Knowing the risks:Theory and practice in financial … broad conventional approaches can be identified. The first focuses on inventory, looking at the flow of trades and how temporal

Knowing the risks: Theory and practice infinancial market tradingPaul Willman, Mark P. Fenton O’Creevy, Nigel Nicholson andEmma Soane

A B S T R AC T Theories about trading in financial markets are well developed and

practically influential. However, traders’ behaviour within these

markets appears to deviate substantially from that predicted by

theory. Using data from a study of traders within financial markets in

London, this article seeks to document this apparent paradox and

assess its implications. General theories about how the financial

world works are distinct from, but compatible with, more instru-

mental behavioural rules about how to work in the financial world.

The latter may be seen as internally consistent recipes for action

which require concurrent belief in both the validity of the general

theories – for example about the relationship between risk and

return – and in the ability of individual agency to secure outcomes

which, in terms of the general theory, have low probability.

K E Y W O R D S financial markets � qualitative analysis � risk � theories in use �

traders

1. Introduction

Financial markets are heavily theorized domains. Academic finance theoryuses some of the most powerful analytical tools available in social science tomodel the operation of such markets, usually on the basis of strong assump-tions about the rationality of actors and the availability of information. There

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are also practitioner theories, which often put less emphasis on rationality;books by and about traders describe trading strategies that may generatelarge rewards. Trading in financial markets sustains heroes and myths; it sus-tains movies and television shows.

People who trade in financial markets are generally aware of both setsof theories. They are trained to understand the academic theories of themarket, but also immersed in the folklore of the practitioner theorists. Thesetwo sets provide, by the nature of the markets they govern, two rather differ-ent things. The first is a theory of how the world of financial markets works.The second is a theory or set of theories which act as guidelines for success-ful action within markets – theories about how to work the world. AsGiddens (1990) notes, financial markets are very specific types of domain.They are institutionally structured risk environments. Risk is not incidentalto their activities; rather, the activities themselves involve the measuredpursuit of risk. They are also domains of sophisticated reflexivity in whichbehaviour is influenced by the type of theory preferred by the actor. In suchmarkets, traders trade according to one or more theories, knowing thatothers act similarly.

In this article, we are primarily concerned with the theories that guidetrader behaviour. Specifically, we explore the relationship between the generaltheories of the market and the specific theories that traders use. Section 2reviews the main theories about financial markets. It looks in particular atthe ways in which economic theories try to explain both the behaviour ofindividuals in the market and the aggregate effects of that behaviour. Section3 looks at some essential properties of individual theories in action in thiscontext. Section 4 describes the data and methods from an empirical studyof trading behaviour in the London financial markets. Section 5 uses the datato explore traders’ individual theories; three features are selected for closerexamination – intuition (‘flair’), the role of reflexivity and the emergence ofcontrarian beliefs. Section 6 looks at the relationship between formal theoriesand theories in action, attempting an assessment of performance impli-cations.

2. Academic work on financial markets

As Coase (1988: 9) notes, financial markets are often cited as examples ofperfect competition. As such, they have three features. First, they are assumedto be efficient; they have prices that fully reflect available information (Fama,1970). Second, they are characterized by perfect information, such that allactors have the information necessary to trade both costlessly and immediately;

Human Relations 54(7)8 8 8


transaction costs are low. Third, they are characterized by perfect competition,such that multiple sellers cannot fix prices (Swedberg, 1994: 274). Paradoxi-cally, as Coase (1988) also notes, these conditions require an authority struc-ture to secure the market ‘involving an intricate system of rules and regulationsto prevent malfeasance’ (p. 7). Swedberg (1994) and Coase (1988) also arguethat, in financial economics, markets are often assumed to exist rather than beempirically analysed.

However, this is a rather incomplete picture. Financial economics doestend to have a rather rigid neo-classical structure in which individuals areseen to maximize utility under the assumption of a close correlation betweenrisk and return (Bernstein, 1996; Brealey & Myers, 1988; Markowitz, 1952).In the aggregate, these actions determine asset prices. To this extent, marketefficiency stems from joint hypotheses about individual behaviour andmarket structure.1 But financial economists do not generally assert that finan-cial markets are perfect and informationally efficient. If this were the case,then there would be no profit opportunities (defined as a return in excess ofthe risk). As Bernstein puts it (1996): ‘at any level of risk, all investors wouldearn the same rate of return’ (p. 297).

In fact, much work on market microstructure has been concerned tounderstand how prices are set and how opportunities for profit may arise.Two broad conventional approaches can be identified. The first focuses oninventory, looking at the flow of trades and how temporal imbalancesbetween supply and demand may arise. The second looks at informationaldifferences between traders. Together, they imply that profits may emergefrom the existence of transaction costs and private information (for a review,see O’Hara, 1995). There is empirical work on financial markets in supportof both approaches (Ito et al., 1998; Lyons, 1998) which remains fully withinthe neo-classical tradition in assuming rational, utility maximizing actors.

Although risk-return relationships emerge in the aggregate in financialmarkets (Brealey & Myers, 1988),2 it is not necessary to assume that all indi-viduals have the same risk-return preferences. The behaviour of individualactors in relation to risk is determined by the asymmetry of their utility func-tions above and below current wealth, which may be variable. Interpersonalvariation aggregates into a demand curve for any given level of risk over arange of returns accruing to such risk. In combination with the aggregatesupply curve for a given level of risk, this produces a market price for risk.

It is thus possible to distinguish the market price for risk from the riskpreferences of individuals in the market. Expected utility theorists canacknowledge evidence, for example, from experimental economics, thatindividuals do not always behave according to the predictions of expectedutility theory, relying on the weaker proposition that, on average, investors’

Willman et al. Knowing the risks 8 8 9


behaviour is consistent with the prediction. Expected utility theory can beshown to be consistent with most aggregate market behaviour most of thetime (Bernstein, 1996).

However, some aggregate market phenomena are difficult to explain.Consider ‘noise’ trading. As Dow and Gorton (1997) note, ‘there appears tobe a consensus that trading volume or turnover (trading volume as a fractionof total market value) is inexplicably high’ (p. 1025); Black (1986) originatedthe term ‘noise’ to describe this excess. Noise traders may be acting ration-ally for liquidity or hedging reasons, or they may be acting rationally asagents in ways that differ systematically from the behaviour of those actingin markets as principals. For example, Dow and Gorton (1997) argue thatnoise trading exists because investors (principals) force traders (agents) totrade rather than be idle. Other approaches take a more radical view. DeLong et al. (1990) argue, first, that noise traders are irrational, having ‘erroneous stochastic beliefs’. Second, irrationality helps, in that it may gener-ate higher expected returns than those accruing to rational traders. Third, therational traders’ reaction to irrationality may cause prices to diverge fromfundamental values.

Close attention to irrationality, the possible benefits from irrationalaction and its aggregate consequences are central to the project of ‘behav-ioural finance’, which seeks not only to account for phenomena such as noisetrading but also for investor behaviour, stock market overreactions andbubbles (Shiller, 2000; Thaler, 1991). In practice, the focus is on ‘quasi-rationality’ (Thaler, 1991), which Bernstein (1996) describes as analysinghow market actors ‘struggle to find their way between the give and takebetween risk and return, one moment engaging in cool calculation and thenext yielding to emotional impulses’ (p. 287).

One theory, prospect theory, has become particularly important inbehavioural finance because of its implications for expected utility theory.The latter posits that individuals are motivated to maximize not expectedfinancial returns but rather the expected utility of their actions. Utility func-tions possess a general form following from the proposal that the disutilityarising from a fall in wealth is greater than the utility arising from an increasein wealth of the same size. This accounts for risk aversion; an individual willrequire a risk premium to engage in a trade with an element of risk in thereturn. A monetary value can be put on risk aversion by examining howmuch greater than the stake the winnings need to be to encourage individualsinto a fifty-fifty bet (Bernstein, 1996; Markowitz, 1952).

Prospect theory (Kahneman & Tversky, 1979; Thaler 1991; Tversky& Kahneman, 1986) argues that individuals treat gains and losses differently;this is depicted in Figure 1. The risk neutrality line is contrasted with the



framing effects of prospect theory depicted in the value line. Framing effectsdepict risk aversion in the domain of gains and loss aversion – risky behav-iour seeking to avoid loss altogether – in the domain of losses. Individualreference points dividing gains from losses may vary, depending on per-formance targets and past history (Wiseman & Gomez-Meija, 1998).

Despite their importance, framing effects are just one example of a setof heuristics and biases which affect decision making under uncertainty (Kahnemann et al., 1982); many have been used to explain deviations fromrationality in financial markets (Shefrin, 2000; Thaler, 1993). An importantpoint to make for the subsequent argument of this article is that within thebehavioural finance approach, rational behaviour and heuristics coexist butthe former is theoretically dominant. A comprehensive list of decisionheuristics does not sum to a theory of individual market behaviour, ratherit sums to a theory about deviations from the neo-classical model (Willman,2000).

In explaining how individuals behave in financial markets, the final setof work to consider concerns social influences. Sociological work on finan-cial markets has tended either to examine the empirical pattern of market


Figure 1 Prospect theory framing effectsSource: Bazerman (1998: 49)


transactions or to examine social and non-rational influences on decisionmaking. The former is the concern of the micro structural work of Bakerand colleagues (Baker, 1984a, 1984b; Baker & Iyer, 1992). Baker conteststhe market model implicit in financial economics by describing highly dif-ferentiated exchange networks in securities markets characterized by inter-personal differences in network centrality. He identifies systematicrelationships between network size and density on the one hand and pricevolatility on the other. His central finding that increased network size isassociated with increased volatility challenges economic assumptions aboutperfect markets.

Keynes (1936) described stock market behaviour as ‘anticipating whataverage opinion expects average opinion to be’ (p. 156), manifesting theconcern with reflexivity which is characteristic of many who focus on thesocial construction of financial markets. A key strand of work is that onherding, where mutually aware actors engage in mimetic, irrational behav-iour (Adler & Adler, 1984). Abolafia and Kilduff (1988), following Kindle-berger (1978) have described panics in which investment manias followed bypanic selling imply that market actors both create and are influenced by theirtrading environments (see also Warner & Molotch, 1993).

Abolafia (1996) combines a social constructionist approach withdetailed ethnographic work in a variety of Wall Street markets. Thesemarkets are seen, not as neo-classical phenomena but as socially producedand reproduced by market makers. In particular, specific market conditionsare linked to bounded rationality phenomena, such that different behavioursmay be understood as responses to different market circumstances. Forexample, bond traders, subject to continued electronic information flow, useinformation editing heuristics foreign to pit traders in commodity marketswhere the main information sources are interpersonal.

These theories have different implications for traders in markets. Apeculiar feature of the efficient markets hypothesis underpinning financialeconomics is that efficiency stems from joint hypotheses about individualbehaviour and market structure. Other approaches differ. In behaviouralfinance, market outcomes are influenced by deviations from rationalitywhose origins do not lie within the market. In sociological approaches, thereis variance in the primacy accorded to structure and action but in all casesindividual action occurs within a socially produced market context. Weargue that all approaches imply that, in addition to an understanding offinancial theory, individuals in the market need a set of theories and heuris-tics to guide their own profit seeking behaviour. We also argue that theseindividual theories have social dimensions. We detail the argument in thenext section.



3. Individual and general theories

The key distinction is between general theories of how the market works andthe personal theories guiding specific individuals working in the market.Markets may be perfect, rational and governed by expected utility theory,but individuals trade within differentiated networks, more frequently thannecessary and with variable risk-return trade-offs. Their behaviour is reflex-ive in two senses. First, they learn idiosyncratic trading strategies based ontheir market experience. Second, they factor predictions of others’ behaviourinto their own trading strategies. The view offered here is that this differenceis systematic. In the following section, we examine the following proposi-tions:

Proposition (i): Traders believe that the market as a whole works inaccordance with general finance theory – this is ‘how the world works’.

Proposition (ii): Theories that guide individual action in the marketinvolve a belief in the existence of exceptions to the general theories –this is ‘how to work the world’.

Consider Figure 2. The figure is a simple representation of an aggre-gate risk return relationship in the market. Traders in the market wish tosucceed, i.e. they wish to trade in the area above the trend line, but withoutmoving the trend line up. A market in which there is perfect informationabout risk-return relationships will, other things being equal, offer few suchopportunities.3 The strategy of the successful trader is thus, from one per-spective, an attempt to identify or create and then subsequently use andprotect the market imperfections that allow the majority of trades to occurabove the line.

The theory of how to work the world differs from the prevailing theoryof how the world works in the following respects.

1. It seeks anomalies in the operation of the general theory; it is often con-trarian in nature.

2. However, it depends logically on a belief in the general validity of thetheory of the world; it involves plays around the trend line whichrequire a general belief in its existence.

3. It deploys this knowledge consciously and instrumentally; this may beto the advantage of the principal, or the trading agent, or both.

4. There are, ex ante, clear incentives to render the individual theory inuse inappropriable both by others operating competitively in themarket and by principals.



5. Interpersonal variation in the content of such theories is likely.

Theories of how to work the world generate variance around the trendline. It is not assumed at this point that they are rational. Such a theory tar-geted at the domain of gains may simply be optimistic bias or illusion ofcontrol, and may be associated with poor trading performance (FentonO’Creevy et al., 1999). They may involve beliefs about fundamental proper-ties of the stock or instrument being traded. They may involve beliefs aboutpatterned movements in the market, accessed by analysis of historic data.They may involve more intuitive responses, based on ‘feel’ for the market.They may thus also vary in their degrees of formality. Such theories are instru-mental recipes for action predicated on the need to generate purposive actionin a world governed by probabilistic rules; they function to generate a senseof control over the environment. They are reflexive in the sense that indi-viduals are aware of the development of their own theories. They are heuris-tic in the sense that they reduce complicated tasks on the assessment of


Figure 2 The relationship between risk and return


probability and value to simple judgement operations. (Kahnemann et al.,1982). They have symbolic functions in at least two – slightly contradictory– senses. First, they are used to render actions accountable; they must fitwithin a broader vocabulary of trading motives (Wright Mills, 1963) to assistpresentation of self. Second, they may be used to articulate a distinctivetrading strategy and identity; they may function to generate self esteem. Aswe discuss below, these two senses may relate to accountability in thedomains of loss and gain respectively.

Work-the-world theories are thus similar in many respects to laytheories, particularly in terms of their functions (Hewstone, 1983). However,they differ from ‘pure’ lay theories in several respects. They are the productof experience in a specific expert context, not pure ‘common sense’(Furnham, 1988). They are theories of process rather than content, oftendeductive rather than inductive, situational rather than individualistic(Furnham, 1988). However, this is not to say that such theories about themarket do not contain pure ‘lay’ elements in their characterization of the psy-chology of other traders.

The difference from, and relationship to, general knowledge is neces-sary rather than contingent. If trading behaviour is routinized application ofgeneral rules, then – as many have argued – it can be automated (Wilhelm,1999). Routines imply fatalistic trading behaviour which can, incidentally,be exploited by others in the market. By contrast, popular accounts stress theexistence of ‘wizards’ and ‘heroes’ who ‘beat the market’ (Insana, 1996); ourown data show not fatalism but unrealistic optimism and illusion of controlamong traders (Fenton O’Creevy et al., 1999). From one perspective, traderssimply avoid what might be termed the ‘actuarial fallacy’, defined as the indi-vidual presumption that aggregate population probabilities apply to indi-vidual action. To say that, for example, 50 percent of trades lead to losses isnot to say that each trade has a 50 percent probability of failure (Nicholsonet al., 1999). Traders routinely display optimistic bias in assigning low prob-abilities of loss to trades based on their work-the-world theories. They inter-pret the aggregate theories as defining an action space rather than providingbehavioural rules; we explore the implications of this in more detail below.

4. Data and methods

The data reported here are part of a larger study of individual and contex-tual influences on the risk-taking behaviour of traders in financial markets.Respondents came from the London operations of four large investmentbanks. They were engaged in trading fixed income and equity products. Both



market making and proprietary trading are included. No open outcrymarkets were covered, since the majority of London trading is now elec-tronic. The total sample size is 118, of whom 57 were traders, 46 trader/man-agers, 15 desk managers and 10 senior managers who did not trade.

The data collection process had three stages. First, participants com-pleted a short questionnaire covering demographic, tenure, career history andearnings data. Second, semi-structured interviews were conducted with allrespondents. Interviews addressed a range of issues, including risk, emotions,trading strategies, the experience of gain and loss and questions aboutorganizational culture. Interviews took between 35 minutes and 1.5 hours.The key variable influencing the length of interviews was the amount of timeparticipants could be away from their desks. Each interview was taped andverbatim transcripts were produced. Third, participants completed a set ofself-ratings concerned with four aspects of performance and a questionnairecovering attitudes towards compensation.

The qualitative data presented here are based on analysis of the contentof the interview transcripts. Table 1 illustrates, by underlining, the main inter-view categories used. The approach to analysis was phenomenological, as inseveral other studies of this type (Furnham, 1988; Heath, 1999) althoughreference is made to whether a type of comment reflected a broadly sharedopinion or not. Interview data were coded for analysis using the QSRNUD*IST Vivo (NVivo) program. The program enables sections of tran-scribed interview text to be coded and categorized. The analysis took place intwo stages. First, every reference to trading style or strategy was coded. A newdocument comprising each strategy-related quotation was created. Second, thequotations in this document were sub-coded. The new set of codes was derivedfrom the data. This method of categorization, rather than theoretically basedcoding, was used because it fitted with the exploratory nature of the article.Each of the authors read the document containing all references to strategies.The key data sub-categories were reached through discussion of the data. Thesecond set of codes comprised categories agreed upon by the authors. TheNVivo program was again used to code the interview data into categories.

The categories of interest were concerned with traders’ beliefs abouthow to trade, beliefs about the trading world, tacit knowledge, contrarianbeliefs and boredom trades. The quotations presented in this article have beenselected as representative examples of the material from each category.

Although the relevant data are reported elsewhere, it is appropriate, inlight of the previous discussion, to note the evidence on the existence of non-rational behaviour in the sample. These include pervasive loss aversion(Willman et al., 1999), illusion of control (Fenton O’Creevy et al., 1999) andemotional influences on trading behaviour (Soane et al., 1999). In addition,



there were dispositional differences in risk propensity (Nicholson et al.,1999). These traders used heuristics and biases in their own recipes foraction.

5. Working the world

In this empirical section, we explore the content of working-the-worldtheories. Three elements are important. The first is a belief in the importanceof flair and intuition; traders believe technical knowledge is not enough. Weargue that this belief sustains the generation of tacit and inappropriableknowledge. The second is a belief in the importance of reflexivity; we arguethat this belief sustains learning by doing which in turn may generate noisetrading. The third involves contrarian strategies. These are, in Giddens’s(1990) sense, counterintuitive beliefs that generate divergent behaviour. We


Table 1 Main interview categories

Trader interview items1. The trader’s job2. Traders’ decision making processes3. Emotions associated with trading4. Learning and strategy development5. The use of trading limits6. The impact of loss and gain7. Team work8. Management style9. The compensation process

10. Organizational culture

Manager interview items1. Traders’ use of heuristics 2. The effects of reward systems on traders’ behaviour3. Stress management4. Trader development5. Matching of traders to different types of markets6. Traders’ use of information7. Teamwork8. Performance evaluation and management9. Error management

10. Risk management


argue that these generate intractable management problems. They cannoteasily be articulated and their pursuit often involves dissimulation.

(i) Flair and intuition

The following exchange took place between the first-named author and asenior trading manager on an equities desk.

What happens to new entrants?

People join as juniors, answering phones and monitoring positions . . .they sit with traders and move around the desks . . . if they show flair,they try out in trading. We are recruiting MBAs but you need flair; flairplus a good technical base.

What is flair?

Flair is anticipating the market, showing intuition, having a contrary,different view of events; not going with the herd, not following themarket trend.

Traders also had views on flair:

Having a feeling is not the same as experience, it’s like having whiskers,like being a deer . . . you need a certain type of intelligence, but it’smore about intuition.

Knowledge and experience do count for a lot; but there are some peopleyou could never teach trading to in your life. Some people are just tooacademic.

There is an instinct that you have and you build on that.

It was seen by many to be something to do with learning from experience,and with learning about information processing:

Unless you’ve been in a black hole yourself, you can’t explain it toanyone; you see someone just staring at the screen and they can’t getout. When you have a position like that, you just look at it. You needsomeone to come along and say ‘OUT’. So traders have to be approach-able.



The subjective part of trading strategy is the assessment of the qualityof information and how prepared you are to rely on it to assess yourrisk . . . you have to tell the difference between probabilities and possi-bilities.

No-one has told me how to trade since I have been here. I sit next tothe guy that runs the desk, so they’re always asking ‘what’s the P&L,how’s the desk doing, what’s the desk doing?’

Intuition is particularly difficult to justify ex ante and particularly glam-orous ex post. Trader interviews nonetheless abounded with assertions of theimportance of intuition and, conversely, the failure of purely technical abilityto explain success. This differed by market, being more likely in ‘vanilla’ equi-ties trading than in more complicated derivatives trading; however, it was notabsent in the latter:

It is very difficult in my experience to articulate why you get into a tradeand what you do when you’re in a trade.

The importance of intuition was expressed in a variety of contexts. Mostgenerally, managers would note that some people ‘get it’ and some do not,referring to the performance of trainees. Those who did not ‘get it’ went tosales or research. At the most fundamental level, then, displaying some formof intuition was a ticket of entry to the trading floor.

Different trading styles were noted, some focusing on the fundamen-tals of the stock or instrument, some looking at past trading data (‘technical’traders) and others using intuition. The combination of these three differentforms of approach offers managers a portfolio of trading styles that could beused to diversify risk. Finally, there is intrapersonal variance. Tradersremarked on times when they were ‘on a roll’, ‘seeing the ball really well’,‘going for it’, when the intuitive feel for the market had to be exploited tothe full. They reported being more likely to trade frequently in such circum-stances. Use of intuition may be associated with noise trading and, conceiv-ably, with risk taking:

I think one of the keys to successful trading is to know when you’rehot and there’s no better feeling than when you’re trading fromstrength.

Once this tacit skill was developed, not only was it difficult to articulate, therewere few incentives for many to do so:



Everyone has the right to know arbitrage information, but people donot necessarily think it is their duty to share the information.

This business is not about team spirit. We should be better at tradingas a group . . . but in reality people get very parochial and very pro-tective of what they’re doing.

If I didn’t know something and went and asked someone, this givesthem bargaining power.

Systems are not set up to be able to see what other people are doing.Information is not shared enough and there is too much Chinese whis-pers about position taking.

In short, traders believe both that they have private information and that theydevelop tacit knowledge.

(ii) Reflexivity

Financial markets are highly reflexive domains in which traders ‘colonise thefuture’ in terms of models from the past (Giddens, 1990). There is awarenessof the general theories, awareness of one’s personal theory in use and aware-ness of the existence of variance between one’s own and others’ theories.

I think it’s the blending of the quantitative skills with an understand-ing of how people react. Let’s face it . . . what is the market? It’s youand me and a hundred other people sitting around and playing pokerthrough screens; you are trading emotion, but you do have quantita-tive value running through it.

You have to build a framework on how you believe the world isworking . . . you have your mental picture of what’s going on. Whenyou are making money, this mental picture is being reinforced.

Some traders reflected on the contradictions involved:

When we make a lot of money, I tend to worry about it. I think wecan’t be making all that money, something is wrong. Our systems arenot all that good.

Once you’ve entered a position, you realize no rule book prepares you



for it. And we’ve all read the world’s best trader books. Every personhas a different approach.

One way of approaching this issue is to examine three of the theoriesabout loss and gain which pass the membership test (i.e. traders talk in theseterms). They are depicted in Figure 3. Figure 3a depicts risk neutrality in theface of loss and gain, which is generally assumed in finance theory. Whateverthe wealth or performance position of the trader, each trade is dealt withaccording to its own risk level, analytically and rationally. In interviews,traders would acknowledge the need to behave in this way but would alsopoint to the experience of losses, the progress towards bonus targets and their‘mood’ as influences on trading behaviour. Figure 3b presents the prospect-theoretic pattern of risk aversion in the domain of gains and loss aversion inthe domain of losses. This was to be avoided; several traders quoted themaxim ‘you don’t cut profits and you don’t chase losses’. Figure 3c illustratesthe ideal; chase profits, cut losses. Figure 3a represents what traders know theyshould do; Figure 3b what they should not do – but apparently, on occasion,find themselves doing (Willman et al., 1999); and Figure 3c what they wouldlike to have done ex post. They are simultaneously aware of the rules, the needto break the rules and the risks involved in so doing. They know the risks ina fairly sophisticated way, but not in the way predicted by finance theorists.

Finding out about the risks involved learning by doing. Experience wasseen to be important:

To trade anything well, you need at least a year’s experience of tradingthat stock.

The year is a continuous progression of trading experiences.

You can never know enough and you can never learn too much.

Traders were more likely to report trading more frequently whenmaking money and less frequently when losing:

When I make money I think it shows I’m doing something right. If I’mright, I will try and do more of them, to increase the size of my pos-ition to make more money.

I think if you are on a roll, that is when you are prepared to put moremoney at risk. When you are not sure what is going on and you havea few losses, that is when you pull back.



I think it is a good idea when you lose money, especially a lot of money,to sit back, take it easy and maybe stop trading for a while.

On average, people will trade more often when they are making moneycompared with when they are losing money because their risk aversionand loss tolerance change.

Other random effects may be evident:

You do boredom trades because you can be sitting up there doingnothing and you think, well I’ll do that because it gives me somethingto do. The next thing you know you are wrong and you’ve lost moneyon it. Or you’re right and you’re inclined to do it again in biggervolumes.


Figure 3 The theories about loss and gainthat pass the membership test: (a) risk neu-trality; (b) prospect-theoretic prediction; and(c) ideal trader

(a) (b)

(c)


These quotes imply a rather different view of noise trading. If trading is seenas a rational activity affecting the portfolios of investors, the volume oftrading in markets is high. If trading is seen as the generation of expertisethrough learning by doing, this high level of trading can be explained inrational terms as leading to the acquisition of tacit knowledge by traders inthe market.

(iii) Contrarian beliefs

Behaviour manifesting flair was often seen as the opposite of herding. Goodtraders must be better than average:

A lot of people in the market are trend followers and that’s whereopportunities are created.

You need to believe in your own ability because when things go wrong,how will you cope? You have to think you are better than average inthe market.

Every year my filtering processes have improved, so now I can beat themarket.

I think that to get to the top you would have to adopt a higher riskstrategy because otherwise it is a risk-return trade off. If you’re happyto take bigger risks then you might end up getting fired or getting tothe top.

Contrarian beliefs use the language of the general theory to articulatevocabularies of motive. The general laws of the market are interpreted inidiosyncratic ways as part of the vocabularies of motive used to justify situ-ated action by traders (Wright Mills, 1963). Consider the following, from atrading manager in equities:

Six weeks ago [i.e. mid-October 1998] I woke up on Sunday, read thepapers and decided that the market was going to turn. On the Monday,I told the traders ‘you can be level or long but don’t come back short’.We made a lot of money . . . it’s been the best six months in a while . . .when everyone tells you something, don’t be with the crowd. It was abad market . . . we decided it was better to do something than donothing.



Another trader manager remarked:

I ask my team what they are doing and then I do the opposite.

Of course, by definition, everybody cannot be contrarian. A tradingcliché is ‘the trend is my friend’. However, individuals ‘on a roll’ may havethe confidence to buck the trend. Managers may use contrarians or deliber-ately encourage a contrarian position on their desks in order to hedge againstthe trend. If all traders on a desk are trading with the herd, the manager maysee benefit in having some contrarian positions which ‘question existingwisdom’.

We asked traders about their best experiences and about their besttrades. Several responded by talking about the times they had made a greatdeal of money, or made a big trade. However, several responded to this open-ended question by talking in explicitly contrarian terms; the best trade wasseen as the one that proved their theory right, in the face of arguments to thecontrary, irrespective of profitability:

Gut feeling is my sixth sense. I would rather go with it and be wrongthan not go with it and be right because if my gut feeling tells me todo something and I don’t and it happens then I am doubly annoyed.

The context in which these beliefs are expressed is important. Traders oftenhave to justify their trading positions to managers. Trader managers in thissample were predominantly ex-traders who continued to run their ownbooks. They had fairly wide spans of control and could not monitor thetrading of individuals directly; they simply knew inputs (i.e. trading strat-egies) and outcomes (profit or loss). Overwhelmingly, they focused on ensur-ing traders avoid losses rather than encouraging them to maximize gains(Willman et al., 1999).

Because they do not wish to rely solely on retrospective outcome data,they frequently resort to monitoring inputs, i.e. the logic of the trading pos-ition. In one interview, a manager reported firing a trader because of defi-ciencies in his explanation of his position, rather than on the basis of outcomedata. This episode epitomizes the social aspects of ‘work-the-world’ theories.There may be strong incentives for traders to offer ‘herding’ representationsof contrarian positions, or simply to conceal such positions from managersex ante, until the hypothesis prompted by the theory in use has been tested.Ex post, a successful contrarian strategy may be publicized as the basis forreputation building.

Consequently, the contrarian views expressed in the interviews involve



retrospection and are thus likely at best to be a biased sample of the con-trarian positions actually taken. The failed ones will not form part of thefolklore of trading, while the successful ones achieve a cachet perhaps out ofproportion to their significance. Successes from herding or simply from luckmay be retrofitted to a contrarian argument. Traders who strongly believe intheir own efficacy may be subject to fundamental attribution error. In short,there may be strong incentives for traders ex post to emphasise the use offlair for the purposes of reputation building. As Frankfurter and McGoin(1999) note, those who work in financial markets may be predisposed tointerpret beneficial outcomes which are the result of chance events as theresult of deployment of tacit skill. We concur to the extent that we foundtraders had unrealistic beliefs in their ability to control events in this sample.

6. Knowing the risks

Our central argument has been that the knowledge deployed by traders infinancial markets consists of general, quasi-scientific knowledge aboutmarkets and tradable instruments on the one hand and, on the other, specificand idiosyncratic recipes for trading success. Perfect markets and rationaldecision making in the aggregate models may be set against the interest inthe generation of imperfections, the use of heuristics and learning by doingat the individual level.

It is beyond the scope of this article to argue that these differencesbetween, on the one hand, aggregate theories and, on the other, individualones serve to undermine the former.4 It may be that, as in the philosophy ofscience literature, one regards individual decision making by traders asinvolving the critical and reflexive process of ‘depending on ideas whileassessing their dependability’ (Sabel, 1994). From this perspective, over-coming what we have termed the actuarial fallacy, (i.e. believing aggregateprobabilities apply to specific actions) is similar to maintaining Feyerabend’s(1970) notion of the ‘principle of tenacity’ under which scientists maintain abelief despite acknowledging its infirmity. The fundamental belief here is thatone can beat the market and its infirmity lies in the fact that many tradershold it and they cannot all be right.

We conclude by examining three issues. The first is the pursuit of theperfect market, particularly by regulators. The second is the pursuit of learn-ing by doing by traders. The third is the generation of contrarian strategiesby traders. All three have both theoretical and policy implications.

Financial markets are not perfect and traders do not have perfect infor-mation. Moreover, their actions keep things that way. Imperfections are



essential to making money but are intrinsically unknowable to the majorityof traders at any time. The markets are in fact a complicated mixture offormal, publicly available knowledge and knowledge which is tacit in one oftwo senses (Willman, 1997).

1. It may be necessarily tacit, based on intuition about market change.This knowledge is typically generated in learning-focused noise tradingwhich results in experientially grounded ‘feelings’ about the marketwhich provide a basis for risk exposure. It may be conceptualized as adeeply embedded heuristic which traders describe as flair, the precisecontent of which cannot easily be articulated. We refer to this as a Type1 inappropriability.

2. It may be contingently tacit – kept so for profit by the originating traderor traders in order to be locally rather than generally appropriable. Thisknowledge may contain facts about arbitrage possibilities or marketmaker reactions that are in principle easily articulated but which willbe protected by individuals as the basis for continued trading success.This is Type 2 inappropriability.

As Hayek (1945) points out, in information terms, markets are highlydecentralized. Relevant knowledge ‘never exists in concentrated or integratedform, but solely as the dispersed bits of incomplete and frequently contra-dictory knowledge which all separate individuals possess’ (p. 519). Winningstrategies in such markets may not last long and individuals have incentivesto prevent others from smothering the advantage their strategy creates (Bern-stein, 1996).

Both forms of knowledge are used by traders to target the domain ofgains outlined in Figure 1. Use of know-how which is Type1 inappropriableis not fully rational, but it may generate profit. Use of know-how which isType 2 inappropriable is rational for traders but may cause problems formanagers or regulators; it will generally be covert. Ex ante, traders may mis-represent or dissimulate the bases of their trades in both cases. In the firstcase, because of the irrationality; it would be very difficult to explain to man-agers and peers. In the second, because of appropriability issues; if everybodyfollows the strategy, it may yield no advantages. The vocabulary of motivesthat traders use to describe their trades to managers or other position mon-itors is that underlying Figure 3c.

This relates to both noise trading and contrarian strategies. Ourapproach to noise trading is that a significant amount of it consists oflearning by doing. Traders are experimenting with their work-the-worldtheories. Since these trades may not be condoned by managers, often the



basis of the trade will be couched in language which conceals intent.Similar considerations affect contrarian strategies, but to a rather morecompounded extent. Ex ante I will wish to conceal my contrarian strat-egy not only, first, because my manager will not necessarily endorse a risk-seeking counter-intuitive trade but also, second, because it can onlysucceed to the extent that it does not immediately induce mimetic herding.Ex post, it may be the basis for reputation building. Not only do thesemotives underpin the dual symbolic role for work-the-world theories out-lined above, they also suggest a link between trader learning, contrarianstrategies and noise trading. In short, the structure of the knowledge basewhich sustains trader success generates covert behaviour which also sus-tains the occasions of trader malfeasance which attract great publicity; themost famous, that of Leeson in Barings, contains intuition, contrarianbeliefs and the attempted exploitation of a market imperfection (Bank ofEngland, 1997).

Heath (1999) argues that extrinsic incentives bias – assuming othersare more motivated by extrinsics than intrinsics – is endemic in princi-pal–agent relationships. A priori, there is a strong case for suspecting thisexists in financial markets where traders are rewarded predominantly bybonus payments. Heath argues that this bias emerges because principals useinappropriate lay psychological theories to structure incentives for agents.Our analysis of the practical theories trader–agents themselves use mayoffer hints for the design of incentives and constraints in financial marketswhich tap into the intrinsic elements of motivation that underpin suchtheories.

Our focus here has been on knowledge intensive work rather thanknowledge intensive firms. All our respondents worked within large invest-ment banks but we have chosen the individual and the market as our unitsof analysis. Knowledge in markets is fragmented. Work may be knowledgeintensive but the knowledge used and generated in work cannot easily be cod-ified. It thus illustrates a particular form of knowledge management problem,but the appropriability considerations may be relevant to a variety of con-texts.

Acknowledgements

We are grateful for comments on this article from attendees at the Oxford Know-ledge Management Conference, as well as from anonymous reviewers of thejournal. We are also grateful for the co-operation of the four institutions that sup-ported the field research, who wish to remain anonymous.

This research was sponsored by ESRC grant No. L211252056.



Notes

1 We are grateful to an anonymous reviewer for this point.2 This is not necessarily the case for other markets; see Wiseman and Bromiley (1991),

McNamara and Bromiley (1999).3 Traders can of course profit simply by making markets using liquidity to benefit from

bid-offer spreads.4 For a view of financial economics as wholly ideological, see Frankfurter and McGoin

(1999).

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Paul Willman is Professor of Management Studies at University ofOxford. He holds degrees from the Universities of Cambridge andOxford and has held teaching posts at Imperial College, London, Cran-field School of Management and London Business School.He is a researchfellow at the Centre for Economic Performance, LSE. He has publishedseveral books and numerous articles in the fields of labour relations andhuman resource management and is currently researching perceptions ofrisk and their relationship to risk behaviour.[E-mail: [email protected]]

Mark Fenton O’Creevy is a Lecturer at the Open University. [E-mail:[email protected]]

Nigel Nicholson is Professor of Organizational Behaviour at LondonBusiness School.[E-mail: [email protected]]

Emma Soane is a Research Officer at London Business School[E-mail: [email protected]]


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Knowing the risks:Theory and practice in financial … broad conventional approaches can be identified. The first focuses on inventory, looking at the flow of trades and how temporal