Finance - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 21
  • Item
    No Preview Available
    Emotional Engagement and Trading Performance
    Bossaerts, P ; Fattinger, F ; Rotaru, K ; Xu, K (INFORMS, 2020-04-27)
    Extensive research in neuroscience proves that rational decision-making depends on accurate anticipative emotions. We test this proposition in the context of financial markets. We replicate a multiperiod trading game that reliably generates bubbles, while tracking participants’ heart rate and skin conductance. We find that participants whose heart rate changes in anticipation of trading at inflated prices achieve higher earnings. In contrast, when such trades precede heart rate changes, earnings decrease. Higher (lower) earnings accrue to participants whose skin conductance responds to the market value of stock (cash) holdings. Our findings demonstrate that emotions are integral to sound financial decision-making.
  • Item
    Thumbnail Image
    Exploiting Distributional Temporal Difference Learning to Deal with Tail Risk
    Bossaerts, P ; Huang, S ; Yadav, N (MDPI AG, 2020-12-01)
    In traditional Reinforcement Learning (RL), agents learn to optimize actions in a dynamic context based on recursive estimation of expected values. We show that this form of machine learning fails when rewards (returns) are affected by tail risk, i.e., leptokurtosis. Here, we adapt a recent extension of RL, called distributional RL (disRL), and introduce estimation efficiency, while properly adjusting for differential impact of outliers on the two terms of the RL prediction error in the updating equations. We show that the resulting “efficient distributional RL” (e-disRL) learns much faster, and is robust once it settles on a policy. Our paper also provides a brief, nontechnical overview of machine learning, focusing on RL.
  • Item
    No Preview Available
    Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism
    Markovic, D ; Glaescher, J ; Bossaerts, P ; O'Doherty, J ; Kiebel, SJ ; Einhäuser, W (PUBLIC LIBRARY SCIENCE, 2015-10)
    For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects' behavior and found that attention-like features in the behavioral model are essential for explaining subjects' responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects.
  • Item
    No Preview Available
    The impact of disappointment in decision making: inter-individual differences and electrical neuroimaging
    Tzieropoulos, H ; de Peralta, RG ; Bossaerts, P ; Andino, SLG (FRONTIERS MEDIA SA, 2011-01-06)
    Disappointment, the emotion experienced when faced to reward prediction errors (RPEs), considerably impacts decision making (DM). Individuals tend to modify their behavior in an often unpredictable way just to avoid experiencing negative emotions. Despite its importance, disappointment remains much less studied than regret and its impact on upcoming decisions largely unexplored. Here, we adapted the Trust Game to effectively elicit, quantify, and isolate disappointment by relying on the formal definition provided by Bell's in economics. We evaluated the effects of experienced disappointment and elation on future cooperation and trust as well as the rationality and utility of the different behavioral and neural mechanisms used to cope with disappointment. All participants in our game trusted less and particularly expected less from unknown opponents as a result of disappointing outcomes in the previous trial but not necessarily after elation indicating that behavioral consequences of positive and negative RPEs are not the same. A large variance in the tolerance to disappointment was observed across subjects, with some participants needing only a small disappointment to impulsively bias their subsequent decisions. As revealed by high-density EEG recordings the most tolerant individuals - who thought twice before making a decision and earned more money - relied on different neural generators to contend with neutral and unexpected outcomes. This study thus provides some support to the idea that different neural systems underlie reflexive and reflective decisions within the same individuals as predicted by the dual-system theory of social judgment and DM.
  • Item
    No Preview Available
    Positive temporal dependence of the biological clock implies hyperbolic discounting
    Ray, D ; Bossaerts, P (FRONTIERS RESEARCH FOUNDATION, 2011)
    Temporal preferences of animals and humans often exhibit inconsistencies, whereby an earlier, smaller reward may be preferred when it occurs immediately but not when it is delayed. Such choices reflect hyperbolic discounting of future rewards, rather than the exponential discounting required for temporal consistency. Simultaneously, however, evidence has emerged that suggests that animals and humans have an internal representation of time that often differs from the calendar time used in detection of temporal inconsistencies. Here, we prove that temporal inconsistencies emerge if fixed durations in calendar time are experienced as positively related (positive quadrant dependent). Hence, what are time-consistent choices within the time framework of the decision maker appear as time-inconsistent to an outsider who analyzes choices in calendar time. As the biological clock becomes more variable, the fit of the hyperbolic discounting model improves. A recent alternative explanation for temporal choice inconsistencies builds on persistent under-estimation of the length of distant time intervals. By increasing the expected speed of our stochastic biological clock for time farther into the future, we can emulate this explanation. Ours is therefore an encompassing theoretical framework that predicts context-dependent degrees of intertemporal choice inconsistencies, to the extent that context can generate changes in autocorrelation, variability, and expected speed of the biological clock. Our finding should lead to novel experiments that will clarify the role of time perception in impulsivity, with critical implications for, among others, our understanding of aging, drug abuse, and pathological gambling.
  • Item
    Thumbnail Image
    Neural Mechanisms Behind Identification of Leptokurtic Noise and Adaptive Behavioral Response
    d'Acremont, M ; Bossaerts, P (OXFORD UNIV PRESS INC, 2016-04)
    Large-scale human interaction through, for example, financial markets causes ceaseless random changes in outcome variability, producing frequent and salient outliers that render the outcome distribution more peaked than the Gaussian distribution, and with longer tails. Here, we study how humans cope with this evolutionary novel leptokurtic noise, focusing on the neurobiological mechanisms that allow the brain, 1) to recognize the outliers as noise and 2) to regulate the control necessary for adaptive response. We used functional magnetic resonance imaging, while participants tracked a target whose movements were affected by leptokurtic noise. After initial overreaction and insufficient subsequent correction, participants improved performance significantly. Yet, persistently long reaction times pointed to continued need for vigilance and control. We ran a contrasting treatment where outliers reflected permanent moves of the target, as in traditional mean-shift paradigms. Importantly, outliers were equally frequent and salient. There, control was superior and reaction time was faster. We present a novel reinforcement learning model that fits observed choices better than the Bayes-optimal model. Only anterior insula discriminated between the 2 types of outliers. In both treatments, outliers initially activated an extensive bottom-up attention and belief network, followed by sustained engagement of the fronto-parietal control network.
  • Item
    Thumbnail Image
    Explicit neural signals reflecting reward uncertainty
    Schultz, W ; Preuschoff, K ; Camerer, C ; Hsu, M ; Fiorillo, CD ; Tobler, PN ; Bossaerts, P (ROYAL SOC, 2008-12-12)
    The acknowledged importance of uncertainty in economic decision making has stimulated the search for neural signals that could influence learning and inform decision mechanisms. Current views distinguish two forms of uncertainty, namely risk and ambiguity, depending on whether the probability distributions of outcomes are known or unknown. Behavioural neurophysiological studies on dopamine neurons revealed a risk signal, which covaried with the standard deviation or variance of the magnitude of juice rewards and occurred separately from reward value coding. Human imaging studies identified similarly distinct risk signals for monetary rewards in the striatum and orbitofrontal cortex (OFC), thus fulfilling a requirement for the mean variance approach of economic decision theory. The orbitofrontal risk signal covaried with individual risk attitudes, possibly explaining individual differences in risk perception and risky decision making. Ambiguous gambles with incomplete probabilistic information induced stronger brain signals than risky gambles in OFC and amygdala, suggesting that the brain's reward system signals the partial lack of information. The brain can use the uncertainty signals to assess the uncertainty of rewards, influence learning, modulate the value of uncertain rewards and make appropriate behavioural choices between only partly known options.
  • Item
    Thumbnail Image
    Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning
    Prevost, C ; McNamee, D ; Jessup, RK ; Bossaerts, P ; O'Doherty, JP ; Sporns, O (PUBLIC LIBRARY SCIENCE, 2013-02)
    Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world, and a model-free system in which values are updated without encoding such structure. Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning. In the present study, we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol. After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data, we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts. These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning, as well as providing evidence of a role for the human amygdala in model-based inference.
  • Item
    Thumbnail Image
    Do not bet on the unknown versus try to find out more: estimation uncertainty and "unexpected uncertainty" both modulate exploration
    Payzan-LeNestour, E ; Bossaerts, P (FRONTIERS RESEARCH FOUNDATION, 2012)
    Little is known about how humans solve the exploitation/exploration trade-off. In particular, the evidence for uncertainty-driven exploration is mixed. The current study proposes a novel hypothesis of exploration that helps reconcile prior findings that may seem contradictory at first. According to this hypothesis, uncertainty-driven exploration involves a dilemma between two motives: (i) to speed up learning about the unknown, which may beget novel reward opportunities; (ii) to avoid the unknown because it is potentially dangerous. We provide evidence for our hypothesis using both behavioral and simulated data, and briefly point to recent evidence that the brain differentiates between these two motives.
  • Item
    Thumbnail Image
    The chronometry of risk processing in the human cortex
    Symmonds, M ; Moran, RJ ; Wright, ND ; Bossaerts, P ; Barnes, G ; Dolan, RJ (FRONTIERS MEDIA SA, 2013)
    The neuroscience of human decision-making has focused on localizing brain activity correlating with decision variables and choice, most commonly using functional MRI (fMRI). Poor temporal resolution means these studies are agnostic in relation to how decisions unfold in time. Consequently, here we address the temporal evolution of neural activity related to encoding of risk using magnetoencephalography (MEG), and show modulations of electromagnetic power in posterior parietal and dorsomedial prefrontal cortex (DMPFC) which scale with both variance and skewness in a lottery, detectable within 500 ms following stimulus presentation. Electromagnetic responses in somatosensory cortex following this risk encoding predict subsequent choices. Furthermore, within anterior insula we observed early and late effects of subject-specific risk preferences, suggestive of a role in both risk assessment and risk anticipation during choice. The observation that cortical activity tracks specific and independent components of risk from early time-points in a decision-making task supports the hypothesis that specialized brain circuitry underpins risk perception.