Demystifying Rational Financial Decision-Making: Insights from Neurofinance
Is the human brain wired for wealth? The setting is the high-velocity financial environment. Undoubtedly, the development of sophisticated derivative instruments has improved the allocation of risk across economies, highlighting the nexus between banking and finance and economic development. But bouts of irrational exuberance raise concerns about the unprecedented pace of financial innovations. The recent past has witnessed the rapid growth of algorithmic and automated trading as competitive strategies to capture gains, for example, from zero-latency trades. At the same time, financial headlines have been set ablaze by behavioral treatises related to our evolutionary hardwiring for such emotions as greed and fear; seemingly, we are not predisposed to make rational financial decisions. This research takes a step back and explores to what extent the human brain is well adapted to assess risk and reward in financial markets. The research implemented three novel behavioral experiments, one of which used high resolution electrical neuroimaging and is organized along two distinct themes that relate to specific features of financial trading. The first theme concerns the speed of financial decisions. Are decisions made under short time constraint more likely to be biased than under less time pressure? How fast does the brain process financial information? The second theme is (financial) volatility. To what extent are individuals adept at making financial forecasts? The dearth of knowledge on these topics prompted the inductive and transversal bent of this research. Several new insights emerged that challenge the behavioral views of financial decision-making. First, fast decisions as observed on the trading floor is well-captured by moment-based theory, a workhorse of Classical Finance. This is in marked contrast to the generally accepted view that fast decisions impose a bound on rationality; rational decisions are assumed to take time-to-build. Surprisingly, biases loom larger with longer decision time. Second, analysis of electrical brain signals indicates that the brain is very fast at extracting complex monetary reward features from the environment. The behavioral and electrophysiological findings highlight the need to better calibrate the speed of decisions. Third, decision-making (here, learning and forecasting) may go awry when individuals face financial volatility; but not always. The overall findings lead to the proposition that the border between rational and irrational financial decisions is much more razor-thin than opined by behaviorists. The research paints a complex picture about how emotions affect decisions. High emotional quotient and long investment time horizon respectively distinguishes professional traders and long-term investors from novice traders and investors chasing short-term gains; the latter two groups are more likely to react emotionally. Real experts in high-velocity environments are rare and forecasts are bound to be imperfect; algorithmic and automated trades are important aids to diverse players in financial markets. Last but not least, extrapolating from the findings, regulatory policy should concern the transparency of financial products and technology-enabled trades and preferential access to trading platforms. Financial institutions, on the other hand, ought to review their business models paying particular attention to their reward system.
Keywords: Financial Decision-Making ; Risk ; Time Pressure ; Volatility ; Electrical Neuroimaging ; PARAFAC ; Time-to-Build ; Mean-Variance Theory ; Prospect Theory ; Reinforcement Learning ; Least Squares Learning ; Rationality ; Behavioral Biases ; Regulation ; Prise de Décisions Financières ; Risque ; Contraintes de Temps ; Volatilité ; Neuroimagerie électrique ; PARAFAC ; Théorie 'Time-to-Build' ; Théorie Moyenne-Variance ; Théorie des Perspectives ; Apprentissage par Renforcement ; Apprentissage par la Méthode des Moindres Carrés ; Rationalité ; Biais Comportementaux ; RégulationThèse École polytechnique fédérale de Lausanne EPFL, n° 5125 (2011)
Programme doctoral Management de la technologie
Collège du management de la technologie
Institut de management de la technologie et entrepreneuriat
Laboratoire de Prise de décisions dans l'incertitude
Record created on 2011-06-16, modified on 2016-08-09