Tag Archives: market crash

Episode 14. Seeing and doing in the market

What better week to tackle fear and greed in the stock market? Under the shadow of global financial meltdown, this episode explores the nature of cognition in the markets: how market actors see, choose and act. Moving from the model of homo oeconomicus in the efficient market to the irrational animal spirits of behavioural economics, I find neither satisfactory, and explore an alternative, sociological concept of decision: that it is distributed across social and technical networks. We revisit the non-professional investor, and find that a distributed model of decision making can help us understand their sometimes idiosyncratic actions. *Updated with postscript!*


Well, it’s been quite a week in the markets, hasn’t it. The old saying has it that when Wall Street sneezes, the world catches a cold. It is probably in bad taste to observe that it is not Wall Street doing the sneezing, not yet at least, and that the rest of the world is doing its very best to avoid colds and much worse. Unless you have been living on Mars you will have noticed that there is a global pandemic on the way and that, as well as shutting down everyday life for an increasing chunk of the world’s population, it is playing havoc with industrial production in China, and, thanks to global supply chains, business everywhere else. Amazingly it took until the middle of last week for Goldman Sachs to point out that the wildfire spread of COVID-19 across the globe might damage US earnings – important to stick to consequences that matter – and already nervous stock markets collapsed. As did Flybe, the UK regional airline, already once rescued by the government, with other travel firms sure to follow. The Federal Reserve’s move to cut interest rates had little effect, the screens are bathed in red; money managers are working long nights and shoppers are hoarding loo rolls. What better week to discuss greed and fear – what Keynes famously called ‘animal spirits’ – in the stock market?

But are we so irrational after all? And is that even the right question?

Hello, and welcome to How to Build a Stock Exchange. My name is Philip Roscoe and I am a sociologist interested in the world of finance. I teach and research at the University of St Andrews in Scotland, though I’m on strike quite a lot of the time at the moment, squeezing these episodes out in the odd day back at the desk. Anyway, to business: I want to build a stock exchange. Why? Because, when it comes to finance, what we have just isn’t good enough. If you’ve been following this podcast – and if so thank you – you’ll know that I’ve been talking about how financial markets really work, and how they became so important. I’ve been deconstructing markets: the wires, and screens, the buildings, the politics, the relationships, the historical entanglements that make them go, all in the hope of helping you understand how and why finance works as it does. As well as these, I’ve been looking at the stories we tell about the stock market. You might be surprised how much power stories have had on the shape and influence of financial markets, from Daniel Defoe to Ayn Rand. I’m trying to grasp the almost post-modern nature of finance, post-modern long before the term was invented, the fact that finance is, most of all, a story. Start-ups are stories, narratives of future possibility; shares and bonds are promises based on narratives of stability and growth. Even money is a story, circulating relations of trust written into banknotes, credit cards and accounts. Stories set the tone, make the rules, determine what counts and what does not. A good stock market needs a good story, so if we’re serious about rebuilding financial institutions then we need to take control of those stories.

This episode is about how people see and do in the market: how they think and how they choose.

It’s probably best to start our thinking about thinking by thinking about what economists think when they think about thinking.

Economists have an idealised vision of decision which centres on the computation of potential payoffs multiplied by the probability of them taking place. Very crudely, if you have a 50% chance of making £10 and a 25% chance of making £20 your payoff from both is identical – 5 pounds – and you can do either. The economist is indifferent to other factors, such as the ethics of your course of action. The maths says the outcomes are identical and all else is metaphysics.

Of course, we can’t all be like that. The idealized creature that is able to purge such exogenous factors from his reasoning is the economic man, homo oeconomicus. I choose the pronoun wisely, because there is a long history both of fictional accounts and of scientific practices that locate reason firmly in the male person, while the female is emotional, irrational, and hysterical. This economic man is instrumentally rational, solipsistic and maximising; I am not sure whether the model of man or of decision comes first but the two are intimately linked. In the case of finance this translates into the efficient market hypothesis and its variants, which we have encountered already. The market, full of agents able to calculate the odds efficiently and accurately, makes sure there are no opportunities for profitable trade; these can only come from uninformed, noise traders whose sole purpose appears to be messing things up enough for the economic men to make a living trading.

There is an obvious problem with our friend homo oeconomics and his rational decision-making. Computationally this is very difficult, if not impossible. We can manage the sums okay when it comes to the roulette wheel, perhaps even the odds in a poker game – although those are already too much for me. But in any kind of real-world situation the possibilities are enormous and proliferate rapidly, one decision leading to another in chains of cause-and-effect that soon become infinitely complex.

Another possibility was suggested in the late 1950s by computer scientist and all-round polymath Herbert Simon. He proposed that decision-makers did not seek to find the best possible option, simply one that was good enough. Having picked the most promising option, they follow through a train of reasoning testing out consequences. If things look as if they will work out badly the thinker simply ditches one option and tries out the second best. Research has shown that emergency services and others working in high-pressure situations follow such protocols: firefighters arriving at a burning house, or doctors triaging patients arriving in intensive care. It’s a robust, quick and effective means of taking decisions under severe informational or time constraint. Simon called it ‘satisficing’.[1]

[siren sound][2]

There is another reason to doubt the existence of homo oeconomicus. In 1974, two experimental psychologists, Daniel Kahneman and Amos Tversky, published an article in the prestigious academic journal Science. It showed, on the basis of solid laboratory evidence, that humans, or human brains, were so programmed as to systematically and consistently miscalculate chance. The authors called these biases heuristics.[3]

There were, the scientists argued, three main categories of bias. The first is representative, where existing patterns are extrapolated into the future. The second is availability, where the ease with which an event can be imagined is linked to its perceived likeliness, and the third is to do with anchoring, where estimates may be skewed by the parameters suggested by, for example, an interviewer. These things have been empirically demonstrated in laboratories, and we may recognise them from everyday life. There is what is known as the hot hand phenomenon, where the sportsperson’s run of good form is deemed likely to continue. Every would-be lawmaking politician who asks their audience to imagine some terrible violation knows intuitively that imagining and expecting are closely linked. In 1979 Kahneman and Tversky added ‘Prospect theory’, the demonstration that people weighed losses more heavily than gains, making makes us naturally risk averse in our calculations.

For economists schooled in the theory of optimizing trade-offs, this was dynamite. People did not behave like the model said, and markets would not be entirely efficient. But – wonderfully – they behaved in a way that was predictably irrational, and a whole field of empirical science could be built around this. Dan Ariely, one of the most famous of these ‘behavioural economists’, as they became known, wrote a bestselling book with exactly that title: Predictably Irrational.

Kahneman and Tversky’s work has been enormously influential. The ambitious young graduate students of the mid 1970s who took their insights and built them into research programmes are now among the most senior members of the economics profession. We have been treated to a slew of popular books, each full of examples of the strange and wonderful (to economists) way that we think about things. Did you hear, for example, about the day nursery that introduced fines for parents who picked up children late, and found that lateness got worse? Of course you did! And were you surprised to hear that parents treated the fines as fees? I doubt it.

Names like Ariely, Richard Thaler, George Akerlov, Robert Shiller, and Kahneman himself, are well known outside the academy. They have influenced policy and practice, with governments embracing the behavioural tactic of nudging to get what they want. These theories have made their way into finance. It helped that the efficient markets model was bursting at the seams, unable to explain the persistent habit of bull and bear periods in financial markets that should be – logically – organised and stable. The behavioural perspective has become the default explanation for stock market boom and bust. Alan Greenspan famously referred to the ‘irrational exuberance’ of the dotcom era. People just got carried away! It was the same with the credit crisis. The film The Big Short includes a cameo from Richard Thaler and Selena Gomez. They are billed (in more than a nod to the film’s own gender politics) as President of the American Economic Association and father of behavioural economics, and international pop star. They are explaining the synthetic CDO, the device that caused so much financial destruction in 2008. Thaler’s monologue highlights the hot hand aspect of the fiasco – the sense that property had been going up for so long, and people had been making so much money from it, that observers thought it would just carry on going. The crash was just a matter of our innate behavioural biases.

We can apply this model elsewhere. In the last episode, we started thinking about non-professional investors. We heard how finance research thinks of them as “noise traders”, a polite way of saying what the Wall Street professionals call “dumb”. Nonprofessional investors buy shares that are going up. Now we know that’s the hot hand fallacy, the representativeness heuristic. They buy shares that have been in the news, or shares of firms when they like the products. This is the availability heuristic. They are predictably irrational in their calculations of profit, refusing to sell shares that are tumbling for fear of crystallising their loss. This is Prospect Theory. People account for things in irrational ways, saying things like – and I heard this or its variants many times – “if you take out all the bad trades I had a great year”. This is mental accounting.

Proof! QED! Nonprofessional investors are noisy, dumb, predictably irrational, and behavioural economics has the answer to everything.

Well okay, up to a point. Of course, people do overvalue and undervalue and treat fines as fees and do all the other things that economists say they do, but I can’t be alone in feeling that these explanations are a little, well, thin. At the heart of the behavioural perspective lies the model of the individual agent choosing between outcomes, just getting the sums a little bit skewed. Behavioural economics has been so successful because it is the kind of radicalism that allows you to leave the underlying assumption unchanged, the individual decision-maker, the brain in a vat. I do not think that is really how we choose, certainly not in financial markets.

We are embodied, for a start, and we are embedded in webs of social relationship. This embeddedness has been a persistent theme throughout the podcast as we have discussed how markets have evolved over time, their path shaped by friendships and alliances. The sociological theory of embeddedness emerged in 1973, at roughly the same time as behavioural economics. It too was a challenge to the orthodoxy of the instrumental economic agent, but from a sociological perspective. Mark Granovetter, whose article kicked it all off, suggested that information flows through social ties. He argued that people would prefer to buy from people that they knew and trusted, and would pay more for the privilege of doing so.[4]

As with behavioural economics, a whole field of literature emerged demonstrating that this was the case. To give you an example, one famous (if dated) study showed how the garment industry in New York subsisted on network relationships, with firms offering each other generous credit terms and even loans. The author, Brian Uzzi, suggested that firms embedded in these tight networks had better survival chances than those keeping rivals at arm’s length.[5] In purely economic terms, these findings don’t seem to make sense. We would expect instrumentally rational economic agents to always pay as little or charge as much as possible and to be glad when their rivals went out of business. If you look carefully, however, you will notice that the model of decision remains broadly unchanged: economic agents still choose the optimum outcome but merely recognise the value of social relationships, in terms of better information, collective insurance, a critical mass of providers in a geographical area, or whatever it may be. Social bonds reduce uncertainty, the great enemy of economic decision-making. Like behavioural economics, the embeddedness thesis is a challenge that doesn’t tear the building down around it; it’s the kind of in-house radicalism that goes well with ambitious young researchers looking to make a mark but not alienate the tenure committee. The fact that one can swiftly reduce the notion of embeddedness to the mathematical modelling of network structures can only help here, radical but still demonstrably rigorous quantitative social science.

The concept of embeddedness can help us understand some of the things that nonprofessional investors do. If social relationships provide better information and reduce uncertainty then maybe it does make sense to invest in the firm that you work for, or the corporation in the nearby town that employs some of your friends. Perhaps you can compensate for a lack of diversification with an insider’s sense of how things are coming along. But, as critical sociologists have pointed out, networks are sparse social structures. Networks may show who knows who, but not how they know them. Power relationships, differences of capital, gender and race, all the structural inequalities that are reproduced through networks are rendered invisible by this form of analysis.[6]

We are still circling the point, I think. Our picture of these non-professional investors may be getting more nuanced but there is still a void at its centre. How do people choose? Bearing in mind that non-professionals have not, as de Bondt complained, managed to infer the basic principles of portfolio management from their mediocre performance, what sort of tools do they use to navigate the markets?

Zooming out to a more macro perspective it seems to me that both behavioural economics and theories of economic embeddedness are asking the wrong question. It is more interesting to ask how people manage to be rational in the market at all, even if they don’t quite carry it off. Commentators may complain about the irrational greed and fear that fuelled the credit crisis, the hot hand backing up the synthetic CDO, when the more extraordinary aspect of the disaster was that one could buy financial instruments that reflected, and I’m being precise here, the future revenue streams of wagers on the future revenue streams of wagers on the repayment of mortgages on houses half built in another part of the globe. Extraordinary, but not in a good way! The economic explanation just does not cut it. It is as if, in discussion of the collapse of a series of bets on a baseball game on Mars, Richard Thaler were to tell us that people just got carried away because the green aliens had been doing so well, up to now.

Cast your mind back to our discussion of facts  in episode nine. We saw how facts are made – the clue is in the name – carefully built up through processes of measurement and theorisation, held together in what the sociologist Bruno Latour has called network relations. As Latour has endlessly pointed out, saying that facts are made doesn’t make them any less true, and certainly doesn’t mean that there’s no such thing as reality. It does mean, however, that scientific activity of any kind is dependent upon previous advances in techniques buried in the everyday equipment of the laboratory. Every standard, everyday machine unnoticed in the lab itself contains an entire history of laboratory work and technological advances folded into its programs and circuitry. One simply couldn’t do science if one had to start afresh every day.

The construction of decision and fact are tied together. When we take a decision we do so in conjunction with the material artefacts that surround us. We use these as cognitive prostheses to navigate contemporary life. I wrote a book about this, a few years ago. It was before the whole smart phone app thing had really kicked off, but even then it was clear that we couldn’t get by in the world without the props-for-thinking that came through our screens and web browsers. I was interested in the moral consequences of our construction as cyborg-economic agents, and if you’re interested the book was called I Spend Therefore I Am, republished as A Richer Life. I worried about education, healthcare and love, but let’s concentrate now on weightier matters, such as thinking in financial markets.

Actors don’t drift around markets like disembodied brains in vats. They are enmeshed in social relationships and they use material and technological devices. Processes of observation and decision-making, of seeing and of doing, are shared across these networks. The trader sits at her screens, scanning numbers that have already been parsed and processed by numerous socio-technical systems. She will run additional calculations, send messages, have conversations with colleagues and counterparties. She will buy and sell. Where does the decision-making begin and where does it end? If we claim it is all in the human agent we are performing what the quantum physicist and philosopher Karen Barad calls an ‘agential cut’, artificially slicing between the human and the material because it suits us to give an account of the world in these terms. We could simply say that the decision is performed across this heterogeneous socio-technical assemblage, which we might call, if we were feeling fancy, an ‘agencement’.[7]

Let’s take an example. We hear a great deal about hedge funds. They have done this, or that, betted against the pound, raided our pensions, or funded a political party to achieve certain nefarious aims. The language we use gives it away; the hedge fund is a thing, a composite, a single market agent. It is an agencement, a socio-technical assemblage. A fascinating study by Ian Hardie and Donald MacKenzie treats the hedge fund as exactly that.[8] These piratical, globally domineering organizations turn out to be rather small. The one Hardie and MacKenzie examine has just five employees, including the “sometime intern”. They sit around a large, central desk occupying a trading room in some small, non-descript offices in a desirable part of central London – hedge funds prefer Mayfair and St James’s to the City. The sociologists spent a week in the trading room watching what was going on and reported that much of the day was spent in complete silence: the whirring of fans, or the tapping of keyboards broken only by the occasional cryptic exchange about the valuation of bonds or a telephone call to place an order, several million here, several million there. The room is an epicentre of information gathering, with the three trading partners’ specialised knowledge paired with bespoke calculators, often built in that room, making sense of the deluge of conversations that pours in through email and newswire. “If human beings had unlimited powers of information processing, calculation and memory,” they write, “a single unaided human could perhaps turn the information flowing into the room into an optimal trading portfolio. Since human capacities are limited, as Herbert Simon emphasised long ago, the necessary tasks are distributed across technical systems and multiple human beings: what goes on in the trading room is indeed distributed cognition.” Hardie and MacKenzie show how conversations between the three partners and their counterparties elsewhere converge on eventual trading strategies, wrapping together the output of their tools and calculators. They quote Ed Hutchins, who coined the term distributed calculation: “work evolves over time as partial solutions to frequently encountered problems are crystallised and saved in the material and conceptual tools of the trade and in the social organisation of the work.” The hedge fund is a computational agencement, combining the social and the technical to manipulate market information.

This hedge fund seems very small, at least in terms of its physical presence and organizational structure. How can it wield financial firepower so substantial that, when hedge funds gather in packs – or perhaps shoals, for they are the financial equivalent of piranha – governments tremble? Like any contemporary knowledge business, the hedge fund can only exist in a network of outsourcing relationships with firms that can offer competitive advantages in their own fields, be that cost-efficient manufacturing or in this case clerical services. It delegates the painstaking business of settlement to Dublin to an organisation that itself employs hundreds of workers in Mumbai double checking trades and smoothing problems while the London market sleeps. The pulldown menus of the trading system, leased from another provider, are the front end of this settlement operation, the visible tip of a computational and administrative iceberg. The fund’s deals are conducted by a “prime broker”, an international investment bank that transfers the money necessary to make a trade on the fund’s behalf. The bank effectively underwrites each trade, and this tiny Mayfair office now enjoys the credit rating of a global investment bank. Hedge funds are themselves allowed to borrow, and when this is coupled with the bank’s creditworthiness the combination is quite formidable. Embeddedness matters here too. Mackenzie has shown how fund managers, embedded in a tight social network, imitate each other leading to a super portfolio with enormous power and occasionally disastrous results. No wonder governments tremble when they face them.[9]

As the hedge fund shows, in a market where information is ubiquitous and overwhelming, calculation is both a problem and an opportunity. It is beyond the capacity of the individual human agent, in purely computational terms, and, in an echo of the efficient market hypothesis, if everyone has all market information, it no longer confers an advantage. Advantages must derive from socio-technical processes of interpretation – from calculation – and this must be better, meaning faster, more accurate, more sophisticated. In another classic study, Daniel Beunza and David Stark explore how traders in a bank’s dealing room try to discover arbitrage opportunities in the extraordinary complexities of market information.[10] Arbitrage is the pursuit of risk-free profit: if you can buy goods from Sarah at one pound and sell them to Sidney for two, in the very same moment and without the risk that the goods might break or be stolen in transit or that Sidney might not want them when you get there, that is an arbitrage. In textbook theory, entrepreneurs earn their profit because arbitrage never exists in the real world. In financial markets, it’s arbitrage that keeps prices the same in New York and London: arbitrage exists purely to prevent itself from existing in real life. Beunza and Stark suggest that arbitrage can be found, if traders are clever enough. By breaking down financial instruments so that individual properties such as the exposure to a particular sector or currency can be isolated, traders might find that property is priced differently in one instrument than in another. That’s an arbitrage. If it sounds complicated in theory, it’s much worse in practice. These arbitrageurs are highly educated, users of complex tools and theory; but they depend also on social fluidity built into the space of the office. Unlike the staid and hierarchical spatial arrangements of corporations, Beunza and Stark find the physical layout of the trading room organised to maximise social fluidity, interaction, and the transfer and overlap of ideas. Individual desks – clusters of traders and equipment specialising in one particular kind of trade and organised around a dominant evaluative principle and associated devices – create differing versions of the market from the same data, and when these overlap opportunities can be identified. It’s the role of the office manager to keep these overlaps happening, which she does by moving things around, giving the back office equal status in the trading room, rotating the positions of individuals. Benuza and Stark see the traders’ terminals as ‘workbenches’, heavy  with instrumentation. Calculation happens on the screens, across the desks, and between the desks: it is distributed throughout the trading room. That’s how professionals see and think in the market.

Non-professionals, on the other hand, are consumers. I need to make an important distinction here, for they are not consumers of investments but consumers of investment services. At the most basic level, this insight explains the way that investment services are sold to them, as exciting, or risky, or complicated. It’s an echo of the narratives that we found Tom Wolfe popularizing about finance in episode ten,  mass produced for the commodity market. You will recall the roar of the trading room he describes, young men baying for money in the bond market in the morning. Researching my doctorate, I watched non-professional investors acting out a noisy, carnival-esque version of Wolfe’s market in investment shows, all crowds and screens and exciting investment tech.

What exactly do they consume, these non-professional investors? Everything, the whole market. Again, remember how the sociologist Karin Knorr-Cetina characterizes the market – everything, how loudly he’s shouting, what the central bank is doing, what the president of Malaysia is saying.[11] The market is experienced by professionals as an extraordinary barrage of information, which they wrestle into profitable submission with their workbenches and algorithms. Non-professionals buy a commodified, simplified version of this world. It comes with everything: its own rules and understandings of market function, information sources and the requisite tools for making sense of these. Non-professional investors haven’t been to finance school and don’t know how markets ‘should’ be understood. Instead, they choose the method that feels right to them. Choosing investments is as much as anything a choice of what kind of investor to be – which of many competing investment service packages to adopt – and that is a consumer choice. We all know how to be consumers. Once entangled in a particular kind of investment practice, individuals distribute calculation across the agencement organized by the investment service provider. Their choices spread across a calculative network within which everything hangs together, reasonably and rationally, even if it sometimes looks bizarre from the outside.

A couple of examples will help here. Some investors specialize in the shares of smaller companies, or ‘growth stocks’. This is presumably an ironic name, as many growth stocks would do anything rather than grow. These share are often cheap, and are also known as ‘penny shares’ – the great advantage of a penny share is that it only has to jump to twopence, and you have doubled your money. There is a long tradition of snake oil here. I’m not sure what the inflation-adjusted equivalent is, but the principle is the same. In thin (illiquid) markets, small company shares can move around a great deal, netting their owners valuable paper profits, profits that disappear as soon as the owner tries to cash them in. Most small company investors are smarter than this. They are heirs to another investing tradition, one that can be traced back at least to the 1940s, when the investment guru Benjamin Graham published his book the Intelligent Investor. Graham argued that investors should pursue value, buying stocks when the market price of the shares is less than the parcel of assets each share represents.

These days Graham’s approach is more problematic, because asset values can contain all sorts of intangible capitalised goodwill – branding and so forth – but Warren Buffett has shown what this method can do when it works well. Growth company investors, however, do not look for value that has already shown up on the balance sheet; their endeavour is to find unrecognised future possibility. They believe that the costs of researching growth stocks are such that the “big boys” – whoever they may be – are unable to spot opportunities, but the nimble individual can. It is all about rolling up your sleeves and working hard, getting to know the companies you are investing in. For the financial economist risk management is a matter of portfolio construction. Here, managing risk becomes a matter of diligence and self-discipline. This discourse, this narrative account of how the market works and how we should behave in it is embedded in the tools and devices that these growth company investors use to navigate the market – the tip sheet that proclaims its delight in getting into the opportunity ahead of the big boys, or the pundit who explains that there is value to be found if you are prepared to roll your sleeves up. You can hear it widely:

[finance pundit]

It is framed in an antagonistic relationship with the big guys. One investor described his practice as a way of “outsmarting the large brokers, finding good opportunities that are likely to do really, really well but nobody knows about them, because nobody investigates them.” And, he says, “It’s really satisfying”. Or, as one pundit says ‘I love banking big stock market gains – especially if it’s on the blindside of other investors. Seven years ago I quit my high-flying career in the Square Mile to join a newsletter called…’ There is money to be made, and the investors I interviewed were hoping for 30 percent annual returns, all at the expense of these big guys; but not entirely, because the whole practice depends on the possibility that sooner or later a big guy will spot the value as well, and the stock will be teleported to its rightful price, taking the plucky investor with it. It is a kind of delayed efficient market hypothesis – the market will be efficient but only after I have got there first.[12]

Can you see what is going on here? The investor, lacking a formal education in finance, adopts – buys into – a particular market identity. With that comes an understanding of the way the market works, and a set of tools to negotiate the marketplace in pursuit of profits.

These investors like numbers, but simple ones, so company financials are rendered down to single figure indicators like the PEG, popularised by investment guru Jim Slater, and easy to understand: less than one means buy. Slater’s catchphrase was elephants can’t jump, and I must have heard that in a dozen different formulations. Investors would tell me that small companies are a great place to make money, or would be if they could at least get their formula right.

Another popular kind of investing practice is that of charting, or technical analysis. This too claims a rich investing heritage, dating right back to the arrival of the tickertape and linear time in the markets. In essence, the practice aims to predict future prices from the pattern of previous ones. From the point of view of economic theory, this is madness. The main factor affecting stock prices is news, and news is by its very nature unpredictable. It is news! Think COVID-19 and red ink – the global rout of shares by virus that didn’t then exist was impossible to predict just a few weeks ago. For the behavioural economist, there is a little more sense in the method. If we know that people herd, and that they are irrational and over-emotional, we may expect prices to overreact, to have some momentum, as the jargon goes. So it makes sense to chase the trend, and research shows there are small profits to be made by doing so.[13] Although this is treacherous, and I read that nonprofessional investors have been prevented from making excessive bets on the falling market, lest they be wiped out by the smallest “dead cat bounce”.

Chasing trends does not really capture the chartist’s endeavour. He (always!) has signed up to a view of the market predicated upon some kind of underlying order. The noisy mass of random prices is nothing less than a code that can be deciphered using Fibonacci numbers or Elliott waves. Through elaborate retrospective testing he seeks to discover the perfect pattern of indicators, tests like long term moving averages crossing short term moving averages, for example, or a plotted cloud of stock prices shifting from a supporting position underneath a share’s graph to a position above, weighing it down. This is the holy Grail of charting – to be able to fit a curve so perfectly to historical data that it will be able to predict the future. The only problem, as social scientists know, is a methodological one. The more precisely a curve fits historical data, the less its predictive power. Oh dear.

And even this does not really capture the chartist experience, because the actual practice of being chartist involves paying for some expensive software, configuring it on your PC and leaving it running overnight. Just like those tip sheets the computer takes away the burden of the difficult computational problem, sifting through the market to find profitable investment opportunities. It is about what kind of consumer you are. Does this advertisement appeal to you?

[charting advert]

Thanks Rebecca. That advert really needs to be seen for its full effect, but let me assure you Rebecca is very beautiful and is wearing a very low-cut dress. It is all here, the secret knowledge made simple, the Wall Street bad guys, the fancy jargon and the actually quite easy investment strategies. Yes, charting is really for guys that like messing about with computers and then explaining what they are doing at great length. Here’s Dave, explaining the same trading tactic as Rebecca, but with different emphasis and this time, a ratty beige pullover.

[charting explanation]

Let me also assure you that Dave’s video is not improved by visual, though he has had half a million views on youtube, which is a lot better than I have done. Chartists like to explain things:

“Elliott’, one told me, ‘is a wave structure, a simple wave structure which is basically a series of impulse waves followed by a series of retracement waves, and the impulse is broken into a series of five simple waves upwards, and then you have two retracement waves, and then a series of ‘a’, ‘b’ and ‘c’ waves…a series of five simple waves up followed by three simple waves down. And when you see a movement such as the share price or a commodity price in the stock market you’ll very often see the series of five smaller impulse waves up followed by two retracement waves, an ‘a’, a ‘b’ and a ‘c’…” But at the end of the day all one does is pay some money, and run some software. One click and it’s done.

That’s my point. Nonprofessional investors may sound crazy, but they are not really, because they are not just investors, they are consumers as well. They consume an entire market ontology – a vision of how the markets actually are – linked to an account of how one should behave in them, which is linked to or inscribed into the devices they buy to distribute their calculation across the market place. We all know how to consume, and as consumers we buy things that reflect our preferences and enact how we understand ourselves, the plucky underdog or the tech savvy market savant with a soft spot for Rebecca and her little black dress.

One could be quite cynical about investment service companies here and their role in promoting such a variety of investment practices. Many of which can only be described as bad for the recipients’ financial health, and sometimes their physical health too, for investing is a lonely and stressful business. Alex Preda, who we have met before, videoed nonprofessional day traders at work and found them chatting to their screens as they re-narrate the combat of market action – give me a break buddy, and that kind of thing. This is something they share with their professional counterparts, the need to work the numbers back into bodies, stories and narratives – to make sense of the vast, lonely thing that is the contemporary financial market…[14]

But seeing as saving the world has been cast as a consumer problem – recycling and buying sustainably, cutting down meat and that sort of thing – perhaps we should start consider the reworking of finance as a consumer project as well. When we get round to assembling our new, fit for purpose stock exchange I am almost certain it will not fit with the criteria of rationality circulating in financial econometrics, precisely because those criteria have done so much to contribute to finance as it is today. Narratives around sustainable finance, or impactful investing may help to deal with some of the problems that I have been highlighting from the outset, and nonprofessional investors will be able to participate on their own terms, as consumers, and reasonable, rational people. Not economic men or women, just people.

I’m Philip Roscoe, and you’ve been listening to How to Build a Stock Exchange. If you’ve enjoyed this episode, please share it. If you’d like to get in touch and join the conversation, you can find me on Twitter @philip_roscoe. Thank you for listening. Join me next time, when we’ll get back to our story, see how the noughties commodity boom powered stock-markets and learn just how hard it is digging things out of the ground.

I’m adding a postscript. In the two weeks since I wrote this episode the coronavirus has continued to spread and country after country has been forced into lockdown. The markets have fallen and fallen. Surely this must be irrational, a global panic? Or perhaps a rational assessment of the threat of global recession? It’s neither. The model of cognition still holds. We are witnessing a massive, collective endeavour of figuring out stretched across conversations, tools and trading algorithms. The latter are working especially hard, selling. The fact that markets keep having to be switched off, with circuit breakers cutting in to stop precipitous falls, shows just how much calculation has been delegated to those algorithms. These aren’t panicking at all, simply doing what they have been programmed to do. But I do think, more than anything, this is a project of re-embodying and re-storying the nature and future of finance. What we can see at the moment is a future of closed borders and sick bodies, a dystopian, panicked imagining, a place of pure uncertainty and unknown. There’s an element of the availability heuristic here, of course, but hey, it doesn’t seem so unlikely at the moment. Sell! Sell!

[1] Gerd Gigerenzer and Peter M Todd, “Fast and Frugal Heuristics: The Adaptive Toolbox,” in Simple Heuristics That Make Us Smart, ed. Gerd Gigerenzer and Peter M Todd (Oxford: Oxford University Press, 1999).

[2] Siren, from https://freesound.org/people/Nahlin83/sounds/220424/

[3] Amos Tversky and Daniel Kahneman, “Judgement under Uncertainty,” Science 185 (1974).

[4] M Granovetter, “The Strength of Weak Ties,” American Journal of Sociology 78, no. 6 (1973); ———, “Economic Action and Social Structure: The Problem of Embeddedness,” American Journal of Sociology 91, no. 3 (1985).

[5] Brian Uzzi, “The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect,” American Sociological Review 61, no. 4 (1996).

[6] GR Krippner, “The Elusive Market: Embeddedness and the Paradigm of Economic Sociology,” Theory and Society 30, no. 6 (2001).

[7] Michel Callon and Fabian Muniesa, “Peripheral Vision: Economic Markets as Calculative Collective Devices,” Organization Studies 26, no. 8 (2005).

[8] Iain Hardie and D MacKenzie, “Assembling an Economic Actor: The Agencement of a Hedge Fund,” The Sociological Review 55, no. 1 (2007). Quotations below from p66-67.

[9] Donald MacKenzie, “How a Superportfolio Emerges: Long Term Capital Management and the Sociology of Arbitrage,” in The Sociology of Financial Markets, ed. Karin Knorr Cetina and Alex Preda (Oxford: Oxford University Press, 2004).

[10] Daniel Beunza and David Stark, “Tools of the Trade: The Socio-Technology of Arbitrage in a Wall Street Trading Room,” Industrial and Corporate Change 13, no. 2 (2004).

[11] Karin Knorr Cetina and Urs Bruegger, “Global Microstructures: The Virtual Societies of Financial Markets,” American Journal of Sociology 107, no. 4 (2002).

[12] See my paper Philip Roscoe, “‘Elephants Can’t Gallop’: Performativity, Knowledge and Power in the Market for Lay-Investing,” Journal of Marketing Management, no. 1-2 (2015).

[13] N Jegadeesh and S Titman, “Profitability of Momentum Strategies,” Journal of Finance 56 (2001).

[14] Alex Preda, “Brief Encounters: Calculation and the Interaction Order of Anonymous Electronic Markets,” Accounting, Organizations, and Society 34 (2009). See also Caitlin Zaloom, “Ambiguous Numbers: Trading Technologies and Interpretation in Financial Markets,” American Ethnologist 30, no. 2 (2003).