Why Do We Chase Past Performance And What Can We Do About It?

“Money flows into most funds after good performance, and goes out when bad performance follows.” (John Bogle)

We have all seen the wording discretely appended to mutual fund marketing stating that ‘past performance is no guide to future results’. Despite the ubiquity of this message we struggle to heed its warning.  Instead, we operate in the powerful grip of outcome bias; where we assume that good results must be the consequence of skill and will persist, and that poor results are a prelude to ongoing disappointment. This leads to the damaging behaviour of performance chasing, where we sell our holdings in laggard fund managers and reinvest in recent winners.

Whilst such an activity may offer near term relief, it often comes with a long-term cost.  In unpredictable financial markets relying on historic performance as a primary measure for quality is fraught with problems.  There is no better example of how misleading past performance can be than the tale of a pharmacist turned fund manager.

Jayesh Manek was born in Uganda in 1956 and, after moving to the UK, studied to become a pharmacist at Brighton College before opening his own pharmacy in west London.  He had great success in this field and eventually sold the chain of stores he had built, Dallas Chemists, to Alliance Unichem in 1999.  His real passion, however, was for investing.  A fact demonstrated by his success in a fantasy fund management competition run by The Sunday Times, which offered a £100,000 first prize.

In 1994, Manek beat thousands of entrants to win the stock picking contest after turning a paper £10m into a staggering £502m.  Remarkably, he managed to repeat this feat in 1995, growing another £10m into an impressive, though modest by previous standards, £58m.

Manek had a predilection for small cap growth companies, and was ideally placed to benefit from the emerging bubble in technology stocks.  It has also been suggested that he astutely exploited loopholes in the competition by making multiple entries[i], materially increasing his chances of success.  But, in the face of stellar outcomes, we can often treat such details as something of an irrelevance. The results were leading us to believe that a major new investing talent had been unearthed, operating above a pharmacy in Ruislip.

Emboldened by his unprecedented success and the publicity surrounding it, Manek took the inevitable next step of launching his own fund; offering investors the opportunity to benefit from the acumen that had seem him twice crowned a champion stock picker.  The story of pharmacist to fund manager not only captured the attention of the investing public, he also received £10m from revered investor Sir John Templeton.  Manek had the validation, the captivating narrative and a track record of success – what could go wrong?  Plenty, as it turned out.

Manek’s foray into fund management didn’t immediately turn sour.  In the early years of his fund he persisted with his penchant for speculative, high growth stocks, which had brought him great such success in the competition.  As Manek himself noted “at its peak, the fund was 75%-80% in technology”[ii]. From launch in 1997 to the height of the technology bubble, the fund outperformed the UK stock market by 152%.

The strong initial performance only served to bolster his reputation, and his fund grew to in excess of £300m.  As the fervour for technology companies imploded however, so did Manek’s fund.  In the three years from its peak at the end of March 2000, it fell in value by 75%.  The poor results did not stop there and its travails continued in the years that followed, before its belated closure in 2017.  Over its twenty year life Manek’s fund proved to be a disastrous investment, falling in value by 56%, whilst the broad UK stock market delivered 236%. This yawning performance disparity meant that Manek was landed with the unfortunate sobriquet: ‘Britain’s worst fund manager’[iii].

The experience of Manek is a stark reminder of the dangers of making inferences about both skill and future returns from historic investment performance.  Manek not only had the unique achievement of consecutive victories in a fund management competition, he delivered dramatic outperformance with real money in his eponymous fund for two and a half years.  Yet this sustained period of material stock picking success proved a prelude only to years of disappointment. Given how misleading past performance can be, do we really rely on it when selecting active managers? The evidence would suggest so.

Chasing Past Glories

In a 2008 study[iv] Amit Goyal and Sunil Wahal observed the investment decision making of 3,400 intuitional investors between 1994 and 2003, which included 8,755 active manager selection decisions across assets totalling £627bn.  The researchers were seeking to identify patterns in the hiring and firing of fund managers.

The results were clear. The institutional investors typically appointed new managers following a period of strong excess returns, only to witness the outperformance evaporate in the following years.  This behaviour not only applied to new manager purchases, but also firing decisions.  Managers were frequently relinquished after a period of poor results often to witness an upturn in fortunes.

What is of particular note in this study is the type of investor on which it trained its focus.  These were not inexperienced, individual investors, but large institutions benefitting from sophisticated and seasoned insights from trustees and investment consultants.  Investment practitioners who should have been well aware of the problems inherent in performance chasing, yet still they succumbed.

The findings of this research were corroborated in a broader context in a study conducted by Itzhak Ben David, Jiacui Liu, Andrea Rossi and Yang Song[v].  They analysed flows into US mutual funds between 1997 and 2011 and sought to observe the determinants of flows across a range of risk factors.  The two primary drivers identified were Morningstar ratings and recent returns. Whilst the Morningstar rating evolved through the evaluation period, it is a quantitative metric based on peer group relative outcomes (risk and return).  In other words, it is a measure based largely on past performance.  The authors argue that investors seek: “easy to follow signals”.

It is not only that performance chasing is reliant on data that provides no guide to future returns, but that strong performance in one period can be a forebear of poor future returns.  Research by Brad Cornell, Jason Hsu and David Nangian found[vi] that managers with recent weak performance earned higher benchmark relative returns in the future than those that had outperformed.  The tendency of mutual fund returns to experience mean reversion shows that our propensity to sell stragglers and buy recent winners is not just pointless; it is often the exact opposite of what we should be doing.

Whilst performance chasing is endemic in mutual fund selection, it is not necessarily deliberate.  Although some of us will explicitly target historic returns, many will consider a broad range of factors and undertake a rigorous due diligence process.  The problem is as soon as we observe past performance it shapes our view on every other aspect of our research.  The judgements we make about factors entirely unrelated to performance are indelibly tainted.  We chase performance even when we are not consciously attempting to.

This damaging behaviour is driven by outcome bias.  Its influence over our decision making is not just that it gives results pre-eminence over all else, but that it changes the way we perceive the other relevant issues.

The Origins of Outcome Bias

The classic origin study for outcome bias was carried out by Jonathan Baron and John Hershey[vii]. They asked participants to rate the quality of a hypothetical medical decision:

A 55 year old man with a heart condition was offered a bypass operation that would improve his standard of living and increase his life expectancy; however, 8% of patients who undertook such an operation died.  The decision was taken by the physician to carry out the operation.

Participants were presented with this scenario twice (amongst an array of different questions) with a single difference – in scenario one the patient lived and in scenario two they died.  Despite the result being irrelevant to the quality of the decision, and it being the same participant making the choice, the scenario that ended with success was rated as being a significantly superior judgement.

Outcome bias has also been shown to be hugely influential in areas where we might expect our views to show more stability – our ethics and values. Francesca Gino, Don Moore and Max Bazerman carried out a study[viii] where they presented participants with a range of scenarios featuring ethically dubious behaviour.  In one case this involved a company knowingly selling a toy containing harmful chemicals that was hazardous to children. The ethical condemnation of this activity by participants was far greater when the outcome was negative – six children died – than in the positive version, where no children were injured.

Even when it should be inconsequential, results have a material influence on the views that we hold and the judgements that we make across many domains, and investing is no exception. Indeed, there are features of financial markets which make us particularly vulnerable to its more damaging consequences.

Outcome Bias and Randomness – A Potent Cocktail

Although it is easier to observe the negative elements of outcome bias, in many aspects of life it can be viewed as an effective adaption.  A shortcut to making quick judgements based on limited information.  I can with some confidence assume that a carpenter has skill by observing their finished item. I don’t need to watch her work with a chisel for hours.

The challenge for investors is that outcome bias is only useful when we can be confident that a good process will consistently result in positive results.  If outcomes are unstable or subject to randomness then they become hugely misleading. Unfortunately, we don’t adjust sufficiently for this when using outcomes to inform our judgments (we are poorly calibrated).  We can even be swayed by outcomes when an activity is purely and categorically the result of pure chance.

In a 1975 study[ix], Ellen Langer and Jane Roth asked a group of Yale University students to predict a sequence of 30 coin tosses. Although the overall outcome for each participant was fixed to be right 50% of the time, the study was designed so that half of the group enjoyed early success with their predictions and the other half struggled at the start.  Emboldened by indications of their otherworldly foresight, those who were correct with their initial guesses were more likely to consider themselves to be better than average at predicting the toss of a coin.  Startlingly, 40% of the students also believed that they could improve with practice (which reflects well on the ethos of Yale students, though not necessarily their grasp of probabilities).

The coin toss example is consistent with Langer’s broader research on the ‘illusion of control’, which is the idea that we consistently believe that random events are under our own or someone else’s influence.  If we are prejudiced by the outcomes of a coin toss (where the randomness is certain), our desire to see order in the vacillations of capricious financial markets is a given.

Whilst the result of a coin toss is pure chance, active fund management combines some fortune and some skill.   Although exactly how much luck or skill is involved is itself highly uncertain, our performance chasing behaviour suggests that we greatly overstate (either directly or indirectly) the role of skill.  On an individual basis the influence of outcome bias on our investment decisions is profound, but we also need to consider how our collective susceptibility to the bias serves to compound the problem.

The Collective Impact of Outcome Bias

When we discuss behavioural biases it is typically about their influence on us as individuals, however, it is important not to neglect their impact on group behaviour. What does it mean if we are all subject to outcome bias?   If the majority of investors are in the grip of outcome bias and engage in performance chasing behaviour as a result, then taking action that is contrary to that becomes impossible for most professional investors.  Imagine the reaction of your clients and colleagues to investing money with a poorly performing fund.

Investing in underperforming managers and selling strong performers might be a beneficial strategy over the long-term, but it comes with profound career risk.  It is acceptable to buy an outperforming manager that subsequently struggles – this is what we are all prone to do.  Investing in a poor performer that continues to deliver weak returns is likely to be seen as unpalatable. Wasn’t it obvious that they had no skill?

Attempting to mitigate outcome bias and prevent performance chasing behaviour means overriding our natural instincts and also having a willingness to fail unconventionally.  Neither of these is simple, but that does not mean there is nothing we can do.

How Can We Prevent Performance Chasing?

Outcome bias cannot be switched off.  Whilst awareness is a starting point, it is evident from our continued performance chasing behaviour that it alone is insufficient.  We need to make clear and focused interventions to change our behaviour:

1) Stop Using Performance Screens: Mutual fund performance screens are ubiquitous across the investment industry, and I have never worked in a team where they have not been employed in some form. Wealth managers, investment consultants, ratings agencies all use some form of historic performance screen to rank funds.  Whilst each will differ based on the metrics and time horizon used, they are all fundamentally doing the same thing – assessing funds based on past performance.  They are a behavioural disaster.

Outcome bias and the performance chasing behaviour that follows is difficult enough to avoid even if you are not actively employing tools that encourage it.  Investors will argue that it is difficult to reduce such large universes of funds without some kind of performance filter, and also that it is only a starting point. However, we know that observing performance in this way will have a dramatic impact on the decisions we make. Virtually any other way of cutting down a potential investment universe would be better.

2) Create decision rules: A simple step to avoid performance chasing behaviour is to create fixed decision rules that strictly prohibit it. For example, you might state: “We will not purchase a fund that has outperformed its benchmark by more than 10% over three years”. This sounds straightforward but is fiendishly difficult to apply given that it runs counter to our typical approach to selecting active managers.

It will prohibit buying of those anomalous managers that continue to generate strong returns even after a stellar run. On average, however, it should be an effective means of avoiding the cost of purchasing active managers with a high potential for severe mean reversion.

3) Go Passive: The best behavioural interventions are the simple ones. Anything that requires behavioural discipline or continued effort raises the prospect of failure. Given this, what is the best way to avoid performance chasing in active mutual funds?  We can restrict ourselves to buying only passive market trackers.  This changes the context of the decision and entirely nullifies the influence of the bias from our fund selection decisions at least.

4) Specify the activity in which you believe skill exists. When investing with an active manager, we are taking the view that the underlying manager has some form of skill. We tend, however, to be very vague about what we actually mean by this.

When seeking to identify whether a fund manager is skilful, we need to be specific about the activity in which someone may possess it.  If we suggest that they are skilled at ‘beating the market’ then we are entirely beholden to whether performance is good or bad, irrespective of the reason.  If we instead make precise claims about where they may hold an advantage – for example, in identifying companies where the potential earnings growth is greater than market expectations – we can at least begin to assess whether the outcomes are directly related to an explicit process.

There is no such thing as a ‘skilled investor’ as much as there is a ‘skilled surgeon’ – I wouldn’t be entirely comfortable with even the most skilled urologist performing brain surgery on me.  When discussing skill always ask – skilled at what, exactly? Then test it.

5) Understand how much randomness there is an activity. In his book ‘The Success Equation’[x] Michael Mauboussin suggests an intuitive check for resolving the quandary about gauging whether a particular activity is more subject to luck or skill – simply ask the question: 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.

The more randomness there is in any given activity, the higher the threshold must for claiming that skill exists. Always apply the ‘can you fail deliberately’ test.

6) Extend your time horizons: Our susceptibility to outcome bias is greatly influenced by the time horizons involved. Whilst investment management is always a combination of luck and skill, the length of the assessment period alters how much we can glean from performance alone.  If we assess investment performance over one day it can be considered to be pure luck, but as we extend the period skill can exert more of an influence.  You can still be lucky over ten years, but the outcomes should be less influenced by chance.

We would not necessarily expect Warren Buffett’s stock picks to beat those of my six year old daughter tomorrow, but over a decade we would.  The fact, however, that over a ten year period you couldn’t be 100% certain that the world’s most revered stock picker would beat the returns of a six year old tells you a great deal about the problems of performance chasing and outcome bias.

Unfortunately, if anything we are seemingly becoming more myopic in our investment judgements. Three years is considered long-term, and performance is increasingly assessed over ridiculously brief periods of time such as one month and one quarter. If you are assessing outcomes over such time horizons you should understand that they are likely devoid of meaning.

Performance chasing behaviour is, of course, not isolated to our selection of active fund managers.  It is readily apparent in how we invest in everything from major asset classes to individual stocks. It is also not entirely driven by outcome bias.  There are a multitude of factors at play; including our willingness to extrapolate recent history into the future and our susceptibility to a good story.

Active manager selection decisions are, however, the perfect breeding ground for outcome bias to run amok.  The use of past performance to guide judgements seems to offer some sense to a highly uncertain environment – accepting that past performance is largely random may lead to some difficult introspection.  Its importance is also embedded in the system. It is what everyone cares about – boards, clients, trustees, risk teams, CIOs – it is measured and therefore it matters, and usually a great deal to your career.

This is not to say that outcomes do no matter.  Of course, all investors are seeking better long-term results for their clients.  The problem is not that we care about performance, it is that we have become reliant on historically noisy data to make unsubstantiated or plain incorrect inferences about the future.  If we want to invest in active managers, we need to think far more about decision quality and process, and far less about yesterday’s performance.

[i] https://www.moneyobserver.com/our-analysis/maneks-journey-zero-to-hero

[ii] https://www.theguardian.com/business/2001/dec/29/4

[iii] https://portfolio-adviser.com/uks-worst-manager-shuts-up-shop/

[iv] Goyal, A., & Wahal, S. (2008). The selection and termination of investment management firms by plan sponsors. The Journal of Finance63(4), 1805-1847.

[v] Ben-David, I., Li, J., Rossi, A., & Song, Y. (2019). What Do Mutual Fund Investors Really Care About?. Fisher College of Business Working Paper, (2019-03), 005.

[vi] Cornell, B., Hsu, J., & Nanigian, D. (2017). Does Past Performance Matter in Investment Manager Selection?. The Journal of Portfolio Management43(4), 33-43.

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

[viii] Gino, F., Moore, D. A., & Bazerman, M. H. (2009). No harm, no foul: The outcome bias in ethical judgments. Harvard Business School NOM Working Paper, (08-080).

[ix] Langer, E. J., & Roth, J. (1975). Heads I win, tails it’s chance: The illusion of control as a function of the sequence of outcomes in a purely chance task. Journal of personality and social psychology32(6), 951.

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

 

Five Behavioural Resolutions for Investors in 2020

The notion of a New Year’s resolution now seems more associated with lofty aspirations and hasty failures, rather than substantive and prolonged change.  Yet for investors caught in the daily cacophony of market volatility, financial news and perpetual performance assessment; opportunities for genuine reflection are scarce. Most of us spend our time dealing with the noise of the present rather than considering how we might make better long-term investment decisions.

Even if introspection over the New Year period doesn’t result in dramatic change, any occasion that affords investors even a modicum of space for contemplation away from the everyday should be grasped readily.  And for those investors in need of resolutions to follow, here is a short list of simple (but not easy) goals driven by insights from behavioural science:

Make a set of market / economic predictions for the year ahead: Most of us are aware of our general incompetence at making forecasts and predictions, but most of us engage in it anyway.  In order to disabuse any notions of prescience, simply write down some market forecasts for the year ahead and then review them in 12 months’ time. This will provide a cold dose of reality.  This task should be carried out each year to prevent us getting carried away if we get lucky with one round of predictions.

Check your portfolio less frequently: The best defence against most of our debilitating behavioural biases is to engage with financial markets less regularly. The more we review short-term performance and pore over every fluctuation in the value of our portfolios, the more likely it is that we will make poor, short-term decisions. The common wisdom that being ‘all over’ our portfolio is some form of advantage is almost certainly one of the most erroneous and damaging beliefs in investment.

Read something / someone you disagree with each week: Confirmation bias is incredibly damaging for investors and its influence appears to have been exacerbated by the rise of social media. We follow those who share our principles, and read articles we know we will agree with, whilst haranguing those with contrary views.  This is easy and it makes us feel good.  The problem, however, is that there are things that we currently believe that are wrong, and if we live in an echo chamber we are unlikely to find out what they are.

Keep a decision log: Our memories are incredibly fickle. It is not simply that we forget things, but that we re-write history based on information that we receive after we have made a decision. It is almost impossible to learn from our past judgments unless we have a clear and simple record of what we were thinking (and feeling) at the time we made a decision.

Be comfortable doing nothing: Particularly for professional investors, the pressure to act can be overwhelming. Financial markets are in a perpetual state of flux and uncertainty, and investors are expected to constantly react: “something has changed, what are you doing about it?” Doing nothing can be seen as lazy, negligent or incompetent, when in the majority of cases it is the best course of action.  Sticking to your principles and enjoying the long-term benefits of compounding sounds easy, but the behavioural realities of investment means that this is far from the case.  Doing nothing requires a great deal of effort.

10 Things Fund Managers Say and What They Actually Mean

Communication is a major problem in the fund management industry.  Whilst financial markets exist in a state of constant uncertainty and flux, asset managers talk with unwavering confidence.  Their utterances are driven by the sales imperative and a general reticence to admit mistakes. They therefore often require some unraveling to get to the true meaning:

Below are some favourites (with translations provided):

“ESG is in our DNA”

“We have always done some company management meetings, but have started to take environmental and social factors seriously now we can see it sells”.

“What we have witnessed is a 10 standard deviation event”

“This didn’t show up in our backtest”

“We don’t have a specific capacity limit in mind for this fund, but we can say that our current asset size has had no impact on our investment process”

“You must be mad if you think we are going to close down this revenue stream”.

“Private equity and ESG are a natural fit”

“We hope you haven’t noticed that the private equity model has largely been built on shareholder value creation at the expense of other stakeholders”

“We are benchmark aware not benchmark constrained”

“We are benchmark constrained”.

“The rise of passive investing and quantitative easing has materially distorted how markets function”

“Performance hasn’t been great”.

“The performance fee structure means that my interests are perfectly aligned with my clients”

“If I can just have one or two good years then I am made”.

“Active management will prove its worth in the next bear market”

“I wonder if I can appeal to your loss averse tendencies”

 “Cognitive diversity is incredibly important to us” 

“We have one woman on the team”.

I am genuinely excited to join this company, with the resources they have and the freedom they give fund managers, it is a great place to run money”

“They offered me a share of revenues”


 

 

Are You Sticking to Your Investment Principles or Suffering from Escalating Commitment?

Our investment behaviour is a tangle of contradictions.  Whilst there is an understandable desire to draw clean lines of causality – X bias leads to Y action – things are rarely so simple.

One example of such confusion is the inherent friction between keeping faith with your investment principles and the problem of escalating commitment.  I often try to extol the virtues of holding a set of well-defined investment principles and maintaining them through the vicissitudes of economic and market noise.  It is clear, however, that a great failing for many of us is becoming increasingly devoted to a particular view in spite of reams of disconfirming evidence about the validity of that perspective.

Escalating commitment is a situation where an individual or a group persist with a course of action despite facing negative results and feedback.  This topic has been well covered by psychological research and there are some excellent meta-analytic reviews of the studies carried out in this area[i] [ii].

A research paper by Theresa Kelly and Katherine Milkman[iii] specifies four primary explanations for our propensity to double-down in the face of adversity:

Self-Justification Theory:  Repudiating previously held views is painful and creates a dissonance between our failure and belief in our own competence.

Confirmation Bias:  We actively seek information that corroborates our view or course of action.  We might be wilfully blind about the fact we are failing.

Loss Aversion: The crystallisation of lost time, money and credibility is deeply unpleasant. We weigh the sunk costs heavily when deciding whether to persist.

Impression Management:  How we are perceived by others is crucial to our self-worth and (in many cases) our career.  Changing our mind means admitting that we have erred and risks us being perceived as ineffectual or inconsistent.

The issue of commitment escalation is a particular challenge for investors because the feedback we receive (typically in terms of profit and loss) is noisy and erratic. Markets don’t consistently reward good decisions.  Sometimes they take time to work; sometimes they don’t work at all.  Whilst in many fields the failure of a project or decision is unambiguous, for investors it can be difficult to tell whether we are wrong or just not right yet.

This feature of financial markets creates a stark juxtaposition between the benefit that can accrue from persevering with sound investment principles, and the cost of the failure to abandon poor decisions.  How can I tell if I am diligently keeping faith with my investment approach or naively escalating commitment?

Is it an Investment Principle or Investment View?

The most crucial distinction to draw is between what constitutes an investment principle and what constitutes an investment view.

An investment principle is a belief that informs our decision making – for example, I may believe in regular rebalancing, or in long-term holding periods.  All of our investment decisions should be framed by these principles.

By contrast, an investment view is some form of explicit or implicit prediction or forecast about the future.  For example, it might be that there will be a recession in 2021 or that US equities will underperform other developed markets next year.

In reality, for most decisions it is not possible to specify a binary classification between an investment principle and an investment view; rather there is a spectrum between the two extremes.

At one end, an investment principle should be broad and supported by robust and clear evidence, and assumed to be invariable.  Of course, there may be times where an investment principle shifts or evolves but the threshold for such a change should be very high.  Commitment to a principle should generally be seen as a virtue.

At the other end of the spectrum is an investment view, which is specific and temporary.  Whilst it should also be supported by evidence, the threshold for change based on the receipt of new information should be reasonably low.  The probability of being wrong is significant and unwavering support is damaging:

  Investment Principle Investment View
Context General Specific
Durability Longer Shorter
Evidence Stronger Weaker
Confidence Higher Lower
Threshold for Change Higher Lower
Commitment Positive* Negative

It is easy to think of examples at each extreme – I don’t buy funds with leverage (principle) / the market will fall 20% next year (view) – but many of our investment opinions fall somewhere in-between.  For example, let’s assume I believe that smaller companies provide a long-term return premium and are inefficiently priced.  As a result I place an active allocation to this area within my portfolio.  This perspective is neither purely a principle nor a view.  It is structural long-term rule, but is also specific and based on mixed evidence.

The closer an investment decision is to being defined as a view, the more the escalation of commitment becomes a challenge.  Whilst we should be willing to reconsider either a principle or view in light of new evidence, by definition our commitment to principles should be significantly more durable than our willingness to persevere with an investment view.

The escalation of commitment is particularly problematic for investors when they come to be identified by an investment view rather than principle.  Warren Buffett can be defined by a set of core investment principles, but many investors become known for a view.  This is most common with bearish prognosticators consistently forecasting recessions or severe market declines.  In such situations individuals seemingly operate in ignorance of new contrary evidence and their identity becomes intertwined with their particular outlook meaning that it becomes impossible for them to recant**.

How to Avoid the Escalation of Commitment

With any investment decision (wherever it resides on the spectrum between view and principle) one of the best protections against the escalation of commitment is a decision log.  This is a simple approach but one that seems to be applied sparingly.  Although the precise structure can vary; in broad terms a decision log should be a concise document detailing the key drivers of a particular decision at the time we are making it.  This should include aspects such as: what the decision actually is, the time horizon involved, the key supporting evidence and potential risks / threats.

Not only does a decision log provide some protection against our hazy and unreliable recollections of past investment decisions; it should also mitigate the danger of us becoming increasingly committed to an investment view.  If we are specific about the key drivers of a decision at the point of initiation it becomes far more difficult to become emboldened in our conviction as the evidence wanes.  Creating a decision log also forces us to consider the prospect of being wrong at the very start of a position, which subsequently makes it (somewhat) less damaging to our ego if this negative outcome comes to fruition.

Using a decision log is not, however, painless.  Looking back at what we previously believed when we made an investment decision can be a unpleasant experience; it’s far more agreeable to allow your memory to construct a flattering story about it.  This is perhaps why they seem something of a rarity.

There is no simple way to manage the escalation of commitment.  Taken to its extreme, bluntly attempting to negate its influence would lead to us all becoming short-term investors, destroying value at the first sight of trouble and abandoning sound investment decisions based on the meaningless fluctuations of markets.  Contrastingly, ignoring it would make us prone to increase conviction in failing ideas with scant regard for new evidence.  A more measured approach is required, where we better understand the nature of each decision we make and are clear about the reasons we are making it.

* Investment principles can of course be wrong and therefore commitment to them a negative. I am assuming here that the investment principles are robust and well-founded.   

** Whilst becoming defined by a certain viewpoint may often be imprudent and costly in investment terms, it can be a rational course of action for an individual pursuing such a strategy.  This is particularly true for those of a bearish disposition who appeal to the fearful nature of risk averse investors and of course will, one day, be right.  Also, the half-life of credibility for market crash / recession forecasters seems to be far longer than those of a more positive bent.

[i] Sleesman, D. J., Conlon, D. E., McNamara, G., & Miles, J. E. (2012). Cleaning up the big muddy: A meta-analytic review of the determinants of escalation of commitment. Academy of Management Journal55(3), 541-562.

[ii] Sleesman, D. J., Lennard, A. C., McNamara, G., & Conlon, D. E. (2018). Putting escalation of commitment in context: A multilevel review and analysis. Academy of Management Annals12(1), 178-207.

[iii] Kelly, T. F., & Milkman, K. L. (2013). Escalation of commitment. Encyclopedia of management theory, 257-260.

 

 

Stale Pricing Does Not Equal Low Risk or Low Correlation

Alternative asset classes are in something of a sweet spot. Not only do they offer the prospect of a diversifying source of return in an environment when bond yields are at historically low levels, but they also provide a new revenue source for active managers. In the current landscape, strategies where passive replication is problematic or impossible provide a particular allure for margin-pressured asset management firms.  Whilst the attention being lavished upon this area is unsurprising there are certain aspects of discussions about such investments, which are troubling and often misleading.

Alternative assets represent a broad church and can encompass anything that falls outside of the core traditional mix of equities and bonds, from private equity to fine wine.  The nebulous nature of this definition means that it is difficult and unfair to discuss the credibility of the grouping in general terms; however, one common feature tends to be the approach to pricing and valuation.  Whereas the majority of traditional assets are regularly traded and marked to market; alternative assets are typically far less liquid and, in the absence of a regularly traded market price, are valued on some form of model / appraisal basis.  This approach to valuation is not a problem in and of itself – there is often no simple answer to appropriately valuing such assets – however, it does have profound implications for how you might express the risks of such strategies and compare them to traditional asset classes.

The first, seemingly obvious, point is that volatility is a woefully inadequate measure of risk for most alternative assets, particularly if used in comparison with public equity returns, for example.  The pricing of any mark to model asset is smoothed; it is largely immune to the vagaries of human behaviour that drive the vacillations of listed assets – imagine the volatility of the S&P 500 if it was valued on a monthly basis based on projected future cash flows.  Volatility has come to be the primary term for how we express investment risk, even where it is inappropriate for the assets in scope.  This incongruence has been exploited by some to suggest that alternative assets in general terms are inherently lower risk, turning a structural limitation* into a sales message.

Deeply intertwined with the issues surrounding volatility and mark to model pricing in alternative assets is the issue of correlation and diversification.  Whilst some alternative assets will have genuinely distinctive attributes when compared to traditional equity / bond portfolios, these should be driven by the underlying economics / cash flow profile of the assets rather than the valuation methodology or liquidity structure.  The most egregious example often comes in the form of some private equity strategies, where a portfolio of private, medium sized companies can be said to offer material diversification benefits compared to a portfolio of public, medium sized companies.  Clearly, the holdings of the two portfolios are highly economically correlated, even if their differential approaches to valuation provide an optical sheen of differentiation.

The narrative supporting alternative assets is often built around the impact that their addition can have on a traditional portfolio (such as a 60/40); whilst there may be merit to this viewpoint, the primary evidence given is often fatuous. The argument tends to run as follows: ‘look at the beneficial Sharpe ratio and volatility impact of adding XYZ alternative strategy to your portfolio’.  Alternative assets exhibit artificially low volatilities and therefore abnormally high Sharpe ratios, they can also appear to have a low correlation to traditional assets – of course they look wonderful when added to a leaden portfolio of equities and bonds.

The problem is that as an industry we have come to use volatility and Sharpe ratio as default metrics for the analysis of traditional portfolios and are now prone to view everything through these frames even when their usage is entirely inappropriate.  Furthermore, given that many asset allocators are assessed on such metrics the rational course of action for them is to game these measures by utilising alternative assets with depressed volatility and low correlations to ‘enhance’ the overall results of their portfolios.

That is not to say that there is no role for alternative assets but any investment case for them should be driven by an understanding of their economic merit and cash flow profile, we should always ask – do arguments around diversification and low volatility make intuitive sense?  Such assets can appear low risk when viewed through a volatility lens – attractive in risk models and optimisations – but such smooth returns can often cloak an unpleasant tail.  Beware the dangers of mistaking pricing and liquidity characteristics for fundamental ones.

* As I have previously argued, one indirect benefit of illiquid assets is behavioural – if it is difficult / impossible to trade, we are more likely to stay invested for the long-term.

How Probabilities are Expressed Can Impact Our Investment Decision Making

Imagine you are in a team meeting discussing a potential investment with three colleagues, you ask them how probable it is that your investment thesis for a particular position comes to fruition, each of them states that they see it as ‘likely’.  In an alternate universe, you are in the same situation the only difference being the responses from your colleagues – on this occasion they each say ‘60%’.  Does your colleagues’ shift from a verbal to numeric expression of probability impact your confidence in the investment decision?  A new paper from Robert Mislavsky and Celia Gaertig contends that it would – their research suggests that when we are given numeric probability forecasts we average them and when given verbal forecasts we count them.  A succession of ‘sixty percents’ leads you to a 60% average, whereas a similar number of ‘likely’ responses sees your own view become ‘very likely’.

We often talk in probabilistic terms without realising it – when we state something is ‘likely’ or ‘very likely’ we are expressing some form of view on the probability of an occurrence, although it is admittedly a vague one.  Research around this area has typically focused on the comparison and translation of verbal probability expressions into numeric ones, and vice-versa – when we say something is unlikely, what probability do we actually mean?   As Mislavsky and Gaertig note in their paper, verbal probabilities have the benefit of being clear in their direction (you can tell if it is positive or negative) but suffer from imprecision, whereas numeric probabilities are specific but the direction is not always clear (whether a 45% probability is positive depends on the context).

Mislavsky and Gaertig’s research develops the thinking around this subject by moving on from identifying specific differences between individual verbal and numeric probability expressions, and showing that there is a material change in outcome when we combine a number of verbal probabilities, compared to when we combine a selection of numeric probabilities.  Their research incorporates a range of experiments (7 in total) wherein participants were required to make a decision or predict an outcome after receiving one or two expert forecasts – these forecasts were either both numeric or both verbal.

For example, in their second study, participants were provided with some details about a company and asked to judge how likely it was that its share price would be higher in a year’s time.  Some participants received expert guidance from advisers in numeric form and some from advisers in verbal form.  The results of this study – which were consistent with all the experiments carried out in the research –  was that “participants became more likely to make extreme forecasts as they saw additional advisor forecasts in the verbal condition but less likely to do so in the numeric condition”.  We can see this in the chart below:

Probability

The predictions of the participants clearly became more extreme when they received an additional verbal forecast but not when an additional numeric forecast was provided.  By ‘extreme forecast’ the authors mean when a participant’s forecast is in excess of that given by either adviser.  Similar results were observed when the study moved from looking at a hypothetical stock price, to predictions of Major League Baseball games with probabilities given by genuine experts.

There is clear evidence from the study that the verbal probabilities lead to a counting process, whereas numeric probabilities are averaged. There are good reasons for both approaches – counting works on the basis that each adviser is providing new information, whereas averaging is prudent if we assume each forecast is founded on the same information.  There is no requirement, however, to associate the different expressions of probability with different processes for their combination – two 60% forecasts could just as easily be driven by different information as two ‘likelys’.  So what is happening?

The authors conclude the paper by reviewing and largely discounting a range of potential explanations (I would urge everyone to read the paper directly).  My best guess of the cause of the phenomenon would be a combination of some of the factors mentioned by the authors, in particular how individuals are liable to perceive numeric and verbal probabilities in different fashions.  Numeric forecasts feel precise and objective, and more consistent with an ‘outside view’ driven by the base rate or reference class – more likely to contain all relevant information.  Contrastingly, verbal probabilities seem personal and subjective, more akin to an ‘inside view’ where an individual providing a forecast will be doing so based on their own unique knowledge or perspective – therefore an additive approach can seem justified.

This idea is pure speculation about which the authors are sceptical, however, whilst the drivers of this contrasting approach to combining probabilities are uncertain; the results, from this initial study at least, are clear, and there is an important lesson for investors to heed.  It is crucial to consider not only the type of guidance and advice we are receive when informing a decision, but how it is being expressed.  This is relevant whether it relates to an individual’s decision using a range of external information sources, or a team based decision making process where we are seeking to synthesise the insights of a number of individuals into a single view.

Mislavsky, R., & Gaertig, C. (2019). Combining Probability Forecasts: 60% and 60% Is 60%, but Likely and Likely Is Very Likely. Available at SSRN 3454796.

Active Management is Reliant on the ‘Inside View’

I have an investment decision to make.  I need to allocate money to a particular asset class and have to decide whether to use a passive market tracker fund to gain exposure or invest with an active manager.  The odds are not in favour of the active option – over the last decade only 10% of managers in the asset class have outperformed the benchmark – however, I have identified a manager with unsurpassable pedigree in the area, a fantastic performance track record and a robust investment process.  Which option should I choose?

The stark contrast of perspectives underpinning this question is an example of what Kahneman and Tversky would label as the ‘outside view’ versus the ‘inside view’[i].  The outside view in this scenario is that 10% of active managers achieve success in the asset class, and is what we can consider to be our base case or reference class – it provides a statistical framework for informing a decision.  My experience of being impressed by a particular manager is the inside view, which is developed using information specific to my individual case which, as Michael Mauboussin notes, may include “anecdotal evidence and fallacious perceptions”[ii].   We can broadly characterise the outside and inside view informing any decision as having the following features:

Outside View Inside View
Reference Class Personal Experience
Evidence Narrative
Similarity Difference
General Specific
Realism Optimism
History Current

Our general tendency is to focus on the inside view – we adore narratives, tend to believe that our own experiences are exceptional and are overconfident in our abilities.  Use of the inside view is particularly prevalent in the active asset management industry as, of course, it must be – if something does not work on average then it must be forged on the notion of edge, competitive advantage and exceptionalism.

The inside view is also so much more compelling – those wonderful and usually superfluous stories of active managers gaining an advantage by visiting the factory of a target company (it always seems to be a factory) or meeting management are both diverting and persuasive.  The problem is that they do not change the odds; rather they simply encourage us to forget them.  We often think that the additional insights from detailed research are improving our decisions, but in many cases they are simply making us neglect the base rate (whilst erroneously increasing our confidence)[iii].

Returning to the question with which I began this post; if I select the active manager option then I need to support that decision with one of two claims.  I can argue that the base rate is incorrect and therefore the odds are more favourable than they appear – there is something about historical experience which means it is not representative of the future.  Alternatively, I can accept the probabilities but possess such belief in my active manager selection capabilities that I am not concerned by them.  In most cases we don’t actually make either of these arguments explicitly, we simply ignore the outside view and make the case using our inside view – which is usually sufficiently captivating to overwhelm more prosaic considerations.

This is not to suggest that the inside view is of no merit, but rather it should be used only as a complement or adjustment to the outside view. Our starting point should always be a consideration of the reference class or general evidence that frames a particular scenario.  We can then revise this (usually modestly) if we obtain relevant information that is specific to our case.  A failure to follow this approach means that we will consistently make decisions which ‘feel’ right but where the odds are stacked against us.

[i] https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/daniel-kahneman-beware-the-inside-view

 

[ii] Mauboussin, M. J. (2012). Think twice: Harnessing the power of counterintuition. Harvard Business Review Press.

 

[iii] https://behaviouralinvestment.com/2019/01/09/can-more-information-lead-to-worse-investment-decisions/