As noted by Jason Collins in his excellent behavioural economics blog, Daniel Kahneman’s next book is expected to focus on the concept of ‘noise’ and how it impacts our judgements.  Although often conflated with behavioural biases, noise is a distinct phenomenon that relates to the random variability in our decision making.  Whilst biases exhibit a consistency of effect (at least in direction, if not magnitude), noise is defined by the absence of consistency.  A watch that loses time each day is biased; a watch that can either gain or lose time during any given day is noisy.

In an article in Harvard Business Review, Kahneman, alongside his collaborators, discussed how individual choice is “strongly influenced by irrelevant factors” and gave examples of how professionals are prone to contradict their own previous conclusions.  The problem of attempting to grasp the idea of noise is that it is so amorphous – whilst we can at least develop a (limited) framework for defining and understanding biases, by definition noise is hard to isolate and anticipate. Noise can stem from entirely spurious factors – such as mood, weather or hunger – or variables that we perceive to be meaningful, but are in fact meaningless.  It is certainly possible to test whether noise exists in any given scenario – by observing decision making consistency – but this is only the start of understanding the issue.

Noise has profound implications for investors, but is often ignored or, at least underappreciated. It can be difficult to accept that our judgements can be shaped by erroneous, often farcically minor, factors.  Furthermore, we are often uncertain about the key variables that define any given problem.

In the realm of investment decision making, we can define two separate forms of noise:

• When given the same objective data and relevant variables we are unlikely to make the same decision. This is consistent with Kahneman et al.’s article – even if we hold the meaningful factors constant, other irrelevant issues will lead to inconsistent choices.

• We don’t know what the relevant information is and therefore make decisions based on what we perceive to be ‘signal’ but is in fact noise. This is such a major problem – one which the industry perpetuates – that it is difficult to know where to begin.

We could crudely define these as unconscious noise and conscious noise.  In the first case there are many factors that impact our decision making over which we have no real awareness and we would be reticent to acknowledge had any influence over us.  In the second case, the issue is uncertainty about what constitutes relevant information and what is superfluous noise – this will vary by context and discipline, but it is difficult to think of an industry with a greater ratio of noise to signal than asset management. Conscious noise is the oxygen on which the industry, in its current form, exists.

Second by second coverage of random market movements (with accompanying narratives), heroic forecasts (usually wrong), luck masquerading as skill, complex products and every decreasing time horizons are just a few of the factors that contribute to the maelstrom of noise that investors are forced to navigate.  Of course, this is good for the industry – simplicity and inaction are not typically an aid for revenue generation – but it fosters a situation where decision consistency becomes close to impossible for most investors.

Kahneman et al. proceed to argue that a “radical solution” for the problem of noise is the replacement of human judgement with algorithms, or a structured set of decision rules. They also acknowledge, however, that such processes are less effective in environments where uncertainty is high or where consistency is difficult to attain.

Can algorithms be effective  in muting the incessant noise in investment markets, and even exploit it, to improve decision making?  To a certain degree.  One effective and humble decision rule / heuristic, is portfolio rebalancing.  A structured and consistent approach to rebalancing a portfolio back to target weights is proven to be effective and cancels out a great deal of market noise.  It ensures both that your portfolio doesn’t stray too markedly from its desired allocation, and that you consistently sell assets that have become more expensive and reinvest in those that have become cheaper.  Whilst this might seem a simple course of action, rebalancing into assets that have struggled (amidst the prevailing negative market narrative that will inevitably accompany the poor performance) can be difficult without a formal / systematic decision rule.

The oft-mooted remedy to the problem of noise and inconsistency in human-led investment decisions is the movement to full automation and the use of complex algorithms / machine learning.  It deals, at least in part, with the aforementioned ‘unconscious noise’ angle as the feelings of the decision maker are no longer a direct issue, however, at some point human judgement will inevitably exert an influence – for example, in the decision to initially invest in a strategy or to redeem, thus it does not provide full immunity.

More importantly, algorithms do not necessarily resolve the issue over noise in regard to the use of irrelevant information.  Many an ETF has been created based on factors with no empirical credibility; therefore although the decisions within the process can be dispassionate, the very existence of such strategies is reliant on the fact that there is noise in the market.  Furthermore, even the most sophisticated systematic approaches are vulnerable to trading based on patterns than are simply a consequence of random market movements, with no structural, technical, economic or behavioural reason to exist or persist.

Even without full automation, there are means of dulling the noise in human-led investment decision making, such as checklists.  Whilst checklists are particularly effective in areas such as aviation and surgery where many of the checks can be simple and objective –  whether the correct leg is being operated on, for example – they can still improve discipline and focus around decisions with inherently more subjectivity. Formally reviewing a list of your key criteria prior to making an investment can serve to highlight noise driven deviations from your core process and also acts as a useful record for reviewing historic decisions.  Of course, using checklists when answers are subjective means the potential for manipulation is ripe, so it pays to be as rigid as possible when defining questions; however, even if this is not possible, checklists remain a useful means of reaffirming your investment principles amidst the market noise.

Noise is an inescapable feature of human judgement.  In random and uncertain investment markets its influence is profound. Although it is impossible to eradicate, acknowledging its presence and taking steps to simplify and systematise certain decisions can be an effective way of turning down the volume.

Key Reading:

Kahneman, D., Rosenfield, A. M., Gandhi, L., & Blaser, T. (2016). Noise: How to overcome the high, hidden cost of inconsistent decision making. Harvard Business Review, 94(10), 38-46.

Please note all views expressed in this article are my own and are not necessarily shared by my employer.

“It infuriates me to be wrong, when I know I am right.” (Moliere)

In a previous job, I worked for an investment consultant who employed a psychologist to assist in their fund manager research process.  I vividly recall her making a particularly astute point about how we constantly hear about the failings of active fund managers, but rarely anything about how they can improve.  Whilst inevitably there are exceptions to this view, it is certainly reasonable to suggest that evidence of fund manager learning and development is scant.  I do not mean that there is a lack of generic, box ticking courses with no specific purpose, rather an apparent absence of weaknesses being openly identified and addressed.

This may seem odd as active management should be a ripe environment for constant improvement – the difficulty level is high, there is continual feedback (though not necessarily the right sort) and sufficient latitude in the role to change behaviours.  There is, however, a major problem; a key aspect of learning is the ability to identify areas of limitation and, in particular, admit mistakes.  This is something that fund managers are typically not very good at.  Although it is unfair to imply that the profession is unique in this regard – it is a constant struggle for us all – the environment in which a fund manager operates makes it particularly difficult to be open about failings, and therefore seek remedies.

“When an expert is wrong the centrepiece of his or her professional identity is threatened” (Tavris & Aronson, 2008)

The primary driver of our inability to admit errors and failings is cognitive dissonance – the battle to deal with situations where we hold contradictory beliefs, attitudes or feelings.  Such instances are deeply uncomfortable and we seek to resolve these inconsistencies as rapidly as possible, in order to preserve a coherent sense of self.  The most common ameliorative is some form of simple justification: “I am a safe and considerate driver, but the message on my phone could have been urgent, and the road was clear”.  This sort of internal monologue is a crucial part of how we navigate everyday life and look to validate our own behaviour.

Whilst cognitive dissonance is focused on self-perception, a similar dynamic is apparent in how we seek to present ourselves. In The Presentation of Self in Everyday Life (1959) sociologist Erving Goffman describes how individuals seek to manage and control the information that we offer others, and are consistently engaged in some form of performance, where we try to shape the impression that we leave.  Thus, we are often behaving in a way that we perceive to be consistent with what we believe are the key traits of the particular ‘role’ we are hoping to play – am I meeting the criteria expected of this type of person?

We are constantly faced with the challenge of attempting to manage how we perceive ourselves and how others perceive us.  To develop their career, and sell their own capabilities, fund managers are required to present themselves as assured experts with an ability to opine on virtually any investment issue and present their ideas with little hint of doubt. The expectations of fund managers almost entirely contradicts the reality – adding value in markets is fiendishly difficult and a hit rate north of 60% should be deemed heroic; furthermore, any edge possessed by a particular manager will likely be focused and specific, not all encompassing. Markets are chaotic and unpredictable, even for the most talented manager, mistakes will occur with alarming regularity.

Yet it is hard to sell your fund to clients if you talk about failed positions, and approach your investment ideas with circumspection rather than unabashed conviction. Fund managers need to convince themselves and others that they have expertise, and a level of expertise that is superior to peers. Experts don’t make mistakes…

The other major problem for fund managers is one of commitment. A crucial part of their job involves convincing themselves, colleagues and clients of their opinion, and the more this opinion is repeated the more committed they become to it. In the face of overwhelming disconfirming evidence, fund managers often persist with a view or indeed become even more emboldened in it – rather that than lose face and recant on their previously high conviction ideas.  On countless occasions I have witnessed fund managers take such forthright positions that it becomes obvious that even if they are proven wrong it would be entirely unconscionable for them to perform an about face.  Often an investor’s entire professional identity will be forged on one particularly bold viewpoint.

In addition to simply ignoring mistakes, there are a number of tactics fund managers can employ to protect their ego.  These were first raised by Phillip Tetlock (1999) when he analysed how forecasters dealt with having particularly poor records in forecasting, and also highlighted by James Montier in his Seven Sins of Fund Management. My favourites include:

–  “If only”: If only the company hadn’t decided to engage in that expensive acquisition, my view would have been correct…

– “Ceteris paribus”:  Some external factor, which was impossible to foresee, impacted my otherwise robust analysis.

– “Not happened yet”:  A favourite of the dogmatists and those with commitment bias issues – I am right; it has just not been reflected in markets, yet

The problem is not that such excuses are always without merit, but that they subvert any need for introspection. As financial markets exist in a constant state of flux, it is all too easy to move forward with minimal consideration of the decisions that have come before.

The mistake I can recall most readily from my own career was a decision to sell a high yield bond fund early in 2009; if this was not at the ultimate peak of credit spreads, then it was impressively close.  I have, of course, made many errors since, but this is one that resonates sharply, I think because it was particularly stupid. It was not a mistake because junk bonds generated strong performance following the sale (you cannot decide the quality of a decision based solely on noisy market outcomes), but because, based on the evidence available, there was a (very) high probability of subsequent returns being significantly above the historical average.  This was a clear behavioural error driven by the sheer tumult of the period, leading to an ignorance (wilful or otherwise) of the data and historical precedent. I actually remember very little about the decision, except that it felt good for a few days and then bad for a few years.

This sort of mistake can be considered simple, in that there is the combination of a bad process followed by a bad outcome – these are the easiest to acknowledge and most difficult to ignore.  Investment markets, however, aren’t often as straightforward; their randomness and unpredictability often mean that sensible decisions are regarded as mistakes, whilst poor judgements can masquerade as investment acumen. Given this, it is naive to review a fund manager’s underperforming positions and suggest that they all constitute mistakes.  Relative stock price performance is far too capricious to use as a reliable arbiter of decision quality.

Identifying and understanding a mistake requires knowledge of the initial rationale for a particular course of action and then some comparison with the outcome.  This does not mean making forecasts, but simply comprehending the key drivers behind a decision.  For example, if part of my rationale for investing in a particular company is its strong balance sheet and it subsequently struggles with its sizable operating lease commitments, that is a specific error of judgment. Approaching mistakes in a process / outcome manner is the only way to address shortcomings and seek to make improvements.

We can think about most mistakes made by fund managers as either analytical or behavioural.  In the former, specific areas of flawed analysis can be pinpointed – either we looked at the wrong evidence or misinterpreted it, as in the example on lease commitments in the previous paragraph.  For behavioural mistakes, we make poor decisions despite having the appropriate evidence available to support a decision; my mistake of selling high yield at the most inopportune time was made in spite of their being sufficient information for me to know ex-ante that this was likely to be an error.  It was simply that a swathe of behavioural factors (most likely recency, availability, probability neglect and affect-led decision making) became overwhelming.  Of course, these two broad categories of mistake are not mutually exclusive; many analytical mistakes will be driven by behavioural flaws, but spending some time at least attempting to decipher how mistakes were made, and perhaps identifying patterns, can be a valuable process.

The concept of learning from mistakes can sound horribly trite, but it is actually an integral element of improved decision making.  In many ultra-competitive professions, such as fund management, where expectations about performance are significantly above any semblance of reality, being open about lapses and faults can damage ego, harm career prospects and lose clients. However, choosing to ignore or obfuscate rather than engage with things that have gone wrong is undoubtedly of detriment to future behaviour.  Taking steps to better engage with mistakes is not technically difficult, and there are a number of simple steps that can make a material difference, such as:

– Documenting decision making appropriately:  Make sure that prior to committing to a decision you detail the underlying rationale and key risks. It is impossible to assess a mistake after the event if you have not explicitly noted your thinking at the time a decision is made.  As much as you might think you can recall the reasons behind a past course of action – your memory is too fickle to be a reliable source.

– Performing a regular mistake audit: On an annual or semi-annual basis you and your colleagues should write down two / three mistakes they have made over the period.  They have to be genuine mistakes, with no caveats or excuses allowed.  This sort of process can go at least some distance toward creating a more favourable climate for embracing the times when we err.

These steps, however, can be fruitless (or indeed counterproductive) if you are operating in an environment that continues to stigmatise mistakes. In such organisations and teams, mistakes are ignored, excuses created and responsibility apportioned elsewhere.  Although this may enhance the esteem of the team (internally and externally); it is a terrible waste of a great resource to improve future decision making.  As the structural pressures on active fund managers increase, they need to grasp every available edge.

Key Reading:

Goffman, E. (2002). The presentation of self in everyday life. 1959. Garden City, NY.

Montier, J. (2005). Seven sins of fund management.

Tavris, C., & Aronson, E. (2008). Mistakes were made (but not by me): Why we justify foolish beliefs, bad decisions, and hurtful acts. Houghton Mifflin Harcourt.

Tetlock, P. E. (1999). Theory-driven reasoning about plausible pasts and probable futures in world politics: are we prisoners of our preconceptions?. American Journal of Political Science, 335-366.

Please note all views expressed in this article are my own and are not necessarily shared by my employer.

The idea of loss aversion – that losses ‘loom’ larger than gains – is one of the most established and prominent findings in behavioural economics, and could be considered a foundation stone for the entire discipline.  Recent research, however, has questioned the validity and robustness of the supporting evidence, suggesting that it is at worse a false concept and at best overstated (in particular see Yechiam 2018, Gal & Rucker 2017).  Given the issues surrounding p-hacking and failed replications, placing such widely-accepted beliefs under scrutiny should be applauded yet, in this instance, some of the claims appear exaggerated.

One prominent criticism of loss aversion has been that its presence is reliant on the stakes involved being of a sufficient magnitude –  the evidence evaporates if the potential loss or gain is not meaningful.  Harinck et al. (2007) argue that the loss aversion phenomenon actually reverses when the amounts of money involved in a decision are small.  This makes intuitive sense – if an individual were gambling a minor amount of ‘play money’ on a slot machine during a visit to a casino, they are unlikely to be particularly loss averse, indeed the ‘utility’ from winning could easily outstrip any pain from losing.  Conversely, if a risky decision has the potential to incur a material cost, then the classical features of loss aversion should take hold.

The extent to which a loss is considered meaningful or an amount of money considered ‘small’ will also be heavily dependent on its size relative to wealth.  The same monetary amount at stake could be viewed as inconsequential to an affluent individual, yet incredibly valuable to another with less resources – the ‘marginal utility’ of the lost money would be far greater in the latter instance.

The influence of bet size and relative materiality are two reasons why it is difficult to create general rules around the concept of loss aversion; however, more vital, and certainly less prominent, is the importance of reference points.  Reference points dictate what is considered a loss or gain – we can think of them as a break even point. Although in certain circumstances defining a loss may seem simple, this is far from the case – losses are subjective rather than objective. Understanding reference points is crucial in ascertaining how and where loss aversion may occur.

The major problem is that reference points are not fixed, but subject to a multitude of behavioural biases and heavily dependent on individual differences, environment and decision context.  Let’s take a simple investment example to illustrate the point:

There are three investors, A, B and C, each have been invested in the same portfolio for the previous 12 months.  Over this period, the portfolio has fallen in value 10% and its benchmark has lost 15%.  From the perspective of loss aversion and reference points, how does each investor feel about this outcome?

– Investor A is satisfied with the performance relative to the benchmark and considers it to be a ‘gain’.

– Investor B is disappointed with the absolute loss suffered by the portfolio over the period.

– Investor C is pleased with the returns as he just spoke with his friend and their portfolio lost 22% over the same period.

This is a heavily stylised example, but aims to emphasise the point that reference points can vary between individuals and within individuals, and in many cases it is impossible to know what that reference point is.  Absent that information, it becomes difficult to precisely understand or predict behaviour consistent with loss aversion in all situations.  Of course, in certain circumstances the reference point might be obvious or we might be able to decipher it from analysing individual behaviour, but they should still be considered highly variable and vulnerable to manipulation.

Given that the genesis of behavioural economics was, at least in part, a reaction to the rigidity of classical economics and the ultra-rational assumptions made about individual / collective behaviour, it seems nonsensical to criticise loss aversion for not being universally applicable.  It is, however, useful to be reminded that circumspection is required when making broad claims about any research findings.

Will loss aversion appear in a consistent fashion, irrespective of context or individual difference?  No, but in some ways that it the point.

Key Reading:

Gal, D., & Rucker, D. (2017). The Loss of Loss Aversion: Will It Loom Larger Than Its Gain?.

Harinck, F., Van Dijk, E., Van Beest, I., & Mersmann, P. (2007). When gains loom larger than losses: Reversed loss aversion for small amounts of money. Psychological science, 18(12), 1099-1105.

Yechiam, E. (2018). Acceptable losses: the debatable origins of loss aversion. Psychological research, 1-13.

Please note all views expressed in this article are my own and are not necessarily shared by my employer.