Neuroeconomics May Enable a Self-Regulatory Policy for Preventing Asset-Price Bubbles

Saturday, 14 February 2015
Exhibit Hall (San Jose Convention Center)
John L. Haracz, UC Berkeley, Berkeley, CA
Background: Asset-price bubbles challenge the explanatory and predictive power of standard economic theory, so neuroeconomic measures should be assessed for a capacity to improve the predictive power of standard theory. Methods: This objective is achieved by reviewing results from functional magnetic resonance imaging (fMRI) studies of lab asset-price bubbles and herding behavior (i.e., following others' decisions). Results: In subjects exposed to replayed visual displays of lab-market bubbles, activations were found in the medial prefrontal cortex (mPFC), possibly reflecting subjects' attempts to sense peers' intentions (De Martino et al., 2013). Another study showed displays based on historical records of Lehman Brothers stock prices (Ogawa et al., 2014). Exposure to the Lehman Brothers bubble increased functional connectivity between dorsolateral PFC and the inferior parietal lobule, possibly suggesting a future orientation during the bubble. Smith et al. (2014) found that nucleus accumbens (NA) activity, when calculated as a moving average across all subjects, tracked lab bubble-related price changes and predicted crashes. These studies may have limited external validity: fast-growing lab bubbles differ temporally from years-long real bubbles (e.g., the housing- and stock-market bubbles of 2000-2008). Herding may be hypothesized to occur during these prolonged real bubbles, in which case fMRI evidence for the involvement of evolutionarily ancient brain areas (e.g., NA, amygdala, hippocampus) in various forms of herding, including that related to financial decision-making (Burke et al., 2010; Edelson et al., 2011; Zaki et al., 2011), could be informative for predicting bubbles. Crucially, the same choice (e.g., buying a stock) could be generated by herding-related neurocircuitry during bubbles, or by deliberative neocortical circuitry during non-bubble periods. Using functional near-infrared spectroscopy headband technology (Hofmann et al., 2014), it may be possible to identify herding behavior and thus predict bubbles. Bubbles could then be prevented by implementing neuroimaging-based financial-system regulation without government involvement. For example, traders could monitor an open-access aggregated data stream of processed brain activity, collected from consenting traders, for real-time signs of over-heated markets, enabling them to exit these markets and thereby prevent major bubbles voluntarily. Conclusion: Neuroimaging-based financial-system regulation may be useful for distinguishing bubble and non-bubble periods and preventing bubbles.