greater than those computed from the adjusted history the skill is positive, in an amount equal to the difference. Alternatively, if the actual results are less than those computed from the adjusted history the skill is negative, in an amount equal to the difference.

Here is how it works conceptually. Consider assessing a manager’s skill at adding capital to losing positions. A management tactic that is often referred to as adding on the way down. Measuring the manager’s skill at this involves first identifying each time over the fund’s history more capital was added to positions whose price had recently dropped. Then an adjusted history would be created in which each such add would be reversed – the additional capital would be subtracted from the position whose price had recently dropped. Then the excess returns for the adjusted history would be computed. The difference between the excess returns of the actual fund and those of the adjusted history would indicate the manager’s skill at making these incremental investments.

A number of asset owners are using decision-based analytics today to strengthen their fund assessment processes and make more effective allocation decisions. (4) These investors are benefiting from a deeper understanding of which manager skills are helping to generate excess returns as well as the persistence of such skills. Decision-based analytics are also helping asset owners confirm elements of each manager’s investment processes – further informing likely repeatability of success going forward.

CONCLUSION

Identifying which funds have recently outperformed is straightforward. Knowing if such outperformance is based on inconsistent or lucky decisions versus manager skill remains a challenge. Active share was initially believed to possess information able to identify skilled managers. This is no longer the case and active share is now viewed as a useful metric for understanding a fund’s position bets versus benchmark tracking. It’s demotion regarding skill assessment was confirmed in a later paper by Cremers himself: “It is only for managers with strong individual stock picking skills that a high Active Share may be beneficial.” (5) In other words, high active share accentuates position bets much like leverage. Positions with large active weights are able to contribute substantial positive or negative returns to a fund. Ultimately the direction and magnitude of the returns from high active share positions depends upon fund manager skill.

Fortunately, newer decision-based analytics provide unambiguous measures of fund manager skill. When combined with conventional analytics these newer skill metrics enable asset owners to better understand manager ability and make more effective allocation decisions.

ENDNOTES

  1. Martijn Cremers and Antti Petajisto, “How Active is Your Fund Manager? A New Measure That Predicts Performance,” The International Center for Finance at the Yale School of Management, March 2009.
  2. Ibid.
  3. Michael A. Ervolini, “Skill Versus Luck – Taking The Guessing Out Of Equity Fund Selection,” MIT Press, February 3, 2026. Available for pre-order on Amazon.
  4. Ibid.
  5. Martijn Cremers, “Active Share and the Three Pillars of Active Management: Skill, Conviction and Opportunity”, Financial Analyst Journal, 2016.

MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

Categories
Essays

Broadening Diversification To Include Skill

Smart Thinking Issue 3
ESSAY

Broadening Diversification To Include Skill

Smart Thinking: A Skill Versus Luck Essay Series | Issue 3

Written by Michael A. Ervolini

8 min read

Broadening Diversification To Include Skill

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INTRODUCTION

Few investors, if there are any, doubt the benefits of diversification. Deciding precisely how to achieve it, however, remains a thorny challenge.

SEEKING DIVERSIFICATION

The practice of diversification (AKA risk management) gained considerable analytic rigor with the 1952 paper “Portfolio Selection “by Harry Markowitz. (1) In his paper Markowitz informs us that a portfolio’s riskiness is not simply the average risk of the assets or funds it owns. Instead, portfolio level risk is based upon the degree to which the prices of the various assets and funds move together. Said differently, do portfolio assets and funds provide offsetting or compounding riskiness? Assets and funds with positively correlated price movements tend to exacerbate risk while those whose price movements are uncorrelated or negatively correlated ameliorate it. One of Markowitz’s enduring contributions is showing that many sources of risk can be analytically identified, quantified and, therefore, managed.

Diversification and risk analytics have been greatly expanded since the 1950s. Within equities alone this includes the analysis of asset price movements in relationship to company location (country, global region) and relative to a host of factor exposures, such as market value (size), financial dynamics (growth vs. value), price momentum, balance sheet quality (debt levels), and earnings growth. These and similar characteristics are commonly integrated into portfolio construction processes. The intended result is a portfolio whose assets are sufficiently diverse such that the net effect provides an attractive trade-off between quantifiable risks and hoped for returns.

The diversification benefits from many traditional factors are in decline according to numerous studies. (2) This may be due to the effects of globalization and/or other market forces. What’s clear is that more positive correlations are now being observed among a number of traditional diversification characteristics. This dynamic, in good part, motivates the continued search for newer and alternative sources of incremental diversification.

THE HUMAN FACTOR

One idea that is gaining traction involves expanding the sources of diversification to include fund manager skill. While this may seem obvious implementing it has become possible only recently due to newer analytics. Traditional efforts to capture elements of manager skill have relied upon conventional analytics such as relative return, multi-factor alpha, attribution, upside/downside capture, information ratio, and active share. These metrics are very helpful in describing how an equity fund has performed. They say little about manager skill, however. Their shortcoming with regard to describing skill is inherent in the data they use. These data consist of two forms of outcome, namely fund returns and/or daily holdings. The outcome-driven results they produce can only hint at the presence or absence of skill. These analytics cannot identify nor quantify skill directly.

Fortunately, newer analytics for computing skill now exist. These newer analytics use as inputs the decisions made by the fund manager. These newer methods are referred to as decision-based analytics. These newer analytics relate the decisions made by the manager with the returns they generate. They connect cause and effect. This is precisely how skill is computed in other demanding endeavors like sports, auto racing, jet piloting, and surgery. These newer analytics provide capital owners and allocators enhanced insights into manager skills, decision consistency, and heretofore undetectable sources of risk. (3) Examples of how these newer analytics are helping asset owners and allocators are discussed next.

BETTER INSIGHTS, BETTER DECISIONS

Incremental diversification can be captured through the use of manager skills. Importantly, it is accessible even from funds whose managers are pursuing the same style and strategy. This opportunity exists when the stocks they purchase reflect different alpha time horizons or alpha generating profiles. (4) One manager might buy stocks that typically take off soon after initial purchase and continue to generate excess returns for 9 to 18 months (relatively fast and short time horizon). A second manager with the same mandate might be purchasing stocks that are slow to show any price movement but once they get going can generate alpha for two and three years or more (slow starters with extended time horizon. These two managers are fishing in essentially the same pond for the same species. Yet the fish they land are quantifiably different.

Alpha time horizon and other manager skills are now being used by JANA Investment Advisers, a manager search and OCIO consultancy. According to Justin Tay, JANA’s Head Of Global Equity Research: “Knowledge of a manager’s alpha time horizon can support allocations to multiple top managers even in the same style –while avoiding overlapping decision processes. It’s a subtle but important form of diversification.” (5) This type of diversification can improve the odds of capturing the full potential from each style/strategy blend by finding managers whose buy processes yield stocks that outperform over different time horizons. 

Another aspect of skill involves the management of significant losers, defined as positions down by 20% or more. Some managers are skilled at culling out significant losers unlikely to recover and/or retaining those that eventually do rebound. The opposite also is observed where managers are too quick to sell depressed stocks which soon rebound and/or have trouble letting go of weak positions that never recover. Knowing how effectively managers (even great ones) deal with significant losers provides additional insights into how risky a fund may become in a market downturn.

Then there is the question of position sizing. Deploying meaningful capital into stocks before or as soon as they begin to take off enables funds to capture the full benefit of strong buys. In contrast, habitually under sizing or chasing strong buys undermines the potential available from purchases that outperform. Knowing how effectively a manager builds up their best buys (winners) can shed light on the likelihood of capturing excess returns going forward.

These and other newer analytic results are enabling asset owners and allocators to better assess equity managers and strengthen their allocation processes.

IT’S CATCHING ON

Increasingly investors are integrating the newer analytics into their equity allocation processes. Pension funds and sovereign wealth funds are using the newer analytics to both assess external allocations and to obtain clearer insight into how effectively internal teams are managing their equity funds. Endowments, Family offices, and large asset management companies are using the newer analytics to better understand the strengths and shortcomings of their third-party equity managers. The results include a deeper understanding of which skills are driving results and which skill deficits may represent previously unknown risks. The insights obtained also support more productive discussions between investors and fund managers – during due diligence, at regular update meetings, and especially when fund results are disappointing.

CONCLUSION

Portfolio diversification is a common objective. Globalization and other market forces are increasing the correlation among various diversification characteristics (e.g., funds, asset types, factors). Stronger positive correlations make diversification ever more difficult. 

In response asset owners and allocators are expanding how they view and implement their approaches to diversification. One transformative effort underway is the integration of manager skills as an additional alpha and risk characteristic. Doing this requires use of newer decision-based analytics. These newer analytics provide superior measures of manager skill by relating decisions taken by a manager to the impact such decisions have on fund results.  

It’s time. Time to improve the industry’s knowledge about manager skill. Time to capture more effectively the potential available from actively managed equities. Time to make better equity allocation decisions.

ENDNOTES

  1. Harry Markowitz, “Portfolio Selection,” The Journal of Finance, Vol. 7, No. 1. (Mar. 1952), pp.77-91.
  2. For example, see: Richard Yasenchak, “Correlation Conundrum: How Will You Fix Portfolio Diversification?,” Intech Investment, Inc., March 8, 2023. https://www.intechinvestments.com
  3. Michael A. Ervolini, “Skill Versus Luck – Taking The Guesswork Out Of Equity Fund Selection,” MIT Press, February 3, 2026. Available for pre-order on Amazon.
  4. A fund’s alpha generating profile or information advantage indicates, on average: when new stock purchases begin to outperform, how much excess return they generate, and when outperformance tends to be depleted.
  5. Interview by the author with Justin Tay, Head Of Global Equity Research, JANA Investment Advisors, Inc., June 2025.
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MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

Categories
Essays

Making Active Equities More Stylish

Smart Thinking Issue 2
ESSAY

Making Active Equities Mo

Smart Thinking: A Skill Versus Luck Essay Series | Issue 2

Written by Michael A. Ervolini

5 min read

Making Action Equities More Stylish

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INTRODUCTION

Style analysis is used regularly to support equity fund assessment and allocation decisions. While this analytic offers useful insights, its value in determining a fund’s desirability may be more limited than generally perceived. It can deliver unintended consequences that diminish rather than enhance ultimate outcomes. This essay clarifies the benefits available from style analysis and offers an improved approach for its use in supporting active equity fund assessment.

GOT STYLE?

In his landmark 1988 paper “Determining a fund’s effective asset mix”, William F. Sharpe described a method for assessing the style characteristics of an equity fund.*1 Since then style analysis has become an integral element of fund assessment, allocation decisions, and confirmation that a fund is acting in accordance with its strategy and purpose (i.e., identifying style drift).*2

The debate continues regarding whether Sharpe’s return-based analysis or the alternate holdings-based method yield the more creditable result.*3  The returns-based approach resolves which asset indices best explain (i.e., are most correlated) with the return series of a fund or portfolio. Return-based style analysis is commonly referred to as a top-down approach. Holdings-based analysis takes a more bottom-up path by first determining the style or factor characteristics of each of a fund’s holdings over time. For example, some holdings may be more growth oriented while others more value oriented. Factor levels are then aggregated across holdings to formulate the fund’s overall style.

WHAT YOU GET

Both approaches have their strengths and shortcomings. But each serves a basic role in fund assessment. In the words of Sharpe: “All that style is is exposure. If I say your style is 60% growth and 40% value that means you’ll move 0.6 times whatever happens to growth stocks plus 0.4 times whatever happens to value stocks.”*4

The algebra cited by Sharpe holds to the extent that the fund continues to be managed in the future much the way it was managed in the past. Meaning that the fund owns an array of stocks such that the returns going forward, or the factor composition of its holdings are based on similar bets as were previously taken (i.e., exposures to large/small caps, growth/value, interest rates). 

A diversified fund that is highly consistent in the stocks it owns fits this bill. So does a diversified fund that rotates from one factor to another regularly. Style analysis can be less helpful for a highly concentrated fund. Here a couple of positions can dominate the fund’s factor exposures. And the dominant positions can change in response to market forces rather than manager intention. This can result in substantial style shifts that may be of limited value in estimating future exposures. A similar diminution in usefulness of style analysis is encountered for a fund that has recently and permanently changed its strategy or alpha sourcing. Although it is important to be mindful of these two latter considerations the bigger issue is the frequent misapplication of style analysis and the unintended consequences therefrom.

THE STYLE TRAP

What can style analysis say about effective fund management? In good part it depends on the question being investigated. One common use of style analysis is the assessment of style drift. Drift is indicated by a set of current factor exposures that differ meaningfully from the fund’s historic exposures (i.e., the style changed). Typically, the desired outcome is the absence of style drift or, stated in the affirmative, consistent factor exposures. However, exposure consistency frequently comes at a high cost.

Consider a value fund. It is fully expected that this fund is purchasing mostly (if not exclusively) stocks with a clear value signature. Which means that at time of initial purchase these stocks fit the value style. But what about new buys that then go on to generate significant excess returns? As these positions experience improving fundamentals and upward price movement, they begin to shed their value characteristics and edge into growth territory. One assumes that this evolution is a primary reason for buying value stocks in the first place. The intention being that enough of these value stocks will outperform sufficiently so that the fund itself can generate excess returns.

All too often, however, value funds significantly trim or liquidate their strongest performing positions prematurely. These actions are taken well before such successful buys have exhausted their ability to generate excess returns. It’s done to ensure that the fund is not perceived as drifting outside of its value style box. Managers that engage in such activities believe it is what clients want. They are often told that: “The client is allocated to the fund based, in good part, on its style or factor exposures. Departures from historical style are likely to complicate (or even compromise) the client’s overall risk management.” In Such situations adherence to style or overall factor exposures supersedes the capture of excess returns. Clearly, managing risk indirectly through style allocations brings with it unintended consequences.

GOING STYLISH

There is an alternate and for many better ways of confronting the question of style. It involves selecting funds based on the types of stocks they purchase rather than all the stocks they own.*5 Within this formulation value funds are expected to purchase value stocks, growth funds are expected to purchase growth stocks, and so forth. Once a position is established, however, the fund is then expected to maximize its contribution to excess returns subject to prespecified levels of risk control. This enables value fund to own growthy stocks that were initially purchase when they were clearly value. It allows a small cap fund to own mid cap stocks that have performed well and grown out of their small cap designation. It improves the potential for funds to realize excess returns while purchasing stocks that fit a specific style designation.

Allocating to funds that operate as described requires some rethinking and retooling on the part of asset owners/allocators. In particular they need to take a greater role in managing the overall risk exposures across their equity platforms. By analyzing the complete set of all their equity positions (internally and externally managed) they are able to offset over and under exposures more comprehensively. And in doing so they give a greater degree of freedom to each manager that can be used in the pursuit of excess returns.

CONCLUSION

Actively managed equities continue to be an important asset class for many investors. Institutional asset owners/allocators can increase the value available from this asset class today by focusing style analysis directly on the types of stocks being purchased rather than on conformity of fund overall exposures.  This will allow funds to hold on to their strongest positions longer and capture even greater excess returns. Replacing traditional style conformity and using a “purchase what is expected” approach is already helping a growing number of asset owners/allocators improve the results from their equity programs. These investors are capturing greater excess returns while making equity investing ever more stylish.

ENDNOTES

  1. William F. Sharpe, “Determining a fund’s effective asset mix,” Investment management review, 1988.
  2. “RETURNS – VS. HOLDINGS – BASED STYLE ANALYSIS”, Beacon Pointe Research White Paper, Beacon Pointe Advisors, LLC, September 2022.
  3. Paul D. Kaplan, “Holdings-Based And Returns-Based Style Models”, MorningStar, Inc. June 2023.
  4. Barry Vinocur, “Setting The Record Straight On Style Analysis”, A Newsmaker Interview, Stanford University, 1999, http://www-sharpe.stanford.edu/fa.
  5. Michael A. Ervolini, “Skill Versus Luck – Taking The Guesswork Out of Equity Fund Selection”, MIT Press, February 2026 (forthcoming).
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MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.

Categories
Essays

Measuring Manager Skill Versus Fund Outcomes

Smart Thinking Issue 1
ESSAY

Measuring Manager Skill Versus Fund Outcomes

Smart Thinking: A Skill Versus Luck Essay Series | Issue 1

Written by Michael A. Ervolini

3 min read

Measuring Skilled Managers Versus Fund Outcomes

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Active equity investors want their capital in the hands of skilled managers.

The reasoning is straightforward: Managers with the greatest levels of skill should do better than their less skilled peers over time, all other things being equal. Identifying who is highly skilled and who is less so, however, remains a difficult if not utterly impossible task for most investors. And make no mistake about it, this problem is equally vexing to large and highly sophisticated asset owners as well as individuals.  The inability to effectively assess skill lies in the analytics commonly used for such investigations. Such conventional analytics provide useful information for sure – just not about skills.

What’s meant by conventional analytics is the myriad of metrics regularly used in support of fund assessments. These analytics include relative return, multi-factor alpha, information ratio, upside/downside capture, active share, attribution, hit rates, batting average, and slugging ratio. These metrics are effective in describing how a fund generated its returns. They indicate whether the fund’s returns were the result of high concentration, market factor cyclicality, wisely overweighting or underweighting sectors, or taking on additional risk (i.e., volatility). What these analytics cannot do is identify or quantify skill.

The reason is elemental. Conventional analytics are computed using a fund’s return series and/or its holdings history as their data. These data are themselves outcomes. They reflect the performance of the fund which constitutes its returns and also substantially determines the size of its holdings over time. These conventionally derived metrics, therefore, are referred to as outcome-based analytics. And while outcomes do reflect the presence or absence of skill they are not themselves measures of skill.

Meaningful measures of skill are found in the relationships between types of manager decisions and the results they generate. Said differently, skill measurement involves capturing cause and effect. A golfer’s skill is not measured by how many games they win. It is assessed by the distance and placement of drives off the tea, the ability to hit shots with irons close to or onto the green, and the accuracy of the putting. As each of these skills improve we’d expect the golfer to achieve stronger results (i.e., win more games).

As mentioned, manager skills are observed by investigating specific decisions or actions taken and the results they generate. Examples of manager skills that can be isolated and calculated include:

  • Buying new stocks that more often than not outperform their sectors or benchmarks.
  • Selling positions such that the alpha from strong stocks is captured while the drag from underperforming stocks is minimized. 
  • Sizing positions so that sufficient capital is invested in the strongest stocks (i.e., allowing them to lift overall fund returns).

Metrics such as these reflect what are known as decision-based analytics. These analytics capture the cause and effect relationships between manager actions and fund returns. They provide unambiguous measures of skill. Using the results of decision-based analytics enables investors to more effectively assess a fund’s desirability and to make better allocation decisions.

Currently there are a number of firms which provide decision-based analytics.1  The question that the industry needs to address is why aren’t these superior measures of skill being used regularly today? Absent such analytics investors simply cannot make their best decisions, which can lead only to bad outcomes for all market participants.

ENDNOTES

  1. Firms providing decision-based analytics include: Inalytics, LTD, FactSet Research Systems, Inc., Alpha Theory LLC, Essentia Analytics LTD, and Behavioral Lab LTD.
Michael Ervolini headshot

MICHAEL A. ERVOLINI, AUTHOR

The ideas expressed on this website are developed and/or curated by Michael Ervolini. Mike has spent his entire 35 year + career leading efforts to improve and strengthen active management.