Performance Fees aren’t the Solution to Active Management’s Problems

In my previous post I explored how quality uncertainty for both buyers and sellers of active management created a bloated market structure with homogenous fees and average pricing greater than that justified by average quality.  One potential solution to these inefficiencies is the use of performance fees for active management; which, it is argued, brings alignment between fund manager and client, by ensuring that fees are linked to the ultimate objective of the end investor.  Performance fees are also said to limit the desire of fund managers to engage in asset gathering – conduct that is detrimental to long-term returns.

Whilst there are optical attractions to the wider implementation of performance fees within the field of active management, it is highly questionable whether they incentivise the correct activities or provide genuine alignment between fund manager and client.  It is more likely that they exacerbate harmful behaviours.  The main drawbacks are as follows:

Process versus Outcome: The importance of focusing on process over outcomes has become better understood in recent years, and is particularly crucial in active fund management where the randomness and variability of results means that outcomes can be grossly misleading when attempting to discern skill.  Despite awareness of this issue, the clutches of outcome bias are difficult to escape and the industry remains obsessed by headline past performance.  Linking fees directly to performance inflames the issue – it expressly ignores the quality of process / decision making, whilst delivering substantial rewards for positive outcomes, whether they be driven by skill or pure chance.

Incentives Matter:  Although not entirely in unison on the subject, both mainstream economics and behavioural science focus on incentives as a key determinate of an individual’s actions.  As Charlie Munger commented:  “Never, ever, think about something else when you should be thinking about the power of incentives.”  Whilst it may appear that performance fees for active management represent a perfectly aligned incentive structure, this is far from the case. Performance fees create a very singular dynamic – reward is related to outperformance, irrespective of how it is generated.  It is naïve to believe that the behaviour of a long-term investor won’t alter based on potential near-term payoffs; it could result in increased risk taking or protective strategies to preserve potential performance fees.   Furthermore, the asymmetric nature of most performance fee structures also creates disconnect between the interests of the fund manager and client.

Reference Points Matter:  Intrinsically related to the power of incentives is the manner in which reference points can dominate our perceptions and behaviour.  Performance fee structures often create reference points for fund managers that are inconsistent with the long-term goals of an investor.  For example, based on the lessons of Prospect Theory, we might expect the employment of a high watermark to lead fund managers to engage in more risk seeking behaviour when performance falls below this threshold, than when they are above it.  Performance fees can also foreshorten fund manager investment time horizons – whilst their stated investment philosophy might be focused on five year periods, performance fees often create short-term reference points focusing on relative returns over the next quarter or year.

Structural Challenges:  Performance fees are difficult to apply in a daily dealing structure in a manner that treats all clients equitably – despite the myriad of methodologies employed.  For example, if levied annually, four years of modest outperformance could lead to handsome profits for the fund manager, even if the fifth year is disastrous and returns for the client over the entire period are disappointing (depending on clawback arrangements).  Furthermore, fee structures that ratchet the base management charge higher following a prolonged spell of outperformance lead to new investors paying higher fees based on historic excess returns that they never enjoyed.

Active Management Fees are not about Performance Alone

It may sound an absurd contention given that outperformance above some benchmark or passive investment is the ultimate goal of employing active investment management, but the fees levied should not be about performance in isolation.  Active management fees should be paid because the fund investor believes that the underlying investment process (in the broadest sense of the term) is of sufficient quality that it materially increases the probability of delivering market outperformance over the long-term.  There can be no guarantees – in a random and variable system even good decisions can lead to disappointing outcomes.

It is a misnomer to believe that performance fees bring better alignment between clients and fund managers, in many cases they are likely to encourage behaviours that are inconsistent  with investor expectations and even the manager’s own investment philosophy. Performance fees are an unnecessary distraction from what is required to improve the market for active fund management, which is lower flat fees, genuinely distinctive investment approaches and patience.

Is Active Fund Management a Market for Lemons?

Prior to being awarded the Nobel Memorial Prize in Economic Sciences, economist George Akerlof authored the seminal paper: “The Market for Lemons: Quality Uncertainty and the Market Mechanism” (1970). The piece focused on the used car market in the United States with a central contention that an information asymmetry existed between buyer and seller, which led to low quality cars (lemons) being overpriced and high quality vehicles under-priced; the consequences  for the market were considered as follows:

– Withdrawal of higher quality vehicles.

– Reduced size of market.

– Reduced average quality.

– Reduced average willingness to pay.

Though the ‘information asymmetry’ term is somewhat oblique, the core concept is simple – where the seller knows more about the product than the buyer, this can be used to their advantage.  Whilst the prospective purchaser can make a general assessment of a car’s qualities, they are likely to have limited knowledge of its detailed history.  In the absence of this information, it is difficult for the buyer to differentiate between cars of contrasting quality except by judging headline factors such as appearance.  This leads to a price convergence between low and high quality cars as buyers are unable to accurately distinguish between options, and a subsequent withdrawal from the market by those offering higher quality vehicles.

I previously held the view that the active fund management industry was consistent with the ‘market for lemons’ concept, but, on reflection, whilst there are certain echoes, the impact of quality uncertainty in active management is distinct.  Most notably, in Akerlof’s example, the condition is created by a significant disparity in the awareness of a product’s quality between buyers and sellers – the aforementioned information asymmetry.  However, in the case of active management, doubt over the quality of the product is true for both buyers and sellers – neither party is certain that skill exists. Although there may be an informational edge held by asset management groups regarding the underlying quality of their active offerings, this is likely to be marginal and often erroneous.

The central problem of the market for active fund management is the subjectivity around what constitutes quality (or skill) and the spurious use of past performance as an indicator of said quality.  From a buyer’s perspective, at the point of purchase it is difficult to know with certainty whether one has purchased a manager with skill or a ‘lemon’.   In addition to this, given the randomness of outcomes inherent in financial markets, even if a manager with skill is correctly identified – there is no guarantee that positive outcomes will be delivered.

In the majority of purchasing decisions – a washing machine or TV, for example, – there is a reasonable level of clarity over what the key indicators of quality are and how they might influence the product’s cost.  In the case of active fund management, it is far more difficult to ascertain what characteristics define quality and how they should be valued.  Given this uncertainty the temptation is to depend on past performance as the best indicator of quality / skill; a situation which allows many ‘lemons’ to masquerade as high quality active funds merely due to good fortune.

The reliance on past performance as the primary marker for quality also leaves investors vulnerable to a distinctive aspect of the active fund management market –  ‘evidence’ of historic high quality (strong past performance) may actually increase the probability that future outcomes will be of a lower quality (poor performance through mean reversion).  This is a perverse situation, akin to a scenario where a hotel that has received consistently five star reviews on TripAdvisor is more likely to deliver disappointing holidays to future guests.

Given the majority of active funds producing sustained underperformance will close or be subject to manager change; we are left with a pool of active managers, most of which will have delivered outperformance for certain periods, some through luck, others skill (and a combination of the two).  Within this collection of managers the quality will inevitably vary significantly, and it is the challenge of differentiating between these (for both buyers and sellers) that gives the market for active management its most distinguishing features:

– Proliferation of active strategies / Reduced average quality:  The subjectivity around what constitutes quality and the randomness of performance (particularly over shorter-time horizons) means that a vast number of low quality / unskilled active strategies can exist, creating a bloated market.

– Homogeneous pricing:  The problem of discerning between different levels of quality leads to minimal distinction between active fund costs.  Active funds with no evident skill (which should cost zero – at most), are priced under the assumption that they do possess skill; whilst the highest quality offerings may struggle to charge a ‘premium’ price to the wider market because the buyer is uncertain over their true quality.

– High average price relative to average quality: The entire market is priced as if skill is pervasive.  On balance, there are a greater number of lower quality funds overcharging, than there are higher quality funds ‘undercharging’; thus, the average price for active management is skewed upward.

– Withdrawal of highest quality operators:  This is perhaps a factor at the margins, with certain high quality operators moving away from the mass market and into (even more) rewarding fields, such as hedge funds.  This move, however, will also be attractive to unskilled participants, who wrongly believe they possess skill.  Overall, the market is currently sufficiently lucrative for the majority of participants to remain.

In essence, the structure of the market for active management is defined by a cocktail of random markets and our own behavioural frailties.  Our focus on the short-term, obsession with outcomes and susceptibility to compelling narratives serves to cultivate its core characteristics.  Whether these features are indelible or materially vulnerable to the changing investment management landscape witnessed in recent years, remains open to question.

Key reading:

Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 488-500.

MIRRORS – Creating a Behavioural Checklist for Investment Decision Making

Despite the paramount importance of the findings of behavioural science to our investment decision making, there is limited evidence of its lessons and principles being applied.  Given the long-term benefits from engagement with this area, why has there been such a reticence to embrace behavioural concepts?  There are four factors, which I consider to be the major impediments:

  • Individual Acceptance: The investment community has certainly not ignored behavioural science, but there is a tendency to consider how it affects other people. Acknowledging personal behavioural vulnerabilities is not easy; particularly for professional investors operating in an industry where accepting mistakes and limitations can have a deleterious impact on career prospects. However, individual ownership is crucial if behavioural issues are to be tackled.
  • Amorphous Ideas: The range of biases highlighted and heuristics identified in behavioural research is vast; furthermore, they frequently overlap, are sometimes contradictory, can suffer from replication issues and are often not directly related to the field of investment. Given these factors, the struggle to develop a coherent means of addressing the topic is unsurprising.
  • Challenging Application: Developing solutions that serve to mitigate the impact of behavioural weaknesses is difficult; they often face criticism for being too simplistic (‘surely re-ordering the presentation of performance data won’t impact our decision making’ ) or are contrary to conventional wisdom (‘I need to be on top of my portfolio, so checking it less is not a feasible option’). More broadly, there is limited empirical evidence on successful ‘de-biasing’ strategies that are directly applicable to investment decision making.
  • Investment Process Afterthought: Through a combination of the aforementioned factors, behavioural concepts are often an afterthought in an investment process, serving at best as an adjunct to the ‘real’ investment decision making and, at worst, a pure marketing ploy designed to capture some kudos from the current interest in the topic, but lacking in any substantive value.

These issues are by no means insurmountable, but action is required to effectively incorporate behavioural science in a meaningful and consistent fashion. As simplicity is at the heart of most successful behavioural interventions; an ideal starting point is to develop a checklist encompassing the most significant and influential behavioural hurdles. Although a seemingly minor advance, such a step could have a material impact on investment decision making.

As detailed in the popular and engaging ‘The Checklist Manifesto’ (Gawande, 2009); concise checklists are an incredibly effective means of encouraging behavioural consistency, whilst limiting mistakes and omissions.  Furthermore, if correctly structured, they can easily be integrated within existing processes and rapidly applied.  Of course, a behavioural checklist for investors cannot be as specific or definite as those that might apply in surgery or aviation; however, they can serve to ensure that the consideration of behavioural issues becomes an integral part of the decision process.

Although it may seem superfluous, making a checklist memorable is also a vital means of ingraining its core ideas.  Behavioural science is particularly fond of acronyms; notably, the MINDSPACE and EAST structures employed by the UK Behavioural Insights Team when designing policy (EAST, for example denotes: Easy, Timely, Attractive and Social).  More prominent, on a global scale, is Thaler and Sunstein’s highly influential NUDGES framework.

Given that the purpose of this behavioural checklist is to better reflect on our own and others’ investment decision making, I propose the use of MIRRORS, where each letter pertains to a prominent behavioural factor that exerts a material influence on investors:

M Myopia We are overly influenced by short-term considerations
I Integration We seek to conform to group behaviour and prevailing norms
R Recency We overweight the importance of recent events
R Risk Perception We are poor at assessing risks and gauging probabilities
O Outcomes We focus on outcomes when evaluating the quality of a process
R Reference Points We make judgements using, often erroneous, reference points
S Stories We are frequently beguiled by compelling narratives

Whilst there is depth and complexity underpinning the behavioural issues included in the checklist, which I will endeavour to explore in future posts, the fundamental problems and potential implications of each should be readily apparent.

The checklist is not designed to be exhaustive, thus there will inevitably be pertinent issues not adequately captured; however, it incorporates what I perceive to be the major behavioural impediments encountered, and those which forge a significant ‘behaviour gap’ between underlying asset performance and the returns realised by investors.  Moreover, employing a concentrated list makes it simple to bring these crucial considerations from the periphery to the core of investment decision making

Creating a checklist is, of course, no panacea and one cannot hope to ameliorate the impact of ingrained biases and predilections, simply by ticking boxes.  However, the starting point for improvement in our investment choices and judgements is an awareness and acceptance of our behavioural flaws.  Employing a checklist is an acknowledgement of susceptibility and an expression of willingness to engage with the issues in a consistent and rigorous fashion.

In my next post, I will explore in greater detail how such a checklist might be utilised as part of an investment decision making process both to stimulate debate and to develop interventions. However, even without a precise application in mind, simply beginning to think about and discuss the areas covered in the checklist when making decisions should prove a major benefit to investors.

Key Reading:

Dolan, P., Hallsworth, M., Halpern, D., King, D., & Vlaev, I. (2010). MINDSPACE: influencing behaviour for public policy.

Gawande, A. (2010). The checklist manifesto : How to get things right. London: Profile Books.

Insights, B. (2014). EAST: Four Simple Ways to Apply Behavioural Insights. London: Behavioural Insights.

Sunstein, C., & Thaler, R. (2008). Nudge. The politics of libertarian paternalism. New Haven.

When Will You Participate In An Asset Price Bubble?

Do investors holding an asset in the midst of a price bubble all come to share similar beliefs, even if they did not do so previously?  Asset price bubbles are unfailingly supported by a captivating narrative, which is ‘validated’ by price movements, draws in investors and allows for traditional valuation considerations to be discarded.  It is therefore plausible to assume that an asset price bubble involves the acceptance of new norms or a shift in regime, by those that choose to participate.

This notion is consistent with certain theories on collective behaviour – where a crowd or herd act in concert it is because they have developed shared ideas, which drive their actions. However, work by sociologist Mark Granovetter takes a different perspective; contending that concentrating on the motivations or attitudes of individuals was insufficient to describe group behaviour.  His focus, instead, was on the participant’s ‘threshold’ – using the example of a riot, Granovetter describes the concept as such:

“A person’s threshold for joining a riot is…the proportion of the group he would have to see join before he would do so” (1978, 1422)

There are three crucial insights provided by Granovetter in his work on collective behaviour:

– A group does not have to share (or come to share) values and preferences for them to engage in similar behaviour.

– The behaviour of others within a group has a material impact on the choices made by other individuals in that collective.

– The distribution of ‘thresholds’ within a group matters greatly for its overall behaviour.

In Granovetter’s simple model, individuals are faced with a binary decision – to join the riot or not – and their threshold is dictated by their assessment of the benefits and costs of action or inaction.   A ‘radical’ will have a low threshold, whereas a ‘conservative’ will have a high threshold.  In essence, Granovetter is assuming that an individual’s threshold is dictated by their attempt to maximise utility.  Whilst we may question the underlying assumption of rationality, the core tenets of Granovetter’s approach remain powerful.

Although not directly related, Granovetter’s insights have profound implications for how the composition of a financial market (the activity and preferences of its participants) impacts the development of asset price bubbles.  It allows us to move away from the notion that a bubble involves the sweeping acceptance of a new narrative / norms, or an alignment of exalted expectations, and instead consider the importance of the threshold for participation across different types of investor.

We can apply the threshold model of collective behaviour to the incidence asset price bubbles.  As with the riot example, individuals are faced with a binary decision – whether or not to purchase the asset /security in question – a choice over which their threshold for participation will have a material influence.  By creating heavily stylised and simplified groupings of market participants we can speculate as to how this threshold varies:

Investor Type Threshold for Participation
Speculative Very Low
Growth Low
Momentum Low / Moderate
Market Cap Passive (Soft Momentum) Moderate
Value High

A speculative investor is akin to the ‘radical’ in a riot, they are content to be initiators often participating in the absence of others.  They are likely to have latched onto (or helped create) a beguiling narrative, and perceive the potential benefits (vast monetary profits) to outweigh the costs of failure. By contrast the value investor adopts the role of ‘conservative’; their threshold for involvement is high as, by definition, owning an asset priced at a level significantly above intrinsic or ‘fair’ value, would threaten their reputation, process and beliefs.

The threshold for pure momentum strategies is ingrained, with their participation in an asset class / security directly driven by the behaviour of other participants.  The situation for market cap passive is somewhat different; whilst the approach can be considered a form of soft momentum there is no tipping point for its involvement, rather it will serve to reflect and potentially exacerbate the aggregate behaviour of other participants.

Working within this framework, the composition of a market is crucial in understanding the incidence of asset price bubbles.  For example, in a market with a significant proportion of speculative and momentum investors (with low thresholds for participation), the frequency of bubbles is likely to be higher and momentum can rapidly build.  Contrastingly, in a market with a preponderance of value (or at least valuation sensitive) investors, price bubbles should be less common and their development protracted, as their threshold for involvement is far higher.

Of course, many valuation sensitive investors do eventually succumb to an asset price bubble, when the weight of participation becomes overwhelming and their threshold is reached.  Using Granovetter’s theory, this will occur when the costs of not participating outweigh the benefits – as an asset price bubble grows, so do the costs to the value sensitive investor – underperformance / career risk / stigma / regret. The impact of such factors will depend on the individual and their environment, thus thresholds will differ markedly.

Asset price bubbles are an incredibly complex phenomenon with narratives, behavioural biases and information dissemination all vital components in their formation and persistence.  Using Granovetter’s threshold model of collective behaviour provides us not with overarching theory of price bubbles, but offers some crucial insights, most notably:

–  The development of asset price bubbles is heavily dependent on who participates in a particular market / security.

–  Asset price bubbles are about how individuals react to the behaviour of others, as much as the acceptance of the underpinning narrative.

– Market participants will have different thresholds for participation in an asset price bubble.

The most fertile ground for a price bubble is where an asset has heavy speculative interest (very low threshold) and is impossible to value by any traditional means;  this combination of features can lead to dramatic and (for a time) unchecked positive price momentum.  Examples of such situations spring readily to mind.

Key Reading:

Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology83(6), 1420-1443

Granovetter, M., & Soong, R. (1983). Threshold models of diffusion and collective behavior. Journal of Mathematical Sociology9(3), 165-179.

Five Simple Behavioural Tips for Better Long-Term Investment Decision Making

Although behavioural finance has become an in-vogue topic in recent years and provided many valuable insights into how investors make decisions; it is often fiendishly difficult to put these into practice.  Whilst there are some notable exceptions (such as Save More Tomorrow in the US), much of the work in this field describes what we do, not necessarily what we can do about it. The purpose of this short piece is to highlight five ideas, influenced by behavioural science, which could lead to better long-term investment decisions:

1) Check your portfolio less frequently:  Whilst the benefits of transparency and access are significant they create a range of behavioural problems for the long-term investor.  Quite simply – the more frequently we check our portfolios, the more myopic and risk averse we are likely to become in our decision making. Viewing our portfolios on a daily basis creates an often irresistible urge to react and trade, often at the worst possible times. Although difficult, once we have a sensible investment plan in place, we should try to restrict our observations to a meaningful and realistic level – once a month / once a quarter / once a year.  A gentle nudge for private investors is to set a password for your investment account that is difficult to remember and store that password somewhere it takes a modicum of effort to retrieve. Making something that little bit more difficult, can have a dramatic impact on our behaviour.

2) Don’t make emotional decisions:  How we ‘feel’ at any given point in time can have a material influence on the manner in which we perceive risks and assess opportunities. Making an investment decision in an emotional state – such as excitement or fear – is fraught with problems. If there is any chance that emotion is overwhelming your thinking – postpone the decision. If the idea was a good one, it is still likely to be tomorrow, or next week.

3) Make doing nothing the default:  I vividly recall sitting in an investment meeting at one point in my career and debating what our reaction should be to a particular period of market tumult. Certain participants advocated taking the opportunity to increase risk, others proposed becoming more cautious – both contrasting views were considered by the group to be credible. However, my suggestion of doing nothing was treated with incredulity – something is happening, we must react.   The more we are bombarded with news, information and opinion the greater the temptation to be busy fools and justify our existence as investment managers by taking action, any action.  For a variety of reasons doing nothing is the hardest decision to make for an investor, but it is often the correct one.

4) Choose sensible reference points: Loss aversion is a well-understood concept, but the important role of reference points is probably understated.  We experience losses relative to a particular level, value or benchmark, and what that reference point is can materially impact both how we think about investment performance and the decisions we make.  Unscrupulous investment managers can attempt to exploit this phenomenon by shifting the benchmark, or trying to create a new reference point when reporting to clients: “Whilst your balanced portfolio lost 7% during the period, the NASDAQ Biotechnology index fell by 23%…”  There is no perfect solution here and the decision will be specific to each individual, but choosing a prudent, consistent reference point (or selection of reference points) at the outset of an investment portfolio can mitigate future behavioural pitfalls. An example of a reference point problem would be comparing the performance of your cautious portfolio to a broad equity index in a bull market, experiencing this as a ‘loss’ and deciding to abandon your investment discipline to assume more risk.

5) Write a pre-mortem before making an investment: An idea developed by Gary Klein whereby, prior to embarking on a course of action, you imagine a future state where this action has ended in failure, and then list the reasons why it has gone wrong. This approach can be easily applied prior to making an investment, where you envisage the future failure of the decision and attempt to identify the causes.  It can be particularly useful in a group situation as it gives individuals the freedom to play devil’s advocate.  The technique is effective as it forces us to engage with the prospect of being wrong (which is often unpalatable) and can serve to puncture the overconfidence that can often plague investment decision making.  It is also a useful learning tool as we can retrospectively review what we considered to be the primary risks at the time of making an investment.

The Asset Management Industry Must Confront Biases to Address its Diversity Problem

A recent survey on UK asset managers carried out on behalf of the Diversity Project, highlighted a dispiriting, though not unsurprising, lack of diversity in the industry. Investment management roles are dominated by White men (often privately educated); with significant variation from the broader population composition across a number of categories, including: gender, education, disability and ethnicity.  Whilst these findings would not shock anyone with direct involvement in the field; it is beneficial to have sample data rather than rely on anecdotal evidence.

It would be unfair to claim that the problem is asset management specific, rather than a societal issue; however, this does not exonerate or excuse the industry, which at best reflects the phenomenon and at worst serves to exacerbate it.  The situation is an obvious problem for the groups that suffer from restricted opportunities, but also for the asset management firms that are starved of cognitive diversity.

Although the subject and its consequences are palpable, there is no simple solution.  Many of the behaviours that contribute to the lack of diversity are caused by entrenched biases that are often either unconscious or difficult to acknowledge; a fact that has been evidenced in a range of studies across many years, and a variety of domains.

Bertrand and Mullainathan (2004) carried out a field experiment seeking to analyse the treatment of race in the job hiring process. They sent fictional applications for jobs advertised in Chicago and Boston newspapers, the CVs created were randomly given “African-American or White-sounding names” and the responses monitored. The results of the study were stark:  White-sounding names received 50% more interview requests; furthermore, the difference in response between high and low quality CVs was significant for White-sounding names (close to 30%), but markedly lower for African-American sounding names.  Whether conscious or unconscious, the differential labour market treatment based solely on race evidenced in this study was pronounced.

The issue of gender discrimination was tackled by Goldin and Rouse (2000) through an analysis of the audition process for symphony orchestras. They observed the gender diversity of eight prominent orchestras between the 1950s and 1990s, and sought to isolate the impact of blinding or screening (obscuring the identity of the player), a policy that had been adopted at different junctures by the orchestras under analysis.  The authors found that the use of screening increased by 50% the probability of women progressing from certain preliminary rounds, and accounted for “possibly 25% of the increase in the percentage female in the orchestras from 1970 to 1996” (738, 2000).  These are significant impacts from a simple procedure that obviates the potential for sex-based discrimination.

In a study of racial bias and leadership, Rosette, Phillips and Leonardelli (2008) found that “being White” was considered a typical characteristic for a business leader. The authors suggest that consistent exposure to White individuals in leadership positions and the history of White leaders in business and politics served to perpetuate “being White” as a typical feature or attribute of a business leader.  This view was supported by Gündemir, Homan, de Dreu,, & van Vugt (2014), who displayed that “race neutral” classical leadership traits were more strongly associated with White-majority group members and that “a major cause of the underrepresentation of ethnic minorities in leadership positions in the Western world is that this group does not fit the predominant image or prototype of a leader.” (2014, 1)

Whilst these studies focused on the importance of race as a means of leadership categorisation, we can assume a host of other factors (such as gender, disability, sexuality, social class) are also erroneously (and often unconsciously) used to evaluate an individual’s suitability for a particular role.  It is simple to link this type of thinking to the lack of diversity within the investment management industry – the majority of fund managers share a range of traits (white, male, middle class), which come to be viewed as archetypal, and are interwoven with other features and skills that one might associate with the profession. Thus, a vicious circle is forged where the dominance of a particular group in a role, leads to their aforementioned factors being viewed as characteristic and favourable.

These studies represent only a fragment on the research undertaken on bias and discrimination in the workplace; but serve to provide an insight into ingrained prejudicial behaviour and its potential consequences.  Whilst there is a growing awareness of the issues, there remains a great deal of uncertainty about how to effectively tackle a problem that often seems intractable.

Although businesses are increasingly keen to place diversity at the forefront of their employment practices, such simple signalling is possibly problematic; not only because these declarations do little to address the unconscious nature of many of our biases but, more importantly, it raises the spectre of moral licensing.  First detailed by Monin & Miller in 2001, moral licensing is a situation where “past moral behaviour makes people more likely to do potentially immoral things without worrying about feeling or appearing immoral” (Merrit, Effron and Monin 2010, 344).  As an example, in a study by Effron, Cameron and Monin (2009) participants that expressed support for Barack Obama (shortly prior to the 2008 US election), were more likely to make ‘pro-White’ decisions in an ensuing scenario.  The subjects’ view on Obama provided them with ‘moral credentials’, absolving them of the need to appear non-prejudicial subsequently. Although research on moral licensing is nascent and the subject complex, it is possible that proclamations of diversity are not simply insufficient, but counter-productive.

Given the behavioural hurdles of improving the level of diversity within senior roles in the asset management industry, it is apparent that bold steps need to be taken.  One example, in a different field, is The Rooney Rule in professional American Football (NFL), which requires that one minority candidate is interviewed for all head coaching positions and senior operations jobs.  The rule was designed to address the lack of opportunity for minority coaches in the NFL.  Although there remains much debate about the efficacy and desirability of such an approach, DuBois (2015) estimated that a minority head coach candidate was 19-21% more likely to acquire the role following the imposition of the Rooney Rule.  Furthermore, it has been contended that the Rooney Rule decreased discrimination by reducing “the archaic biases regarding the intellectual ability of minority candidates” (Collins 2007, 870).

In the absence of a counterfactual, it is difficult to precisely assess the impact of the Rooney Rule; however, given the potential strength of our biases it is easy to see how a policy that compels behaviour change from employers and alters the typical candidate pool could serve to both improve short-term opportunities for under-represented groups, whilst eroding harmful stereotypes and biases over the long-term.  For asset managers, an adapted version of this approach could be utilised when hiring for both senior positions and also more junior levels, such as graduate programs.

For each possible ameliorative strategy there will be imperfections and the potential for negative behavioural spillovers (where an intervention backfires and produces the opposite effect of that intended).  However, there is little doubt that the biases that impact recruitment in the asset management industry are deep-rooted and material; for meaningful change, bold thinking and actions are required.

Key Reading:

Blanken, I., van de Ven, N., & Zeelenberg, M. (2015). A meta-analytic review of moral licensing. Personality and Social Psychology Bulletin41(4), 540-558.

DuBois, C. (2015). The Impact of “Soft” Affirmative Action Policies on Minority Hiring in Executive Leadership: The Case of the NFL’s Rooney Rule. American Law and Economics Review18(1), 208-233.

Collins, B. W. (2007). Tackling unconscious bias in hiring practices: The plight of the Rooney rule. NYUL Rev.82, 870.

Claudia, and Cecilia Rouse. (2000). Orchestrating Impartiality: The Impact of “Blind” Auditions on Female Musicians. American Economic Review, 90(4): 715-741.

Effron, D. A., Cameron, J. S., & Monin, B. (2009). Endorsing Obama licenses favoring whites. Journal of experimental social psychology45(3), 590-593.

Gündemir, S., Homan, A. C., de Dreu, C. K., & van Vugt, M. (2014). Think leader, think white? Capturing and weakening an implicit pro-white leadership bias. PloS one9(1), e83915.

Lavergne, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. The American Economic Review94(4), 991-1013.

Merritt, A. C., Effron, D. A., & Monin, B. (2010). Moral self‐licensing: When being good frees us to be bad. Social and personality psychology compass4(5), 344-357.

Monin, B., & Miller, D. T. (2001). Moral credentials and the expression of prejudice. Journal of personality and social psychology81(1), 33.

Rosette, A. S., Leonardelli, G. J., & Phillips, K. W. (2008). The White standard: racial bias in leader categorization. Journal of Applied Psychology93(4), 758.

The Perils of Past Performance

The role of a fund investor is to identify active managers (individuals or teams) with some form of edge or skill that can deliver excess returns relative to a specific index or hurdle rate.  We seek to uncover a process (application of skill) that delivers a positive outcome (outperformance).  Whilst this may appear to be a simple concept, isolating skill in the field of investment is a complex task, particularly given the undue focus on how well a fund has performed historically – which as a standalone measure provides no indication of skill and can materially skew our (seemingly) objective judgements.

To evidence skill there must be a direct and deliberate link between process and outcomes. If I am playing golf, take aim at the flag and proceed to shank the shot, but the ball ends up in the hole after ricocheting off a tree, that is not skilful – even though the ultimate outcome is both the one I intended and positive.  Furthermore, there must be a large sample to validate skill – a single successful golf shot could be mere fortune, after one hundred attempts the influence of luck is significantly reduced.

The ability to discern skill also depends on the type of activity undertaken – some activities are dominated by randomness, others can be influenced by skill.  Michael Mauboussin (2012) suggested an intuitive test for judging whether something is more subject to luck or skill – is it possible to fail deliberately? Take a lottery – it is impossible to purposely fail when playing; provided the ticket is correctly completed, the probability of success cannot be impacted by the combination of numbers selected, and the result is entirely arbitrary. Contrastingly, chess is a game dominated by skill, with limited influence from chance or randomness; it is easy to intentionally lose a game by recklessly sacrificing key pieces.

We can apply such a framework to active fund management; however, we rapidly arrive at definitional problems.  Even if we assume that outperformance of a benchmark is a robust indicator of a successful outcome – over what time horizon should this apply? For example, an active equity manager could attempt to deliberately fail (or underperform their benchmark) by selecting a range of companies with characteristics contrary to those in which they typically invest; yet if the efficacy of this strategy was assessed over a short time period (one day, for example), the outcomes would be driven by luck – did the market rise or fall on the day? What was the key macro-economic news? If, however, we extend the time period then the impact of random short-term market vacillations should wane and fundamentals exert more influence.

Lengthening time horizons in our assessment of active management certainly improves our ability to distinguish between luck and skill – but it is by no means failsafe. Even over the long-term, a randomly selected portfolio can (and often will) improve upon a market cap allocation (absent fees) and active managers frequently outperform for reasons that are not directly related to their process. Thus, whilst long-term past performance information for a fund is preferred, as a measure of skill it is severely limited.

The problem with historic fund performance is that it is devoid of context and impacted by a blizzard of variables over which the manager has no control. Returning to the golfing analogy – viewing headline fund performance in isolation is akin to judging that I am a skilful golfer by observing on my scorecard that I recorded an eagle, without knowing that I dented a tree.  It is all outcomes and no process.

This presents a challenge for investors; we seek coherent and consistent narratives and want to believe that strong active fund performance is a direct result of skill as this is the simple, coherent explanation.  The world becomes incredibly complicated when a good process delivers a bad outcome, or vice-versa.

The issue of outcome bias – our propensity to judge the quality of a decision or process by its outcome – has been consistently evidenced in behavioural research (See Baron & Hershey, 1998), yet our awareness of the phenomenon seemingly does little to shake its influence. Survivorship bias, return driven ratings and performance screens still suggest an undue focus on historic performance. Whilst the common behaviour of investors selling funds that have struggled after three years and investing in those that have outperformed over the same period is well-documented.

Professional fund investors are aware of this issue, of course, and most have thorough due diligence processes for analysing active investors, which downplay the influence of the historic excess returns delivered by a fund. The problem, however, is that as soon as we are aware of the headline performance / outcomes of a strategy then it will impact our perception of all elements of the approach.  Outcome bias would suggest that our assessment of the quality of two identical strategies would differ markedly if one reported strong historic headline performance and the other weak. Therefore, it is not just that past performance can take undue precedence in our assessment of an active fund manager, but that knowledge of it can influence all other considerations.

This is not to suggest that past performance analysis contains no relevant information, or that it should be ignored entirely; however, where considered it must be framed by the appropriate context, rather than employed as a binary good / bad marker. The primary consideration should be – is the behaviour of the strategy consistent with expectations, given my knowledge of the philosophy and process adopted?  Even then, whilst providing some useful evidence about the historic characteristics of the strategy, assessment of past performance in this more nuanced manner provides minimal guidance on whether a manager possesses skill because it remains difficult to link the outcomes to any underlying process.

Identifying skill in an active fund manager is incredibly difficult – long-time horizons are a pre-requisite and past performance alone does more to mislead us than guide us – but it is not impossible. Crucial to the endeavour is thinking about different types of outcomes; focusing on specific decisions through time, rather than top-level fund performance. This involves understanding the rationale for holdings and positioning within a portfolio and observing their impact – this provides us with a far greater sample from which to judge skill and offers a more direct link between process and outcome (though admittedly not free from noise)

Such a method also allows us to pinpoint areas of skill and locate potential weakness.  There is no skill of ‘active fund management’, rather individuals or teams may possess expertise in certain facets (this could be areas of the market, types of security or particular disciplines, such as portfolio construction); disaggregating decision making and outcomes is a crucial means of understanding where competitive advantages or particular abilities may exist.   Blunt past performance evaluations provide no information on this aspect.

That isolating skill requires us to find evidence linking process to outcomes also highlights the importance of maintaining due diligence once invested in a particular active strategy.  When initially researching a manager much of the questioning will relate to historic decisions, and the manager will be vulnerable to post-hoc rationalisation and hindsight bias, where (consciously or unconsciously) the rationale for historic decisions is prone to revision.  Regular monitoring meetings with active managers offer reams of ‘live’ decision making evidence, which is far less susceptible to the aforementioned problems. This is particularly crucial when invested in a fledgling managers / approaches where the level of confidence that skill exists and will offer persistent advantage must necessarily be limited.

Given that outperformance is the ultimate objective of active fund selection, it is unsurprising that so much attention is lavished upon historic results; yet in a chaotic and unpredictable system focusing on such a context-free number is highly problematic.   Not only does it leave us exposed to mean reversion and conflate skill with luck; it serves to influence our perspective on all other relevant evidence. Although it is impossible to expect investors to research funds without an awareness of its past performance, we should continue to strive to reduce its relevance in any due diligence process and move to a point where investing in a fund with a strong recent track record might be seen as much a problem as it is a validation.

Key reading:

Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of personality and social psychology54(4), 569.

Mauboussin, M. J. (2012). The success equation: Untangling skill and luck in business, sports, and investing. Harvard Business Press.