We can build better financial models if we acquire an understanding (and a subtle one at that) of neuroscientific advances in how brains make decisions. Neuroeconomics, or neurofinance as it is also called, examines why we make the financial choices we do by looking at activity in the brain. Recent discoveries about how we calculate risk and reward indicate that our brain may be able to make financial predictions even when we have no knowledge of finance. Understanding why this is will improve the theory of financial decisionmaking.
When confronted with an economic or financial decision, how does our brain function and what makes it react in a particular way? Do we know the real underlying underpinnings of the financial brain? Is it careful? Is it logical? Or is it emotional? We still do not have any definitive answers. In the past few years a new way of looking at decision-making, as it relates to financial markets and risk taking, has evolved. Sometimes called neuroeconomics or neurofinance, it seeks to understand why we make the financial choices we do by looking at activity in different parts of the brain. New discoveries are beginning to call into question the classical economic hypotheses of economic rationality and market efficiency. Our brain it seems is capable of making financial predictions even when we have no knowledge of finance. Understanding why this is could revolutionize financial modeling. This article looks at some of the recent theories and discoveries in this nascent field.
We will focus our attention on the several key areas of the brain where decisions and emotions regarding risk and reward are known to take place. It is sufficient for readers to know that isolating these regions and understanding how they interact is the basis for neurofinance.
Economic science often offers its views on the behavior of economic agents, that is decision makers, but has not come up with a definitive answer on why they act. Behavioral finance has brought to light a large number of cognitive biases or behavioral deviations from the theory of economic rationality. In this respect behavioral finance does not essentially differ from behavioral economics whose broader purpose is to incorporate psychological explanations of seemingly irrational behavior into economic models. The biases reflect systematic cognitive deficiencies in people with regard to memory, reasoning, risk anticipation, etc. But other internal factors (e.g. emotions) or external factors (e.g. mimetism and emotional contagion or herd instinct) may also cause behavioral deviations from the two classical hypotheses of economic rationality and market efficiency.
The founding principles of neuroeconomics are that the behavioral deviations observed when financial decisions are made have a relevant explanation, and that this explanation involves brain mechanisms. Neuroeconomics is based on the theory that explaining these mechanisms will lead to a better understanding of the internal factors of irrationality and will contribute to improvements in understanding financial decision-making. It could, therefore, be applied to designing better and more accurate financial modeling.
Initial work on the neuroeconomics of financial decisions focused on two areas of the brain that seemed to influence these decisions, namely, the nucleus accumbens septi and the insula, both part of the subcortical brain structure. In particular, U.S researchers Camelia Kuhnen and Brian Knutson, in a 2005 study using functional imaging (fMRI), scanned the brains of subjects who had to make financial decisions involving two stocks and one bond. At the start subjects have no information on the quality of these products. Over the course of the experiment the subjects discover that one of the stocks has a favorable yield and the other, an unfavorable one while the bond offers a lower yield than the favorable stock, albeit with a reduced risk level. Subjects have to make 200 financial decisions involving these three possible choices and in the process of doing so gradually discover the nature and consequences of their choices.
Initially, Kuhnen’s and Knutson's observations appeared to confirm only what neuroscientists already knew about the role of subcortical brain structures in the apprehension of gains and losses. The nucleus accumbens is activated in the expectation of a reward, whatever its nature, whereas the insula responds to negative pro-prioceptive states, i.e. nausea, disgust, anxiety or even the anticipation of pain. Kuhnen and Knutson, however, were able to show that the brain reacts differently when it comes to the anticipation of gains and losses. This represents a first step in answering why decisions are adapted or not adapted to the contexts in which they are made.
To be a bit more precise, Kuhnen and Knutson observed that the nucleus accumbens is activated two seconds before a person makes a risky and/or wrong choice (i.e one that results in an unprofitable outcome). Why does understanding this matter? Because it means that we have neurobiological markers that indicate the rightness or anticipation of a risky decision. The brain, so to speak, knows before we become fully aware of it that we are on the verge of yielding to a misleading decisional impulse. Institutions that favor risky behavior, like casinos, and those that seek to discourage such behavior, like insurance companies, will find it in their interest to know and master this neurobiological data.
On the other hand, the insula responded significantly only at the moment preceding a suboptimal and riskless choice (i.e picking the bond). We know that when people look at photographs of accidents, catastrophes and mutilated bodies this activates the insula. So what Kuhnen’s and Knutson’s experiment shows is that the insula's activity is also correlated with a diminished risk-taking behavior. An active insula inhibits rashness. We have then a rather complete neurobiological mapping of the anticipated consequences of our decisions in settings embodying various levels of uncertainty. This data constitutes a first body of work that deserves close scrutiny as the main concepts of financial decision-making may well not be mere mathematical artifacts but rather mirror neurobiological reality.
Recent work carried out by Peter Bossaerts’s team at Ecole Polytechnique Fédérale de Lausanne, Switzerland, has highlighted differences between how the financial industry models risk and how the brain itself perceives financial risk. When the brain calculates risks, it does not only learn from past mistakes (as, in the case of the choice of poorly yielding stocks in the experiment mentioned above), its internal algorithm is also sensitive to rare events that financial models fail to take into account. Apparently unpredictable events, if we use classical financial forecast models, would in fact be predictable if we were able to build adequate algorithms that mimic internal emotional dynamics. The brain consistently resorts or responds to these anticipatory reactions, as we have seen, when evaluating future risky prospects. Bossaerts’ idea is “simply” to extract the implicit model that the brain uses in connecting anticipatory emotions, risk and potential rewards, and import it into the financial realm. Behavioral finance assumes emotions interfere with financial decision-making. Neuroeconomics, on the other hand, says emotions and rationality are not necessarily opposed. To neuroeconomics, emotions are an integral cog of rationality.
Theories of regret, developed in neuroscience and in decision-making theory, are excellent examples of realistic models that could be introduced into the study of financial behavior. The neurobiologist Antonio Damasio studied the manner in which decision-making and emotional processes interacted to generate optimal behavior. Damasio showed somatic signals move up from the depths of the limbic system toward the regions of the prefrontal cortex where the neural foundations of our thinking and decision-making abilities are situated. The brain activity of a subject was also observed at the precise moment when, having made a decision, the subject faced its outcome and simultaneously learnt of the outcome had he picked the alternative choice. In this situation, before feeling any emotion of relief or regret, the subject compared the results. He worked out the difference between what he had and what he could have had. This comparison was at times favorable (thereby bringing contentment and even relief, had he been anxious) and at other times unfavorable (causing regret for not selecting the other choice).
From recent studies, we know that the orbitofrontal cortex (a part of the prefrontal cortex) is the area in the brain that operates as the interface between the evaluation of consequences of our choices (at the same time a calculation and a counterfactual reasoning, i.e what could I have got had I chosen otherwise?) and the emotions we feel when the results of our choices are understood. More accurately, activities in the orbitofrontal cortex are differentiated depending on whether subjects feel regret or disappointment. Regret is the emotion we feel when we are able to compare what we have with what we could have had. This presumes information on the consequences of options that weren't chosen is available. Disappointment is different; it is the emotion felt when we are unhappy with the results obtained without actually knowing what would have happened had we behaved otherwise. Only regret brings about significant activity in the orbitofrontal cortex. We even know the amount of neural activity linked to regret depends on the difference between obtained gains and unattained gains.
In most situations information on consequences of unselected choices is unavailable. In financial markets, however, this information is known. So modeling the activities of the orbitofrontal cortex as a function of regret becomes extremely relevant. We can use our understanding of regret, as registered by our analysis of the activity in the orbitofrontal cortex, to provide modeling resources for behavioral finance.
In the 1950s, the Nobel prize-winning economist Harry Markowitz theorized that the interaction between hope for gain, or expected utility, and variance of risk interacted influenced economic decision-making. The brain was able to analyze the degree of uncertainly in a probability distribution. Markowitz looked at several risk-taking decisions simultaneously and narrowed down his analysis to take into account these two variables – hope and variance. This made the process of revision and adaptation to different scenarios relatively simple. In fact, this turns out to be the process the brain actually uses in repetitive decision-making contexts, when, for example, deciding whether or not to buy or sell a share. It is a learning process.
Since the mid-1990’s, as we have discussed above, neuroscientists have shown how the activities of the subcortical structures and especially basal ganglia of the brain (see illustration) are correlated with the anticipation of rewards and risk. In these regions, the brain encodes these two key dimensions of modern finance. What is to be noted is that this encoding of risk and reward by the brain is almost instantaneous and definitely much faster than if an individual were to explicitly factor these measures. In a manner of speaking, our brain, we believe, is capable of making financial predictions even when we have no knowledge of finance.
The basal ganglia that carry out this encoding of reward and risk are common to several animal species; in particular, one can find these mechanisms in rats and mice. A long-held but naive view maintains these regions in the brain are responsible for irrationality. This is not the case. This part of the brain is responsible for fine-tuning decisions and the decoding and anticipation of information. Any malfunction within these regions, however, can cause irrational behavior such as addiction. This is the case when there is an imbalance of dopamine, for example. But the mistake often made is to attribute the cause of irrationality to a conflict between two large types of regions in the brain, roughly the limbic system and the prefrontal cortex.
A famous study in neuroeconomics carried out by Samuel McClure and David Laibson in 2004 explains anomalies in intertemporal choices as a result of competition among these two broad regions within the brain. This is an accurate but maybe somewhat oversimplified picture of how we yield to poor decisions, and it overshadows how emotions may more often than not contribute to rational and optimal decision-making.
It is reasonable to presume that a set of brain structures, both common to several species and relatively primitive, contribute efficiently to the adaptation of an organism to its environment. These brain structures support behavior that maximizes individual utility, i.e behavior classical economists coined as “rational” in man. It is therefore certainly wrong, or simply too binary in nature to oppose a “rational” prefrontal cortex and “emotional” subcortical structures. These two large brain systems and their interaction have favored the emergence of adaptive and rational behavior in natural environments as well as in modern economic decision-making settings. The study of the regions of the brain at the interface of these two large systems is therefore of great interest to the neuroeconomist, as seen in the case of the orbitofrontal cortex.
In theory, it should be possible using neuroscientific and financial models to come up with a theory of optimal decision-making. But to do this, we need to understand the parts of the brain that process risk and reward. We are beginning to do this.
Financial risk, as we know it, is very specific and is perhaps the most unpredictable, artificial form that risks present in nature. The beginnings of the stock exchange date only from the 15th century and our brains have not had the time to adapt to it. It is therefore more reasonable to try to adapt finance to our brains.
The societal goal of neuroeconomics is to improve the theory of decision-making. If we better understand the reason why we make mistakes and sub-optimal financial choices we can develop tools to improve our decision-making. The brain has gotten used to the processing of incomplete information ultra-fast. The objective is to base artificial intelligence both on an improved theory and a better grasp of the underlying brain mechanisms involved in this kind of decision-making. We cannot expect the brain, over a short period of time, to adapt to something as historically new and complex as financial markets. But understanding brain mechanisms will help make these environments more comprehensible.