Stale Pricing Does Not Equal Low Risk or Low Correlation

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

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

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

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

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

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

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

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

How Probabilities are Expressed Can Impact Our Investment Decision Making

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

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

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

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


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

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

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

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

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

Active Management is Reliant on the ‘Inside View’

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

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

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

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

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

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

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



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



Why Are So Many Fund Managers Men?

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.



[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 Review22.





Your Investment Time Horizon Might Be Shorter Than You Think

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.

* 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.

Why Are So Many Investment Decisions Based on Biased and Contrived Stories?

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.

[i] The Science of Storytelling by Will Storr


When to Ignore a Fund Manager

The active asset management industry is overpopulated and hugely competitive, and, as with any sales activity, delivering the ‘appropriate’ message to prospective and incumbent clients is hugely important.  They are a range of common utterances from active fund managers, which feel as if they are intended to cultivate a certain image or manage client concerns, rather than present a realistic assessment of crucial issues. The types of statements listed below should be disregarded, or at least considered with a liberal dose of scepticism:

“ESG factors have always been at the heart of our investment process”.

“The growth in assets under management has not had a negative impact on our investment approach”

“We have reviewed strategy capacity and increased it by £1bn from our previous estimate”

“There is a bubble in passive strategies”

“Markets are not rewarding fundamental analysis”

“My (asset class / style / factor) is historically cheap”

“My performance has been driven by individual company selection, with no major impact from factor exposures”.

“We rarely use sell-side research”

“Even though half of the team have left to join a new firm we can continue to run an unaltered investment process”

“I spend 95% of my time at my desk on pure investment work”

“My CIO has given me their full support despite the difficult period of performance”

“The merger / acquisition / restructure has not been a distraction”.

“I think that interest rates are going to…”

“The performance fee structure means that we are perfectly aligned with our clients”

“We have an approach that can adapt to different market conditions”

“Having a team of 27 doesn’t hinder our decision making”

“All of our excess returns are from credit selection, being overweight credit risk hasn’t really been a factor”.

“I don’t think we suffer from any biases in our recruitment policy, it just so happens that the best candidates all went to the same universities and look the same”.

Although this post is somewhat tongue-in-cheek, there is an important point at its heart. If all firms and teams present themselves with a similar sheen, then there is a significant cost to being an outlier that is frank about problems, challenges and limitations.  This fosters an environment where openness and transparency are viewed as business risks.