“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.

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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.

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[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 Finance, 63(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 Management, 43(4), 33-43.

[vii] Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of personality and social psychology, 54(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 psychology, 32(6), 951.

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

 

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.

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):

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“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”.

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

“This didn’t show up in our backtest”

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“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”

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“We are benchmark aware not benchmark constrained”

“We are benchmark constrained”.

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

“Performance hasn’t been great”.

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“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”.

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

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

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 “Cognitive diversity is incredibly important to us” 

“We have one woman on the team”.

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“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”

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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.

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* 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 Journal, 55(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 Annals, 12(1), 178-207.

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

 

 

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.

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* 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.

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:

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.

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

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.

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[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/

Citywire’s 2019 ‘Alpha Female’ study[i] reported that only 10.8% of the fund managers in their global database were women; a figure that has largely flat-lined since its first publication in 2016.  If we disregard the absurd notion that men hold some form of gender based advantage in the skills required to be a successful fund manager, then this represents a staggering anomaly.  There are inevitably deep-rooted societal features* that contribute to such disparities and whilst these are apparent across industries[ii]; there are also features and behaviours specific to asset management – and our perception of what it means to be a talented fund manager – which serve to exacerbate the issue. These must be acknowledged if we are to even begin to remedy the situation.

In her excellent book ‘Invisible Women’[iii] Caroline Criado-Perez highlights the idea of brilliance bias and research showing that “the more a field is culturally understood to require brilliance or raw talent to succeed…the fewer women there will be studying and working in it.”  She goes on to make the case that we struggle to associate women with being “naturally brilliant”. Criado-Perez cites areas where this bias presents a major impediment for women including: maths, physics and science, but this group could easily include active fund management – particularly given our long standing obsession with ‘star’ fund managers.

It is unquestionable that we are often in thrall of successful active managers.  Given how difficult it is for active managers to deliver excess returns; we are prone to laud those that do as possessing some form of exceptional or even innate investment aptitude (even where it may very well be luck). One of the (many) problems with this is that, as Criado-Perez notes, we are more likely to erroneously associate possession of such inherent talent with men.  There is also the complication that nearly all examples of this type of fund manager adulation involves men, which only serves to perpetuate and exaggerate the trope that those truly exceptional individuals able to buck the trends in active management are inevitably male.

Whilst brilliance bias is often focused on opaque and unexplainable characteristics – intangible concepts such as talent – the challenges faced by women seeking to enter the fund management industry are also caused by the wrongful assumption that certain (traditionally male biased) characteristics are associated with fund management skill, and these are often the very characteristics which overwhelm investor decision making.  This idea is eloquently put forward by Tomas Chamorro-Premuzic in his paper (and subsequent book) ‘Why Do So Many Incompetent Men Become Leaders’[iv].  Although about executive management positions rather than fund management roles the parallels are stark, and there is one particular trait which resonates pointedly – overconfidence.

We are liable to mistake confidence for competence.  This is especially relevant in the fund management industry where identifying the features that are truly indicative of skill is so difficult that we are likely to rely on how compelling or convincing an individual is.  Chamorro-Premuzic argues that the advantage granted to overconfident individuals presents two challenges for women attempting to attain leadership positions – they are generally perceived as less confident than men and if they do display ‘extra’ confidence we become concerned that they are not conforming to their gender stereotype.

A related concept highlighted by Chamorro-Premuzic is the influence of charisma, another feature which clouds our ability to judge an individual’s aptitude for a given activity – there are few things more dangerous in fund manager selection than a charismatic manager who has been lucky.  Although there is far less research around how gender impacts our perception of charisma, Chamorro-Premuzic argues that there is a circular relationship at play – more leaders are men, leaders are perceived to be charismatic and therefore charisma has come to be considered a male-dominated trait.  He goes on to produce a pertinent quote from Margarita Mayo about our attraction to charisma:

 “The research is clear; when we choose humble and unassuming people as our leaders, the world around us is a better place…Yet instead of these unsung heroes, we appear hardwired to search for superheroes: over-glorifying leaders who exude charisma”.

I could quite easily replace ‘leaders’ with ‘fund managers’ in the above quote and it would prove an appropriate description of the type of fund managers to which we are often drawn.

A report by capital markets think tank New Financial:  ‘Diversity in Portfolio Management’[v] explicitly attempts to identify the barriers to progression for women and those from diverse backgrounds within the asset management industry.  One of the 18 highlighted was the ‘loss of performance continuity through leave of absence’- this is a material concern and one which is directly related to our perception and glorification of star fund managers.  Our false perception of the archetypal successful fund manager as being a supremely talented individual with a unique skill set not only plays into tired gender stereotypes, but also increases the requirement to have named fund managers and avoid extended leaves of absence.  The belief that superior performance must be down to one individual who is constantly poring over the portfolio further raises the hurdle for women, who are more likely to have extended time way from the office due to factors such as maternity leave and the traditional division of childcare responsibilities[vi].  This imperils the ability of a female portfolio manager to develop an undisturbed track record as an individual, which is often valued highly by fund selectors, and seen as a ‘requirement’ for asset management firms to successfully market their funds.

Two other extremely valid issues raised in the report are the lack of meritocracy and the shortage of role models.  The meritocracy obstacle in fund management is caused both by asset management firms valuing the wrong characteristics (such as confidence over competence), and also the self-perpetuating tendency of people to hire individuals who are most like them – the mirrortocracy – if most fund managers are men, this becomes an implicit characteristic / requirement for such positions.  The lack of role models is a related and also pernicious problem (and by no means just relating to gender) – if the vast majority of fund managers are men, there are inevitably few role models with whom people of other groups can easily identify.

The causes of gender imbalance in the asset management industry are deep and structural, and far more complex than I could possibly hope to convey.  The purpose of this post is to highlight how asset management groups and fund buyers can be considered complicit in the under-representation of women in fund management roles because of our desire to identify, extol and sell ‘exceptional’ individuals (unfortunately almost always men) whilst being beguiled by gendered traits such as overconfidence and charisma – which have no proven relationship with the skills required to be a successful fund manager.  Until we start taking substantive steps to improve the situation – such as moving to a team based fund management culture rather than one focused on star individuals – progress in this area will remain glacial.

*These vary across region, there is an admittedly UK / US bias to this article.

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[i] https://citywire.co.uk/new-model-adviser/news/alpha-female-2019-female-fund-managers-stuck-on-10/a1253711

[ii] https://researchbriefings.parliament.uk/ResearchBriefing/Summary/SN07068

[iii] Perez, C. C. (2019). Invisible Women: Exposing Data Bias in a World Designed for Men. Random House.

[iv] Chamorro-Premuzic, T. (2013). Why do so many incompetent men become leaders. Harvard Business Review, 22.

[v] https://newfinancial.org/diversity-in-portfolio-management/

[vi]https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/705898/Return_to_work-parental_decision_making.pdf

 

 

I was recently asked by a friend for my opinion on UK assets following the renewed weakness in sterling and the general Brexit induced pessimism surrounding the UK economy.  I am deeply reticent to talk about this type of investment related issue outside of work; primarily because it usually ends up with the person wondering if I really have a job in the investment industry – I don’t know where markets are headed, I don’t have any good stock tips and have no unequivocal opinions on key economic issues.

Although it is inevitably a conversation killer, I instead try to turn to sensible and broad investment principles; on this occasion I said something along the lines of: “It depends on your time horizon, if you are investing for the long-term – like your pension – then buying unloved and undervalued assets can be a good idea, but if you are looking for a short-term trade the risk and uncertainty is extremely high”.   Whilst I think the general point here is sound*, on reflection I made a major behavioural omission, which I think is fairly common when thinking about time horizons.

When we talk about investment time horizons we often focus on only two discrete points – when we invest and when we plan to disinvest. If I make an investment in my pension today, which I hope to draw upon in 30 years’ time; my time horizon is clear**.  Whilst this is an incredibly important element of any investment decision, our tendency is to focus on the start and the end, and neglect what we might do in the intervening period.

What I should have said in response to my friend’s question on the UK is: “it depends on your time horizon…and even if you have a long-term objective, are you going to be checking the valuation and poring over the news every day?  If so, then your time horizon might be a great deal shorter than you think”.

Even if our circumstances do not change, our behaviour can lead to our realised time horizon for any given investment being materially different to what we may have stated at the outset.  The overwhelming driver of this is how we engage with financial markets – how frequently are we checking our portfolio?  How easily can we trade?  How anxious do daily price fluctuations make us? Are we eagerly watching the financial news?  Are we checking on short-term performance?

Making a long-term investment is not simply investing money with the aim of meeting a temporally distant goal; but understanding the behavioural discipline required to be a long-term investor. Where possible the ‘easiest’ route is simply to disengage from the daily cacophony of market and economic news, and commit to a long-term investment plan.  For many^ this is not feasible and in the constant battle between long-term objectives and short-term behavioural pressures there is typically only one winner.  Unfortunately, for most of us, this means that investing for the long-term is simply making a succession of short-term decisions.

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* I am making the very simple point that valuation matters to long-term expected returns.

** Of course, circumstances may dictate a change in your time horizon.

^ This is a particular problem for professional investors.

I recently read Will Storr’s excellent book ‘The Science of Storytelling’[i]; which is an exploration of how our brains process stories and why they are such a fundamental component of human experience.  Whilst it is primarily designed as an aid for writers seeking to better craft their narratives; for me, it also served to underscore the underappreciated but essential role that stories play in financial markets.

Linking storytelling and investments is not a revelatory observation – investors are often lured into a poor decision by a compelling story and no financial bubble in history has been absent some beguiling narrative – however, their importance is hugely understated.  Rather than a susceptibility to stories being a behavioural quirk; the human brain’s reliance on narratives to make sense of the world means that they define much of our investment behaviour.  The noisy, chaotic and unpredictable nature of financial markets is anathema to our mind’s desire for order and clarity; stories reduce our discomfort and allow us to navigate the ever-capricious waters. It seemingly matters not that most of the yarns we spin are works of fiction.

Storytelling in financial markets is always driven by cause and effect.  There are two forms of this – either we predict a cause and effect before the event, or we look to rationalise an effect by defining a particular cause subsequent to the occurrence.  Although we might not always consider them as such both of these scenarios are about creating narratives – Y will happen because of X, or Y has happened because of X.  Investors do this for every conceivable situation – from justifying the hourly change of a stock price to the impact of a fifty-year demographic shift on asset class returns.  Everything must have a cause and effect underpinning the narrative.

In many other circumstances a straight and true line can be drawn between cause and effect – even if something might not be forecastable, it can be understood after the event. We might not be able to foresee a plane crash; but in most cases we have sufficient information to gauge its cause after the incident. Unfortunately, in the random and reflexive world of investment, accurately defining these aspects in either predictions or during a post-mortem is hugely problematic.  We know only too well about the hopelessness of forecasting and observe on a daily basis the myriad competing explanations for even the most irrelevant market or economic change.

If gleaning cause and effect in financial markets is so difficult, why do we spend most of our time talking about it?  As Storr succinctly notes: “Cause and effect is the natural language of the brain. It understands and explains the world.”  The alternative would be to exist in a financial environment defined by randomness and chaos – whilst this may be closer to the reality, it would not make for a comfortable existence.

In addition to the importance of cause and effect for our narrative-driven brains; Storr also highlights how the types of stories we tell are dependent on the mental models that we develop and then seek to defend. This has unquestionable relevance for investors – we all utilise models for interpreting the financial world; these can be structural (I might be a Keynesian economist) or temporary (I might be bullish on equity markets over the next six months). The belief sets that we maintain mean that when we create our stories we do so in a prejudiced fashion; as Storr explains: “Our storytelling brains transform reality’s chaos into a simple narrative of cause and effect that reassures us that our biased models…are virtuous and right”.

The importance of these models to our sense of identity means that much of our tale telling is in the defence of those models. Financial market participants are not coolly and impartially attempting to identify cause and effect; rather we are creating stories that corroborate our views and our beliefs, and often rail against those with conflicting perspectives or opinions. We see this constantly in the varied and often entirely contradictory interpretation of the latest release of economic data. If you understand an individual’s angle, mental model or incentive, then you are likely to have a good idea of the story that they may tell.

Does the importance of narratives in how our brain interprets the world mean that investors are condemned to exist in an environment of incessant, often meaningless and always biased storytelling?  To an extent, but it depends on how much we choose to engage. Financial markets provide an unrelenting torrent of outcomes which we can seek to forecast or explain via stories – indeed, much of the industry is fuelled by these precise activities – and whilst the notion of storytelling seems harmless, for investors it is likely to mean over-trading, over-confidence, stress and partiality.  Investors need to be comfortable stepping away (it doesn’t matter what the market did yesterday or why), be willing to say they have no idea what will happen or why it happened; and focus on some robust, evidence based, principles – all supported by a good story, of course.

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[i] The Science of Storytelling by Will Storr