Value in Mean Reversion?

Many shall be restored that are now fallen; and many shall fall that are now in honour (Horace, Ars Poetica)

From Horace to Graham and Doddsville

The quote above is now a couple of thousand years old but was used by Ben Graham and David Dodds in their seminal book on value investing, Security Analysis, a term invented by the book's title. At root it’s a simple plea for understanding that current market conditions, no matter how placid or tempestuous, will pass. The job of the conscientious investor is, at worst, to ignore the short-term forecasts or, at best, to take advantage of them.

Some psychological quirk means that a small subset of humanity latches onto this concept instantaneously and holds to it, like an investing life preserver, through thick and thin. The rest of us either learn the slow, hard and painful way or, more likely, continue to be storm-tossed. Occasionally someone gets washed up on a tropical paradise and is accounted a genius but mostly we drown, quietly, where no one can see us waving.

Mean Reversion is Mental

Value investing is the behavioural finance equivalent of Tantric sex; it takes a long time to work and requires considerable abstinence for long periods of time. But the end result is extremely satisfying. Well, so I’ve heard.

The point, the main point and possibly the only point about value investing is that it’s as much a psychological exercise as a technical one. Of course, we can’t hope to be great investors without hard work but most of us can be better than average merely by learning to control our emotions – which means building a financial platform that can withstand the worst excesses that the markets can throw at us.

The Thaler-De Bondt Hypothesis

In essence, mean reversion theories state that stocks that have previously done badly will tend to outperform and those that have done well will underperform. Much of the recent work on this topic originates with Richard Thaler and Werner De Bondt – see, for instance, Does The Stock Market Overreact? and Further Evidence On Investor Overreaction And Stock Market Seasonality:

“... the paper provides new evidence consistent with the simple behavioural view that investors overreact to short-term (i.e. a few years) earnings movements. Certainly, within the framework of the efficient markets hypothesis, it is distinctly puzzling that a dramatic fall (rise) in stock prices is predictive of a subsequent rise (fall) in company-specific earnings.”

Subsequent studies have confirmed this effect. In Death, Taxes and Reversion to the Mean, a report by Michael J. Mauboussin for Legg Mason Capital Management, we find:

"Various studies conducted over multiple decades document this reversion-to-the-mean pattern. We have reproduced the results here, using data from over 1000 non-financial companies from 1997 to 2006 ... We start by ranking companies into quintiles based on their 1997 ROIC. We then follow the median ROIC for the five cohorts through 2006. While all of the returns do not settle at the cost of capital (roughly eight percent) in 2006, they clearly migrate toward that level".

To summarise: mean-reversion is alive and well. The study's well worth reading for some ideas about predicting companies that don't mean revert. We'll look at that another day.

People Don’t Do Stats

If the idea behind mean reversion is so obvious then why doesn’t everyone eventually adopt it? Roughly we can point to a couple of issues. Firstly human psychology isn’t built around statistical concepts like mean reversion, we prefer to trend follow rather than go against the flow. Secondly, in the bubble of time in which we exist from moment to moment it’s all too easy to convince ourselves that the mean itself has changed: indeed this does happen from time to time – so what may have been an acceptable valuation criteria in the 1930’s or the 1990’s will prove to be far from the average in the 2010’s.

It’s this inherent uncertainty that makes value investing so difficult, along with the market’s tendency to stay irrationally high or low for extended periods of time. As social creatures we’re programmed to take our cues from other people and when these are sending strong signals about what we should be doing then it’s terribly hard to stay aloof and plough our own furrow.

Mean Regression Alone Won’t Make You a Good Investor

As we explored in Regression to the Mean: Of Nazis and Investment Analysts, mean regression won’t automatically make you a better investor – a below average analyst will get below average results. The idea that investing is somehow easy and that anyone can consistently make above trend results through some process that doesn’t involve thinking until the blood seeps out of the pores in your forehead is utter nonsense.

In essence performance depends on a combination of skill and luck. The rare combination of great skill and good fortune can lead an investor to amazing returns. Aggregating no skill and bad luck leads to horrendous ones. Most of us lie somewhere in the middle. However, luck has a habit of evening out over long enough periods: investor performance mean reverts as well as company performance.

Based on what we can see from studies of investor returns and mutual fund cashflows we can be pretty certain that the majority of people – and a significant majority at that – fail to even match the indices. Behaviourally induced overtrading, disaster myopia, lack of emotional control, poor use of gearing and a failure to manage the costs associated with dealing all contribute to terrible results, to the extent that most people would be better off using passive funds for investing purposes and spending their leisure time doing less self-harming stuff. You know, things like do-it-yourself body piercing with a rusty nail and trimming their fingernails with a chain-saw.

Mean Reversion Makes Momentum Trading Successful

Yet, as ever in the murky psychological waters of finance, you can't find a commonly understood trend without finding someone betting against it, often accidentally. The observation that momentum trading, buying stocks that are winners or shorting those that are losers, suceeds over short-time scales of up to a year or so has led some researchers to wonder whether this is also a facet of mean reversion and human psychology.

Kevin Wang, in Mean-Reversion and Momentum, looked at this, the idea being that the wide-spread knowledge of mean-reversion effects may lead investors to sell winners, expecting them to fall back, and buy losers for opposite reasons. Intuitively, he points out, this seems to be wrong - companies which have released good results might reasonably be expected to carry on benefiting from whatever trend they're riding for a while: the invisible hand may be inevitable, but it's not instantaneous.

This is exactly what he found when he tested this simple behavioral theory. Note also that this has nothing to do with the probabilities of actual returns - it's simply investors reacting to changes in stock prices, not the underlying earnings and fundamentals. It's emotion and behavioral bias, not reason and dispassionate analysis: people appear to be reacting to a popular idea, a so-called meme rather than logically assessing a corporation's real outlook.

Emotional Management is Value Investing

All of which suggests that investors can lose out long-term and short-term by not appreciating mean-reversion effects and just goes to show that we can no more separate our emotions from our investing than we can separate our minds from our bodies. However, once our emotions are out of control so are our investments, no matter what returns we’re making and what popular ideas we're following. There’s skill – in terms of the technical business of flushing out undervalued stocks – and there’s art – in terms of learning to control ourselves by insisting that we Get an Emotional Margin of Safety.

Mostly investment gurus major on the skill aspect of investing – which is fair enough, because it’s the bit you can teach. Value investing based around an understanding of mean reversion is one of the few ways of making money through active investing – for the private investor it may be the only way. However, the art to being successful is to make sure you're in control of yourself because when things go wrong that’s just about the only thing that matters.


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Trust Is In the Eye of the Beholder

Is Beauty More Than Skin Deep?

There’s been quite a bit of debate down the years about so-called beauty biases. Various bits of research have suggested that us humans have a bit of a soft spot for other humans who are easy on the eye, to the extent that we tend to attribute abilities to them that they haven’t got. Merely looking attractive is enough to help you get on in life, it seems.

However, there are usually potentially confounding problems with these experiments, in particular the possibility that lovely looking people might actually be better at stuff than the rest of us ugly has-beens. Fortunately a combination of the banking crisis and the proliferation of the internet has offered a glorious opportunity for field experimenters to conduct a bit of real-world research. Even better, it’s suggesting that automating risk management removes a vital layer of protection for lenders and that scammers can play on these traits to defraud us. So are the beautiful also brighter?

Confidence is the Key

Of course, we might look askance at the idea that someone might actually be better at stuff simply because of appearances. However, this might not be quite as stupid a suggestion as it seems. Although it does seem unlikely that the handsome amongst us are born smarter, at least on the evidence of celebrity reality shows, there’s a distinct possibility that they could learn to be so. There’s rather a lot of research that shows that attractive looking children tend to receive more praise, are forgiven their transgressions more easily and get more opportunities. Unsurprisingly they tend to be more confident in their abilities, and confidence often begets its own rewards.

As most of us know, the right sort of encouragement can work wonders in getting us to work harder, while being continually discouraged and ignored is likely to create people who don’t put as much effort in. So, strange as it may be, it’s at least possible that more attractive people are better at intellectual stuff. The problem for researchers is how do you separate these effects – how do you tease apart the possibility that beautiful people are genuinely better at stuff from the idea that they’re given preferential treatment due to an accident of genetics?

The Beauty Premium

The so-called beauty premium was originally demonstrated by Hamermesh and Biddle, who showed that physically attractive people earn upwards of 10% more than their plug-ugly cousins. When Mobius and Rosenblatt in Why Beauty Matters decomposed these results in an experimental situation they both replicated the original findings and came up with a rather precise formula for explaining this wage premium: 20% of it was down to confidence, 40% due to visual perception of supervisors and 40% due to the attractive people having superior oral skills, which is generally regarded as an outcome of greater confidence. However, they also demonstrated that the additional wages earned by the beautiful are actually unjustified – simply looking better doesn’t actually make you better at doing your job. Now there’s a surprise.

One of the problems with these types of experiments is that they’re laboratory based. As we saw in Be A Sceptical Economist unreal experimental scenarios can often trick people into behaving in an abnormal fashion, as their brains try to figure out what the experimenters want them to do rather than just behaving “naturally” – whatever that is.

Although the beauty effect and premium are interesting findings they’re also exactly the kind of daft idea that gets generated from commonsense expectations and exactly the kind of result that you might artificially generate from an unrealistic experiment. To confirm this idea what we really need is a real-world situation that we can mine for data. In particular we need a way of showing that attractive people get an economic reward for looking different and not for being smarter.

Trust, Not Beauty

In Trust and Credit Jefferson Duarte, Stephan Segal and Lance Young found just such a natural setting in which to investigate the beauty effect but went on to discover something slightly different: that us humans are rather good at judging whether or not to trust people based on their looks. What they latched onto were the opportunities for behavioural economic researchers in the development of peer-to-peer lending sites where individuals lend money to other individuals without the overhead of the banking system. They hypothesised that there might be a correlation between the way people appeared in their photographs and the likelihood of them getting access to lending.

The idea that a lender would put their money at risk based on a perception of trust from nothing more than a photograph is, of course, a bizarre and stupid one. Naturally, then, that’s exactly what the researchers found as the people perceived as being more trustworthy received more loan offers at lower interest rates than those perceived otherwise. Of course, this may just be another example of people being stupidly and unconsciously biased but they found otherwise: they found that this perception of trust is genuinely successful at predicting loan defaults even after controlling for stuff like credit scores. As they remark:

“This indicates that borrowers’ photographs offer relevant information about trustworthiness that is not embedded in the standard model used for credit scoring.”

Which suggests, at the very least, that the financial industry’s determination to reduce costs and automate lending decisions comes at a cost: the loss of individual human judgement about the trustworthiness of individuals. If this finding is correct it indicates that there is more to assessing trustworthiness than a credit report.

Trust and Beauty

Clearly, assessing trustworthiness from a photo is not exactly the same as assessing attractiveness. However, there’s obviously some relationship between the two. In fact the researchers also controlled for attractiveness as well as trustworthiness and found that although the two are related attractiveness doesn’t appear to increase the probability of a loan being funded – in fact, if anything, more attractive borrowers seem to have less chance of getting a loan funded than everyone else.

So this field experiment seems to suggest that the beauty premium may be less of a factor than laboratory based experimenters may think, if it's really a factor at all, and that that researchers are getting confused with perceptions of trustworthiness. It also raises some intriguing questions about the nature of trust and our propensity to engage in risky transactions based on our perceptions of it. The peer-to-peer lending data appears to be showing not just that lenders will take risks with their money based purely on their judgement of trustworthiness from photographs alone but also that this judgement is remarkably accurate.

All of which shows that we shouldn't accept every believable theory we hear even when, or perhaps especially when, it seems intuitively correct. Of course, we already knew that removing human judgement from credit scoring was a bad thing, but it's also nice to know that appropriately incentivised individuals can do better than sophisticated computer systems: when it comes to judging people, people are still the best.


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Finance: Where The Law Of One Price Doesn't Apply

Differentiating Financial Products

Even the smartest amongst us can be fooled by the pricing structures of relatively simple financial products. In any normal industry we would expect the law of one price would be prominent – in efficient markets all identical goods must have only one price.

Now whether or not the market for financial services is efficient or not is a moot point but the industry’s ability to create a vast swathe of differentiated products could almost have been designed to prevent the law of one price from operating. With the documentation for even simple financial products running into several pages of hieroglyphics in a convoluted and slightly sinister attempt to promote “clarity” the chances of anyone actually recognising that any two products are identical is minimal. In such a situation efficiency is a pipe dream.

Avoiding Competition

Competition is, of course, at the heart of capitalist economic systems – the idea being that it’ll keep prices in check to the benefit of consumers and, ultimately, to the whole financial system. This process happens even as individuals and corporations seek to maximise their profits and should have the outcome of stimulating innovation because new products and services will command excess profits until the competition catches up.

However, corporations will go to considerable lengths to maintain their profit levels while not having to increase their costs by investing in new products. Those rare companies for whom this is a natural situation, because they have some in-built but non-monopolistic advantage, usually trade at a premium. These are the classic “moated” businesses that come with built in defences against competitive forces.

Creating Your Own Moat

For the most part, however, companies need to create their own artificial moats. At one end of the spectrum are illegal monopolist behaviours where companies collude with their competitors to artificially keep prices high. At the other end is a more difficult to discern, but equally troublesome problem – an industry that provides a vast array of slightly different, highly complex and difficult to compare products. Now, does that sound familiar?

As we’ve repeatedly seen, financial services seem to be a special case amongst consumables. Part of the problem is that humans are attracted to today’s successes, which is unfortunate in the mean reverting world of finance because they’re likely to be tomorrow’s flops. It’s no coincidence that a lot of marketing effort and manipulative spin goes into focussing on the performance record of the best products – see Survivorship Bias in Magical Mutual Funds for an example.

Hidden Fees

However, the whole industry can’t survive on promoting the top performers when, underlying all of the flim-flam around wondrous new offerings you actually only have a few tired old products that have mostly been around for eons. At this point you need to get really creative in order to avoid having your profit margins decimated by the law of one price.

Partly this is done by hidden fees – some people are pretty good at focussing on the actual fees charged, but often miss loadings on sell prices, for instance. There are other sorts of hidden charges, as well. For example, George Serafeim looked at the effects of the SEC introducing new regulations in 2004 banning funds from using directed brokerage. He speculated that brokers who recommended clients to specific funds, thus boosting funds under management and fees, would have trades directed to them by the funds, thus earning the brokerages extra commissions.

Overall, his research concluded that the new SEC regulations had reduced this problem and, not uncoincidentally, had improved the returns of the funds by giving them less reason to overtrade. Of course, funds producing better performance doesn't matter to the fund managers if their fees are reducing and, so, Serafeim also showed that funds seemed to be finding new ways of replacing their lost revenues at customer expense.

Complexity Through Differentiation

The most likely source of creativity in financial services, though, is around the plethora of complex products promoted by the industry. If these products were genuinely different then this wouldn’t represent a market failure, merely a very complex market. Unfortunately the research suggests that the complexity is artificial and that the underlying products are homogenous – that is, they’re much of a muchness. The effect of this artificial complexity is to raise the barriers to understanding sufficiently to prevent consumers from being able to make an informed decision and the result is a failure of competition on a grand scale.

Bruce Ian Carlin in Strategic Price Complexity in Retail Financial Markets has looked at this issue and argues that “Producers of retail financial products create ignorance by making their prices more complex, thereby gaining market power and the ability to preserve industry profits”. In particular he suggests that “Industry confusion is the way high-price firms gain market share”.

This actually leads to what seems at first sight a completely contradictory hypothesis: that as more companies enter the market the price of products will rise. In normal markets, of course, new entrants will cause prices to fall – we’ve seen this in the last two decades in telecoms markets, for instance. However, in a market which is dominated by complexity Carlin argues that the addition of new entrants will increase the overall complexity – even if they’re actually offering simpler products – and this will increase the barrier to understanding for consumers and, hence, increase costs.

Sidestepping Disintermediation

One argument against this model is the intervention of intermediaries whose business model is to reduce the complexity and make it intelligible to consumers. Advisors, cost comparison websites and the like can all offer these services, for a fee, and given the excess returns in the industry should be able to make a turn for themselves. However, this assumes that the industry is prepared to stand aside and let this happen. Carlin suggests several ways in which this can be prevented:

"...they can raise industry complexity, decrease the benefit to becoming informed, or offer the advice channel incentives to share in industry profits. Rising industry complexity causes the cost of education to rise, making it less profitable for the advice channel to market its information services. Decreasing the expected benefit of information involves making industry prices more concentrated (decreasing price dispersion). Concentration in prices is not equivalent to decreasing prices, however, as it is possible for prices to rise, while still preventing the advice channel from enforcing competition. Signing incentive contracts makes it more profitable for the advice channel to hold back information from consumers and preserve industry profits".

Limit Complexity Or Enforce Separation

If Carlin is right this has some significant implications for the approach of regulators in future. He suggests that either regulating to limit industry complexity in terms of pricing disclosure or enforcing separation between financial services companies and advisors or improving financial education would improve matters. However, as we’ve seen elsewhere, better financial education doesn’t seem to improve financial decision making so that’s unlikely to be a useful starting point.

To end with a caveat on this research: it starts from the assumption that financial products are all the same. Although that’s probably a fair starting point it’s not entirely true to argue that all products are the same (Carlin doesn’t). However, ensuring that product information is simple and that comparison is relevant is essential to making the financial services industry transparent. More pertinent, though, is introducing regulation to enforce separation between product providers and advisors which can be policed. Until this is done the complexity of the finance industry will ensure that the law of one price doesn’t apply in the one place that it ought to above all.


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Putting Down the Credit Cards

Deviant Cards

Credit cards are one of the modern era’s great financial innovations, and have benefited the financial institutions that issue them terrifically. Unfortunately many of the people who use them get rather less rewards, mainly because of the unconscious biases that compel their every conscious move.

One of the few manifest and practical advantages of credit cards, though, is that analysis of their use is a practical primer in many of the behavioural biases which so entrance us on these pages and of the way in which ill-considered legislation magnifies their effects. Credit cards are a marvellous mechanism for a study of our deviant debt-laden ways and the ineptitude of our legislative representatives. However, they’re also the thin end of a very dangerous wedge.

Minimum Payment Palaver

In both the US and the UK credit card providers are legally bound to collect the minimum interest payment outstanding on cards each month, to prevent the issue of compounding interest and evermore spiralling debt. So every month, in nice large and friendly figures, the statement shows the minimum amount that the account holder has to pay. The idea, of course, is to prevent consumers getting themselves into financial difficulties. Which is a nice idea but the law of unintended consequences, allied to the supercharged effect of behavioural biases has other ideas: it turns out that this minimum payment works an anchor, and biases people into only paying the minimum amount outstanding.

We shouldn’t really be surprised by this, because the evidence of people anchoring on completely random numbers in areas of financial uncertainty – which for most of us is anything at all to do with money – is something for which we’ve seen plenty of evidence – see Anchoring, the Mother of Behavioural Biases for example. What’s more interesting, perhaps, is the lack of understanding of regulators who continue to think they can enact laws to protect people from their own stupidity without understanding the underlying, albeit basic, psychology.

Credit Card Anchors

Neil Stewart in The Cost of Anchoring on Credit-Card Minimum Payments has followed up on some ideas of Thaler and Sunstein in 2008 to demonstrate a strong correlation between the minimum payment required and the actual payments made. What the research has shown is that the presence or otherwise of minimum payment targets made no difference to the number of people repaying in full. However, when minimum payment anchors were removed from the statements the average partial payment went up by 70%.

All of which suggests that the inclusion of the minimum payment amount has the effect of reducing the amount paid by the people targeted by the legislation. In fact the research suggests that including these targets roughly doubles the amount paid in interest. The credit card companies will, no doubt, be very grateful for the profit boost enacted by our governments.

Out of Control Consumers

Anchoring isn’t the only effect visible through the use of credit cards, however. Perhaps a more obvious area of study are the issues that surround the inability of many people to avoid acquiring stuff they really want now as long as they can delay until later the pain involved in the outlay of actual money. This issue, the so-called problem of self-control, seems to surface almost anywhere and anytime there’s a significant time delay between acquisition and payment. Credit cards are, obviously, the ideal vehicle for people who must have that must-have item right now.

The traditional way of looking at self control assumed that consumers rationally discount the future – the so-called exponential discounting function. To behave thus is, in the jargon, to be time consistent. However, rumours from the outside world that people don’t actually behave like this keep surfacing in the economic ivory towers, suggesting that humans are time inconsistent and discount hyperbolically not exponentially.

To simplify this drastically, if humans discount hyperbolically they basically disregard future costs entirely, while if they do so exponentially they don’t. Exponential time discounters have a budget while hyperbolic time discounters have a really nice wardrobe, a cool car, all the latest gadgets and some really ugly looking debt collectors banging at the door.

Time Inconsistency

Most hyperbolic discounting studies have been laboratory based which runs the risk of putting people in situations that don’t make human sense. There’s copious evidence that such scenarios produce results that don’t transfer well to real-world scenarios. To get round this problem Shui and Ausubel in Time Inconsistency in the Credit Card Market developed a large-scale real-world experiment. To cut a long story short, the results suggest that consumers have “severe self-control problem” with credit cards. Now, don’t tell me you’re not surprised. Please don’t tell me that.

To simplify the study somewhat, they made two credit card offers, both with low introductory rates. The first offered a very low rate for a short period, then rising rapidly for the rest of the experimental period. The second offered a higher, but still discounted, rate for the whole timescale. Over the entire period the rational discounter with constant debt would have taken the second card, because this produced the lowest total interest payments.

You’re way ahead on this, aren’t you? Yes, the respondents overwhelmingly preferred the first card with its low initial rates. This makes perfect sense, however, if they then switch to another low rate card at the end of the offer. Only they overwhelmingly didn’t and carried on paying higher rates. That consumers don’t always prefer the credit card offer demanding the lowest overall interest is, more or less, evidence that consumers are not rational in their consumption approach. That credit card companies bombard us with low introductory rate offers is, by the same criteria, entirely so, as are the relatively high rates charged for normal credit card rates, a competitive anomaly that this research at least partially explains: consumers don’t select credit cards based on the lowest standard rate.

Behind this is an important differentiator between the exponential and hyperbolic models. The first model would lead us to assume that the consumer is intelligently deciding to use credit card debt to fuel current consumption rather than saving in advance. However, the second model suggests something rather different – it suggests that people don’t have control over their spending and their lack of self-control is what’s leading to racking up the card bills.

What Do We Do About It?

Of course, at one level a simple explanation of these facts is that the spendthrift credit card debt addicts simply aren’t very good at doing sums about compound interest and, moreover, wouldn’t care even if they did as long as they can get their hands on the latest piece of consumer electronics today. While those of us with the self-control to manage our spending impulses may well feel that we can take advantage of the mentally less well endowed there is, unfortunately, a side-effect of this borrowing that affects us all. As Stewart points out:

“About three quarters of credit-card accounts attract interest charges. In the United States, credit-card debt is $951.7 billion of a total of $2,539.7 billion of consumer credit. In the United Kingdom, credit-card debt is £55.1 billion of £174.4 billion of consumer credit.”

That’s one hell of a lot of unsecured credit sitting on already stretched balance sheets much of it weighing on consumers whose job security remains fragile. Expecting the global economy, at least in the West, to charge ahead with this ball shackled to its ankle is to mistake hope for expectation and credit expansion for productivity fuelled growth. That the sheer pressure of uncontrolled self-interest can put the livelihoods of millions of people at risk compels us to look at how we manage this in future.

Protecting the Unwary

Of course, the new US Credit Card Accountability, Responsibility, and Disclosure (CARD) Act is designed to stop some of the more egregious abuses of cardholders. Unfortunately this relies on cardholders behaving with some level of vague rationality. The evidence, such as it is, isn't encouraging.

Credit cards are valuable tools as long as they’re controlled, but the lack of control of many owners puts you in mind of the legislative response to the owners of dangerous dogs: put the dog down and ban the people from owning any more – although responsible dog owners suggest that this is the wrong way around. In our free world that may be a step too far but it’s about time bureaucrats sat down with the people who actually have some understanding of this stuff and started designing proper responses. Demanding that people can actually pass a simple test to calculate, or even understand, the compound interest on their debt would be a good start.


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Behavioral Portfolios

Beer Balancing Behavioralism

Perusing the literature on behavioural finance you might be inclined to the thought that although this stuff is all very interesting and even occasionally amusing it’s not much use when you come to actually investing. It’s a bit like posture – it’s easy enough to point out to someone that they slouch like a sloth with a dose of haemorrhoids, it’s entirely another to explain to them how to retrain their muscles to start balancing pitchers of beer on their head on the way back from the bar.

Digging deeper, though, what becomes apparent is not that we're especially bad at understanding investing but that the way we go about constructing our portfolios is radically different to what theorists expect. Whether it's the theorists who are wrong or the investors is entirely dependent on your perspective. Either way recognising what's going on is an important step on the way to getting a beer balancing musculature.

Behavioral Portfolio Theory

As an example it's a strange fact that investors tend to prefer dividend paying companies to non-payers; a finding that, as we saw in The Psychology of Dividends, can't be predicted by standard portfolio theories. It can, however, be predicted by behavioural finance along with a bunch of other results that are otherwise hard to explain.

As far back as 1997 Hersh Shefrin and Meir Statman published a fascinating paper on Behavioral Portfolio Theory which starts to get into some core body building. What they pointed out is that behaviourally biased investors don't have investments which look like the mean variance portfolios that Markowitz’s Portfolio Theory suggests they should.

Markowitz's Main Variance Portfolios

Portfolio Theory has been the basis for most portfolio design for the best part of half a century. It relies on reversion to the mean and aims to create portfolios to capture maximum returns at the least risk to the investor. Importantly it treats the portfolio as a whole, not as separate parts to be individually managed. Each part is there in order to ensure that the overall portfolio meets its risk-return goal. Above all, each part has to be analysed in terms of its correlation with other parts. So, for instance, if emerging market stocks and commodities are closely correlated there would be no need to have both in an efficient portfolio, since there would be no diversification benefit to doing so.

In mean variance portfolios you would also expect to see short positions and even securities bought on margin. Individually these may be highly risky investments, but if they allow the portfolio designer to achieve the maximum-reward for least-risk position – lying on the so-called Efficient Frontier – then the individual risk is irrelevant.

Investor Zeitgeist

Shefrin and Statman contrast this mean variance portfolio design with what investors actually create. By and large you don't find many investors calculating the historical co-variances of their portfolio constituents to create efficient portfolios. In general they rely on a large amount of intitution about what's undervalued and/or what's about to catch the zeitgeist of the moment.

Moreover, investors rarely regard their portfolios as a holistic whole. At worst they account for each security separately, applying anchoring, loss aversion and framing biases to create portfolios that look like they were designed by a spider on caffeine, if they can be said to be designed at all. Shefrin and Statman suggest, however, that there is an underlying structure to such portfolios which is underpinned by what they call Behavioral Portfolio Theory.

Downside Protection, Upside Potential

They argue that investors of all kinds, private and professional, for the most part don’t integrate their portfolios to achieve the minimum risk, maximum return outcome but, rather, the standard behavioural portfolio is a simple two layer affair – a downside protection layer designed to protect against worst case scenarios and an upside potential layer which provides a chance of getting rich. Simple this may be but the architecture allows the authors to propose explanations for a wide range of behavioural biases.

The downside protection layer is supposed to be a zero risk combination of stuff like of cash and bonds, investments with low levels of risk with high certainty of future returns. The upside potential layer is where we find risky investments such as stocks and derivatives; securities with higher levels of risk and lower levels of certainty. The behavioral investor will, once they've satisfied themselves that their downside protection is in place, often regard the upside potential layer as money they can afford to lose – which leads to investments being made with low levels of analysis or understanding and often ends up being a self-fulfilling prophesy.

Lotteries on the Upside

This two layer structure can explain a remarkably large number of behavioral finance phenomena. Consider, for instance, the oft-noted tendency of investors to buy both insurance and lottery tickets. As the writers put it:

“Lotteries contain no fundamental risk, meaning risk that is related to economic events. Instead, they have risk that is manufactured artificially. Behavioral [investors] buy lottery tickets for their upside potential layers when their aspiration levels are very high relative to the amount they allocate to upside potential layers. Investors with $1 cannot have a shot at a $5 million aspiration level other than through lottery tickets. Investors who allocate more money to the upside potential account and investors who have lower aspiration levels might satisfy their aspiration levels by buying call options rather than lottery tickets. Of course, mean-variance investors never buy lottery tickets.”

Mental Accounting of Portfolios

The behavioral portfolio layering concept is completely foreign to mean variance investing of the Markowitizian kind where all elements of the portfolio lock together. At root, of course, the behavioral portfolio is Mental Accounting writ large and holders of such portfolios will go to extreme lengths to avoid the losses in the upside potential layer impacting the value of their downside protection layer. This leads to the avoidance of investments in the upside layer that can overrun such as short positions and buying on margin - both of which are essential to achieving efficient mean variance portfolios.

Dividends in Behavioral Portfolios

What then of dividends? Well, the behavioral portfolio structure suggests a reason why dividends continue to exist despite economic logic dictating they shouldn't, provides a hint as to why changes in dividend policy can provoke significant changes in stock prices and explains why dividend cuts from major corporations can create a furore amongst private investors. Shefrin and Statman suggest that the reason is simple – although the capital gains from stocks are allocated to the upside potential layer the income from dividends is placed in the downside protection layer. It's the violation of this sacred protection that triggers the excessive response.

If the idea behind behavioral portfolio theory is correct – and it certainly feels like it touches a lot of investing nerves - then it's fairly obvious why Harry Markowitz's ideas are largely ignored by swathes of private investors. It's not that Portfolio Theory goes wrong surprisingly often – it does, due the breakdown of historical co-variances between asset classes leading to inadvertent acceptance of excessive risk at the extreme ends of market variation – it's that most people can't see beyond their beyond their own psychological desire to protect themselves.

Value Investing on the Downside

It's just unfortunate that these attempts at protection often to lead to portfolios that offer greater risk for less return than anything standard Portfolio Theory could come up with. Mean variance investing may go wrong more often than you'd expect but basic logic has, so far, always re-asserted itself. Not so in the behavioral portfolio, where crashing and booming markets often leads to investors changing their upside/downside ratios at exactly the wrong time: just ask all those people who took on larger mortgages and riskier investments right at the top of the market and then did the opposite at the bottom.

So we have a situation where the analytical approach to investment is ignored by the masses in favour of bias driven intutition, but where the former often fails due to the biases of the latter causing unpredictable and extreme market behavior. The irony is that if more people invested analytically then the analytic models would fail less often. Of course, between these two stools lies the value opportunity, if you're knowledgable enough to take advantage of it.

Forearmed is forewarned, as they say. Time to practise some beer balancing.


Related Articles: Markowitz's Portfolio Theory and the Efficient Frontier, Correlation is not Causality (and is often Spurious), Mental Accounting: Not All Money is Equal

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Irrational Numbers: Price Clustering & Stop Losses

Universal Number Theory

One of the odder things about the universe is that the small set of numbers that define its structure, the so-called universal constants, don’t seem to have any structure of their own. You’d have thought that whatever immortal deity breathed life into the whole shebang would have at least have bothered to make sure that reality was defined in simple integer values your average gameshow contestant could remember. Yet someone’s just calculated Pi to more decimal places than you can read in a lifetime. The universe is strangely irrational, it would seem.

More likely, however, is that the irrationality lies in our heads. If you look at the way we treat numbers for investment purposes it’s probably a good job the infinite cosmos is specified in irrational numbers, because if it were otherwise we’d probably have sold it to the lowest bidder eons ago. Humans, it seems, treat numbers as an approximation to reality, unlike reality; which treats humans as an approximation to nothing.

That Friday 13th Feeling

Under standard economic theories one price should be much the same as another but all experienced practitioners know that this isn’t so – some numbers are much more likely to occur than others. Anyone with even a basic appreciation of behavioural psychology would expect no more or no less – people are as arbitrarily inconsistent about numbers as they are about everything else. In western culture, for instance, thirteen has acquired negative connotations to the point where many tower blocks omit the number from their floor numbering plans, presumably on the grounds that the universe can’t count. Beware, for fourteen is the new thirteen. Ha!

Despite the obvious irrationality of ascribing luck to a number many people are petrified of Fridays falling on the thirteenth of the month. Such is the human propensity to translate mental muddle into actual behavioural nonsense that it turns out that more accidents do occur on these days. So either there’s a malevolent demon tripping us up or our incipient fears are causing us to fall over our own feet. Mental confusion in our heads often turns into real problems in the real-world.

Round Number Attractions

It’s no surprise to find this numerological naughtiness feeding across into investment, a discipline many stockmarket dabblers may be surprised to discover should involve a basic understanding of numbers and the use of simple mathematical operators (don’t tell them). In particular we should note that most humans, but not all, are addicted to round numbers and, because of this, securities prices tend to huddle together like waifs in a storm.

There are various effects associated with round numbers – especially those that end in zeros or, to a lesser extent, those ending in 5 or 25. So $1.00 is preferable to $1.50 and that to $1.25. They’re all preferable to $1.13, a figure that causes a goodly proportion of the population to start getting all touchy-feely with the nearest piece of tree-related product, lopping feet off innocent lagomorphs and chiselling shoes off any nearby loitering pack animals.

The Clustering Conundrum

This attraction to round numbers was first noted in research on price clustering – the tendency of prices to cluster around specific numbers rather than spreading themselves out randomly over the whole spectrum of possible values. As far back as 1962 Osborne showed that NYSE closing prices clustered around whole numbers and common fractions – halves and quarters, prior to decimalisation. Since then price clustering has been demonstrated time and again in all sorts of securities – stock indexes, stock prices, commodities, bonds, foreign exchange and futures.

Quite why this happens is a source of much debate, although the main theory is that it’s something to do with our cognitive limitations. Perhaps it’s a retrieval problem – the easy availability of round numbers to our mental processes makes them attractive. Perhaps it’s an anchoring problem, where investors unconsciously anchor on easily available round numbers.

Whatever it is, it has an effect on investment returns – Herrmann and Thomas (2005) have shown that financial analysts tend to round their forecasts and that market reaction to earnings surprises is based on these rounded numbers. Of course, the more cynical reader might view the rounding off of analyst forecasts as more evidence that these are one step removed from guesses, although a more generous view might be that all forecasts are rough estimates at best.

Beat The Markets

Johnson, Johnson and Shanthikumar have investigated the round number phenomena and have concluded that round numbers are implicated in stock movements – investors appear to trade differently when closing prices are just below a round number than just above. To whit, you get more selling in the former case and more buying in the latter. This doesn’t appear to be a naive trader or a technical analysis problem either; the result appears to be robust for institutional investors as well as private ones. Rather startlingly the researchers state:

“Our estimates show that returns following prices ending in 01 through 09 cents are, on average, 12.9 basis points higher than those ending in 91 through 99 cents. This corresponds to an annualized rate of over 38% per year....” (To be continued)

Now that’s what I call an effect.

Stop the Stop Losses

More evidence for this was uncovered by Joep Sonnemans, who looked at Dutch investor behaviour before and after the introduction of the euro in 1999. What he uncovered was that price clustering around round guilder prices disappeared overnight and turned into price clustering around round euro prices. As he states:

“Apparently, not only for a consumer 19.90 looks much less than 20.00, it also looks that way for an investor.”

Naturally this effect can be used against investors. The idea of using a stop-loss – a pre-set lower price limit at which securities are automatically sold – is a very common one, encouraged by many market practitioners. However, small traders are often heard to complain about being “stopped out” of a stock by an unexpected and short-lived downward spike in the price, bitterly breathing of conspiracy theories. Looking at the round number effect it’s pretty clear that you don’t need to invoke insider dealing to create this effect, you can rely on the traders' own behavioural biases.

Chinese Whispers

Price clustering around round numbers seems, therefore, to be a robust effect causing measurable effects. However, a number of researchers have wondered whether this is not just a cognitive limitation but a cultural effect. Brown and Mitchell used the multi-tier nature of the Chinese stockmarkets to investigate this. They hypothesised that if clustering is a cultural effect then the number eight, which is synonymous with good luck in China, would appear more often in prices than the number four, which is similar to the Cantonese word for death.

The structure of the Chinese markets was originally divided between A shares for Chinese local investors only and B shares for overseas investors only, although this distinction is slowly being eroded. By examining prices of the different classes of stock the researchers concluded that Chinese investors were being influenced by their cultural preferences for the number eight: opening, closing, high and low prices were far, far more likely to end in an ‘8’ than a ‘4’.

Controlling for the same features in western markets produced no such equivalent preference. In conclusion the researchers also observed that these cultural effects started to break down as external investors started to dominate, although the wider evidence might suggest that this was just replacing a local cultural preference with a wider behavioural bias.

Get Your Lunch Elsewhere

Clustering around round numbers is clearly not a function of any underlying valuation process, but is a cognitive simplification that we use to avoid complicating investment issues. In aggregate individual preferences for nice round numbers adds up to short-term irrationality as prices bounce above and below resistance points. All of which, rather startlingly, suggests that technical analysts may be on to something.

To rescue intelligent investing from the dustbin of humanity’s insatiable appetite for immediate gratification, however, we can conclude with the second part of the Johnson, Johnson and Shanthikumar quote from above:

“... It is unlikely that these return differences alone could be used to form a profitable trading strategy given the almost daily rebalancing required, but they may have a significantly positive impact on returns if exploited for optimizing the timing of order execution.”

Always remember the universe’s rule #3.14159265: no free lunches.


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The Case Against Re-Emerging Markets

Brave Punditry

If you’re of a contrarian viewpoint you might cast your eyes across the pundit’s tips for the best performing sectors of 2010 – or, indeed, any year – with a slightly jaundiced eye. This year’s favourite flavour of investment is emerging markets. There are strong and powerful arguments in support of this particular long-term trend but you’ll rarely find the short-term value counterargument.

Counterarguments are critical for sensible value investors, because they force us to consider what could go wrong despite what our deceitful and biased brains are telling us. Any idiot can see what can go right – and, indeed, they spend a lot of time telling us about it – but putting a purely positive spin on any investing situation doesn’t come close to providing a sensible basis for allocating our valuable and scarce capital.

Re-emerging Markets

The first thing to remember about emerging markets – that subset of the world’s stockmarkets that are representative of economies which aren’t fully developed – is that most of them were in the same state a century ago. To be precise most emerging markets have already emerged at least once, had a good look around at the benefits of a developed economy and then promptly sunk back into oblivion out of some combination of political maladministration, war, famine or simple sheer bad luck.

You’d be hard pressed to detect this from the performance figures associated with these markets because most of the statistics about them reflect only the period since their re-emergence. Such is the nature of survivorship data that these not infrequent busts have been expunged from the records as though they never existed. Viewed from today’s vantage point the concept that these markets could once again collapse is hard to credit, but history suggests something different – that emerging markets are dangerous, delicately balanced affairs and the path of true capitalism rarely runs smooth.

To Lebanon and Beyond

Perhaps the greatest exponent of this concept is Nicolas Taleb whose experience of his home country, Lebanon, has been hugely influential in shaping his world view. Lebanon, oft-described as Switzerland on Mediterranean, was a country blessed with wonderful resources and a thousand year history of peaceful co-occupation by people of multiple religions and ethnicity. Then, in the 1970’s, it descended into a civil war that destroyed it and its economy and from which it’s still struggling to recover. As Taleb relates, many of the people displaced in the subsequent diaspora never accepted that the world had changed. Many spent the rest of their lives waiting for “normality” to re-assert itself.

Emerging markets are never a one-way bet but, to take the alternate view, it’s also worth pointing out that today’s biggest markets were once emerging in their own right. The USA spent most of the nineteenth century doing so, whilst undergoing a series of eye-watering debt defaults and stockmarket crashes. Even so, the spoils of such development often don’t go to investors: whereas in developed markets you’ll normally see your equity investments diluted by 2% to 3% a year as companies raise more capital, in emerging markets those figures may be as high as 20% to 30%. Believe the raw numbers if you will, but do so at your peril.

Value Investing in Emerging Markets

Performance within the range of emerging markets may be hugely varied as well, yet exhibits many of the same features as we see with standard investments. A Brandes Report on the value attributes of these markets finds that just as in developed markets both the worst performing stocks and the best performers end up mean reverting, only to a greater extent: the report suggests that from 1980 to 2007 the value premium – the excess return of value stocks over growth stocks – in emerging markets was 17%, over double that in non-US developed markets.

This, of course, poses a particular problem for most overseas investors engaging in these markets through the use of index trackers targeting large market capitalisation stocks – i.e. those stocks most likely to mean revert in the wrong direction. Although, as we shall see, even that’s probably better than trying to time your investments.

As the report points out, the overall performance of emerging markets over the past decade has been stunning but has largely been linked to the commodities boom which itself has been fuelled by the growth of China and India, two markets which appear to be emerging rather faster than others. We’ve seen such booms before and they’ve rarely ended happily for those countries in possession of the basic commodities: astonishingly typical constant-price GDP of an African non-agricultural commodity exporter appears to drop by 26% in the 25 years after a commodity boom.

The Growth of Liberal Democracy

It’s a remarkable fact that just over 200 years ago there were only three liberal democracies in existence – the USA, the UK and Switzerland. Since then over 70 countries have joined the club and, without exception, their economies have boomed, notwithstanding frequent busts along the way. However, the simple correlation that a liberal democratic political system leads to economic growth is, probably, perfectly wrong: get your economics right and the tax-paying middle classes eventually demand the representation that their contributions require. Wealth first, political freedom second is the rule: proponents of regime change need to develop stockmarkets, not democratic reforms.

If you fail to get your politics correct, however, then economic boom almost invariably leads to economic collapse. At the heart of the equation are property rights, because if people don’t get to keep the rewards of their industry and face the potential consequences of arbitrary confiscation by the state then the rationale for investing time and effort in business activities is destroyed. Incentives matter, even at the national level.

This isn't a question of the type of political doctrine; both left and right wing administrations have managed to wreck their underlying economies through various demented policies. Sometimes the destruction is visited upon economies from outside, sometimes from within. Sometimes regimes are changed by force, other times they implode under the weight of their own myopia – each collapse has its own pathology, traceable only in retrospect and unpredictable in advance.

Extreme Kurtosis

The politics of emerging markets, then, are peculiarly dangerous for investors. Yet not only do we have to manage the range of political issues but these markets are characterised by extreme behaviour, even more so than those of developed nations which, as we well know, can be frightening enough. Javier Estrada in Black Swans in Emerging Markets records:

"On average across all 16 markets, missing the best 10 days resulted in portfolios 69.3% less valuable than a passive investment; and avoiding the worst 10 days resulted in portfolios 337.1% more valuable than a passive investment. Given that 10 days represent 0.15% of the days considered in the average market, the odds against consistently successful market timing are staggering. Hence ... of the countless strategies that academics and practitioners have devised to generate alpha, market timing is one very unlikely to succeed."

These markets exhibit extreme volatility and, for the most part, their returns demonstrate severe levels of kurtosis, the fat-tails at the edges of the typical normal distribution of returns: things get extreme far more often than classical analyis would predict. Typical human behavioral biases seem to get exaggerated in these situations, just as in developed markets, but the nature of emerging markets appears to make this problem even worse, such that trying to time them is clearly nonsensical. If you can’t invest in value based vehicles and you have to be in these markets then being in them full-time is the only real option. Which, of course, brings us back to face the mean reversion issue discussed earlier.

There’s a secondary problem, identified by Bekaert and colleagues back in 1998 – that the process of a market moving from a developing to a developed state can itself lead to anomalous returns. The researchers point out that as a market changes in this way the marginal investor stops being a local and starts being a foreigner. This change can temporarily drive up returns only for them to drop back to a more normal distribution as the local index becomes more diversified. So excess gains from an emerging market may simply be a temporary phenomena due to a surge in liquidity.

Never Forget the Counterarguments

None of the foregoing means that there isn’t a bullish case for some emerging markets. The problem is that markets don’t emerge smoothly or linearly, but do so stutteringly with the ever-present possibility of failure. Following a decade in which developing markets have outperformed developed ones it’d be a brave person who bet that this would continue without some kind of setback, especially given the faltering nature of debt-laden western consumers and the continuing deflationary effects of adding the odd billion or so low-paid workers to the global economy.

Fortunately the world is full of brave pundits prepared to put our money on the line by telling us that these markets are a one way bet. Just as we’d be foolish not to have a small percentage of our investments in such markets we’d be equally so to be betting the farm on them. They're also situations where looking for value oriented investing vehicles may be safer than straightforward large cap index trackers.

These are the counterarguments: do with them what you will.


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