Summary: Increased activity in the nucleus accumbens forecasted an increase in stock price within the next day, whereas increased activity in the anterior insular was predictive of whether a stock price would flip or change direction.
Forecasting changes in stock prices may be possible with the help of brain activity in regions associated with how people feel before making investment choices. Scientists could accurately forecast market price changes based on the average brain activity among a group but failed when using only prior stock trends or people’s investment choices, according to new research published in Journal of Neuroscience.
Scientists have used the average brain activity among a group to predict which videos will go viral and which crowdfunding campaigns will receive funding. In a new study, Stallen et al. investigated if this relationship extends to a more complex and dynamic arena: the stock market.
Participants examined real stock price trends from 2015 as they decided if they wanted to buy or sell the displayed stocks. During the task, the researchers used fMRI to measure activity in the nucleus accumbens and anterior insula, areas involved in seeking reward and avoiding risk, respectively.
Using the group’s average brain activity in these regions, the researchers could forecast how a stock would behave.
Increased nucleus accumbens activity forecast when a stock’s price would increase the next day, while increased anterior insula activity forecast when it would flip or change direction.
Prior stock market trends and the participants’ own investing choices could not forecast stock price dynamics.
About this neuroscience research news
Contact: Calli McMurray – SfN
Image: The image is credited to Stallen et al., JNeurosci 2021
Original Research: Closed access.
“Brain Activity Foreshadows Stock Price Dynamics” by Mirre Stallen, Nicholas Borg and Brian Knutson. Journal of Neuroscience
Brain Activity Foreshadows Stock Price Dynamics
Successful investing is challenging, since stock prices are difficult to consistently forecast. Recent neuroimaging evidence suggests, however, that activity in brain regions associated with anticipatory affect may not only predict individual choice, but also forecast aggregate behavior out-of-sample.
Thus, in two experiments, we specifically tested whether anticipatory affective brain activity in healthy humans could forecast aggregate changes in stock prices.
Using Functional Magnetic Resonance Imaging (FMRI), we found in a first experiment (n=34, 6 females; 140 trials per subject) that Nucleus Accumbens (NAcc) activity forecast stock price direction, whereas Anterior Insula (AIns) activity forecast stock price inflections. In a second preregistered replication experiment (n=39, 7 females) that included different subjects and stocks, AIns activity still forecast stock price inflections. Importantly, AIns activity forecast stock price movement even when choice behavior and conventional stock indicators did not (e.g., previous stock price movements), and classifier analysis indicated that forecasts based on brain activity should generalize to other markets.
By demonstrating that AIns activity might serve as a leading indicator of stock price inflections, these findings imply that neural activity associated with anticipatory affect may extend to forecasting aggregate choice in dynamic and competitive environments such as stock markets.
Many try but fail to consistently forecast changes in stock prices. New evidence, however, suggests not only that anticipatory affective brain activity may not only predict individual choice, but also may forecast aggregate choice. Assuming that stock prices index collective choice, we tested whether brain activity sampled during assessment of stock prices could forecast subsequent changes in the prices of those stocks.
In two neuroimaging experiments, a combination of previous stock price movements and brain activity in a region implicated in processing uncertainty and arousal forecast next-day stock price changes – even when behavior did not.
These findings challenge traditional assumptions of market efficiency by implying that neuroimaging data might reveal “hidden information” capable of foreshadowing stock price dynamics.