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08.21.17 WorldQuant Perspectives

The Human Brain, Markets and Financial Decision Making Recent breakthroughs in the burgeoning field of neuroeconomics have revealed several important patterns in brain function that may explain investors’ demand for particular assets and make financial decisions more predictable.

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THIS MONTH MARKS THE 73RD ANNIVERSARY OF THE PUBLICATION documented, the first coherent economic decision-making model of Theory of Games and Economic Behavior, a groundbreaking was presented in 1979, in a groundbreaking paper by psychologists book by mathematician John von Neumann and economist Oskar Daniel Kahneman and Amos Tversky titled Prospect Theory: An Morgenstern. The two Princeton University professors provided Analysis of Decision Under Risk. Kahneman (who would receive a the basis for game theory by creating a coherent framework that Nobel Prize in economic sciences in 2002) and Tversky provided a predicts how rational beings are likely to behave. Back then, coherent framework that explained some decision-making biases capturing individual preferences with a single number seemed called heuristics. Their theory highlighted that financial decision impossible. Nevertheless, von Neumann and Morgenstern came making is frame-dependent (it depends on the given context) and up with an example from physics that, in their view, foretold the humans are prone to loss aversion (losses are much more painful future of economics: Economic utility (the pleasure or pain related than gains of similar magnitude). to the outcome of a choice), they wrote, “is strongly reminiscent of the conditions existent at the beginning of the theory of heat: that Behavioral economics and neuroscience evolved independently too was based on the intuitively clear concept of one body feeling until the early 2000s, when research in each field began to warmer than another, yet there was no immediate way to express include methods and results from the other. Eventually, this led significantly by how much, or how many times, or in what sense. to the emergence of neuroeconomics. As its name suggests, … Even if utilities look very unnumerical today, the history of the neuroeconomics combines elements of both disciplines, experience in the theory of heat may repeat itself.” borrowing methods from neuroscience to explain how economic decisions are made by directly measuring brain activity during Von Neumann and Morgenstern’s prediction appears to have situations involving monetary rewards. The literature accumulated become reality. Recent breakthroughs in neuroscience have so far has provided biological evidence for patterns previously allowed us to quantify pain, gain, happiness and fear — and to detected by behavioral economics. Studies observing how the discover the factors underlying our behavior. With the emergence human brain processes preferences for risk, expected outcome or of neuroeconomics — a field that connects brain-related delay apply a handful of techniques borrowed from neuroscience. neuroscience with economics — the analysis of human choice involving monetary rewards is no longer an abstract task. We The most common techniques are related to brain imaging and have never been closer to understanding the patterns behind aim to localize the regions and layers in the human brain that people’s financial decisions. Although neuroeconomists still are are specifically involved in particular tasks. Region-specific brain far from fully understanding how the human brain works, they activity can be analyzed in three ways: by attaching electrodes have uncovered many important patterns in its function that make to the scalp to measure electrical activity — a method called our financial choices more predictable. This kind of predictability electroencephalogram (EEG); by measuring blood flow using at the brain level is exactly what the financial industry needs in positron emission tomography (PET); or by measuring blood today’s era of Big Data, in which the collected information is so oxygenation through functional magnetic resonance imaging vast that it must be filtered and standardized. (fMRI). Using these techniques, researchers have been able to identify and separate the layers in the brain that are responsible for specific tasks (see table, below). The Birth of Neuroeconomics Modern neuroscience dates to the early 1900s, when a Spanish Measuring Up doctor, Santiago Ramón y Cajal, published his neuron doctrine, Here are the most popular methods that researchers use to analyze brain activity. suggesting that the human nervous system is made up of discrete individual cells connected by synapses. Ramón y Cajal’s pioneering results earned him one of the first Nobel Prizes, in 1906, and Method Description Main uses influenced others to focus on this emerging field of research. Electroencephalogram Electrodes are attached to the EEG has high temporal (EEG) scalp to measure electrical resolution (i.e., it can measure responses to given stimulus. immediate reactions to a given Behavioral economics didn’t make its appearance until the second stimulus), it is not expensive, half of the 20th century, when studies began to be published on and it is easy to apply, hence seemingly irrational human behavior. Although many separate accessible to a large number experiments about anomalistic patterns already had been of researchers.

Method Description Main uses predictable in similar situations. From a financial point of view, this direct, “hard-wired” predictability is highly relevant, and Positron emission A small amount of positron- PET has lower temporal tomography (PET) emitting radionuclide is resolution and lower its importance will rise as the amount of data collected introduced into the body and accessibility but compensates increases exponentially. then moves with blood flow. for its weaknesses with spatial By measuring its concentration resolution (i.e., it allows in certain areas, neural mapping of different brain Neuroscience and Price Dynamics activity in those parts can be regions). Nevertheless, it is ascertained. often avoided because of its Recent findings in neuroeconomics have revealed biological invasive nature. evidence for a large number of patterns that behavioral finance had already noted. The importance of having a fundamental Functional magnetic Stimulus-related response Similar to PET, fMRI is used for explanation for financial anomalies by analyzing the nerve resonance imaging is measured by detecting the mapping brain regions due to (fMRI) difference between magnetic its high spatial resolution, but structure in our brains lies in the fact that our neural system properties of the blood flow for because it is noninvasive it is is highly robust in time: Our hard-wired way of perceiving and changing blood oxygenation. growing more popular. thinking has evolved to its current form over thousands of years, and is expected to remain mostly unchanged in the future. In fact, studies of our closest primate relatives have shown that some Other promising areas of research include experiments in which basic decision-making biases emerge when the subject faces researchers analyze humans with damaged brains and determine risky choices. UCLA economist Keith Chen, and Yale University how their performance differs from that of humans with normal economists Venkat Lakshminarayanan and Laurie Santos found brains. For healthy subjects a method called transcranial that in an experiment involving trading for coins, capuchin magnetic stimulation (TMS) is often applied: The noninvasive monkeys demonstrated exactly the same behavioral patterns that procedure, which uses magnetic fields to temporarily disrupt brain had been documented with human subjects. When the primates function in specific areas, can create conditions similar to those were given tokens to trade for pieces of food, they behaved in line experienced by people with true brain damage. with reference dependence (outcome distribution presented as a The importance of adopting these techniques for economic gain is preferred over the same distribution presented as a loss) modeling is underscored by the fact that neuroeconomics has and loss aversion (riskless outcome is preferred over the same redefined the principles of economics. As California Institute of outcome presented as a loss), as described in prospect theory. Technology behavioral economist Colin Camerer and others have When it comes to investing, persistence is a key property of a noted, the assumption-based “‘as if’ approach made good sense trading signal. Unlike some well-documented market anomalies, as long as the brain remained substantially a black box.” Now, such as seasonality (the January effect or the post-IPO drift), however, scientists can directly locate and measure our brain that fade away quickly after they are discovered, biases that are activity, allowing us to relax assumptions previously defined in hard-coded in the human brain may survive the test of time. The economics: The analysis of choice is no longer subject to the following examples reveal a small slice of what neuroeconomics standard rationality (making the optimal choice that takes into account all the information related to a problem) and homogeneity might contribute to investments and, in particular, to algorithmic (similar preference of different market participants in a given trading through its ability to identify robust patterns. situation) conditions of decision making. The first study is connected to the predictive nature of brain imaging As University of Pennsylvania psychology professor Joseph in regard to the preference for assets and to the dynamics of risk Kable and New York University neuroeconomist Paul Glimcher preference. University of North Carolina neuroeconomist Camelia have shown, “the subjective value of potential reward is explicitly Kuhnen and Stanford University neuroscientist Brian Knutson represented in the human brain.” Their 2007 study found that provided neural evidence for the well-known pattern of negative neural activity (as measured by increased blood oxygenation) in autocorrelation, or mean-reversion, in returns. They created a certain regions of the brain is identical to subjective utility, as it dynamic experiment in which subjects were able to choose from reflects preference by predicting choice. This evidence implies two stocks and a bond in each of ten trials. During the process that the once-abstract term of “utility” has become an exact, the subjects were monitored by fMRI, and at the end of each trial quantifiable measure; through it our choices have become highly they were told about the realized outcome of their previous choice.

The authors found that when the subjects held a stock, both the nous positive or negative arousals are followed by increased outcome and the cumulative earning had a significant negative neural activity in the NAcc and AI regions, leading to overly risk- effect on the probability that they would subsequently choose seeking or risk-averse behavior, respectively. By presenting highly another stock. In other words, closing a trial with a gain (or a loss) arousing positive pictures (such as erotica), negative pictures induces lower (or higher) demand for risky assets, which translates (such as of rotten food) and neutral pictures before each trial, into a price decline (or increase) in a real stock market, thereby the researchers measured an excessive, insufficient and average creating the familiar mean-reverting pattern. probability of selecting stocks instead of bonds. Although in real situations it wouldn’t be easy to identify such direct signals for the Moreover, the results also indicated that for those previously arousal level, the implied volatility of options and other sentiment holding a stock, activation in the anterior insula (AI) played a indicators might give a clue to the general level of investors’ key role in predicting a lower probability of choosing a stock in emotional state. This pattern supports the well-documented the next trial, while for bondholders activation in the nucleus phenomenon of bull and bear markets happening during low- accumbens (NAcc) was dominant, predicting a higher probability and high-volatility regimes, and stochastic volatility models of picking a stock in the next trial. These two brain regions presenting negative correlation between volatility and residuals. already had been documented as accounting for risk aversion or the anticipation of pain (negative arousal), and the anticipation Neuroimaging can aid the grouping of assets by tracing back of monetary gains (positive arousal), respectively. Given to the source of key characteristics in the human brain. Using confirmation that the human brain is hard-coded to produce a event-related fMRI, Knutson worked with University of Melbourne behavior that eventually results in mean-reverting prices, it is neuroscientist Peter Bossaerts to analyze the part of the brain not surprising that, 30 years after its discovery, mean-reversion involved in pricing and preference tasks. By communicating the remains a significant feature of capital markets. Investors could price of a product but delaying showing the product itself, Knutson exploit this relationship by searching for signals that lead to the and Bossaerts were able to separate the brain functions active activation of these brain regions and then linking these patterns during preference making, price comparison and the subsequent directly to investment decisions. choice of whether to buy the product. Revealing that product preference activated the NAcc and price perception affected the Lobal Domination medial prefrontal cortex (MPFC), the study proved that the two elements of purchasing decisions are controlled by distinct neural Most brain activity related to financial decision making takes place in the circuits. This finding in 2007 highlights the importance of, and frontal lobe. provides an explanation for, a behavioral study on preference reversal that was published by Decision Research psychologists Ventral Striatum (VStr) Sarah Lichtenstein and Paul Slovic more than 35 years before. Parietal Lobe Frontal Lobe The earlier results showed that when facing a choice between Anterior Insula (AI) winning a low amount with high probability and winning a high amount with low probability, most subjects preferred the former. However, when they had to make bids on these two games, the Occipital Lobe Medial Prefrontal Cortex (MPFC) latter was priced higher in most cases, indicating that pricing and preference yield inconsistencies in decision making. As mentioned above, the relevance of these results to algorithmic trading could Nucleus Accumbens lie in clustering: Assets for which demand is driven by preference (NAcc) (for example, mutual funds, where investors choose from Temporal Lobe prepackaged portfolios) should have entirely different dynamics from assets purely driven by a continuous bidding process (individual stocks or options). In another study, Kuhnen, who has degrees in both neuroscience and finance, and Knutson, whose main field of research is the The results of neuroeconomic research can provide an explanation neural basis for emotions, show that emotional states influence for region- or culture-specific patterns in stock trading. As decision making significantly through a similar channel. Exoge- discussed above, risk tolerance and impatience are hard-coded

in our brains. But some of these attributes are different for in 2015 to 180 zettabytes by 2025, according to Framingham, distinct cultures at the neural level. In fMRI experiments Stanford Massachusetts–based research firm IDC. Nevertheless, accessing University psychologists Bokyung Kim and Samuel McClure this trove of data has both advantages and disadvantages. and Korea University psychologist Young Shin Sung analyzed the brain functions of Western (American) and Eastern (Korean) On the one hand, the potential value can be extremely high and cohorts in a simple delay-preference game (choosing between can yield a significant performance edge over competitors. On X today or X+Y in one week). In line with previous results, the the other hand, if the data is not carefully cleaned and filtered, a authors found that activation in the ventral striatum (VStr) region phenomenon called overfitting can result, in which processing significantly changed before decisions involving time discounting. and machine learning techniques can lead to seemingly perfect However, their findings also discovered an important difference pattern recognition that in fact has little predictive value. between Western and Eastern brain functions: Although the VStr was activated in both groups, the brain region showed greater One straightforward way to avoid false patterns is to filter activity for immediate rewards in Western subjects, while in the out input variables that are not relevant to determining the Eastern group greater activity was measured for delayed rewards outcome. Selecting these factors, however, might prove to — providing an explanation for the well-documented higher be a highly difficult task, even for experienced data scientists. discount rate of Western investors. These results highlight the Instead of looking purely at the statistics of the sampled data, importance of regional differences among stock markets and in researchers might also use direct methods that explain the this particular case the yield curve differences of fixed-income processes underlying the outcome. When searching for patterns assets: Higher discount rates mean higher bond yields in different in financial data, neuroeconomics may come in handy because cultures and may offer a basis for carry trade strategies. of its fundamental nature: Given that asset prices are driven by investors, capturing investors’ hard-wired reactions under certain Because of the increasing time and space resolution of circumstances would likely generate valuable information. In brain-imaging techniques, growing access to machines in other words, looking for nonlinear patterns in the input variables neuroscience and a surge in the number of researchers devoted affecting the brain functions involved in financial decision making to neuroeconomics, a wide variety of studies on how humans should yield better out-of-sample performance than simply are hard-wired to make financial decisions is being published scanning a much larger sample space in the hope of finding a every year. Pairing these results with alternative data that can robust pattern. be linked directly to these brain functions may yield a significant contribution to portfolio management. With an ever-growing quantity of data and methods that are expected to improve exponentially, neuroeconomics could shape the future of quantitative finance. By capturing and formalizing The Era of Big Data human behavior in directly measurable ways, neuroeconomics Today it is common knowledge that we live in an era of Big can transform the black box of investors’ decision making into a Data. Those who fail to use this vast quantity of information highly complex machine based on well-defined rules. Quants then to its full potential value will face serious disadvantages. The can apply patterns that are directly connected to the elements of competition related to processing Big Data is particularly strong this decision-making process to try to predict market participants’ in the financial industry, where investors cautiously look for actions and thus asset prices. Although it may seem surprising to profitable opportunities arising from unexplored anomalies in regard human thinking as an analytically tractable model, it is not the capital markets. The data being collected around the world outlandish. After all, as John von Neumann famously put it, “When reflects the demand for and the supply of alternative sources of we talk mathematics, we may be discussing a secondary language information: The volume is expected to grow from 7.9 zettabytes built on the primary language of the nervous system.” ◀

References Werner F.M. De Bondt and Richard H. Thaler. Sarah Lichtenstein and Paul Slovic. “Reversals of Preference “Does the Stock Market Overreact?” Between Bids and Choices in Gambling Decisions.” Journal of Finance 40, no. 3 (July 1985): 793-805. Journal of Experimental Psychology 89, no. 1 (1971): 46-55.

Colin Camerer, George Loewenstein and Drazen Prelec. Brian Knutson and Peter Bossaerts. “Neural Antecedents of “Neuroeconomics: How Neuroscience Can Inform Economics.” Financial Decisions.” Journal of Neuroscience 27, Journal of Economic Literature 43, no. 1 (March 2005): 9-64. no. 31 (August 2007): 8174-77.

M. Keith Chen, Venkat Lakshminarayanan and Laurie R. Santos. Camelia M. Kuhnen and Brian Knutson. “How Basic Are Behavioral Biases? “The Neural Basis of Financial Risk Taking.” Evidence from Capuchin Monkey Trading Behavior.” Neuron 47, no. 5 (September 2005): 763-70. Journal of Political Economy 114, no. 3 (February 2006): 517-37. Camelia M. Kuhnen and Brian Knutson. “The Influence of Affect on Wanjiang Du, Leonard Green and Joel Myerson. “Cross-Cultural Beliefs, Preferences, and Financial Decisions.” Journal of Financial Comparisons of Discounting Delayed and Probabilistic Rewards.” and Quantitative Analysis 46, no. 3 (June 2011): 605-26. Psychological Record 52, no. 4 (September 2002): 479. John von Neumann and Oskar Morgenstern. Theory of Games and Joseph W. Kable and Paul W. Glimcher. “The Neural Correlates of Economic Behavior. Princeton University Press, 2007. Subjective Value During Intertemporal Choice.” Nature Neuroscience 10, no. 12 (December 2007): 1625-33. Jay R. Ritter and Ivo Welch. “A Review of IPO Activity, Pricing, and Allocations.” Journal of Finance 57, Bokyung Kim, Young Shin Sung and Samuel M. McClure. no. 4 (August 2002): 1795-1828. “The Neural Basis of Cultural Differences in Delay Discounting.” Philosophical Transactions of the Royal Society B: Biological Sciences Amos Tversky and Daniel Kahneman. “Loss Aversion in Riskless 367, no. 1,589 (March 2012): 650-56. Choice: A Reference-Dependent Model.” Quarterly Journal of Economics 106, no. 4 (November 1991): 1039-61. Josef Lakonishok and Seymour Smidt. “Are Seasonal Anomalies Real? A Ninety-Year Perspective.” Review of Financial Studies 1, Robin Wigglesworth. “Investors Mine Big Data for Cutting-Edge no. 4 (winter 1988): 403-25. Strategies.” Financial Times (March 30, 2016).

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