Finance - Research Publications

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    Resource allocation, computational complexity, and market design
    Bossaerts, P ; Bowman, E ; Fattinger, F ; Huang, H ; Lee, M ; Murawski, C ; Suthakar, A ; Tang, S ; Yadav, N (Elsevier, 2024-06)
    With three experiments, we study the design of financial markets to help spread knowledge about solutions to the 0-1 Knapsack Problem (KP), a combinatorial resource allocation problem. To solve the KP, substantial cognitive effort is required; random sampling is ineffective and humans rarely resort to it. The theory of computational complexity motivates our experiment designs. Complete markets generate noisy prices and knowledge spreads poorly. Instead, one carefully chosen security per problem instance causes accurate pricing and effective knowledge dissemination. This contrasts with information aggregation experiments. There, values depend on solutions to probabilistic problems, which can be solved by random drawing.
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    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.
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    Task-independent metrics of computational hardness predict human cognitive performance
    Franco, JP ; Doroc, K ; Yadav, N ; Bossaerts, P ; Murawski, C (NATURE PORTFOLIO, 2022-07-28)
    The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of cognitive tasks using a set of task-independent metrics related to the computational resource requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that they predict both time spent on a task as well as accuracy in three canonical cognitive tasks. Our findings demonstrate that performance in cognitive tasks can be predicted based on generic metrics of their inherent computational hardness.
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    Epilepsy and Ecstatic Experiences: The Role of the Insula
    Picard, F ; Bossaerts, P ; Bartolomei, F (MDPI, 2021-11)
    Ecstatic epilepsy is a rare form of focal epilepsy in which the aura (beginning of the seizures) consists of a blissful state of mental clarity/feeling of certainty. Such a state has also been described as a "religious" or mystical experience. While this form of epilepsy has long been recognized as a temporal lobe epilepsy, we have accumulated evidence converging toward the location of the symptomatogenic zone in the dorsal anterior insula during the 10 last years. The neurocognitive hypothesis for the genesis of a mental clarity is the suppression of the interoceptive prediction errors and of the unexpected surprise associated with any incoming internal or external signal, usually processed by the dorsal anterior insula. This mimics a perfect prediction of the world and induces a feeling of certainty. The ecstatic epilepsy is thus an amazing model for the role of anterior insula in uncertainty and surprise.
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    Transferring cognitive talent across domains to reduce the disposition effect in investment
    Rotaru, K ; Kalev, PS ; Yadav, N ; Bossaerts, P (NATURE PORTFOLIO, 2021-11-29)
    We consider Theory of Mind (ToM), the ability to correctly predict the intentions of others. To an important degree, good ToM function requires abstraction from one's own particular circumstances. Here, we posit that such abstraction can be transferred successfully to other, non-social contexts. We consider the disposition effect, which is a pervasive cognitive bias whereby investors, including professionals, improperly take their personal trading history into account when deciding on investments. We design an intervention policy whereby we attempt to transfer good ToM function, subconsciously, to personal investment decisions. In a within-subject repeated-intervention laboratory experiment, we record how the disposition effect is reduced by a very significant 85%, but only for those with high scores on the social-cognitive dimension of ToM function. No such transfer is observed in subjects who score well only on the social-perceptual dimension of ToM function. Our findings open up a promising way to exploit cognitive talent in one domain in order to alleviate cognitive deficiencies elsewhere.
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    How Neurobiology Elucidates the Role of Emotions in Financial Decision-Making
    Bossaerts, P (FRONTIERS MEDIA SA, 2021-07-19)
    Over the last 15 years, a revolution has been taking place in neuroscience, whereby models and methods of economics have led to deeper insights into the neurobiological foundations of human decision-making. These have revealed a number of widespread mis-conceptions, among others, about the role of emotions. Furthermore, the findings suggest that a purely behavior-based approach to studying decisions may miss crucial features of human choice long appreciated in biology, such as Pavlovian approach. The findings could help economists formalize elusive concepts such as intuition, as I show here for financial "trading intuition."
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    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.
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    Separating Probability and Reversal Learning in a Novel Probabilistic Reversal Learning Task for Mice
    Metha, JA ; Brian, ML ; Oberrauch, S ; Barnes, SA ; Featherby, TJ ; Bossaerts, P ; Murawski, C ; Hoyer, D ; Jacobson, LH (Frontiers Media SA, 2020-01-09)
    The exploration/exploitation tradeoff – pursuing a known reward vs. sampling from lesser known options in the hope of finding a better payoff – is a fundamental aspect of learning and decision making. In humans, this has been studied using multi-armed bandit tasks. The same processes have also been studied using simplified probabilistic reversal learning (PRL) tasks with binary choices. Our investigations suggest that protocols previously used to explore PRL in mice may prove beyond their cognitive capacities, with animals performing at a no-better-than-chance level. We sought a novel probabilistic learning task to improve behavioral responding in mice, whilst allowing the investigation of the exploration/exploitation tradeoff in decision making. To achieve this, we developed a two-lever operant chamber task with levers corresponding to different probabilities (high/low) of receiving a saccharin reward, reversing the reward contingencies associated with levers once animals reached a threshold of 80% responding at the high rewarding lever. We found that, unlike in existing PRL tasks, mice are able to learn and behave near optimally with 80% high/20% low reward probabilities. Altering the reward contingencies towards equality showed that some mice displayed preference for the high rewarding lever with probabilities as close as 60% high/40% low. Additionally, we show that animal choice behavior can be effectively modelled using reinforcement learning (RL) models incorporating learning rates for positive and negative prediction error, a perseveration parameter, and a noise parameter. This new decision task, coupled with RL analyses, advances access to investigate the neuroscience of the exploration/exploitation tradeoff in decision making.