What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance

• DOI: http://doi.org/10.1111/jofi.12619
• Published date: 01 June 2018
• Author: MARK DESANTIS,DAVID PORTER,BRICE CORGNET
• Date: 01 June 2018

THE JOURNAL OF FINANCE •VOL. LXXIII, NO. 3 •JUNE 2018

What Makes a Good Trader? On the Role of

Intuition and Reflection on Trader Performance

BRICE CORGNET, MARK DESANTIS, and DAVID PORTER∗

ABSTRACT

Using laboratory experiments, we provide evidence on three factors influencing trader

performance: fluid intelligence, cognitive reflection, and theory of mind (ToM). Fluid

intelligence provides traders with computational skills necessary to draw a statisti-

cal inference. Cognitive reflection helps traders avoid behavioral biases and thereby

extract signals from market orders and update their prior beliefs accordingly. ToM

describes the degree to which traders correctly assess the informational content of or-

ders. We show that cognitive reflection and ToMare complementary because traders

benefit from understanding signals’ quality only if they are capable of processing

these signals.

THE CORNERSTONE MODELS OF FINANCE build on the assumption that a represen-

tative economic agent acts rationally (e.g., Markowitz (1952), Sharpe (1964),

Samuelson (1970)). However, the rationality assumption, which requires that

individuals correctly apply and carry out mathematical and statistical meth-

ods, has been challenged by behavioral finance studies that find a large degree

of variability in individuals’ ability to solve complex problems. When presented

with such problems, most individuals tend to rely on simple heuristics instead

of performing the requisite calculations (e.g., see Thaler (1993,2005), Barberis

and Thaler (2003), Shefrin (2007) for surveys).

The heterogeneity in individuals’ cognitive capacities suggests that we may

observe significant differences in their financial decisions. Broader access to the

stock market coupled with increasing complexity of the financial environment

(e.g., large number of sophisticated financial instruments, interconnectedness

of global markets, etc.) thus renders understanding of the relationship be-

tween individuals’ cognitive capacities and financial decisions of great practical

importance.

Different factors influencing trader performance have been studied in isola-

tion in different strands of the literature using various types of data (archival,

∗Brice Corgnet is with EMLYONBusiness School, GATE L-SE. Mark DeSantis and David Porter

are with Chapman University. We thank the Editor, an anonymous Associate Editor, and three

anonymous referees for insightful and constructive comments. Brice Corgnet also acknowledges

the support of LABEX CORTEX. We obtained Institutional Review Board approval to perform our

experiment and collect data on human subjects. We do not have any sources of financial support to

disclose.

DOI: 10.1111/jofi.12619

1113

1114 The Journal of Finance R

experimental, and neuroimaging) and various measures of performance, such

as forecasting skill (Bruguier, Quartz, and Bossaerts (2010); BQB henceforth)

or stock-picking skill (Grinblatt, Keloharju, and Linnainmaa (2012)). In this

paper, we assess the cognitive underpinnings of trading decisions using a uni-

fied market setting. Todo so, we employ an experimental asset market in which

we control various aspects of the trading environment (see Bossaerts (2009),

Frydman et al. (2014), Noussair and Tucker (2014)). Specifically, we manage

the flow of information into the market so that the effect of an individual’s skill

is not confounded with that of insider trading. This is the case because the

experimental method allows us to randomize the distribution of information

across traders so that traders exhibiting high levels of specific skills are, on

average, just as informed as other traders. This market setting allows us to

control the flow of private information by allocating a specific signal regarding

the true value of the traded asset to each trader (see Plott and Sunder (1988);

PS henceforth).

Our experimental methodology also allows us to collect a large set of char-

acteristics on individual traders. Controlling for an extensive number of

individual-level characteristics (such as financial literacy, personality traits,

educational background, and risk attitudes) in addition to the flow of infor-

mation in the market ensures the robustness of the effect of our conjectured

predictors.

We posit that traders’ ability to learn the true value of an asset using both

private information and market orders is a key to traders’ success. All else

equal, a more informed trader will ultimately post more profitable orders,

which results in higher earnings. In the extreme, a trader who knows an asset’s

true value will be assured of earning positive trading profits by systematically

buying below and selling above the true value. Thus, to ensure positive trading

profits, a trader should attempt to discover the asset’s true value. Weargue that

learning the true value of an asset requires three fundamental skills, namely,

the ability to conduct statistical inference, avoid behavioral biases, and assess

the informational content of market signals.

Statistical inference based on available signals is crucial for traders to

learn the true value of an asset. Such calculations require “fluid intelli-

gence,” which is defined as one’s ability to conduct mental simulations and

engage in abstract reasoning (e.g., Mackintosh (2011)). In short, fluid intelli-

gence is the computational capacity in which one needs to undertake mental

calculations.

In addition to fluid intelligence, “cognitive reflection” is a crucial skill for

traders to learn true asset value. Cognitive reflection is defined as one’s ability

to engage in effortful reasoning (e.g., Stanovich (2009)) and is measured using

the cognitive reflection test (CRT), which consists of questions that have an

intuitive but incorrect answer (e.g., “A bat and a ball cost $1.10 in total. The

bat costs a dollar more than the ball. How much does the ball cost?” Frederick

(2005)). Upon reflection, one may disregard the intuitive answer (“10¢”) in

favor of the correct one (“5¢”). Cognitive reflection adds to fluid intelligence

because it helps individuals avoid commonly observed heuristics and biases