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Undercover Economist

Finance and the jelly bean problem

‘What else might influence portfolio returns? There is literally no limit to the number of variables’

Discomfiting news: most of those financial strategies that claim to beat the market don’t. Even more surprising, many of the financial research papers that claim to have found patterns in financial markets haven’t.

Don’t take my word for it: this is the conclusion of three US-based academics, Campbell Harvey, Yan Liu and Heqing Zhu. What is particularly striking about the way they’ve lobbed a hand grenade into the finance research literature is that Campbell Harvey isn’t some heterodox radical. He’s the former editor of the leading journal in the field, The Journal of Finance.

What’s going on?

Much financial research attempts to figure out what explains the investment returns on financial portfolios. At a first pass, returns follow a random-walk hypothesis. This insight is a century old and we owe it to the mathematician Louis Bachelier. The basic reasoning is that any successful forecast of price movements would be self-defeating: if it was obvious the price would rise tomorrow, then the price would instead rise today. Therefore, there can be no successful forecast of price movements.

A second pass at the problem, courtesy of several researchers in the 1960s, gives us the capital asset pricing model: riskier portfolios will probably offer higher returns. And it seems that they do.

Then, in 1992, Eugene Fama (more recently a Nobel Memorial Prize winner) and Kenneth French found that the returns on a portfolio of shares were explained by three factors: exposure to the market as a whole, exposure to small company stocks, and exposure to “value stocks”.

This is progress of sorts – but it’s also a can of worms. What else might influence portfolio returns? There is literally no limit to the number of different variables that could be examined, because variables can always be transformed or combined with each other, for instance as ratios or rates of change.

In principle, economic logic might limit the number of combinations to be examined – but in practice both academics and quantitatively minded investment managers have been known to throw in all sorts of possibilities just to see what happens. Why not, for example, use the cube of the market capitalisation of the shares? There’s no economic logic behind that variable – at least, none that I can see – but that hasn’t stopped the quants stirring such things into the mix.

The issue here is what we might call the “jelly bean problem”, after a cartoon by nerd hero Randall Munroe. The cartoon shows scientists testing whether jelly beans cause acne, applying a commonly used statistical test. The test is to assume that jelly beans don’t cause acne, then rethink that assumption if the observed correlation between jelly beans and acne has less than a 5 per cent probability of occurring by chance. The scientists test purple, brown, pink, blue, teal, salmon, red, turquoise, magenta, yellow, grey, tan, cyan, green, mauve, beige, lilac, black, peach and orange jelly beans. It turns out that the green ones are correlated with acne!

This is, of course, no way to perform a statistical analysis. If 20 statistical patterns are analysed and there’s no genuine causal relationship in any of them, we’d still expect one of them to look strikingly correlated. (How strikingly? Well, about 19-1 against.)

The finance literature has looked at far more than 20 possibilities. Harvey, Liu and Zhu scrutinise 316 different factors that have been explored by a selection of reputable research studies, of which 296 are statistically significant by conventional standards. That’s just a subset of the factors that have been examined in minor journals, or not published at all because the results were too boring.

For example, a paper might try to explain stock market returns as a function of media coverage of companies; of corporate debt; of momentum in previous returns; or of the volume of trades.

With 316 factors – and probably many more – under investigation, using a 5 per cent significance standard is absurd. Harvey and his colleagues suggest that after trying to correct for the jelly bean problem (more technically known as the multiple-comparisons problem), more than half the 296 statistically significant variables might have to be discarded. They suggest higher and more discerning statistical hurdles in future, not to mention a more explicit role for variables with some theory behind them, rather than variables that have happened to stick after the entire statistical fruit salad has been hurled at the wall.

None of this should astonish us. In 2005 an epidemiologist called John Ioannidis published a research paper that has become famous. It has the self-explanatory title “Why Most Published Research Findings Are False”. The reason is partly the multiple comparisons problem, and partly publication bias: a tendency on the part of researchers and journal editors alike to publish surprising findings and leave dull ones to languish in desk drawers.

Harvey and his colleagues have shown that the Ioannidis critique applies in the finance research literature too. No doubt it applies far more strongly in the advertisements we’re shown for financial products. We should have always been on the lookout for intriguing patterns in the data. But if we’re not careful, our analysis will produce plenty of flukes. And in finance, flukes are just as marketable as the truth.

Also published at ft.com.

Undercover Economist

A passport to privilege

Class matters far less than it used to in the 19th century. Citizenship matters far more

I’ve been a lucky boy. I could start with the “boy” fact. We men enjoy all sorts of privileges, many of them quite subtle these days, but well worth having. I’m white. I’m an Oxford graduate and I am the son of Oxbridge graduates. All those are things that I have in common with my fellow columnist Simon Kuper, who recently admitted that he didn’t feel he’d earned his vantage point “on the lower slopes of the establishment”.

I don’t feel able to comment objectively on that, although we could ask another colleague, Gillian Tett. She’s female and – in a particularly cruel twist – she wasn’t educated at Oxford but at Cambridge. That’s real diversity right there.

All these accidents of birth are important. But there’s a more important one: citizenship. Gillian, Simon and I are all British citizens. Financially speaking, this is a greater privilege than all the others combined.

Imagine lining up everyone in the world from the poorest to the richest, each standing beside a pile of money that represents his or her annual income. The world is a very unequal place: those in the top 1 per cent have vastly more than those in the bottom 1 per cent – you need about $35,000 after taxes to make that cut-off and be one of the 70 million richest people in the world. If that seems low, it’s $140,000 after taxes for a family of four – and it is also about 100 times more than the world’s poorest people have.

What determines who is at the richer end of that curve is, mostly, living in a rich country. Branko Milanovic, a visiting presidential professor at City University New York and author of The Haves and the Have-Nots, calculates that about 80 per cent of global inequality is the result of inequality between rich nations and poor nations. Only 20 per cent is the result of inequality between rich and poor within nations. The Oxford thing matters, of course. But what matters much more is that I was born in England rather than Bangladesh or Uganda. (Just to complicate matters, Simon Kuper was born in Uganda. He may refer to himself as “default man” but his life defies easy categorisation.)

That might seem obvious but it’s often ignored in the conversations we have about inequality. And things used to be very different. In 1820, the UK had about three times the per capita income of countries such as China and India, and perhaps four times that of the poorest countries. The gap between rich countries and the rest has since grown. Today the US has about five times the per capita income of China, 10 times that of India and 50 times that of the poorest countries. (These gaps could be made to look even bigger by not adjusting for lower prices in China and India.) Being a citizen of the US, the EU or Japan is an extraordinary economic privilege, one of a dramatically different scale than in the 19th century.

Privilege back then used to be far more about class than nationality. Consider the early 19th century world of Jane Austen’s Pride and Prejudice. Elizabeth Bennet’s financial future depends totally on her social position and, therefore, if and whom she marries. Elizabeth’s family’s income is £430 per capita. She can increase that more than tenfold by marrying Mr Darcy and snagging half of his £10,000 a year (this income, by the way, put Mr Darcy in the top 0.1 per cent of earners). But if her father dies before she marries, Elizabeth may end up with £40 a year, still twice the average income in England.

Milanovic shows that when we swap in data from 2004, all the gaps shrink dramatically. Mr Darcy’s income as one of the 0.1 per cent is £400,000; Elizabeth Bennet’s fallback is £23,000 a year. Marriage in the early 19th century would have increased her income more than 100 times; in the early 21st century, the ratio has shrunk to 17 times.

This is a curious state of affairs. Class matters far less than it used to in the 19th century. Citizenship matters far more. Yet when we worry about inequality, it’s not citizenship that obsesses us. Thomas Piketty’s famous book, Capital in the 21st Century, consciously echoes Karl Marx. Click over to the “Top Incomes Database”, a wonderful resource produced by Piketty, Tony Atkinson and others, and you’ll need to specify which country you’d like to analyse. The entire project accepts the nation state as the unit of analysis.

Meanwhile, many people want to limit migration – the single easiest way for poor people to improve their life chances – and view growth in India and China not as dramatic progress in reducing both poverty and global inequality, but as a sinister development.

It would be unfair to say that Simon Kuper and Thomas Piketty have missed the point. Domestic inequality does matter. It matters because we have political institutions capable of addressing it. It matters because it’s obvious from day to day. And it matters because over the past few decades domestic inequality has started to grow again, just as global inequality has started to shrink.

But as I check off my list of privileges, I won’t forget the biggest of them all: my passport.

Also published at ft.com.

Undercover Economist

Trading places – with a rat

Financial price data are converted into music, the music is played to a rat, then the rat guesses whether the price will fall or rise

Eighty-five years ago, a young psychologist called BF Skinner developed what is technically called an operant conditioning chamber but is more famously known as a Skinner box, designed to contain and train laboratory animals. The simplest version rewards a rat for pressing a lever. More complex devices can play sounds, display lights and even deliver electric shocks, although Skinner himself preferred to use rewards rather than punishments.

The Skinner box acquired an unpleasant reputation: rumours circulated that Skinner raised one of his own daughters in one, that she lost her mind, that she sued him and that she killed herself before reaching middle age. None of this is true. One explanation for the rumours is that Skinner did design an air-conditioned crib for his daughter and described it with fancy technical terms such as “apparatus”. But perhaps the false ideas circulated because Skinner’s ideas of modifying behaviour with rewards in a carefully controlled environment seemed somehow manipulative and threatening. These days, of course, we call behaviour modification “nudging” and it is perfectly respectable.

I thought of all this when I discovered RatTraders.com, a website offering, in its own words, “a professional service to the financial industry; rats are being trained to become superior traders in the financial markets.”

RatTraders displays photographs and short films of rats in Skinner boxes. As the website explains, “RATTRADERS rats can be trained exclusively for any financial market segment. They outperform most human traders and represent a much more economic solution for your trading desk.”

The brains behind the project is Michael Marcovici, who has a double life: he’s both a conceptual artist and an offbeat investment guru. So is RatTraders a work of art or a business proposition? The basic conceit is that financial price data are converted into 20 seconds of piano music, the music is played to a rat, and then the rat guesses whether to bet that the price will then fall or rise. If the rat is successful, it receives food; if not, a mild shock. After weeks of training, the site says, the best rats outperform most humans.
Rat graph illustration by Harry Haysom for Undercover Economist©Harry Haysom

In an online video, Marcovici deadpans to a “reporter” that “rats are much better at this because you can train a rat on a very specific thing. It will not be distracted.” The interviewer asks how much it would cost to buy one of these elite rats. “It’s hard to put a price tag on a rat. A rat will not help. Rat trading is a system,” responds Marcovici. But he adds that even the full service would be “just a fragment of what a human trader will cost”.

Just when the viewer’s credibility is strained to breaking point, Marcovici announces that investment banks have already installed trading floors full of rats in boxes. His ultimate business vision, he says, is that rats will also be trained to do marketing and general management. The comic timing is impressive. And it’s a piece of art with a message that does not need underlining.

Marcovici says that all this began as a joke. But after he had bought some rats and built a rather beautiful Skinner box, his girlfriend urged him not to get rid of the rats but to see if they actually could learn to trade gold, oil and foreign exchange. Most of them couldn’t but after five months a few of the rats were pretty good – better than tossing a coin, anyway, which is a tougher benchmark than you might think.

So should we start employing rats as traders? The idea is not totally absurd. Ben Vermaercke of the University of Leuven recently published a paper with four colleagues titled “More complex brains are not always better”. They showed that rats were better than humans at distinguishing certain kinds of striped patterns from others – the humans, it was hypothesised, were thinking too hard rather than relying on instinctive pattern recognition.

Perhaps a properly controlled scientific experiment would also find that rats make excellent foreign exchange traders. I doubt it. If rats really can make money after being played a sonified snippet of recent price history, that suggests a basic inefficiency in financial markets. If price movements were predictable in such an elementary way then profit-seeking traders, human or murine, should exploit the pattern and thereby change it. (If there are patterns in market prices, they are not simple.)

RatTraders is five years old but the project has recently enjoyed a fresh burst of media attention. Marcovici tells me that several small hedge funds promptly got in touch with him to find out more. Did he really expect serious interest in financially savvy rats?

“Yes, yes. Honestly I did,” he says. “I’ve been involved with the financial industry for some time and people try anything.”

Fair enough. Greed is a powerful motivator for humans and rats alike. But it is not always a great investment strategy. Some people wanted to believe the worst about BF Skinner. If there seems to be money to be made, others will gladly believe the best about trader rats.

Also published at ft.com.

Undercover Economist

Why are recessions so depressing?

Happiness is around six times more sensitive to economic growth when that ‘growth’ is negative

Have we missed the true cost of the Great Recession of 2008 and 2009? That seems a strange question: the financial crisis, the deep recession that followed and slow growth for many years after seem like the defining economic events of a generation – and it’s not as if we’ve been ignoring them.

But perhaps we haven’t taken the recession nearly seriously enough. That’s the conclusion of Jan-Emmanuel De Neve, an economist at University College London and the London School of Economics. De Neve says that the “untold story” of the recession is its psychological cost. In plain language: recessions make us very sad.

It might seem obvious that recessions are disheartening experiences. It’s not, for the simple reason that the link between economic growth and happiness is itself not obvious.

The opening salvo in a long intellectual battle was fired by Richard Easterlin, an economist who, back in 1974, found that richer people in any society tended to be more satisfied with their lives, and yet richer societies showed no tendency to be happier than poorer ones. Thus was born the “Easterlin paradox”: money buys happiness within a society but money does not make society as a whole happier.

There are several ways of accounting for this finding. One is to say that it’s wrong: that data on life satisfaction weren’t very good in 1974 and now we know better. Recent research by Betsey Stevenson and Justin Wolfers finds that there is no paradox: richer societies are indeed happier. A second response is that it all depends on what you mean by “happiness”. Angus Deaton, an economist, and Daniel Kahneman, a psychologist who won the Nobel memorial prize in economics, have found that income is better at buying some forms of happiness than others. People in rich societies say they are more satisfied with their lives but that does not mean that from day to day they will be in a better mood. A third explanation, favoured by many Easterlin fans, is to say that what Easterlin showed is that we live in a rat race: what makes us happy isn’t money but feeling richer than our neighbours. It’s a race not everyone can win.

This is the backdrop against which De Neve and five of his colleagues began to investigate the impact of recessions on our wellbeing. When you look at surveys of life satisfaction, people in rich countries typically rate their satisfaction at 6.5 or 7 out of 10. The answers are stable, barely changing as economies grow. One might expect that the impact of a recession – of the economy shrinking – would be similarly hard to detect in surveys of life satisfaction.

But a couple of years ago, De Neve was sitting in the Brussels office of Gallup, the polling company, reviewing the latest data on life satisfaction with colleagues. Something strange had happened: Greece and Portugal had disappeared.

On closer inspection it turned out the researchers couldn’t find Greece and Portugal on a graph because they had dropped out of a cluster of EU countries and were suddenly reporting life satisfaction of around 5 or 5.5, on a par with Afghanistan. Greece and Portugal had both suffered severe contractions but not so severe as to put their incomes anywhere near that of Afghanistan. So what had happened?

De Neve and his colleagues believe that the impact of economic growth on happiness is highly lopsided. Their statistical analysis is based on several large international data sets surveying life satisfaction, and finds that happiness is around six times more sensitive to economic growth when that “growth” is negative. If you have six years in which the economy grows a couple of per cent a year, followed by one year when the economy shrinks by 2 per cent, the economy itself will have made substantial progress but the wellbeing of citizens will not.

This is just one research paper but it chimes with a 2003 study by Wolfers, another expert in the economics of happiness. Wolfers found when macroeconomic indicators such as inflation or unemployment were volatile, that volatility was associated with lower life satisfaction.

What might explain the disproportionate impact of recessions on happiness? There’s a longstanding finding in psychology that losses are more keenly felt than gains so maybe that’s the answer.

Yet loss aversion is not the only explanation for why recessions seem to depress us. Another is that recessions are associated with an increase in uncertainty. Uncertainty is unsettling in its own right – and it is also attention-grabbing. When the economy is growing, we take that for granted. A recession, with lay-offs, bailouts, bankruptcies and emergency budgets, is far more noticeable.

Perhaps the connection between the economy and wellbeing is simple: when the economy is doing something that we notice, it affects how we feel – and recessions have a habit of calling themselves to our attention. This suggests a new happiness paradox. Even though we may have underestimated the psychological costs of the recession, those costs would be less if only we’d stop talking about it.

Also published at ft.com.

Other Writing

Why pilot schemes help ideas take flight

There’s huge value in experiments that help us decide whether to go big or go home

Here’s a little puzzle. You’re offered the chance to participate in two high-risk business ventures. Each costs £11,000. Each will be worth £1m if all goes well. Each has just a 1 per cent chance of success. The mystery is that the ventures have very different expected pay-offs.

One of these opportunities is a poor investment: it costs £11,000 to get an expected payout of £10,000, which is 1 per cent of a million. Unless you take enormous pleasure in gambling, the venture makes no sense.

Strangely, the other opportunity, while still risky, is an excellent bet. With the same cost and the same chance of success, how could that be?

Here’s the subtle difference. This attractive alternative project has two stages. The first is a pilot, costing £1,000. The pilot has a 90 per cent chance of failing, which would end the whole project. If the pilot succeeds, scaling up will cost a further £10,000, and there will be a 10 per cent chance of a million-pound payday.

This two-stage structure changes everything. While the total cost is still £11,000 and the chance of success is still 1 per cent, the option to get out after a failed pilot is invaluable. Nine times out of 10, the pilot will save you from wasting £10,000 – which means that while the simple project offers an expected loss of £1,000, the two-stage project has an expected profit of £8,000.

In a real project, nobody could ever be sure about the probability of success or its rewards. But the idea behind this example is very real: there’s huge value in experiments that help us decide whether to go big or go home.

We can see this effect in data from the venture capital industry. One study looked at companies backed by US venture capitalists (VCs) between 1986 and 1997, comparing them with a sample of companies chosen randomly to be the same age, size and from the same industry. (These results were published in this summer’s Journal of Economic Perspectives in an article titled “Entrepreneurship as Experimentation”.)

By 2007, only a quarter of the VC-backed firms had survived, while one-third of the comparison group was still in business. However, the surviving VC-backed firms were big successes, employing more than five times as many people as the surviving comparison firms. We can’t tell from this data whether the VCs are creating winners or merely spotting them in advance but we can see that big successes on an aggregate scale are entwined with a very high failure rate.

The option to conduct a cheap test run can be very valuable. It’s easy to lose sight of quite how valuable. Aza Raskin, who was lead designer for the Firefox browser, cites the late Paul MacCready as his inspiration on this point. MacCready was one of the great aeronautical engineers, and his most famous achievement was to build the Gossamer Condor and the Gossamer Albatross, human-powered planes that tore up the record books in the late 1970s.

One of MacCready’s key ideas was to develop a plane that could swiftly be rebuilt after a crash. Each test flight revealed fresh information, MacCready figured, but human-powered planes are so feather-light that each test flight also damages the plane. The most important thing a designer could do was to build a plane that could be rebuilt within days or even hours after a crash – rather than weeks or months. Once the problem of fast, cheap experimentation was solved, everything else followed.

Some professions have internalised this lesson. Architects use scale models to shed light on how a completed building might look and feel. A nicely made model can take days of work to complete but that is not much compared with the cost of the building itself.

Politicians don’t find it so easy. A new policy is hardly a new policy at all unless it can be unveiled in a blaze of glory, preferably as a well-timed surprise. That hardly suits the MacCready approach. Imagine the conference speech: “We’re announcing a new array of quick-and-dirty experiments with the welfare state. We’ll be iterating rapidly after each new blunder and heart-rending tabloid anecdote.”

A subtler problem is that projects need a certain scale before powerful decision makers will take them seriously.

“The transaction costs involved in setting up any aid project are so great that most donors don’t want to consider a project spending less than £20m,” says Owen Barder, director for Europe at the Center for Global Development, a think-tank. I suspect that the same insight applies far beyond the aid industry. Governments and large corporations can find it’s such a hassle to get anything up and running that the big stakeholders don’t want to be bothered with anything small.

That is a shame. The real leverage of a pilot scheme is that although it is cheap, it could have much larger consequences. The experiment itself may seem too small to bother with; the lesson it teaches is not.

Also published at ft.com.

21st of October, 2014Other WritingComments off
Undercover Economist

The kettle conundrum

The problem of saving the environment, then, is also the fundamental social problem: how do we come together and co-operate?

I owe Lucy Mangan an apology. Seven years ago she wrote a column for The Guardian about the folly of overfilling your kettle. Ever since then I have harboured the unspoken thought that it was one of the most wrong-headed things I have ever read.

Now, however, the International Monetary Fund itself has planted the banner of economic cost-benefit analysis firmly on the side of Mangan. Perhaps I am the one who was wrong-headed.

Mangan’s point was that the green movement has become “hog-tied” by its insistence that doing the environmentally responsible thing is a selfless act. Greens should point out that we’re constantly doing idiotic things that not only damage the planet but waste our own money. Throwing away one-third of the food that we buy is one of them. Over-filling our kettles is another. Mangan concluded that if you and I would “stop being such a frigging idiot”, the planet would be in much better shape.

That sounds like a simple plan. Alas, thrifty kettle-filling will not help much: the physicist David MacKay, author of Sustainable Energy – Without the Hot Air, reckons that kettle-boiling represents about half of 1 per cent of the typical British household’s energy use. As for one-third of food being wasted, Mangan was misled by a statistic produced by the anti-waste organisation Wrap. In “throwing away food”, Wrap included a failure to compost kitchen scraps and used tea bags. In fact, while it is easy to identify ways to reduce carbon emissions, it’s not quite so easy to find the things that both help the planet and save self-centred individuals time, trouble and money.

This is because the central, defining quality of all environmental problems is that they’re problems of shared resources. Driving a car clogs the streets for other drivers; burning coal dumps acid rain on someone else’s forests; above all, emitting greenhouse gases chiefly harms other people, many of whom have not yet been born. The problem of saving the environment, then, is also the fundamental social problem: how do we come together and co-operate?

Enter the IMF, with the astonishing claim that dealing with climate change can be a self-interested business after all. Two IMF researchers, Ian Parry and Chandara Veung, along with Dirk Heine of the University of Bologna, have been trying to find more credible examples of the overfilled kettle problem – that is, opportunities to be better off right now that would cut carbon dioxide emissions into the bargain.

The Montreal Protocol of 1987 was an international agreement to phase out chlorofluorocarbons, or CFCs. According to a recent analysis by The Economist, this single agreement has done about as much to limit greenhouse gas emissions as all nuclear and hydroelectric power generation put together. The striking thing about the Montreal Protocol, though, is that its purpose was to protect the ozone layer. (It succeeded.) The fact that CFCs are also a potent greenhouse gas was a happy coincidence.

The IMF researchers do not mention the Montreal Protocol but they argue that national governments are leaving similar opportunities lying on the pavement, waiting to be picked up. Let’s say, for example, that the US unilaterally introduces a tax on carbon dioxide emissions of $50 a tonne. That move would raise tax revenue, allowing other taxes to be cut. It would also raise the price of anything that embodied carbon dioxide emissions. Driving would become slightly more expensive, and this would reduce congestion and traffic fatalities. Coal-fired electricity would suffer a competitive disadvantage, and this would encourage a switch to cleaner energy, improving local air quality and saving lives. All these benefits would be enjoyed within US borders.

Is $50 a tonne of carbon dioxide emissions a big tax? Yes and no. It is several times higher than the EU’s emissions trading scheme price; it may even be high enough to serve as the main policy for dealing with climate change, although it is hard to be confident of that. It would also add about $900 to the taxes paid, directly or indirectly, by the typical US citizen, and roughly half that if introduced in the EU. These aren’t trivial sums but they are small enough to be offset with reduced taxes elsewhere.

On the other hand, the tax would add less than 10 per cent to the cost of a return flight from London to Sydney; slightly more than 10 per cent to the cost of petrol in the US, and less than a penny to the cost of overfilling your kettle 20 times a week. Life could, and would, go on.

In short, the IMF researchers are presenting us with the mother of all overfilled kettles: policies that governments could introduce that would promptly help their own citizens, while only incidentally making a major contribution to slowing climate change. What makes this plausible is that while individuals and companies do not habitually waste their own resources, we all understand that governments engaged in political rough-and-tumble waste national resources all the time.

So I apologise to Lucy Mangan. The next round of climate change negotiations should focus on governments encouraging each other to stop overfilling their own kettles.

Also published at ft.com.

Undercover Economist

Pick a fund, any fund…

Most active managers do not manage to outperform passive funds – particularly not when their fees are deducted

The supermarket checkout poses a frustrating puzzle. Which line to choose? The one with fewest people? Pah! An amateur’s mistake. One must first look at the number of goods in each shopping trolley to get a sense of how long each person in the queue will take. Elevating the analysis a little further, consider awkward items such as crisps (hard to read the barcode), fruit and vegetables (which must be weighed), alcohol (requiring proof of age). A still higher form of thought is to evaluate the shoppers themselves. Will they fiddle for change? Pull out a cheque book or a wad of coupons and vouchers? If so, avoid.

Yet expending all this mental energy is the mark of a mediocre mind. The truly sophisticated thinker – an economist, for example – knows that there is no need to waste effort. Since others are keenly searching for the shortest queue, there won’t be a shortest queue at all. Each opportunity will immediately be filled, and each line will on average take the same amount of time. Pick a queue at random. Any will do, for they are all much the same.

This is also the case for passive investment. Why spend time carefully choosing assets to buy, or lavishly paying active fund managers to do the job for you, when every asset’s expected risk-adjusted return is the same?

All this assumes something rather important: that the risk-adjusted return (or the length of the queues) is indeed the same. Or at least that such returns look so similar to the trained eye that it is pointless to try to pick a winner. Another phrase to describe this idea is the “efficient markets hypothesis”. It is often viewed with suspicion because it sounds a bit Reaganite; in fact, it simply means you shouldn’t be too impressed by people who offer you stock tips.

We don’t know for sure that all financial assets have the same expected returns after appropriate adjustments for risk, partly because it is not clear what an appropriate adjustment for risk would be. It seems likely that they’re not far off.

One indicator of this is the performance of actively managed investment funds versus passive funds, which simply try to track some sector or market as a whole. Most active managers in most time periods do not manage to outperform passive funds – particularly not when their fees are deducted. As a matter of arithmetic, the average investor cannot beat the market because the market is the average of all the investors. But we might still expect that skilled active managers would consistently beat the average, and most of them cannot. Apparently skilled active managers often see their performance ebb over time, and for every Warren Buffett there are many one-hit wonders in the investment world.

What is more, active management is expensive. Even if you believe that an adviser could pick a faster-than-average supermarket queue to join, you might well be worse off pausing for a couple of minutes to take this advice, rather than choosing randomly without delay.

Active managers will have us believe otherwise, and occasional bunfights break out over whether actively managed funds are quite as bad as they seem but, for me, the logic in favour of passive investing is persuasive and the data even more so.

As this insight becomes better and better publicised, traditional fund managers are losing market share to low-cost exchange traded funds (ETFs) and, at the luxury end, to private equity groups. (The attraction of private equity is that you don’t shop in the supermarket at all. Whether the personal service at the delicatessen is actually worth what it costs is another question.)

I must confess, though, to a twinge of guilt – not a common emotion for the working economist. By passively investing or randomly choosing a supermarket queue, am I not taking advantage of the hard work of others? If everybody chose the first queue or investment that they came across, there would be no reason to expect a happy outcome. It is only because others are taking such pains to choose that I don’t have to bother.

This insight has become known as the Grossman-Stiglitz paradox, after Sanford Grossman and Nobel Memorial Prize winner Joseph Stiglitz, who back in 1980 published a paper pointing out that if financial markets were efficient, there was no benefit in paying for any sort of research or analysis; yet if nobody paid for any sort of research or analysis, why on earth would financial markets be efficient?

We passive investors like to congratulate ourselves on avoiding those parasites, the active fund managers, who charge high fees without delivering high returns. Yet we are parasites too, waiting for others to pay for research and then following the herd. Little fleas have lesser fleas, and so on, ad infinitum.

Passive investors shouldn’t feel too badly, though. This is a self-correcting problem. If most investors switched to passive funds, or picked supermarket queues at random, the market would be full of obvious errors and an active approach would pay off again.

I am beginning to make a study of supermarket queues already. It’s just a hobby – for now.

Also published at ft.com.

Undercover Economist

When regulators are all out to déjeuner

Just because a problem exists does not mean that a new regulation will solve it

“Each time I visit the city the food gets worse and worse.” Tyler Cowen, economics professor, foodie and author of An Economist Gets Lunch, despairs of Paris. Cowen isn’t the only person to lament the state of French cuisine. This may be why – in a quintessentially French move – the nation’s government has introduced a new law in an attempt to improve standards.

The quixotic law in question is public decree No. 2014-797, more popularly known as the “fait maison” rule, in which restaurants may use a new saucepan-with-a-roof-and-chimney logo on the menu beside any dish that is made on the premises. More accurately, the restaurants must use the saucepan-with-a-roof symbol to denote house-made dishes, but the definition of house-made is rather whimsical, thanks to French legislators.

The entire affair seems unlikely to improve French cuisine but it does provide a nice lesson in practical economics: regulation is a superficially appealing answer to life’s problems but often fails to provide real solutions.

The first difficulty is that regulations are developed by politicians, and politicians pay close attention to lobbyists. In the case of the fait maison rules, it is perfectly legitimate to buy industrially prepared ingredients, provided the dish itself is assembled on the premises. Frozen fish is fine. Skinned and boned chicken is fine. Onion powder seems to be fine. Certain types of factory-made pastry are fine, although others are not. Exceptions are baffling: frozen pommes frites are not allowed unless, of course, the fries are to be oven-baked. Diced, vacuum-packed vegetables are fine but be sure to add a home-made sauce. Eliminating prepared sauces was a priority – but still, it is hard to understand these rules as anything other than the outcome of a prodigious lobbying effort by industrial food companies.

Even if the rules were more logically laid out, the French government would still be committing a classic managerial blunder. To borrow the title of a 1975 article by management professor Steven Kerr, they are engaged in “the folly of rewarding A while hoping for B”.

What France demands, naturellement, is good French food. But insisting on home-made food ensures neither quality nor Frenchness. Freshly prepared food can be terrible, while some food prepared elsewhere is superb. (My brother-in-law is a master baker operating out of an industrial estate by Oxenholme station in northwest England. I’d back his frozen sourdough loaves against fresh bread baked by a more generalist kitchen any day.) The French parliament presumably hopes that by rewarding house-made food it will indirectly improve quality. This is optimistic.

A third problem is that the regulation may produce unintended consequences. Consider a chef who offers a fresh fruit crumble alongside a selection of factory-made cakes and puddings. By law, he or she must display the fait maison logo beside the crumble, implicitly damning all his or her other dishes. Such chefs might decide to offer no house-made dishes at all, rather than bring unwelcome questions to the forefront of their customers’ minds.

Policymaking is flawed and crude while the world is subtle and unpredictable. That is why regulations are often rigged from the start, are only peripherally related to the real matter of concern and have a tendency to backfire.

“There is no substitute for consumers who demand the right kind of food and who otherwise won’t buy it,” says Cowen. This is true. The British surely get the food we deserve, and because we have become less clueless about food, our food has become less appalling. (Perhaps I am wrong. Perhaps Tony Blair passed a law back in the late 1990s outlawing prawn cocktails and tinned vegetables, and I missed it. But I suspect not.)

Yet if informed and demanding consumers are essential for food, they are essential for other markets too. In banking, there is no substitute for consumers who refuse to be sucked in by teaser rates and fines in the small print. In investment, there is no substitute for consumers who avoid high charges and are unmoved by selective claims about past performance. In medicine, there is no substitute for consumers who can tell the difference between an expert doctor, a defensive pusher of scans and blood tests, and an outright quack. But such customers are rare. In fairness to the customers who struggle, it is far harder to identify a good pension than a good pizza.

Regulators, then, must muddle through. Sometimes they outsource the job to professional bodies who will punish egregious offenders. Sometimes they try to outlaw particularly troublesome practices. Sometimes (too rarely) they decide that anything they did would make things worse.

There are few easy answers. Regulations are sometimes essential; they are also sometimes both burdensome and useless. The UK’s planning laws should ensure an adequate supply of elegant, well-built homes. They do not. International rules on financial stability did not give us financial stability. Just because a problem exists does not mean that a new regulation will solve it.

This summer I went to Italy for my summer break. I have always found the food there far better than in France or Britain. I doubt that the credit for that should go to the Italian parliament.

Also published at ft.com.

Undercover Economist

Crushing the competition – at any price

It was Selten’s chain store paradox that first attracted me to economics, with a heady mixture of logic, psychology and military strategy

Forty years ago a German economist, Reinhard Selten, published a working paper with the title “The chain store paradox”. It was simple and profound and showed a discomfiting disconnect between the fashionable mathematical tools known as “game theory” and the recommendations of common sense.

This is the set-up. Imagine a chain store with 20 branches, one in each of 20 small towns. Lacking any competition, these branches charge high prices and are lucrative. In each town, an entrepreneur is considering opening a rival shop. These 20 local entrepreneurs will, one by one, decide whether to compete against the chain store or to sink their capital into something else.

Much depends on the chain store’s response to a competitor. The chain store could be aggressive, ruthlessly slashing prices. That would make life painful for both retailers. The entrepreneur, finding herself committed to a low-margin business, would wish she’d invested her money in something else. Or the chain store could be accommodating, letting prices stay high and sharing a profitable market. So what will happen?

Selten offered two lines of reasoning. One of them is intuitive: the chain store will launch a brutal price war against the first entrepreneur with the temerity to set up as a rival. It will lose money in that market but other entrepreneurs will take note of the bloodbath and will steer clear. The reward for giving up one or two cosy local duopolies is that in the other 18 or 19 towns the chain store will remain a monopolist. The chain store will use price wars as a deterrent, and the deterrent will work.

The alternative line of reasoning is a matter of inductive logic. Consider the 20th and final town. Deterrence is pointless there, since there are no further entrepreneurs to deter. The chain store may as well eschew a price war and accommodate the competitor. Yet if it is obvious that there will be no price war in the 20th town, what about the 19th? If everyone knows there will be no price war in the 20th, what would a price war in the 19th be designed to achieve? Again, deterrence is pointless. But if neither the 20th nor the 19th town will see fierce competition, what is the point of deterrence in the 18th town? The logic rolls back to the beginning of the game; it shows that deterrence is futile and will not be used at any stage.

Selten, an expert in game theory, knew that the second scenario was logically watertight. But as a practical matter he found the first scenario “much more convincing”. If he were the chain store, Selten wrote, he would launch price wars in the hope of deterring later competitors. “I would be very surprised if it failed to work.”

If deterrence works in practice, how to make it work in theory? Selten devoted himself to sharpening up game theory to deal with such apparent paradoxes, and in 1994 he was one of two men to share a Nobel memorial prize with John Nash, he of A Beautiful Mind.

One step forward is to add some vagueness or uncertainty. Can we be sure that the decision will be repeated only 20 times? Can we be sure that the competitors really understand each other? What if the chain store manager is a thug who loves to crush the competition and cares not at all for his shareholders’ dividends? Deterrence becomes a logical solution as well as an intuitive one.

It was Selten’s chain store paradox that first attracted me to economics, with a heady mixture of logic, psychology and something approaching military strategy. And it is a hypothetical game with some close parallels today.

Brad Stone’s excellent book, The Everything Store: Jeff Bezos and the Age of Amazon, paints Amazon’s founder to be a visionary entrepreneur, dedicated to serving his customers. But it also reports that Bezos was willing to take big losses in the hope of weakening competitors. Zappos, the much-loved online shoe retailer, faced competition from an Amazon subsidiary that first offered free shipping and then started paying customers $5 for every pair of shoes they ordered. Quidsi, which ran Diapers.com, was met with a price war from “Amazon Mom”. Industry insiders told Stone that Amazon was losing $1m a day just selling nappies. Both Zappos and Quidsi ended up being bought out by Amazon.

When the weapons of war are low prices, consumers benefit at first. But the long term looks worrying: a future in which nobody dares to compete with Amazon. Apple is a striking contrast: the company’s refusal to compete aggressively on price makes it hugely profitable but has also attracted a swarm of competitors.

Consider a grimmer parallel. Vladimir Putin’s Russia is the chain store. Georgia, Ukraine and many other former Soviet states or satellites must consider whether to seek ties with the west. In each case Putin must decide whether to accommodate or open costly hostilities. The conflict in Ukraine has been disastrous for Russian interests in the short run but it may have bolstered Putin’s personal position. And if his strategy convinces the world that Putin will never share prosperity, his belligerence may yet pay off.

I feel a little guilty comparing Bezos and Putin. My only regret about Bezos’s Amazon is that there aren’t three other companies just like it. I do not feel the same about Putin’s Russia.

Also published at ft.com.

Highlights

How to see into the future

Billions of dollars are spent on experts who claim they can forecast what’s around the corner, in business, finance and economics. Most of them get it wrong. Now a groundbreaking study has unlocked the secret: it IS possible to predict the future – and a new breed of ‘superforecasters’ knows how to do it

Irving Fisher was once the most famous economist in the world. Some would say he was the greatest economist who ever lived. “Anywhere from a decade to two generations ahead of his time,” opined the first Nobel laureate economist Ragnar Frisch, in the late 1940s, more than half a century after Fisher’s genius first lit up his subject. But while Fisher’s approach to economics is firmly embedded in the modern discipline, many of those who remember him now know just one thing about him: that two weeks before the great Wall Street crash of 1929, Fisher announced, “Stocks have reached what looks like a permanently high plateau.”

In the 1920s, Fisher had two great rivals. One was a British academic: John Maynard Keynes, a rising star and Fisher’s equal as an economic theorist and policy adviser. The other was a commercial competitor, an American like Fisher. Roger Babson was a serial entrepreneur with no serious academic credentials, inspired to sell economic forecasts by the banking crisis of 1907. As Babson and Fisher locked horns over the following quarter-century, they laid the foundations of the modern economic forecasting industry.

Fisher’s rivals fared better than he did. Babson foretold the crash and made a fortune, enough to endow the well-respected Babson College. Keynes was caught out by the crisis but recovered and became rich anyway. Fisher died in poverty, ruined by the failure of his forecasts.

If Fisher and Babson could see the modern forecasting industry, it would have astonished them in its scale, range and hyperactivity. In his acerbic book The Fortune Sellers, former consultant William Sherden reckoned in 1998 that forecasting was a $200bn industry – $300bn in today’s terms – and the bulk of the money was being made in business, economic and financial forecasting.

It is true that forecasting now seems ubiquitous. Data analysts forecast demand for new products, or the impact of a discount or special offer; scenario planners (I used to be one) produce broad-based narratives with the aim of provoking fresh thinking; nowcasters look at Twitter or Google to track epidemics, actual or metaphorical, in real time; intelligence agencies look for clues about where the next geopolitical crisis will emerge; and banks, finance ministries, consultants and international agencies release regular prophecies covering dozens, even hundreds, of macroeconomic variables.

Real breakthroughs have been achieved in certain areas, especially where rich datasets have become available – for example, weather forecasting, online retailing and supply-chain management. Yet when it comes to the headline-grabbing business of geopolitical or macroeconomic forecasting, it is not clear that we are any better at the fundamental task that the industry claims to fulfil – seeing into the future.

So why is forecasting so difficult – and is there hope for improvement? And why did Babson and Keynes prosper while Fisher suffered? What did they understand that Fisher, for all his prodigious talents, did not?

In 1987, a young Canadian-born psychologist, Philip Tetlock, planted a time bomb under the forecasting industry that would not explode for 18 years. Tetlock had been trying to figure out what, if anything, the social sciences could contribute to the fundamental problem of the day, which was preventing a nuclear apocalypse. He soon found himself frustrated: frustrated by the fact that the leading political scientists, Sovietologists, historians and policy wonks took such contradictory positions about the state of the cold war; frustrated by their refusal to change their minds in the face of contradictory evidence; and frustrated by the many ways in which even failed forecasts could be justified. “I was nearly right but fortunately it was Gorbachev rather than some neo-Stalinist who took over the reins.” “I made the right mistake: far more dangerous to underestimate the Soviet threat than overestimate it.” Or, of course, the get-out for all failed stock market forecasts, “Only my timing was wrong.”

Tetlock’s response was patient, painstaking and quietly brilliant. He began to collect forecasts from almost 300 experts, eventually accumulating 27,500. The main focus was on politics and geopolitics, with a selection of questions from other areas such as economics thrown in. Tetlock sought clearly defined questions, enabling him with the benefit of hindsight to pronounce each forecast right or wrong. Then Tetlock simply waited while the results rolled in – for 18 years.

Tetlock published his conclusions in 2005, in a subtle and scholarly book, Expert Political Judgment. He found that his experts were terrible forecasters. This was true in both the simple sense that the forecasts failed to materialise and in the deeper sense that the experts had little idea of how confident they should be in making forecasts in different contexts. It is easier to make forecasts about the territorial integrity of Canada than about the territorial integrity of Syria but, beyond the most obvious cases, the experts Tetlock consulted failed to distinguish the Canadas from the Syrias.

Adding to the appeal of this tale of expert hubris, Tetlock found that the most famous experts fared somewhat worse than those outside the media spotlight. Other than that, the humiliation was evenly distributed. Regardless of political ideology, profession and academic training, experts failed to see into the future.

Most people, hearing about Tetlock’s research, simply conclude that either the world is too complex to forecast, or that experts are too stupid to forecast it, or both. Tetlock himself refused to embrace cynicism so easily. He wanted to leave open the possibility that even for these intractable human questions of macroeconomics and geopolitics, a forecasting approach might exist that would bear fruit.

. . .

In 2013, on the auspicious date of April 1, I received an email from Tetlock inviting me to join what he described as “a major new research programme funded in part by Intelligence Advanced Research Projects Activity, an agency within the US intelligence community.”

The core of the programme, which had been running since 2011, was a collection of quantifiable forecasts much like Tetlock’s long-running study. The forecasts would be of economic and geopolitical events, “real and pressing matters of the sort that concern the intelligence community – whether Greece will default, whether there will be a military strike on Iran, etc”. These forecasts took the form of a tournament with thousands of contestants; it is now at the start of its fourth and final annual season.

“You would simply log on to a website,” Tetlock’s email continued, “give your best judgment about matters you may be following anyway, and update that judgment if and when you feel it should be. When time passes and forecasts are judged, you could compare your results with those of others.”

I elected not to participate but 20,000 others have embraced the idea. Some could reasonably be described as having some professional standing, with experience in intelligence analysis, think-tanks or academia. Others are pure amateurs. Tetlock and two other psychologists, Don Moore and Barbara Mellers, have been running experiments with the co-operation of this army of volunteers. (Mellers and Tetlock are married.) Some were given training in how to turn knowledge about the world into a probabilistic forecast; some were assembled into teams; some were given information about other forecasts while others operated in isolation. The entire exercise was given the name of the Good Judgment Project, and the aim was to find better ways to see into the future.

The early years of the forecasting tournament have, wrote Tetlock, “already yielded exciting results”.

A first insight is that even brief training works: a 20-minute course about how to put a probability on a forecast, correcting for well-known biases, provides lasting improvements to performance. This might seem extraordinary – and the benefits were surprisingly large – but even experienced geopolitical seers tend to have expertise in a subject, such as Europe’s economies or Chinese foreign policy, rather than training in the task of forecasting itself.

“For people with the right talents or the right tactics, it is possible to see into the future after all”

A second insight is that teamwork helps. When the project assembled the most successful forecasters into teams who were able to discuss and argue, they produced better predictions.

But ultimately one might expect the same basic finding as always: that forecasting events is basically impossible. Wrong. To connoisseurs of the frailties of futurology, the results of the Good Judgment Project are quite astonishing. Forecasting is possible, and some people – call them “superforecasters”– can predict geopolitical events with an accuracy far outstripping chance. The superforecasters have been able to sustain and even improve their performance.

The cynics were too hasty: for people with the right talents or the right tactics, it is possible to see into the future after all.

Roger Babson, Irving Fisher’s competitor, would always have claimed as much. A serial entrepreneur, Babson made his fortune selling economic forecasts alongside information about business conditions. In 1920, the Babson Statistical Organization had 12,000 subscribers and revenue of $1.35m – almost $16m in today’s money.

“After Babson, the forecaster was an instantly recognisable figure in American business,” writes Walter Friedman, the author of Fortune Tellers, a history of Babson, Fisher and other early economic forecasters. Babson certainly understood how to sell himself and his services. He advertised heavily and wrote prolifically. He gave a complimentary subscription to Thomas Edison, hoping for a celebrity endorsement. After contracting tuberculosis, Babson turned his management of the disease into an inspirational business story. He even employed stonecutters to carve inspirational slogans into large rocks in Massachusetts (the “Babson Boulders” are still there).

On September 5 1929, Babson made a speech at a business conference in Wellesley, Massachusetts. He predicted trouble: “Sooner or later a crash is coming which will take in the leading stocks and cause a decline of from 60 to 80 points in the Dow-Jones barometer.” This would have been a fall of around 20 per cent.

So famous had Babson become that his warning was briefly a self-fulfilling prophecy. When the news tickers of New York reported Babson’s comments at around 2pm, the markets erupted into what The New York Times described as “a storm of selling”. Shares lurched down by 3 per cent. This became known as the “Babson break”.

The next day, shares bounced back and Babson, for a few weeks, appeared ridiculous. On October 29, the great crash began, and within a fortnight the market had fallen almost 50 per cent. By then, Babson had an advertisement in the New York Times pointing out, reasonably, that “Babson clients were prepared”. Subway cars were decorated with the slogan, “Be Right with Babson”. For Babson, his forecasting triumph was a great opportunity to sell more subscriptions.

But his true skill was marketing, not forecasting. His key product, the “Babson chart”, looked scientific and was inspired by the discoveries of Isaac Newton, his idol. The Babson chart operated on the Newtonian assumption that any economic expansion would be matched by an equal and opposite contraction. But for all its apparent sophistication, the Babson chart offered a simple and usually contrarian message.

“Babson offered an up-arrow or a down-arrow. People loved that,” says Walter Friedman. Whether or not Babson’s forecasts were accurate was not a matter that seemed to concern many people. When he was right, he advertised the fact heavily. When he was wrong, few noticed. And Babson had indeed been wrong for many years during the long boom of the 1920s. People taking his advice would have missed out on lucrative opportunities to invest. That simply didn’t matter: his services were popular, and his most spectacularly successful prophecy was also his most famous.

Babson’s triumph suggests an important lesson: commercial success as a forecaster has little to do with whether you are any good at seeing into the future. No doubt it helped his case when his forecasts were correct but nobody gathered systematic information about how accurate he was. The Babson Statistical Organization compiled business and economic indicators that were, in all probability, of substantial value in their own right. Babson’s prognostications were the peacock’s plumage; their effect was simply to attract attention to the services his company provided.

. . .

When Barbara Mellers, Don Moore and Philip Tetlock established the Good Judgment Project, the basic principle was to collect specific predictions about the future and then check to see if they came true. That is not the world Roger Babson inhabited and neither does it describe the task of modern pundits.

When we talk about the future, we often aren’t talking about the future at all but about the problems of today. A newspaper columnist who offers a view on the future of North Korea, or the European Union, is trying to catch the eye, support an argument, or convey in a couple of sentences a worldview that would otherwise be impossibly unwieldy to explain. A talking head in a TV studio offers predictions by way of making conversation. A government analyst or corporate planner may be trying to justify earlier decisions, engaging in bureaucratic defensiveness. And many election forecasts are simple acts of cheerleading for one side or the other.

“Some people – call them ‘superforecasters’– can predict geopolitical events with an accuracy far outstripping chance”

Unlike the predictions collected by the Good Judgment Project, many forecasts are vague enough in their details to allow the mistaken seer off the hook. Even if it was possible to pronounce that a forecast had come true or not, only in a few hotly disputed cases would anybody bother to check.

All this suggests that among the various strategies employed by the superforecasters of the Good Judgment Project, the most basic explanation of their success is that they have the single uncompromised objective of seeing into the future – and this is rare. They receive continual feedback about the success and failure of every forecast, and there are no points for radicalism, originality, boldness, conventional pieties, contrarianism or wit. The project manager of the Good Judgment Project, Terry Murray, says simply, “The only thing that matters is the right answer.”

I asked Murray for her tips on how to be a good forecaster. Her reply was, “Keep score.”

. . .

An intriguing footnote to Philip Tetlock’s original humbling of the experts was that the forecasters who did best were what Tetlock calls “foxes” rather than “hedgehogs”. He used the term to refer to a particular style of thinking: broad rather than deep, intuitive rather than logical, self-critical rather than assured, and ad hoc rather than systematic. The “foxy” thinking style is now much in vogue. Nate Silver, the data journalist most famous for his successful forecasts of US elections, adopted the fox as the mascot of his website as a symbol of “a pluralistic approach”.

The trouble is that Tetlock’s original foxes weren’t actually very good at forecasting. They were merely less awful than the hedgehogs, who deployed a methodical, logical train of thought that proved useless for predicting world affairs. That world, apparently, is too complex for any single logical framework to encompass.

More recent research by the Good Judgment Project investigators leaves foxes and hedgehogs behind but develops this idea that personality matters. Barbara Mellers told me that the thinking style most associated with making better forecasts was something psychologists call “actively open-minded thinking”. A questionnaire to diagnose this trait invites people to rate their agreement or disagreement with statements such as, “Changing your mind is a sign of weakness.” The project found that successful forecasters aren’t afraid to change their minds, are happy to seek out conflicting views and are comfortable with the notion that fresh evidence might force them to abandon an old view of the world and embrace something new.

Which brings us to the strange, sad story of Irving Fisher and John Maynard Keynes. The two men had much in common: both giants in the field of economics; both best-selling authors; both, alas, enthusiastic and prominent eugenicists. Both had immense charisma as public speakers.

Fisher and Keynes also shared a fascination with financial markets, and a conviction that their expertise in macroeconomics and in economic statistics should lead to success as an investor. Both of them, ultimately, were wrong about this. The stock market crashes of 1929 – in September in the UK and late October in the US – caught each of them by surprise, and both lost heavily.

Yet Keynes is remembered today as a successful investor. This is not unreasonable. A study by David Chambers and Elroy Dimson, two financial economists, concluded that Keynes’s track record over a quarter century running the discretionary portfolio of King’s College Cambridge was excellent, outperforming market benchmarks by an average of six percentage points a year, an impressive margin.

This wasn’t because Keynes was a great economic forecaster. His original approach had been predicated on timing the business cycle, moving into and out of different investment classes depending on which way the economy itself was moving. This investment strategy was not a success, and after several years Keynes’s portfolio was almost 20 per cent behind the market as a whole.

The secret to Keynes’s eventual profits is that he changed his approach. He abandoned macroeconomic forecasting entirely. Instead, he sought out well-managed companies with strong dividend yields, and held on to them for the long term. This approach is now associated with Warren Buffett, who quotes Keynes’s investment maxims with approval. But the key insight is that the strategy does not require macroeconomic predictions. Keynes, the most influential macroeconomist in history, realised not only that such forecasts were beyond his skill but that they were unnecessary.

Irving Fisher’s mistake was not that his forecasts were any worse than Keynes’s but that he depended on them to be right, and they weren’t. Fisher’s investments were leveraged by the use of borrowed money. This magnified his gains during the boom, his confidence, and then his losses in the crash.

But there is more to Fisher’s undoing than leverage. His pre-crash gains were large enough that he could easily have cut his losses and lived comfortably. Instead, he was convinced the market would turn again. He made several comments about how the crash was “largely psychological”, or “panic”, and how recovery was imminent. It was not.

One of Fisher’s major investments was in Remington Rand – he was on the stationery company’s board after selling them his “Index Visible” invention, a type of Rolodex. The share price tells the story: $58 before the crash, $28 by 1930. Fisher topped up his investments – and the price soon dropped to $1.

Fisher became deeper and deeper in debt to the taxman and to his brokers. Towards the end of his life, he was a marginalised figure living alone in modest circumstances, an easy target for scam artists. Sylvia Nasar writes in Grand Pursuit, a history of economic thought, “His optimism, overconfidence and stubbornness betrayed him.”

. . .

So what is the secret of looking into the future? Initial results from the Good Judgment Project suggest the following approaches. First, some basic training in probabilistic reasoning helps to produce better forecasts. Second, teams of good forecasters produce better results than good forecasters working alone. Third, actively open-minded people prosper as forecasters.

But the Good Judgment Project also hints at why so many experts are such terrible forecasters. It’s not so much that they lack training, teamwork and open-mindedness – although some of these qualities are in shorter supply than others. It’s that most forecasters aren’t actually seriously and single-mindedly trying to see into the future. If they were, they’d keep score and try to improve their predictions based on past errors. They don’t.

“Successful forecasters aren’t afraid to change their minds and are comfortable with the notion that fresh evidence might mean abandoning an old view”

This is because our predictions are about the future only in the most superficial way. They are really advertisements, conversation pieces, declarations of tribal loyalty – or, as with Irving Fisher, statements of profound conviction about the logical structure of the world. As Roger Babson explained, not without sympathy, Fisher had failed because “he thinks the world is ruled by figures instead of feelings, or by theories instead of styles”.

Poor Fisher was trapped by his own logic, his unrelenting optimism and his repeated public declarations that stocks would recover. And he was bankrupted by an investment strategy in which he could not afford to be wrong.

Babson was perhaps wrong as often as he was right – nobody was keeping track closely enough to be sure either way – but that did not stop him making a fortune. And Keynes prospered when he moved to an investment strategy in which forecasts simply did not matter much.

Fisher once declared that “the sagacious businessman is constantly forecasting”. But Keynes famously wrote of long-term forecasts, “About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.”

Perhaps even more famous is a remark often attributed to Keynes. “When my information changes, I alter my conclusions. What do you do, sir?”

If only he had taught that lesson to Irving Fisher.

Also published at ft.com.

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Tim Harford is an author, columnist for the Financial Times and presenter of Radio 4's "More or Less".
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