The authors use electroencephalographic (EEG) technology to examine brain activity during the investment decision-making process in two study groups. In one study group, individuals trade first under low-volatility, steady-gain conditions, whereas in the second study group, individuals trade first under high-volatility, trendless conditions. In the first step, the two groups use different brain circuits while making trade decisions. When the groups swap market conditions, they continue to rely on different brain circuits to deal with the new market. The implication is that learning patterns may influence brain activity and thus future trading behavior. The authors recommend that regulators and practitioners consider how recent market conditions influence investors’ reasoning process.

According to the authors’ findings, our brains rely on different circuits based on trading decision type and prior market conditions. If traders indeed learn patterns that influence their future trading decisions, the effects of such learning could be managed before they cause harm. The authors suggest that regulators and practitioners learn how brain activity might affect trading decisions so that they can adapt their oversight and guidance accordingly.

The larger value of this study, given the small amount of research covering brain activity during financial decision making, is to spur the academic community on to more detailed and comprehensive research. Because most investment professionals probably lack a deep background in neuroscience, appropriately cross-trained individuals could help bridge this gap to relate EEG technology and its underlying theory to real-world financial situations, helping decision makers apply their findings responsibly and correctly. And realizing the importance of this branch of inquiry, financial professionals and firms could grant the academic community greater access in order to enhance such studies’ quality and applicability.

The authors select 20 male and 20 female undergraduate business students from a Portuguese university. They assign them to two groups (G1 and G2) that then trade alternately in two markets (M1 and M2). M1 displays steadily increasing prices and low volatility, whereas M2 shows randomly changing prices and high volatility. G1 makes 50 stock trading decisions in M1 followed by 50 decisions in M2. G2 makes 50 stock trading decisions in M2 followed by 50 decisions in M1. Electrodes are attached to the subjects and used to measure brain activity before, during, and after each trade. The data are converted into brain activity maps and then compared across groups and trading scenarios.

The study is designed to determine whether investors use different brain circuits to buy, sell, or hold stocks as well as whether these differ across market conditions (e.g., trending or nontrending). G1 participants learn a pattern of trending markets in M1 before going into M2, whereas the G2 participants do not learn any such pattern before going from M2 to M1. The authors hypothesize that different brain circuits are involved with anticipation (leading to “rule-based” reasoning) than with simply reacting to current circumstances (“instance-based” reasoning).

The authors emphasize that studies blending neuroscience and finance are few. We should also keep in mind the preliminary nature of this research and refrain from forming too many conclusions too quickly. Notably, the study involves inexperienced traders, had no real money at stake, and relied on making 100 stock trading decisions over a very short time frame. The results, therefore, may apply more to those who trade actively throughout the day and less to long-term investors who make only occasional changes. If possible, the study should be replicated using real professionals making real-money trades over a longer period.