Investors Should Embrace Probabilities to Improve Decision Making

One of the many oddities of the investment industry is that whilst it operates in a constant state of uncertainty and flux, its participants talk in the language of certainty.  Confident forecasts and dogmatic opinions are valued far more highly than circumspection and caveats.  This bias stems from the mistaken belief that expertise is related to conviction – that an expert must know the ‘right answer’.  Whilst this might hold in certain situations where the environment is stable and skill dominates outcomes; the reverse is true in random and unpredictable domains, such as financial markets. Here, expertise is more evident in humility, a willingness to revise views and to deal in probabilities.

There is certainly truth to the view that we are poor when it comes to thinking in terms of probabilities, and there are multiple examples of our irrationalities in this area – such as the tendency to neglect probabilities when considering extreme scenarios, and our propensity to overweight or ignore small probabilities.  The notion, however, that we should not talk about probability because we have limitations in this regard is entirely spurious; we cannot make a decision without taking some view on the likelihood of potential outcomes, even if we are not explicit about it.  If we bring our probability assessments into the open; it materially improves our ability to understand our biases, receive feedback on our judgements and learn.

Aside from our general struggle with thinking in such a manner, there are a range of other issues that limit the use of probabilities when making investment decisions and forecasts:

 Lack of expertise: As soon as probabilities are expressed around a variety of potential outcomes, there is an acknowledgement of uncertainty, which is often erroneously viewed as a lack of expertise and akin to the cardinal sin of stating “I don’t know”.  This issue is exacerbated by the fact that there will be others making bold, singular predictions – surely, they must know better?

Spurious accuracy: In uncertain environments views on probabilities are necessarily based on subjective judgements. Applying specific probabilities to a range of scenarios can appear overly scientific – what does it mean to believe the likelihood of an event is 17%?  This view, however, misses the value of applying probabilities.  Its use is not as a precise figure but as a measure of confidence and a means to monitor how our views evolve through time.

Implementation challenges: It is far easier to translate a strident, narrow view into an investment decision, than to attempt to reflect uncertainty and a variety of potential outcomes.

The forty percent problem:  Probabilities can be misused when making forecasts – the classic case is an individual assigning a 40% probability to an outlier event (a recession, typically). The 40% level means that if the event doesn’t occur their forecast was correct (on balance), but if the scenario does transpire it is of a high enough likelihood for them to be feted for the prediction.  This is an example of probabilities being utilised in a strategic, unhelpful fashion.

Changing minds:  When we frame outcomes in terms of probabilities, we allow ourselves the freedom to alter our view.  Whilst this should be considered positive and a reflection of realistic pragmatism; it is more often stigmatised as ‘sitting on the fence’ or providing a pre-prepared excuse for ‘being wrong’.

None of these arguments are particularly compelling and they are outweighed by the manifold benefits of thinking explicitly in terms of probabilities when making investment decisions:

Humility:  Simply by framing potential outcomes in terms of probabilities, we immediately acknowledge the uncertainty and unpredictability of any given judgement. We are divorcing ourselves from hubristic, narrow forecasts from the outset.

Reduce confirmation and commitment bias: When we espouse high conviction and overly precise views we come to be defined by them. Instead of refining or rejecting our beliefs in light of contrary evidence, we instead seek to preserve our ego by becoming increasingly committed and ignoring countervailing information.  By ascribing probabilities to a range of different scenarios we are more likely to remain open-minded and diminish the influence of being committed to any particular outcome.

Understand strength of view: It is often difficult to gauge the strength of someone’s view – this is particularly the case for binary situations where a ‘yes’ decision could either represent a marginal call or be a display of resounding confidence.  For example, if we had to predict the result of two coin tosses one, which was biased 55% in favour or tails and the other 95% towards tails – our ultimate view on the most likely outcome should be the same – but the level of confidence wildly different. Given that the investment industry tends to favour those making binary, conviction calls; such nuances are often lost and marked uncertainty can exit beneath a veneer of confidence – employing probabilities can help to reveal this.

Consider alternative scenarios: The use of probabilities forces us to consider alternative (often negative) scenarios; an individual with a high level of confidence in a specific outcome is still likely to ascribe some probability to other less favourable results.  Even if the chance of these occurring is regarded as minimal, there is an undoubted benefit from considering them, rather than ignoring them entirely and focusing solely on the primary case.  The best example of this would be in assigning a probability of a bullish case for a particular stock – the simple act of not giving the positive scenario 100% likelihood, compels consideration of other potential outcomes, and hopefully encourages debate.

Incorporate new information: A crucial element of successful decision making is the ability and willingness to revise views in the light of new information. If we make binary decisions (such as buy or sell) we can easily avoid incorporating fresh evidence by claiming our view has not altered (“it is still a buy”); however, by specifying probabilities it becomes increasingly difficult to ignore developments that are likely to impact your level of confidence.

For example, imagine you buy an active mutual fund and hold a 70% confidence that it will outperform its benchmark over three years; after one year the fund has generated significant excess returns – do you revise your view? If it is a simple, vague buy decision you are likely to continue to hold; however, if you have to update your confidence level there is likely to be an impact. Given the evidence of mean reversion in active manager returns, can you really still can you really continue to ascribe the same probability of outperformance over the next three years, or should it be revised lower?  Being explicit with probabilities forces us, and others, to challenge and update our thinking.

Evaluate past decisions: Given the scourge of hindsight bias it is often impossible to assess the quality of our historic decisions, as we cannot accurately recall our thought processes and feelings at the particular time. If, however, we consistently review and document our decision / forecast confidence level, we are in a far better position to understand how we came to a particular viewpoint and the manner in which we reacted to new information.  This can become a vital tool in learning from past behaviours.

Ascribing likelihoods to potential outcomes is not easy, as with all our behaviours it will be blighted by noise and bias; however, even if we are not openly expressing opinion in probabilistic terms, they are still deeply embedded in the views we hold.  As highlighted in Philip Tetlock’s Superforecasters and Annie Duke’s Thinking in Bets, being explicit about probabilities when making judgements or forecasts allows us to embrace uncertainty, affords us the freedom to revise our opinion as new information arrives and fosters the ability to learn from previous decisions.  In complex and ambiguous financial markets such features are invaluable.

Momentum Investing is Easy – So Why Does it Work?

Evidence on the effectiveness of momentum investing is overwhelming – it has delivered a return premium across markets and asset classes, and there is over two centuries’ worth of data (see Asness et al. 2014).  Whilst the performance advantage delivered is difficult to refute, it does present something of a puzzle – why should a strategy that is both technically simple to apply and behaviourally attractive deliver excess returns? Before exploring this question, it is worth clarifying these two claims:

Momentum is Easy:  A standard time series, price momentum strategy is relatively easy to construct and operate. Although there are a vast range of iterations and nuances (this is the investment industry, after all) – the basic premise is to go long assets rising in value, and short those falling. The technical barriers to employing this strategy in some form are limited.

Momentum is Behaviourally Attractive: Most of us are wired to be momentum investors. Being part of the trend plays to our desire to be ‘right’, to be a member of the herd and to conform to the compelling narratives that price momentum inevitably generates.  Participating in a momentum trade is psychologically comfortable.

Yet whilst most of us are momentum investors of some variety (often indirectly), rather than obtain the documented return premium – we help to create it.  Our behavioural limitations mean that our attempts at discretionary momentum investing (driven by human decision making) are deeply flawed and incur a cost, which can be exploited by systematic approaches.  It is important to remember that the evidence upon which the case for excess returns to momentum is built necessarily relates to systematic / rules based strategies.

In his book ‘Following the Trend’ Andreas Clenow described a traditional managed futures approach – a basic trend following strategy – as such:

“A statistical game with a slight tilt in your favour and that you just have to keep throwing the dice long enough to get the law of big numbers on your side”

This description bodes ill for the ability of humans to capture a momentum return.  Victor Haghani and Richard Dewey produced a study where financially literate individuals (including some investment professionals) had to bet on the result of a coin flip, which was loaded in their favour 60/40. They started with $25, could bet stakes as small as $0.01 and were given 30 minutes. Despite this favourable setup, only 21% reached the maximum payout of $250 and 28% went bust. Even more curiously, 48% of players bet on tails (which had the 40% probability) more than 5 times. If our behaviour can be so irrational and erratic in such a dispassionate setting, it is no wonder that investors make consistently poor decisions in stimulating and emotion-laden financial markets.

To understand how momentum investing driven by human judgement serves to create a fecund environment for rules based momentum approaches, it is helpful to consider the central tenets of a typical systematic process and understand how this compares to the features of discretionary (or human) decisions:

Momentum Systematic Discretionary
Winners Purchased Earlier Later
Winners Sold Later Earlier
Losers Sold Earlier Later
Diversification High Low
Rules Clear Vague
Persistence High Low

The characteristic approach of a discretionary, ‘human’ investor described above is driven and shaped by a range of behavioural factors:

Buying Winners Late:  Investors tend to underreact to news that fundamentally alters the value of a security.  Whilst this phenomenon may be caused simply by the pace of information dissemination; it is more likely a result of commitment bias (our reticence to recant prior views) and also the gradual development of a new narrative – one piece of newsflow or data is unlikely to shift the prevailing market story, but if this persists the story surrounding it will build strength, drawing in investors.  Also, as highlighted by Mark Granovetter (1978), we all have a different threshold for joining the riot (or herd) simply based on how many other people are participating. Momentum begets momentum.

Selling Winners Early:  As detailed by Shefrin (2010), the disposition effect – the tendency to cut winners and run losers – has a material impact on asset prices and the momentum effect.  In the case of relinquishing winning positions, investor willingness to sell on good news (and capture gains) serves to slow the adjustment of an asset price to its new fundamental value, thereby helping to foster momentum.

Selling Losers Late:  Whilst arriving conspicuously late to the party, discretionary investors often overstay their welcome in loss making positions.  Investors are committed to their viewpoint and suffer from confirmation bias, so may simply ignore new information that runs contrary to the view implicit in their positioning. This situation is exacerbated by cognitive dissonance, which makes them unwilling to crystallise losses and acknowledge error.

Lack of Diversification: Spreading risk across a variety of positions and asset classes is crucial to all sound investment approaches, but is particularly relevant (essential) for momentum-driven investing.  The hit rate for individual positons can be low and predicting where momentum will arise (and be sustained) before the event is difficult. There is also the constant threat of sharp reversals, which can rapidly destroy gains made in previously successful positions.  The danger for discretionary investors is that they become overconfident in their ability to identify the best trades and also overweight the most recent market activity.  This can lead to exceptionally narrow portfolios focused on securities that have the strongest current momentum, rather than holding a diverse range of positions with varying levels of price strength.  Whilst systematic momentum approaches can become concentrated, they consistently maintain a broad opportunity set and (should) have prudent risk controls.

Vague Rules: The best defence against the impulses of emotion or narrative-led decision making is a set of decision rules, which dictate investment behaviour.  Systematic momentum approaches are founded on such principles, and actively divorce decision making from all intrusive elements except the key variable – which is typically price, but can include fundamentals, such as earnings.  Discretionary human decisions are rarely driven by a binding set of rules and, absent strict guidelines, our actions are impacted by a range of factors, most of which have limited relevance to the judgement at hand. These might include: how we feel at the time of making a decision (our emotional state), our prior experience with a particular investment, the most recent news we have read, our belief in a particular narrative, or even how hungry we are (Danziger,  Levav,  & Avnaim-Pesso, 2011), and the weather (Hirshleifer & Shumway, 2003).

Being behaviourally consistent without a set of rules to adhere to is close to impossible; this was highlighted by Daniel Kahnemann (alongside Rosenfield, Gandhi and Blaser) in an article published in Harvard Business Review in 2016.  The authors discussed how the influence of irrelevant factors can lead to huge variability in decision making; unlike biases, which tend to be consistent and persistent, the impact of noise is random and erratic – and therefore more difficult to mitigate.  Even with the best intentions, the ability of a human decision maker to remain disciplined is severely limited.

Low Persistence: Perseverance is undoubtedly a key requirement for successfully capturing the momentum premium, and its return patterns make this an exacting challenge. The hit rate is unlikely to be high – therefore you will be ‘wrong’ frequently; furthermore, there will be many false dawns where momentum appears and rapidly evaporates.  There will also be prolonged fallow periods in choppy markets where returns to momentum are poor, and short-term shifts in markets that see prevailing trends whipsaw and sharp losses incurred.  Whilst a systematic, rules based strategy can be agnostic on such a performance profile (and indeed specifically designed to withstand it); the foibles of momentum are hugely problematic for discretionary investors, for whom it would be a herculean task to remain sufficiently controlled amidst the welter of behavioural impediments.

Whilst most of us are invariably attracted to momentum investing and carry it out in some form (even if it is merely implicit in our actions, rather than an express choice), few of us do it well.  Our decision making is blighted by our behavioural shortcomings and the substantial influence of irrelevant ‘noise’.  Human decision makers are willing but inferior momentum investors, creating the opportunity for systematic approaches to capture a premium.

Key reading:   

Asness, C. S., Frazzini, A., Israel, R., & Moskowitz, T. J. (2014). Fact, fiction and momentum investing.

Clenow, A. F. (2012). Following the trend: diversified managed futures trading. John Wiley & Sons.

Danziger, S., Levav, J., & Avnaim-Pesso, L. (2011). Extraneous factors in judicial decisions. Proceedings of the National Academy of Sciences108(17), 6889-6892.

Granovetter, M. (1978). Threshold models of collective behavior. American journal of sociology83(6), 1420-1443.

Haghani, V., & Dewey, R. (2016). Rational Decision-Making Under Uncertainty: Observed Betting Patterns on a Biased Coin.

Hirshleifer, D., & Shumway, T. (2003). Good day sunshine: Stock returns and the weather. The Journal of Finance58(3), 1009-1032.

Hurst, B., Ooi, Y. H., & Pedersen, L. H. (2017). A century of evidence on trend-following investing.

Kahneman, D., Rosenfield, A. M., Gandhi, L., & Blaser, T. (2016). Noise: How to overcome the high, hidden cost of inconsistent decision making. Harvard business review94(10), 38-46.

Shefrin, H. (2010). How the disposition effect and momentum impact investment professionals.

Five Simple Heuristics to Make Us Smarter Investors

Heuristics, or what we might call ‘rules of thumb’, have become somewhat maligned as a method of decision making in recent years. They are often erroneously conflated with cognitive biases and suffer their negative connotations.  Furthermore, the scepticism regarding the effectiveness of heuristics has been exacerbated by the ubiquitous system one (fast) / system two (slow) thinking construct (popularised by Daniel Kahneman), which can frame system one as the domain of rash, unthinking errors and system two the home of cool, rational and correct calculation.  System one and system two is frequently used as shorthand for ‘bad’ and ‘good’ thinking respectively, with the mental shortcuts that define heuristics firmly in the former grouping.

Even without exploring the limitations of the ‘two systems’ theory (see Osman, 2004); it is clear that heuristics do not fit within such a binary framework.  They can be either instinctive or deliberate, and often deliver results that are more robust than more complex methods of judgement.  For example, see Serwe and Frings (2006) on predicting the result of Wimbledon tennis matches, and McCammon and Hageli (2007) on avalanche risk.  Andrew Haldane’s speech around this topic is also excellent.

Unsurprisingly, the best definition of heuristics comes from Gerd Gigerenzer, who has been the pre-eminent voice on this subject, and the most vocal critic of the work of Kahneman.  He defines the heuristic approach as such:

“A strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods” (Gigerenzer & Gaissmaier, 2011).

Although heuristics do feature in the world of investment there is an undoubted preference for complexity.  This is understandable – financial markets are intricate, unpredictable and often unfathomable; therefore we struggle to believe that simple solutions can be effective – a complex problem must be untangled by a complex process.  Furthermore, it is difficult to justify fees (and our own existence in the industry) by recommending quick and easy solutions.

This is not to suggest that everything in investment can be improved through simplification; rather we should not pour scorn on approaches that seek to streamline decision making in the midst of high uncertainty or difficulty. Sensible heuristics should be viewed as a valuable tool for investors, and below I have suggested five simple rules.

Jam Yesterday Heuristic:  If an investment has produced strong returns in the recent past, reduce your expectations for future performance.

Whilst this is self-explanatory, it often seems to run contrary to the behaviour of most investors. The gains registered by an investment before you decided to purchase, are returns you will not be enjoying in the future!

Upside Down Heuristic:  If an investment has outperformed its benchmark by a significant margin on the upside, it can also do so on the downside by a similar magnitude. If this is not acceptable, do not invest.

The lure of funds that have generated material excess returns can be irresistible, but if they have taken sufficient active risk for their performance to diverge markedly on the upside, they could generate similarly divergent returns in a less favourable fashion. Whilst index +20% is attractive over three years, only invest if you are willing to withstand the reverse 

– Keep it Simple Heuristic:  If presented with similar investment choices that differ in terms of complexity, select the simpler option.  

Simple investments are certainly no panacea, nor should they always be the preferred choice.  However, in situations where it is difficult to differentiate between alternatives in terms of quality, the preference should be towards that which is most easily understood.  Having clear and well-calibrated expectations is crucial for any investor.  If an investment strategy is straightforward, it is less likely to deliver negative surprises and you are more likely to stay invested for the long-term.

Hedge Your Bets Heuristic: If you have any uncertainty over the timing of an investment, phase it into the market in equal tranches.

Phasing investment decisions is an effective behavioural trick, not only does it reduce the potential reference point impact of a single price on entry or exit; it also allows for the positive framing of your decision. If the price of your targeted asset rises as you stagger your investment you can feel content that you initiated the purchase when you did, whereas if it falls you can be glad that you decided not to invest in one hit.

– Double for Drawdown Heuristic:  Your absolute minimum expectation for drawdown in any diversified investment (outside of cash) should be double its expected long-term volatility.  If this is not palatable, do no further work.

This rule is not a comment on the appropriateness on volatility as a measure of risk, nor the expected distribution of asset class returns (which are often skewed); rather it seeks to translate the most commonly employed investment risk metric (volatility) into a simple yes / no decision based on a willingness to bear (temporary) losses.  Of course, volatility is a woefully inadequate measure of risk for illiquid investments with artificially low volatility, and both undiversified or ‘penny in front of steamroller’ strategies, which suffer from sharp tail risks.

None of these heuristics is likely to be revelatory, but amidst the myriad options that investors face as they seek to meet their financial objectives, it is easy to lose sight of crucial principles that can lead to improved investment behaviour and better long-term outcomes.  Although lacking the intellectual cachet of more sophisticated approaches; easy to remember, simple to apply rules of thumb can be remarkably effective way to navigate a complex investment landscape.

Key Reading:

Gigerenzer, G., Todd, P. M., & ABC Research Group, T. (1999). Simple heuristics that make us smart. Oxford University Press.

Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62, 451-482.

McCammon, I., & Hägeli, P. (2007). An evaluation of rule-based decision tools for travel in avalanche terrain. Cold Regions Science and Technology, 47(1-2), 193-206.

Osman, M. (2004). An evaluation of dual-process theories of reasoning. Psychonomic bulletin & review, 11(6), 988-1010.

Serwe, S., & Frings, C. (2006). Who will win Wimbledon? The recognition heuristic in predicting sports events. Journal of Behavioral Decision Making, 19(4), 321-332.

Performance Consistency is not an Indicator of Equity Fund Manager Skill

One of the most commonly employed methods for judging whether an active equity fund manager possesses skill is monitoring performance consistency that is the regularity with which a particular manager outperforms their benchmark index over specific time periods (frequently calendar years, but often briefer).  This shorthand gauge of quality is pervasive across asset management groups (sellers), fund selectors (buyers) and the media.  The use of such a context free number is deeply flawed.  Not only does it disassociate process from outcome, and lure investors into strategies with a high probability of mean reversion; but it biases us against the distinctive, high conviction equity managers that better justify the use of active management.

Although the pull of performance ‘reliability’ is difficult to resist; the use of short-term excess return consistency measures to assess active equity managers is predicated on two flawed notions:

– Active equity managers have the ability to dependably predict short-term market behaviour.

It is surely now incontestable that the short-term movement of equity markets is highly unpredictable.  For an active manager to deliver consistent returns across discrete time periods through the application of skill, they must hold the uncommon ability to anticipate market direction and drivers, and appropriately shape their portfolios to benefit.

– The market consistently rewards good investment decisions.

To believe that short-term performance consistency can be employed to identify skill, one must contend not only that a manager makes consistently good investment decisions (on average) but that the market duly rewards these judgements in the near-term.  Markets are reflexive and for sustained periods can be driven by narratives and momentum, detaching dramatically from any semblance of fundamental reality.  Objectively robust decisions can go unrewarded for prolonged periods.  The implicit assumption embedded in performance consistency analysis of an active equity funds is that the market has unerringly validated the manager’s investment decision making.

Although the point of this post may seem relatively minor, it is critical to how we view and assess active equity management.  Performance consistency is employed in marketing campaigns, is frequently used by fund selectors to ‘screen’ sectors for talented managers, and often drives the external ratings given to funds.  This widespread acceptance of consistency as a robust indicator of skill exacerbates the damaging focus on short-term outcomes and leads to extensive attribution errors.  Furthermore; it must reduce the opportunities available for genuinely long-term focused active equity investors.

As difficult as it is to separate performance consistency from the skill of any active equity manager, unless a specific link can be drawn between the outcomes delivered and the underlying investment process, this should be our default stance.  Markets embark on prolonged trends and are driven for sustained periods by in-vogue themes;  against such a backdrop it is inevitable that groups of active managers will deliver consistent headline performance as they (often inadvertently) hold style biases that benefit from the prevailing tailwinds.  Given the structure of the market it would be surprising if there were not clusters of equity managers exhibiting consistent excess returns.

The behaviour of financial markets creates patterns of outcomes that are ideal for establishing a mirage of skill, and whilst we are hardwired to draw links between these outcomes and the related process, the simple fact is that skill in active equity management cannot be gleaned from performance alone, irrespective of the form it takes.

It is not only that headline performance consistency is a deeply misleading means of assessing the ability of an active equity manager, but as a characteristic it is the exact opposite of what fund selectors should be seeking.  By definition, long-term, high active share, conviction investors will not deliver performance consistency over the short-term.  There will be periods (often prolonged) when their style is out of favour and the ‘market’s perception’ diverges materially from their own.  Through such spells of challenging performance we should expect them to remain disciplined and faithful to their philosophy and approach; not wish them to latch onto the latest market fad in an effort to achieve improved short-term returns.

Consistency is absolutely paramount to the assessment of active equity managers, but we are focused on the wrong sort of consistency.  Rather than obsess over the persistence of short-term outcomes; we should focus our attention on the consistency of manager behaviour relative to their stated philosophy.  Doing so would improve the probability of achieving excess returns from holding active equity managers over the long-term.

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

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

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

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

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

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

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

Active Management Fees are not about Performance Alone

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

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

Is Active Fund Management a Market for Lemons?

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

– Withdrawal of higher quality vehicles.

– Reduced size of market.

– Reduced average quality.

– Reduced average willingness to pay.

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

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

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

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

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

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

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

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

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

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

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

Key reading:

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

MIRRORS – Creating a Behavioural Checklist for Investment Decision Making

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

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

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

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

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

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

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

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

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

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

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

Key Reading:

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

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

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

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