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When Policy Making meets Neuroscience and Social Science

12th-13th February 2019 @ Witten/Herdecke University, Germany
INSOSCI-SYMPOSIUM 2019
12th-13th February 2019
@Witten/Herdecke University, Germany

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The phenomenology of financial trading

04.08.2018

One of the most interesting empirical data about behaviour towards risk comes from sociological and anthropological research on financial trading, as aptly summarized by the chapters by Preda and Zaloom in the Oxford Handbook of the Sociology of Finance. This information allows for a phenomenology of risk taking in an almost laboratory environment. There are many important insights which do not fit into the standard economics picture of rationality, although on the other hand we also notice that at least partly we might state a paradox: Rationality is constructed by ‘irrational’ means.

By this paradox I refer to the observation that emotions matter much, but that at the same time traders adopt certain ‘disciplines’ in channelling the emotions in a specific way. Zaloom identifies four disciplines: strictly separating private lives and trading floor; suppressing emotional responses to loss; strictly focusing on the present; and adopting a stance of continuous alertness. Clearly, we could speculate that some of those disciplines control for loss aversion and sunk cost fallacies. But more broadly speaking, the fundamental issue in the behavioural regime of financial trading is to keep the transactional logic separate from social context, or, whether this is desirable or necessary. Indeed, many observers think that the increasing role of algorithms in trading is a progress in terms of limiting the disturbance by the ‘human factor’. But is that true?

Social context was essential in the traditional pit where ‘the market’ was institutionally defined as the assembly of physically co-present traders who were continuously communicating via shouts and gestures. In this setting, the transactional logic is deeply merged with social context: For example, the pit was also a social hierarchy with placing ‘big’ traders on top of the arena and newcomers at the bottom, which directly shapes the scope of their views and the extent by which others can observe their facial expressions and gestures. Information technology explicitly aims at neutralizing this role of social context, but the interesting observation is that actors always strive to reconstruct it. For example, in open-space offices traders observe fellow traders’ behaviour and continuously communicate, although they do not see the counterpart anymore, or there is an elaborate system of online communication running parallel to the trading.

Why does social context matter? There are two main reasons. First, traders compete against other traders, and making profits on the market is also a tournament in a status game. That means, profit is more than just an amount of money, but success defines the social identities of individuals. Second, ‘the market’ is just the assemblage of people and machines, with people mostly guessing what other people or algorithms think. Algorithms are made by people, and are activated by people: So, even their actions may require imagining other people’s mental states. Especially in the context of high-speed trading in the short term, Keynes’ beauty contest logic certainly holds, because profits are made when one’s own expectations about the expectations of others prove to be right in the sense of enabling short term profits that accumulate over the day.

Apparently, this process is highly emotional. It is illuminating to distinguish between two kinds of reflexivity here.

• Reflexivity in terms of rational calculation is generally seen as detrimental to successful action. Trading is a ‘flow’, requiring deep immersion and spontaneous decisions, without reflective pondering of alternatives. This stands in tension with the popular ‘dual systems’ approaches in behavioural economics. Traders need to act extremely fast, and therefore their decisions are based on intuition. It is important to distinguish this from ‘automatic’ behaviour, such as playing a musical piece by heart. The market constantly produces novelty, and therefore learned responses cannot matter.

• Reflexivity in terms of being able to understand the market as being a mirror of peoples’ mental states, including ones own, matters essentially. In this sense, one can approach the market as a huge network of distributed cognition: Prices are signals that convey information about the mental states of others, but also emotional states. That is why ‘fundamentals’ may be simply irrelevant. The information condensed in markets is about the state of the systems of distributed cognition, undergirded by emotional mechanisms. This is clearly expressed in the trader’s identities, who often explicitly conceive themselves as embodiments of the market: Reacting to market signals is a total emotional and bodily experience.

I think that algorithms cannot avoid the second form of reflexivity. If algorithms predict the market, ideally, they would also need to predict the operations of other algorithms. Their functioning, however, is proprietary knowledge of the financial actors. Even more, in the longer run algorithms would need to predict innovations in algorithms. Until most recently, that would have required to predict the inventor’s actions. But in the age of artificial intelligence, the software changes itself, in ways nobody understands and can reconstruct. In my view, that means that social context is back. In a world of robots who continuously improve themselves, even the individual robots would not be able to know and predict the behaviour of other robots. They would need to create means to indirectly reconstruct the ‘inner life’ of robots. Perhaps that would be the moment when they evolve emotions.

The phenomenology of trading is about the social construction of risk out of the stuff of uncertainty. Therefore, the mathematical theory of probability should not be taken as a benchmark to assess the human behaviour towards risk. Also, it cannot give guidance to action, unless we focus on those systems in which probabilities are empirically meaningful quantities, as in the context of insurance.

Preda, Axel, ‘Interactions and Decisions in Trading’; Zaloom, Caitlin, ‘Traders and Market Morality’, in: Knorr Cetina, Karin, and Axel Preda, eds., The Oxford Handbook of the Sociology of Finance, Oxford University Press, Oxford 2012.

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