Search

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging ‘neuroforecasting’ research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional magnetic resonance imaging (fMRI), we sampled investors’ neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We find that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors’ predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.Significance StatementThe investors’ dream of forecasting the stock market is typically considered to be just that: An unrealistic aspiration. However, we find that forecasting stock performance may in fact not be completely unattainable. Results of our neuroimaging experiment reveal that professional investors fail to accurately predict long-term stock performance. However, while processing complex information pertaining to investment cases, brain activity in a region associated with reward anticipation was increased for stocks that would end up overperforming in the future market. Remarkably, this effect held after controlling for the stock information presented in the investment cases. Our findings add to recent work in ‘neuroforecasting’, demonstrating that market behavior can be forecasted by brain activity of a small sample, here of professional investors.

The processing of rewards and losses are crucial for learning to adapt to an ever changing environment. Dysregulated reward processes are prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield much larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, from now on termed the Brain Reward Signature (BRS), we trained a LASSO-PCR model to predict the signed magnitude of monetary rewards and losses on the Monetary Incentive Delay task (MID; N = 39) and achieved a high significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy for rewards versus losses; N = 12) and on a gambling task from a large sample (73% decoding accuracy for rewards versus losses, N = 1084) from the Human Connectome Project. Lastly, we also provided preliminary evidence for specificity to rewarding outcomes by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). We thus created a BRS that can be used to make specific, generalizable and reproducible predictions about brain responses to rewards and losses.

Many of the economically most costly forms of unethical behavior such as tax evasion, stock manipulations or movie and music piracy relate to the moral domain of (dis)honesty, in which unethical behavior is not targeted at a clearly identifiable victim. While large individual differences in (dis)honesty are evident, the neurocognitive determinants of this heterogeneity remain elusive. We combined connectome-based predictive modelling (CPM) on resting state functional connectivity patterns with a novel experimental task, which measures spontaneous and voluntary cheating inconspicuously, to investigate how these task-independent neural patterns shape our (dis)honest choices. Our analyses revealed that functional connectivity in a network of regions, including the dorsolateral prefrontal cortex and the inferior frontal gyrus, commonly linked to cognitive control processes, but also the medial prefrontal cortex and temporal pole, associated with self-referential thinking, and the caudate nucleus, linked to reward processing, are of central importance in promoting honesty. In a leave-one-out cross-validation analysis, we show that this neural model can reliably and accurately predict how much an unseen participant will cheat on our task. Participants who cheated the most, also scored highest on several impulsivity measures, which highlights the ecological validity of our task. Notably, when comparing neural and self-report measures, the neural measures were found to be significantly more important in predicting cheating. Our findings suggest that a person’s dis(honest) decisions depend on how well the self-referential thinking network is functionally connected to the control and reward networks.

Every day, we are faced with the conflict between the temptation to cheat for financial gains and maintaining a positive image of ourselves as being a ‘good person’. While it has been proposed that cognitive control is needed to mediate this conflict between reward and our moral self-image, the exact role of cognitive control in (dis)honesty remains elusive. Here, we identify this role, by investigating the neural mechanism underlying cheating. We developed a novel task which allows for inconspicuously measuring spontaneous cheating on a trial-by-trial basis in the MRI scanner. We found that activity in the Nucleus Accumbens promotes cheating, particularly for individuals who cheat a lot, while a network consisting of Posterior Cingulate Cortex, Temporoparietal Junction and Medial Prefrontal Cortex promotes honesty, particularly in individuals who are generally honest. Finally, activity in areas associated with Cognitive Control (Anterior Cingulate Cortex and Inferior Frontal Gyrus) helped dishonest participants to be honest, whereas it promoted cheating for honest participants. Thus, our results suggest that cognitive control is not needed to be honest or dishonest per se, but that it depends on an individual’s moral default.