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The spectacular rise of US stock-market prices in the technology sector over the past few years and the even more spectacular crash last year has intensified the well-worn controversy surrounding the rationality of investors. Most financial economists are advocates of the “Efficient Markets Hypothesis” (Samuelson, 1965) in which prices are determined by the competitive trading of many self-interested investors, and such trading eliminates any informational advantages that might exist among any members of the investment community. The result is a market in which prices “fully reflect all available information” and are therefore unforecastable.

Critics of the Efficient Markets Hypothesis argue that investors are often if not always irrational, exhibiting predictable and financially ruinous biases such as overconfidence (Fischoff & Slovic, 1980; Barber & Odean, 2001; Gervais & Odean, 2001), overreaction (DeBondt & Thaler, 1986), loss aversion (Kahneman & Tversky, 1979; Shefrin & Statman, 1985; Odean, 1998), herding (Huberman & Regev, 2001), psychological accounting (Tversky & Kahneman, 1981), mis-calibration of probabilities (Lichtenstein, Fischoff, & Phillips, 1982), and regret (Bell, 1982; Clarke, Krase, & Statman, 1994). The sources of these irrationalities are often attributed to psychological factors fear, greed, and other emotional responses to price fluctuations and dramatic changes in an investor’s wealth. Although no clear alternative to the Efficient Markets Hypothesis has yet emerged, a growing number of economists, psychologists, and financial-industry professionals have begun to use the terms “behavioral economics” and “behavioral finance” to differentiate themselves from the standard orthodoxy. The fact that the current value of the Nasdaq Composite Index, a bellwether indicator of the technology sector, is 1646.34 (October 17, 2001) only 32.6% of its historical high of 5048.62 (March 10, 2000) reached less than two years ago lends credence to the critics of market rationality. Such critics argue that either the earlier run-up in the technology sector was driven by unbridled greed and optimism, or that the precipitous drop in value of such a significant portion of US economy must be due to irrational fears and pessimism.

However, recent research in the cognitive sciences and financial economics suggest an important link between rationality in decisionmaking and emotion (Grossberg & Gutowski, 1987; Damasio, 1994; Elster, 1998; Lo, 1999; Loewenstein, 2000; Peters & Slovic 2000), implying that the two notions are not antithetical, but in fact complementary.

In this study, we attempt to verify this link experimentally by measuring the realtime psychophysiological characteristics skin conductance, blood volume pulse, heart rate, electromyographical signals, respiration, and body temperature of professional securities traders during live trading sessions. Using portable biofeedback equipment, we are able to measure these physiological characteristics in a trader’s natural environment without disrupting his workflow while simultaneously capturing real-time financial pricing data from which market events can be defined. By matching these events with the traders’ psychophysiological responses, we are able to determine the relation between financial-risk measures and emotional states and dynamics. In a pilot sample of 10 traders, we find statistically significant differences in mean electrodermal responses during transient market events as compared with no-event control baselines, and statistically significant mean changes in cardiovascular variables during periods of heightened market volatility as compared with normal-volatility control baselines. We also observe significant differences in mean physiological responses among the 10 traders that may be systematically related to the amount of trading experience.

In studying the link between emotion and rational decisionmaking in the face of uncertainty, financial securities traders are ideal subjects for several reasons. Because the basic functions of securities trading involve frequent decisions concerning risk/reward trade-offs, traders are almost continuously engaged in the activity that we wish to study. This allows us to conduct our study in vivo and with minimal interference to and, therefore, contamination of the subjects’ natural motives and behavior. Traders are typically provided with significant economic incentives to avoid many of the biases that are often associated with irrational investment practices. Moreover, they are highly paid professionals that have undergone a variety of training exercises, apprenticeships, and trial periods through which their skills have been finely honed. Therefore, they are likely to be among the most rational decisionmakers in the general population, hence ideal subjects for examining the role of emotion in rational decisionmaking processes. Finally, due to the real-time nature of most professional trading operations, it is possible to construct accurate real-time records of the environment in which traders make their decisions, i.e., the fluctuations of market prices of the securities they trade. With such real-time records, we are able to match market events such as periods of high price-volatility with synchronous real-time measurements of physiological characteristics.

To measure the emotional responses of our subjects during their trading activities, we focus on indirect manifestations through the responses of the autonomic nervous system (ANS) (Cacioppo, Tassinary, & Bernt, 2000). The ANS innervates the viscera and is responsible for regulation of internal states that are mediated by internal bodily as well as emotional and cognitive processes. ANS responses are relatively easy to measure since many of them can be measured non-invasively from external body sites without interfering with cognitive tasks performed by the subject. ANS responses occur on the scale of seconds, which is essential for investigation of real-time risk-processing.

Previous studies have focused primarily on time-averaged (over hours or tens of minutes) levels of autonomic activity as a function of task complexity or mental strain. For example some experiments have considered the link between autonomic activity and driving conditions and road familiarity for non-professional drivers (Brown & Huffman, 1972), and the stage of flight (take-off, steady flight, landing) in a jetfighter flight simulator (Lindholm & Cheatham, 1983). Recently, the focus of research has started to shift towards finer temporal scales (seconds) of autonomic responses associated with cognitive and emotional processes. Perhaps the most influential set of experiments in this area was conducted in the broad context of an investigation of the role of emotion in decisionmaking processes (Damasio, 1994). In one of these experiments, skin conductance responses were measured in subjects involved in a gambling task (Bechara et al., 1997). The results indicated that the anticipation of the more risky outcomes led to more skin conductance responses than of the less risky ones. The brain circuitry involved in anticipating monetary rewards has also been localized (Breiter et al., 2001). Another study (Frederikson et al., 1998) reported neuroanatomical correlates of skin conductance activity with the brain regions that also support anticipation, affect, and cognitively or emotionally mediated motor preparation. Recent experiments (Critchley et al., 1999) support the significance of autonomic responses during risk-taking and rewardrelated behavior. They provide more details on brain activation correlates of peripheral autonomic responses, and also claim the possibility of discriminating the activity patterns related to changing versus continuing the behavior based on the immediate gain/loss history in a gambling task.

We focus on five types of physiological characteristics in our study: skin conductance, cardiovascular data (blood volume pulse and heart rate), electromyographic data, respiration rate, and body temperature.

Skin conductance response (SCR) is measured by the voltage drop between two electrodes placed on the skin surface a few centimeters apart. Changes in skin conductance occur when eccrine sweat glands that are innervated by the sympathetic ANS fibers receive a signal from a certain part of the brain. Three distinct “brain-information systems” can potentially elicit SCR signals (Boucsein, 1992). The affect arousal system is capable of generating a phasic (on the scale of seconds) response that is associated directly with the sensory input indicating attention focusing or defense response, and the amygdala is the primary brain region involved. The emotional response to a novel or highly complex task is an example of affect arousal. Another system that can initiate a phasic SCR centers on the basal ganglia and is related to a preparatory activation, mediated by internal cognitive processes. The body exhibits increased perceptual and motor readiness, and high attention levels. Expectation of an event or preparation to an important action illustrates the operation of this system. The third system, often called the “effort system”, produces tonic changes in the level of skin conductance and is related to the long-term changes of a general emotional (“hedonic”) state or attitude, indicating a nonspecific increase inattention or arousal. Hippocampal information-processing is believed to be behind this system, which is associated with a higher degree of conscious awareness, while the first two are mostly attributed to subconscious processes.

The cardiovascular system consists of the heart and all the blood vessels, and the variables of particular interest are blood volume pulse (BVP) and heart rate (HR). BVP is the rate of flow of blood through a particular blood vessel, and is related to both blood pressure and the diameter of the vessel. Constriction or dilation of the vessels is controlled by the sympathetic branch of the ANS, and along with electrodermal activity, has been shown to be related to information processing and decisionmaking (Papillo & Shapiro, 1990). HR refers to the frequency of the contractions of the heart muscle or myocardium. Specialized neurons the so-called “pacemaker” cells initiate the contraction of myocardium, and the output of the pacemakers is controlled by both parasympathetic (HR decrease) and sympathetic (HR increase) ANS branches. BVP and HR track each other closely, so that HR deceleration usually causes an increase in BVP. Compared to SCR arousal indications that refer mostly to cognitive processes, changes in cardiovascular variables register higher levels of arousal which are often somatically mediated. These variables may provide supplemental information for interpreting high SCR signals elevated SCR accompanied by an increase in HR may be an indication of extreme significance of the task or stimulus or, alternatively, simply indicate that some physical activity is being performed simultaneously (Boucsein, 1992).

Electromyographic (EMG) measurements are based on the electrical signals generated by the contraction process of striated muscles. Muscle action potentials travel along the muscle and some portion of the electrical activity leaks to the skin surface where it can be detected with the help of surface electrodes. The activation of particular muscles reflects different types of actions. For example, activation of the facial muscle (i.e., the “masseter” muscle) indicates ongoing speech; activation of the forearm muscle group (i.e., the “flexor digitorum” group) corresponds to finger movements such as typing on a keyboard. In addition to their role in motor activity, certain muscle groups exhibit strong correlations with emotional states. Most of the research in this literature has focused on facial muscles since facial expressions have been linked to emotional states (Ekman, 1982). For example, an increase in EMG activity over the forehead is associated with anxiety and tension, and an increase in activity over the brow accompanied by a slight decrease in activity over the cheek often corresponds to unpleasant sensory stimuli (Cacioppo, Tassinary, & Bernt, 2000). Therefore, EMG measurements can capture very subtle changes in muscle activity that can differentiate otherwise indistinguishable response patterns. However, in our current implementation, the role of EMG measurements is limited to identifying and eliminating anomalous sensor readings caused by certain physical motions of a subject.

Respiration influences the heart rate through vascular receptors (Lorig & Schwartz, 1990), and although this variable is usually is not of primary psychological importance, it is a reliable indicator of physically demanding activities undertaken by the subject, e.g., speaking or coughing, which can often yield anomalous sensor readings if not properly taken into account. Respiration can be measured by placing a sensor that monitors chest expansion and compression.

Finally, body temperature regulation involves the integration of autonomic, motor and endocrine responses, and several studies have related the temperatures of different parts of the body to certain cognitive and emotional contents of the task or stimuli. For example, forehead temperature (a proxy for brain temperature) increases while experiencing negative emotions; cooling enhances positive affect, while warming depresses it (McIntosh et al., 1997). Another study reports hand skin temperature increases with positive affect, and decreases with threatening and unpleasant tasks (Rimm-Kaufman & Kagan, 1996).

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