Professional day traders often talk about being “in the zone.” It’s the kind of language that sounds vague, even mystical, when applied to something as data-driven as high-frequency stock trading. But what if that mental state could be measured, second by second, and linked directly to whether a trade made or lost money?
A team of researchers set out to do exactly that. In a working paper circulated through the National Bureau of Economic Research, they strapped consumer-grade brainwave headsets onto 16 professional traders during real, high-stakes trading sessions and tracked their neural activity around thousands of executed trades. What they found is that brief surges in cognitive engagement in the roughly 30 seconds before a trade are linked to better performance, while a persistently alert baseline throughout the day is not.
A question that laboratories couldn’t answer
Neurofinance, the study of the biological signals that accompany financial decision-making, has traditionally been confined to laboratory settings. Clinical electroencephalogram (EEG) equipment requires gel-based electrode arrays and trained technicians. Functional MRI machines are even more restrictive. Neither is compatible with a working trading floor, so research has largely relied on students making hypothetical choices for modest stakes.
The team behind this project, which includes Liang Chen of East China University of Science and Technology, Tse-Chun Lin of the University of Hong Kong, Fei Wu and Xingjian Zheng of Shanghai Jiao Tong University, and Eric Zou of the University of Michigan’s Ross School of Business, wanted to know whether a new generation of lightweight wearable EEG devices could pick up meaningful signals in a real trading environment. If so, they could ask a question laboratory studies cannot: does moment-to-moment neural activity actually predict real profits and losses?
How the study worked
The researchers partnered with a proprietary trading firm in China specializing in intraday scalping. Traders at the firm execute rapid round-trip trades, often holding positions for only minutes or seconds, and their compensation is tied to short-term execution profits rather than longer-term market moves. Because the firm hedges its base inventory with index futures, individual performance reflects skill at capturing brief price dislocations, not broader market bets.
Each of the 16 traders wore a NeuroSky headset with a single dry electrode resting on the forehead and a reference clip on the earlobe. Unlike clinical setups, the device required no gel and could be donned in seconds. It sampled brainwave activity at one reading per second across the standard EEG frequency bands.
Traders wore the device for one hour in the morning and one hour in the afternoon over five-day stretches, then took a week off, then returned. Two staggered cohorts rotated through the six-week study period between October and December 2025. Altogether, the researchers collected 954,692 second-by-second brainwave observations linked to 9,544 executed trades.
From the raw signals, they built a measure called the Engagement Index, or EI, following a formula originally developed at NASA in the 1990s to monitor pilot attention. The index divides beta wave power (associated with alert, focused thinking) by the sum of alpha and theta wave power (associated with relaxed or drowsy states). Higher values indicate a brain state dominated by active task engagement.
The pattern around each trade
When the researchers aligned EI readings to the exact moment of trade execution, a clear shape emerged. Engagement stayed flat until about a minute before a trade, rose sharply in the final seconds, peaked at the moment the order went through, and then fell back.
The post-trade behavior differed depending on the type of trade. After a trader opened a position, engagement declined gradually. After closing one, it dropped sharply, even overshooting the pre-trade baseline before recovering. The researchers interpret this as consistent with the psychological demands of the two situations: opening a position introduces risk that requires ongoing monitoring, while closing one resolves uncertainty and releases cognitive load.
Several checks supported the idea that EI captured genuine cognitive engagement rather than random noise or a novelty effect. Morning EI was higher than afternoon EI, matching known patterns of cognitive fatigue. Traders with higher scores on a standardized attention test (the d2 Test) also showed higher EI during trading. And trading behavior did not differ significantly between periods when traders wore the headset and periods when they did not, suggesting the equipment itself was not changing how they traded.
Spikes matter, baselines don’t
The core finding involves what predicted profitable trades. Trades that ended in profit were preceded by a larger pre-trade surge in EI than trades that ended in a loss. Interestingly, profitable trades had a slightly lower baseline EI in the two or three minutes leading up to execution, but a much sharper spike in the final 30 seconds.
When the researchers examined overall daily engagement patterns and their relationship to daily performance, the picture flipped. Traders whose average EI was higher across the whole day did not perform better. If anything, higher background engagement was associated with worse outcomes. A high 95th-percentile EI reading over the day was also negatively linked to that day’s win rate.
The researchers interpret this as evidence that raw arousal or constant alertness isn’t what matters. What matters is the timely deployment of cognitive resources at the specific moments when a decision is being made.
Engagement and behavioral biases
The team also looked at whether engagement was linked to two well-documented biases in trading.
The first is the round-number heuristic, the tendency for traders to place orders at prices ending in .00 or .50. In the sample, roughly 9% of trades executed at these “strong round” prices, and those trades earned lower average profits than trades at more precise prices. But the penalty was concentrated in low-engagement states. When traders’ pre-trade EI was above their personal average, trades at round-number prices performed about as well as non-round trades. When engagement was low, the round-number penalty was large.
The second is the disposition effect, the tendency to hold losing positions longer than winning ones. In the sample, losing positions were held about 60.4 seconds on average versus 55.9 seconds for winners. Higher engagement during the holding and exit windows shrank this gap. A one-standard-deviation increase in engagement reduced the additional holding time on losing trades by roughly 4 to 5 seconds.
What the study cannot say
The authors are explicit that their findings are correlational. A larger pre-trade EI spike could reflect the presence of a genuinely better trading opportunity that both grabs the trader’s attention and produces a better outcome. The researchers include extensive controls for tick-level market conditions, price momentum, volatility, and order flow, and they use trader-by-date-hour fixed effects so that comparisons are made within the same trader in the same hour. But they acknowledge that these controls cannot fully eliminate the concern.
They also note that the biases engagement seems to reduce (round-number anchoring, riding losses) are ones rooted in inattention or heuristic shortcuts. Biases rooted in distorted beliefs or stable preferences may not respond the same way, because those reflect how people interpret information rather than whether they process it attentively.
The 16 traders in the sample all work at a single firm engaged in a specific type of high-frequency scalping. Whether the patterns generalize to longer-horizon investors, retail traders, or different market structures is an open question. The single-channel headset also cannot localize activity to particular brain regions, so the findings speak to overall engagement rather than to specific neural circuits.
Even with these limits, the researchers argue that the results shift the conversation away from stable trader “types” and toward short-run fluctuations within the same person. Their evidence suggests that performance differences arise most clearly in narrow windows immediately before decision execution, meaning that identifying and cultivating engagement at key moments may matter as much as selecting for long-run traits.
