Real Fortune Telling: Dividend Forecast Indexing

Fallible Analysts, Reliable Dividends

Unfortunately investment analysts are just as prone to the standard behavioural biases as the rest of us and are inevitably more affected by incentives designed to promote short-term outperformance. In particular when market conditions become turbulent most earnings forecasts become slightly less useful than an unsinkable submarine.

Dividends, on the other hand, are less volatile than earnings. Even better, analysts' dividend forecasts are much more reliable than their earnings forecasts. So if dividends are a sign of healthy companies then using analyst dividend forecasts as a forward signalling mechanism may offer a way of obtaining market beating returns at little excess risk. Although, as it turns out, whether this works or not depends on whether your country’s legal system was created by a bunch of Vikings masquerading as French nobles rather than the descendents of Julius Caesar.

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It’s Not Different This Time

The Smoking Cigar of Behavioural Bias

Not all failures of investment logic are based in human psychological flaws but, to paraphrase Freud, although sometimes a cigar is just a cigar mostly it’s behavioural bias. The smoking gun is almost invariably linked to people doing predictably stupid things. Like building shacks on earthquake fault lines, thinking they can banish risk with a spreadsheet and regarding the lessons of history as too remote to be interesting.

Sadly the fact that these things are predictable doesn’t make them any less easy to deal with. Our current set of financial woes is a wonderful test bed for those inclined to point to the short-termist biases inherent in the human conditions. We’d do well to enshrine these lessons in our systems now, because it won’t be long before we’ll start to forget.

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O Investor, Why Art Thou Rational?

Rationality Good, Irrationality Bad

One of the revealing things about a lot of modern financial theory and economics is the underlying assumption that rationality is obviously good. We’ve moved on a little bit from the belief that we’re all omnisciently perfect investment analysts but the ideal of the perfection of rationality haunts us still, like a ghoul in a very wonky machine. Imagine Béla Lugosi in a Ford Edsel.

However, we’ve somehow built a global society without being particularly rational most of the time. A significant proportion of the world’s population manages to simultaneously combine a belief in a benevolent yet invisible deity it’s worth killing for with the imminent prospect of abduction by sex-obsessed aliens. Yet we’ve managed to probe the fabric of the universe, develop great works of art and keep most people fed, most of the time, through the magical wealth creating properties of the global economy. So what need has humanity for rationality when it’s managed to get this far without it?

Sloppy Neurality

Our sloppy thinking is due to design – the fabulous parallel processing capability of the brain comes at the price of reusing lots of bits of neural pathways for different purposes. This means that although we’re pretty good at processing lots of information we tend to do so inaccurately. Which doesn’t matter a lot of the time – we can perform amazing acts of computation practically instantaneously in order to do important things in spite of our neural sloppiness. For instance, we are the only animal that can watch football while walking upright and carrying a tray of beers and a mound of nachos simultaneously. Of course, we’re the only animal that would want to do this, but that’s a whole ‘nother ballgame.

This reuse means that we take processing shortcuts which lead to mistakes in areas where rational thought would often come in handy. Such as deciding whether to cut someone open and jiggle their innards around with a semi-circular saw. Or whether to send them to war against battle tanks armed with hand-held tin openers. These processing shortcuts lead us to assume such things as that attractive people are always intelligent (they’re not), that any short-term trend will continue indefinitely (it won’t) and that we can foresee what’s yet to happen (we can’t). In all of these cases proper rational analysis of the evidence might lead us to make different decisions.

Rationality in Decision Making

There are cases where it’s obvious that rationality is needed in decision making. Surgeons who keep on performing pointless operations, generals who carry on sending troops into hopeless battles and politicians who continue pursuing reckless economic policies are only a few of the situations where a bit more rationality would lead to a lot less unhappiness.

Despite this human irrationality quite clearly hasn’t stopped us being a decently successful species – only 100 million years or so to go now, before we match the dinosaurs. Our question, though, is whether a dose of rationality is beneficial for investors. There’s plenty of behavioural finance theory that shows how our sloppy processing leads to irrational financial decisions – giving different answers to the same investing question when it’s phrased in different ways, refusing to sell hopeless shares because they’re trading below our anchoring purchase price, buying stocks because they’re the popular flavour of the moment rather than because of their investment fundamentals and so on. The list of these mistakes is almost infinite.

Irrationality Can Be Rational

Yet investing isn’t purely a personal affair, it’s a social one. Making an irrational investing decision doesn’t happen in a vacuum, because lots of other people are making similar irrational decisions. So a rational investor can end up betting against the crowd. Is it really rational for a social creature to step outside of the group and expose themselves to what lies beyond?

Although we can surely agree that the behaviour of financial institutions in lending money they couldn’t afford to pay back over the past few years was wantonly irrational it’s not entirely clear that the same is true of the officers and employees of those institutions. If you were heavily rewarded for flogging dodgy loans with no possible comeback what would you do, rationally?

It’s fairly easy to show that what may be irrational for one group of people is perfectly rational for others and this is a real danger for investors. Consider, if fortune tellers and psychics were any good they wouldn’t be writing dodgy horoscopes, they’d be staggering out of Vegas, weighed down by their foresighted winnings. Similarly, if investment fund managers were genuinely able to produce excessive long-term returns they wouldn’t be running investment funds, but would be making fortunes on their own behalf.

Logically and rationally this suggests one of two things. Either fund managers don’t think they can outperform the markets or they think the risk of trying to doing so isn’t worth forgoing the rewards they can obtain by staying in place. Rationally, therefore, private investors shouldn’t invest in their funds. The fact that hundreds of millions of private investor money does go in this direction is evidence enough that investor irrationality does matter.

Professional Exploitation of Irrationality

There’s worse to come since there are people out there deliberately trying to find ways of profiting from the irrationality of investors. This has been done before in an off-hand kind of way. Betting against so-called odd-lot investors – small investors buying odd-sized packets of shares – has long been considered a way of capitalising on amateurs’ involvement in markets.

Now, however, the professional investment industry is putting lots of time, effort and money into developing automated models to take advantage of the findings of behavioural finance. So called Quantitative Behavioural Finance aims to develop models that take advantage of the inherent biases and irrationality of markets. Alan Kirman came up with a model back in the 1990’s that was able to generate suspiciously realistic market behaviour simply by including two sorts of investors – fundamentalists, who invested based on the basis of valuation – and chartists, who invested on the basis of market direction.

The terms ‘fundamentalist’ and ‘chartist’ are overloaded with various meanings but roughly we can equate the former with rational investors trying to take advantage of market mispricing and the latter with irrational investors trying to take advantage of market psychology. Interestingly Kirman finds that generating realistic looking markets depends on the possibility of chartists converting to fundamentalists and vice versa, dependent on market conditions. The ability of people to be rational at some times and irrational at others is well attested to: virtually all financial bubbles are evidence enough of this. Indeed it may be perfectly rational to be a fundamentalist at times and a chartist at other times – the problem is figuring out when to make the switch.

Boring Rationality

Clearly investment houses would love to find models that effectively trade on these rationality deficiencies and one can only speculate on what impact this might have on markets. That some existing models increase market fluctuations by exacerbating the upswings and downturns now seems to a common theory. Whether quantitative behavioural models would damp down these swings or simply cause different kinds of trouble is unknown, but the evidence of history isn’t encouraging.

Keynes is supposed to have said that markets can remain irrational longer than investors can remain solvent, and there’s more than a grain of truth in this. However, unless investors can manage to stay fairly rational it’s hard to see how they can ever make any money on the markets. The alternative, as ever, is to remove oneself from the equation and simply invest in an index tracker. Rationality is not always interesting, but it’s often better than the alternative.


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Mandelbrot’s Mad Markets

Haunted By Statistics

As we've wandered down the echoing corridors of behavioural finance we seem to be haunted by a troublesome spectre, which refuses to go away no matter how much we prove to it that it’s a figment of economists’ imaginations. Discovered by Francis Galton, appropriated by Harry Markowitz and embedded in risk management models ever since, our ghoulish apparition is a mathematical construct, the Gaussian distribution, aka the bell curve.

The Gaussian distribution keeps on reappearing throughout economic theories as a rule-of-thumb description of how markets behave. With which there is just the smallest problem – markets don’t behave as it would predict. We’ve known this for over forty years, ever since Benoît Mandelbrot showed that cotton prices bound around in a decidedly peculiar way. Markets behave madly far more often than the standard models predict so why anyone should be surprised that they fail catastrophically every so often is a bit of a mystery, really.

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What’s Your Financial IQ?

Take the Cognitive Reflection Test

Answer the following questions and then read on.

(1) A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost? _____ cents

(2) If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? _____ minutes

(3) In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake? _____ days

Intuitively Obvious – and Wrong

Each of the questions on the Cognitive Reflection Test has an immediate, intuitive answer – which happens to be completely wrong. If you answered, or even considered, any of 10 cents, 100 minutes or 24 days you’re in good company (yours truly included). Out of 3428 people tested in the original research a third got all three answers wrong while only 17% got all correct. Even many of those who got the answers right had to think at least twice.

These particular questions are the mental equivalent of optical illusions. They appear to be easy, but aren’t. Tests of similar complexity which are more obviously difficult yield more correct answers so it’s not simply a question of basic maths skills. Something else is going on here. Something, it turns out, that may completely screw up psychology’s ideas about the way people think about finance.

Dumb or Smart?

When the respondants were split into groups dependent on how well they’d done on the CRT and (painlessly) probed for underlying differences what was found, broadly, is that those who did well are generally smart and those who did badly are generally dumb. Which is the psychologist’s equivalent of discovering that gravity makes things fall by dropping dumbbells on your feet.

However, when the “smart” and “dumb” groups were investigated further something genuinely interesting showed up. The “dumb” group turned out to behave exactly like behavioural finance predicts but the “smart” group didn’t. Which is a bit of a problem for the economists who’ve spent thirty years developing models for making money which rely on everyone acting dumb. It’s roughly the equivalent of discovering that gravity doesn’t work the same way everywhere; which can be quite disconcerting if your dumbells suddenly float off into the distance.

The Findings of the CRT

There were two main findings from the CRT. The first is that the “smarter” people were better at discounting time. Or, in lay language, they’re prepared to wait longer for a larger reward rather than taking a small, certain amount immediately – as long as the odds favour them. However, this only applies in the short-term – once timelines stretch out this effect disappears, which is also sensible, since the longer you have to wait for your delayed reward the more likely it is to never occur.

It’s the second finding that’s really interesting, though. Prospect Theory, the cornerstone of behavioural finance describing people’s risk taking behaviour, predicts that people are risk adverse when protecting a gain and risk takers when chasing a loss. This was spectacularly true of the low CRT scoring group, but not true at all of the high scoring group – suggesting that cognitive ability, aka I.Q., is critical in evaluation of decision making theories.

Or, to put it bluntly, behavioural finance is wrong.

What Do I.Q. Tests Measure?

Shane Frederick, the researcher, compared respondents’ CRT scores with results from other I.Q. tests and discovered a good but not perfect correlation. The CRT is measuring something similar, but not precisely the same as these other tests. But what do I.Q. tests measure anyway?

The founder of I.Q. testing, Alfred Binet, developed the concept to track improvements in learning, not absolute levels of intelligence. Scientists to a man (and they’re nearly always male) have proceeded to ignore him and spent the century or so since drawing bell curves to “prove” their preconceived hypotheses.

It was the U.S. Army in World War 1 that picked up the idea as a general measure for recruits and it’s been downhill ever since. Stephen Jay Gould in The Mismeasure of Man lays out the whole sorry story. It’s almost impossible to quickly convey exactly how manipulated the idea of I.Q. tests has been, but the concept keeps on rearing its ugly head like a particularly demented, stake-proof, garlic-loving vampire. By way of example, in the U.K. the academic Cyril Burt’s separated twin studies proved that genes are more important than upbringing in intelligence – i.e. that intelligence is innate, not learned. His findings influenced half a century of British education so it was just a tiny little bit of a shame that it turned out he’d faked his results.

So we can say, fairly safely, that whatever the CRT is measuring it’s not exactly intelligence. Frederick argues that it’s showing something he calls “cognitive reflection”—the ability or disposition to resist reporting the response that first comes to mind. If correct, why should this matter so much?

Does the CRT Invalidate Prospect Theory?

Prospect Theory tells us something about how people make decisions under conditions of uncertainty. The new research is telling us that some, thoughtful, people will make different decisions from those predicted by the theory and that we can identify these people, ahead of time, by using the CRT.

Follow up work from Frederick has indeed shown significant differences in risk taking behaviour amongst low and high CRT groups. Which would you prefer – the certainty of $500 now or a 15% chance of $1 million?

High CRT scorers overwhelmingly went for the gamble – 82% of them chose it while only a quarter of low CRT scorers gambled. This leads us straight into the quagmire of moral relativism: who decides what a “better” choice is and who is to say that the higher CRT scorers are making “better” decisions than the lower CRT scorers?

Dumb Money

Well, I will for one. Choosing five hundred bucks over a better than 1 in 7 chance of a life changing amount of money would be a totally bloody stupid decision no matter how poor, ignorant and desperate you are.

Still, this is all theoretical and we just don’t know how this would work in real situations. We’ve seen a similar proposal before from John A. List, whose staged but naturalistic experiments on trading behaviour suggested that experienced traders could overcome the loss aversion traits shown in the original research. Since then, as we’ve also seen, a completely naturalistic study using professional golfers has provided support for the original findings of Prospect Theory, throwing doubt on whether List’s experiment is really replicating real-world conditions or is somehow generating something even more artificial.

Separating genetic capabilities and learned ones is more like unbaking a cake than unwinding tangled threads. However, if the CRT tells us anything about investing it’s that we need to think hard and think properly and to take our time about it. Such skills can be learned and there are techniques investors can use to help develop them: just as long as they recognise they need them in the first place, of course.

Oh Yeah, The Results

The Cognitive Reflection Test (CRT) was developed by Professor Shane Frederick as described in Cognitive Reflections and Decision Making.

The correct answers are:

1. 5 cents (not 10 cents)
2. 5 minutes (not 100 minutes)
3. 47 days (not 24 days).

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Investing In The Rear View Mirror

Time’s Arrow

If we were granted the ability to reverse time’s arrow in order to replay history it’s highly unlikely that the world as we know it would recreate itself. The chances of humanity being here at all are so remote as to be infinitesimal. Our survival is a miracle, the chances of it happening again remote.

Basically, if we were to rewind and replay the video tape of history almost any prediction we might make would almost certainly not come true. And this process continues today – the world is too complex, too contingent, too damned unpredictable to allow for any trustworthy view of the future. Which is why people who think they can predict the course of macroeconomics are wishful thinkers, not serious investors.

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The Halo Effect: What’s in a Company Name?

Angels and Demons

The halo effect is a simple, pervasive and powerful psychological bias which sees us anchor onto a single positive feature of a person and then indiscriminately apply it to all of their other traits. So if we perceive someone as physically desirable we’re likely to assume that they’re attractive in all other ways as well. Which is highly fortunate for those beautiful but bad tempered, foul mouthed and cerebrally challenged personalities who commonly grace our multi-media world.

Companies will often attempt to use the halo effect by getting celebrity endorsements from completely unrelated but popular celebrities. Still, trading on such a simple psychological trait would be unlikely to fool savvy investors, you’d think. Wrongly, of course.

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Quibbles With Quants

Models are not Reality

When we model the world we simplify it, because we have to: to model the real-world accurately we’d need to have all the atoms in the universe and a few more. So, the globe of the world revolving gently on my desk afore me, is a highly simplified representation of the planet most of us live on, leaving out what many of us would consider to be most of the important details – stuff like people, beer, football, lively teenage daughters and the latest episode of House. You fill in your own details.

Quants, the purveyors of quantitative investment analysis, also model the world, although in a much more complex, mathematical way. Yet despite the sophistication and elegance of the mathematics they too make simplifications because they have to, and these matter to us all because they’ve brought the world to the point of financial meltdown thrice within a few years.

Models are Metaphors

What quants bring to investment analysis is a set of metaphors about the way the world actually works, rather than any kind of accurate description of what’s really happening. In fact the world is run by such approximations – models of average life expectancy, mean time to failure of aerospace components, the probability of Michael Jackson’s chimp contesting his will – and, in general, these inaccuracies haven’t mattered much to humanity en-masse. However, we’re now in a world dominated by mass computing power and intricately interconnected such that small perturbations in one area can have dramatic ramifications elsewhere.

From the 1980’s, when Portfolio Insurance turned out to be no such thing, to the 1990’s, when Long Term Capital Management became a glorious oxymoron, through to the 2000’s, when the securitised mortgage market turned into a series of financial time bombs in a greater game of pass the ticking parcel, the quantitative models that mathematically illiterate managements have relied on have given no warning of what was about to occur. Yet these models were created by some of the cleverest minds in the world and supported by some of the most serious back-testing that has ever been carried out.

Yet still they failed.

People Are Uncorrelated, Until They Aren’t

What the models failed to capture was that humans don’t behave in simple, predictable and uncorrelated ways. It’s impossible to overstate the importance of the way these models cope with correlation of peoples’ psychology. To sum it up: they don’t. Let me know if that’s too complex an analysis for the mathematical masters of the universe.

Anyone who’s ever been to a nightclub, a football game or even a very loud party will know that there are situations where we don’t act as individuals, buzzing about doing our own thing. These are occasions when we all suddenly stop being individuals and start doing the same thing – usually involving large quantities of drugs and some very bad singing. Although these sorts of events are specifically designed to trigger this behaviour – which is probably a deep evolutionary adaptation to sponsor group behaviour, useful when it comes to running down tasty antelope and dealing with giant, carnivorous sabre toothed beavers – it can also happen in other situations. Most stockmarket booms and busts are generated by similar group effects.

In general, people behave in an uncorrelated fashion right up until the point they don’t. Then we all suddenly do the same thing together. We stop taking flights in the wake of 9/11, we stop letting our children play in the streets because of a single, heavily reported abduction in another country and we start selling our shares because everyone else is. Fear is an awfully big motivator.

If Something Can’t Continue, It Won’t

Quantitative models don’t handle this sudden polarisation of human behaviour very well. Every so often something surprising happens that causes us all to scurry into the nearest hole and the models promptly fall over, usually accompanied by some whizz-kid earnestly explaining that this was a one in a million year event and that it was just bad luck it happened after three years and can he please have his bonus anyway?

Think about the booms and busts in stockmarkets compared to the relatively stable periods in between. In the stable times it’s possible to roughly model the way that people are going to invest, on average, making certain assumptions about the companies comprising the markets and general economic conditions. While these assumptions hold so do the models and this can go on for a long time, long enough to convince people that stability is the norm.

Only stability is not the norm. When the markets enter one of their periodic manic-depressive phases these general models break down – people start to cluster together in fear or greed and do the same thing. Quantitative models work on the basis that the stable times will last forever, but the reality is that they don’t and when they end they do so in highly unexpected and unpredictable ways. Worse still, if any model becomes too popular it will start to influence the real world, to swing the pendulum one way. The trouble with pendulums is that eventually they swing back.

Credit Rating Madness

We can trace the collapse of the banks over the past couple of years to the excessive risks they were taking in holding Collateralised Debt Obligations on mortgage securities, the idea being that if you took a lot of very safe mortgage debt and bundled it up with a bit of really unsafe mortgage debt you’d end up with a safe investment. Whereas, in fact, you ended up with a lot of really unsafe lending to people who were never going to be able to repay their mortgages.

Yet at the time the models investment analysts were relying on simply didn’t identify these risks. Indeed the models of the credit rating agencies were explicitly changed to take into account the quantitative models showing that such securities weren’t risky.

The sheer nuttiness of the credit rating agencies changing their risk models purely because a quantitative model existed that indicated that the risk of these securities collapsing like dominoes in the event of isolated defaults was remote is still hard to believe. It’s not that the models didn’t indicate exactly that. Nor is it that the mathematics behind the models was particularly stupid. Nor was it that the analysts who dreamt up the models were doing anything obviously wrong.

No, it’s none of those things. It’s the fact that a bunch of smart people can possibly believe that any computer model can accurately reflect the real risks in a world dominated by stubborn, irrational, fearful humankind. You’ve got to ask – did none of the overpaid executives running the world’s financial corporations and regulators actually stop to wonder whether someone might have, just possibly, failed to predict everything that might happen in the real world? Did none of them look at the collapse of LTCM and wonder?

We Need Bridge Builders, Not Quants

Well, oddly enough, some of them did. Back in 2006 when the CEO of Citigroup was still dancing in the last chance sub-prime disco the risk management team at Goldman Sachs got together and solemnly inspected their VaR models which told them that risk levels were still low. Then they inspected their brains – and bailed out. Whether that was luck or judgement is still to be decided.

The great Victorian engineers built bridges that endure to this day because they couldn’t exactly model the risks. They built with a margin of safety not with a bonus on margins in mind. Remember this because the quants are not dead, they’re out there yet. They will rise again.


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Don’t Overpay For Growth

Why’s Value Better?

A moment’s serious thought should tell us that investing in growth stocks ought to be a better stockmarket strategy than any other. Growth stocks, by their very nature, grow their earnings faster than other stocks so – obviously – they ought to be better investments. But investing’s not a zero sum game and the obvious often isn’t what it appears to be.

Investing in growth stocks all too often turns out to be a less than optimal stock allocation strategy but the simplistic argument that companies on lower ratings perform better than companies on higher ones simply won’t do. Value type companies are on lower ratings because their ability to grow their earnings is lower. All things being equal this shouldn’t translate into better share price performance. It does – so what’s going on?

Bubbling Growth

One simple answer, frequently given, is that growth stocks are the stuff of bubbles. There’s never been a bubble in lowly rated, moth-eaten, downtrodden, ex-growth value companies and, offering a rare prediction, I don’t think there ever will be. So when bubbles pop it’ll be the exciting growth pedigree stories that are bursting, not the mangy mutts that no one wants to re-home.

Recent research by the Brandes Institute confirms the long standing evidence of the superior performance of value over growth. Value vs. Glamour: A Global Phenomenon extends existing US based research across the globe to come to the following conclusions:

“Between 1968 and 2008, we found U.S. large-cap value stocks … outperformed U.S. large-cap glamour stocks … by 6.8% on an average annualized basis across 5-year periods. In the small-cap arena, U.S. value stocks returned an average annualized 9.7% more than their glamour counterparts across 5-year periods.

In non-U.S. developed markets, the premium for large-cap value stocks was 8.6% greater than large-cap glamour stocks between 1980 and 2008. Again, returns were annualized on 5-year periods. The difference was even greater for non-US small-cap stocks, where value stocks outperformed glamour stocks by 9.0% annualized. Note that this was not the absolute return for non-U.S. small-cap value stocks – this was the excess return over the small-cap value stocks.”

To paraphrase: everywhere and at all times growth stocks suck.

Clairvoyant Investing

Although the Brandes research brings up to date and confirms what we already knew it doesn’t offer any explanation for why this is so. It’s like Newton’s Laws of Gravity – providing laws that are always applicable without offering the slightest explanation of why this should be so. Many of Newton’s contemporaries regarded the concept of invisible forces acting at a distance as magic, not science. For humans it’s not enough to accept that something is so, we need to understand why it is so. So why do value stocks outperform?

Let’s face it – all the great companies of the world today were once growth stocks, they didn’t get to be big by being low-growth, grungy value mongrels. There’s something counterintuitively weird going on. As usual it’s nothing to do with the intrinsic merits of the companies and everything to do with the warped sense of values in the heads of the people who invest in them.

As previously reported here, recent research by Research Affiliates has come up with an interesting new way of examining past performance of various types of stocks. The so-called Clairvoyant Value analysis goes back to 1956 and assesses future performance of stocks against whether they were then rated as value or growth through a combination of various valuation ratios – price-to-sales ratio, price-to-earnings ratio and price-to-dividend ratio – all relative to the market at the time. These are fairly typical investment ratios, low values of which are often used to define value stocks, high ones to weed out growth.

Given the Brandes research we should be pretty confident in predicting that the research shows that value stocks will outperform growth stocks and we wouldn’t be disappointed. They do, significantly. However, what is interesting and different about this study is that it gives us a window why this is so, rather than simply reaffirming the facts.

Growth Stocks Are Best, But …

The first, notable point is that it turns out that our intuition about growth stocks being superior companies is, by and large, right on the money. Higher rated stocks justify their ratings by growing earnings significantly more than lower rated stocks. Investors correctly identify the growth stocks which will provide superior performance yet lower rated value stocks turn out to be better investments. What’s going on?

The simple answer is that we get excited about growth stories and overpay for them. Generally by about double what it turned out they were worth when we look at the discounted future cashflows of the companies at the various valuation points. The premium paid for growth stocks over value stocks has varied significantly over the period from a multiple of 1.6 times in 1977 – as the Nifty Fifty bubble burst – to 3.3 times in 1999 as dotcom mania exploded. Rarely has the premium turned out to be justified in terms of future, clairvoyant, growth. Here’s what Research Affiliates had to say about this:

“Perfect foresight through 2007 provides an even more powerful result—for spans of 20 or more years, the market never failed to overpay for the long-term realized successes of the growth companies, even though the market chose which companies deserved the premium multiples with remarkable accuracy.”

It’s perfectly obvious that it’s not anything about the intrinsic nature of the stocks that’s causing these effects, it’s human psychology in its purest form. Even when the markets are on their uppers, blood running in the streets, we still manage to push our money into the wrong stocks, incorrectly assuming that a good growth story equals a good investment. Unfortunately the opposite seems to be the truth of it and that’s highly unfortunate for us because we generally operate on stories and not numbers.

And It’s Getting Worse

The research also indicates that the premium that we’re prepared to pay for growth over value is actually increasing, contrary to any expectations about our ability to learn from our mistakes. This increase in “Value Dispersion” could be explained by a number of factors but most likely it means that investors are increasingly overconfident in their abilities to project forward future growth rates, despite all the evidence to the contrary.

One other factor that needs to be considered and doesn’t seem to be is the effect of survivorship on the statistics. You might suspect that higher risk growth stocks would be more likely to go bust than value stocks which ought to offer a margin of safety, but the studies don’t address this point. Intuition tends not to be a good guide to these things, but if it’s correct then growth stocks are even less attractive than they appear.

In general it’s interesting that it’s not our intuitions that let us down when it comes to growth stocks, it’s our number crunching. As ever, price is what you pay, value’s what you get. Invest with the head and not with the heart.


Related Posts: Clairvoyant Value, Overconfidence and Over Optimism, Fairy Tales for Investors