Tag: active management

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Turning Lemons into Lemonade

Issue 11 Turning Lemons into Lemonade
ESSAY

Turning Lemons into Lemonade

Smart Thinking: A Skill Versus Luck Essay Series | Issue 11

Written by Michael A. Ervolini

8 min read

Turning Lemons into Lemonade

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INTRODUCTION

Poor information regarding fund quality does more than frustrate. It costs investors a great deal in terms of underperforming assets and missed opportunities. Moreover, poor information can render segments of the active management industry dysfunctional. These outcomes comport with the lessons available from decades of research in the area known as the economics of information.

Make no mistake about it, active management is awash in poor information. For one thing there are no readily available metrics that provide clear insight into fund manager skill.[1]  Another fact is that well regarded market reports appear to provide unreliable information about general industry performance.[2] This issue of Smart Thinking considers the role poor information is having on investors vis a vis active management, with the usual focus on equities.

PRICE OF BAD INFORMATION

In his landmark paper “The Economics of Information” George Stigler posited several elements of information acquisition and use that contribute to inefficient or dysfunctional markets.[3] One such element concerns the nature of search. Investigating multiple sellers or providers is expensive, consuming both time and mental energy. Consequently, buyers balance making good choices with minimizing their search efforts. Reduced searching, however, often results in unintended consequences. For example, poor insights into quality can result in price differentials across sellers –even within markets of homogeneous offerings, according to Stigler. That’s because information asymmetry between sellers and buyers enables some sellers to exploit buyer ignorance and charge premium prices without delivering premium services or goods (hereafter “services”).

The negative impacts of limited search grows as the quality of services becomes increasingly heterogeneous. When both superior and inferior services are present, and search costs are meaningfully high, Stigler explains that buyers frequently adopt the strategy of assuming all offerings are of inferior quality. This approach allows buyers to feel justified in their limited searching. It also enables buyers to avoid the regret from believing they had purchased a superior service only to find out afterwards that it is actually of inferior quality (what is commonly referred to as purchasing a lemon). There are, however, drawbacks associated with assuming the worse about a market.

One drawback is price. If buyers are unable to differentiate quality across services, they are apt to offer low prices for all services –as a safeguard against overpaying. On the other hand, low prices are unlikely to be well received by those sellers providing very good or superior services. These sellers may limit the quantity of their services supplied or completely withdraw from the market. The outcome is a downward spiral. One where buyers perceive decreasing quality and thus continue to readjust downward the price at which they are willing to purchase. Simultaneously sellers of the higher quality services vacate the market and leave behind ever poorer quality. As George Akerman explains in his famous “lemon paper”: “For it is quite possible to have the bad driving out the not-so-bad driving out the medium driving out the not-so-good driving out the good in such a sequence of events that no market exists at all.”[4]

OPACITY IS NOT A STRATEGY

Currently it is virtually impossible to assess the go-forward potential of a fund to generate alpha. In large part this is because there are no rigorous measures of manager skill. This deficit persists despite the use of active funds for over 100 years. Consequently, investors must draw whatever insights they can from conventional portfolio analytics. And as has been written about frequently in these essays, conventional analytics do not contain meaningful information about skill. They provide tremendous insights into how a fund performed and they describe the risk/return tradeoffs undertaken to achieve such performance. But they fall well short of indicating just what decisions the manager is consistently making that drive the fund’s results. Relying on such analytics to form judgement about skill, therefore, requires tremendous overinterpretation if not downright guessing. The results of which tend to be either highly disappointing allocations or purely lucky ones. 

Further niggling at investor confidence regarding fund selection are aggregate industry reports of questionable value. A clear example of which is presented in a recent paper by Martijn Cremers, Jon Fulkerson, and Timothy Riley regarding the SPIVA Scorecard.[5] Cremers et el describe that the Scorecard indicates 6% or so of equity funds outperformed over the 20-year period ending 2024. When Cremers et el adjusted questionable assumptions used in the Scorecard the results for the same data showed that outperformance was much closer to 50%. Understating equity outperformance as described in this research serves only to heighten investor wariness of active equities as a valuable allocation option. To this point, depending upon the Scorecard might easily lead to concluding that regardless of the diligence expended you’ll likely end up with a lemon.

MAKING LEMONADE

Portfolio exposure to public equities remains commonplace and very deep. However, it is increasingly obtained through passive products. These passive products now account for slightly over 50% of capital invested in professionally managed equities within the U.S and approaching this level in Europe and Asia. Passive equity products certainly offer many benefits. They cannot, by definition, underperform (assuming an index tracking product). They come with low fees. They frequently provide meaningful tax benefits. And they nicely sidestep the headaches of attempting to identify a skilled manager. Their limitation is that the vast majority of passive products afford no possibility of capturing alpha.[6] This may be a fine trade-off for those content with market returns and a good night’s sleep. For many other investors, however, realizing excess returns is crucial to meeting their long-term portfolio requirements.

Portfolio level alpha is, of course, obtainable from a variety of sources, including highly expert asset allocation, timely rebalancing, and so-called alternative asset classes. Nevertheless, many top asset owners strategically rely upon a well-managed active equity program to bolster their performance. They have the budgets and staffs which enable them, more often than not, to identify skilled management teams. They use a diversified group of funds to help assure that the majority of their active equity capital is outperforming at any one time. And these asset owners increasingly are integrating decision-based skill analytics into their fund assessment processes. They are, in practice, overcoming the industry’s information asymmetry with longer and more extensive searching combined with superior skill analytics. By substituting facts over guessing about skill they are squeezing more from their equity allocations – enjoying lemonade rather than souring on lemons.

TIME FOR CHANGE

A century of inadequate information is enough. Investors deserve trustworthy market information regarding overall results – such as the proportion of funds that outperform vs. underperform. Investors also merit rigorous metrics about fund manager skill. Over relying upon conventional analytics has been and continues to be an expensive shortcoming. Guessing about manager skill and a fund’s likelihood of continued alpha generation is woefully insufficient. Critically important is that the availability of decision-based skill analytics make such guessing no longer necessary.

IT’S YOUR MOVE

You have choices. You can continue with the same tired industry practices. This is likely to push you more toward passive equity investing, continued disappointment from your active equity allocations, or deemphasizing active equities altogether. Alternatively, you can demand better information starting right now. This means better information from industry reporting services, fund rating groups, manager search consultants, and from equity managers themselves. Engaging in these efforts will improve your allocation choices and, over time, make improved information the norm rather than the exception. You can think of this alternative approach as enabling you to “lemon proof” your portfolio. Which is a sweet thought.

ENDNOTES

  1. Michael A. Ervolini, “Skill Versus Luck: Taking The Guessing Out Of Equity Fund Selection”, MIT Press, 2026.
  2. Michael A. Ervolini, “SPIVA Scorecard Receives Poor Grade”, a Smart Thinking essay, available at http://www.SkillVersusLuck.com/essays.mon & Schuster, 2007, by Jason Zweig.
  3. George J. Stigler, “The Economics of Information,” The Journal of Political Economy, June 1961.
  4. George A. Akerlof, “The Market for “Lemons”: Quality Uncertainty and the Market Mechanism,” The Quarterly Journal of Economics, August 1970.
  5. 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/
  6. Passive funds that are not index tracking may produce excess returns relative to their closest relevant passive index and thus provide excess returns.
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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

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/
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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

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

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.

Categories
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.

Categories
Essays

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.

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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.
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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

Counterfactual Investing

ESSAY

Behavioral Matters: Insights from the application of Behavioral Finance | Issue 14

Written by Michael A. Ervolini

8 min read

Counterfactual Investing

“So we have the paradox of a man shamed to death because he is only the second pugilist or the second oarsman in the world. That he is able to beat the whole population of the globe minus one is nothing; he has “pitted” himself to beat that one; and as long as he doesn’t do that nothing else counts.”

– William James

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INTRODUCTION

Imagination and creativity help turn good ideas into winning strategies and great picks if they are calibrated to ensure favorable results. Imagination also can push investors toward unproductive decisions — ones that feel right while lowering performance. Known as Counterfactual Thinking, this type of deliberation can affect your interpretation of results and can shape the buys and sells you make.

DARN, I JUST MISSED IT

Emotional responses to outcomes often are influenced by what might have been. Counterfactual Thinking, as such thoughts are known, often ends in regret. The emotional response to an event depends on how easily one can conjure up alternate outcomes that are either better or worse. An often cited example regards missing a flight by five minutes or 45 minutes. People that just miss their flight tend to kick themselves more than those who missed their plane by a mile. The closer you were to making the flight, the easier it is to construct an alternative outcome or counterfactual that triggers regret.

WHEN WORSE IS BETTER

Who should be happier, the person that wins the silver medal or the bronze? The silver medal winner is objectively better off — he or she is closer to gold than the bronze medal winner. Yet, that is precisely why a silver medalist enjoys second place far less than the bronze medalist enjoys coming in third.

In analyzing medal winners from both the Olympics and the Empire State Games, researchers Thomas Gilovich, Victoria Husted Medzec and Scott Madey found that silver medalists were less happy with their accomplishment than bronze winners. The silver medalists created a counterfactual based on not having won gold, focusing upward and ruminating about how close they came to first place. This thinking induced feelings of regret and frustration… the, if-only-I-hads. The bronze medalist, in contrast, focused downwards and formulated a more positive counterfactual. Rather than being among the others hitting the showers, they were at least up on the awards pedestal and very happy to be there. Counterfactual Thinking does more than color how we feel about past events; it can also trigger surprising actions.

PHANTOM GAINS AND LOSSES

Investors sometimes generate counterfactuals when they consider repurchasing previously owned stocks. In their paper “Once Burned, Twice Shy” Terrance Odean, Michal Strahilevitz and Brad Barber observed that investors are more likely to repurchase a stock when its current price is below that at which it was sold, and less likely to repurchase it when the current price is higher. The counterfactuals motivating these choices are explained as: “Investors who buy a stock at a lower price than they previously sold it experience the pleasure of knowing they are better off than if they had never sold that stock. Investors who buy a stock at a higher price than they previously sold it are painfully aware that they are worse off than if they had simply never sold that stock.” The terms “knowing” and “being aware“ express the feelings investors have about these purchases based on their if-only-I-hads.

The more relevant question for these investors is whether any stock they previously owned is likely to outperform alternate stocks going forward. For example, repurchasing a stock at a price higher than at what it was sold might be a good investment decision — even though it might feel bad. The counterfactuals make the investor feel a loss or gain based on price movement when the stock wasn’t even owned. This distracts from focusing on today’s thesis and how the stock will perform going forward. The result can be murky judgment — where emotions emanating from if-only’s override objective analysis.

CAN’T WIN FOR LOSING

Counterfactuals also shape the choices made when adding to current positions, according to Odean et al. Investors tend to add less frequently to holdings whose price has gone up, since being purchased (“winners”) and add more frequently to those whose price has dropped (“losers”). The counterfactual created for winners is, “I should have purchased more when it was cheap.” Investors prefer to avoid feeling the regret that this counterfactual brings, so they tend to avoid these buys. The counterfactual for buying losers seems to be associated with avoiding the pain of losing, as in, “If I buy more now and the price moves up, I can regain my current losses.” This counterfactual is consistent with Prospect Theory and resetting one’s reference point. The team’s research demonstrates, however, that the investors were no better off preferentially adding to losers over winners. Feeding their counterfactuals did not enhance their returns.

CONCLUSION

Counterfactual Thinking can hurt performance. It has the power to make silver medalist feel worse than those taking bronze. It can drive investors to make decisions based on if-only’s rather than facts. Counterfactuals can cause you to consider repurchasing stocks based more on price movement since being last sold than on current thesis. They can also cause you to refrain from adding to current portfolio winners… perhaps missing a huge run-up in the bargain. Selfawareness counters such ineffective thinking — keeping decisions factual.

ENDNOTES

  1. “When Less Is More: Counterfactual Thinking and Satisfaction Among Olympic Medalist”, Thomas Gilovich, Victoria Husted Medzec and Scott F. Madey, Journal of Personal and Social Psychology, Volume 69, 1995.
  2. “Once Burned, Twice Shy: How Naïve Learning and Counterfactuals Affect the Repurchase of Stocks Previously Sold”, Terrance Odean, Michal Strahilevitz and Brad M. Barber, Working Paper, September 2004.

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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