The research Kyra is built on

Loss aversion → revenge trade detection

FindingThe foundational paper of behavioral economics. Kahneman and Tversky demonstrated that losses loom larger than equivalent gains — later work quantified the ratio at roughly two to one: the same dollar amount feels significantly worse to lose than it does good to win. The finding earned Kahneman the 2002 Nobel Prize in Economics.

In tradingLoss aversion is the engine of the revenge trade. A trader who just lost $500 feels the loss more acutely than they would feel a $500 win, and the motivation to "get it back" is what produces the revenge entry: typically larger, less planned, and worse-executed than the trade that produced the original loss.

Guide: How to stop revenge trading →

Disposition effect → premature-exit detection

FindingShefrin and Statman applied prospect theory to investor behavior and identified the disposition effect: investors systematically sell winning positions too early (to lock in the gain) and hold losing positions too long (to avoid realizing the loss). The pattern has been replicated across decades and asset classes.

In tradingThe disposition effect predicts that a trader with a discretionary exit will tend to take profits before their plan would, and tend to widen stops or let stops fail when the trade goes against them. Both errors are P&L-eroding even when the entry was correct.

Overconfidence after wins → oversizing detection

FindingBarber and Odean analyzed trading records from over 35,000 retail brokerage accounts and showed that traders who exhibited higher overconfidence (proxied by gender in this study, but the result generalizes) traded more frequently and earned lower returns. The mechanism: overconfidence leads to oversized positions and excessive trading frequency, which compound into worse risk-adjusted returns.

In tradingA win sequence reliably elevates a trader's confidence in their next setup. The next position tends to be larger than the trader's risk discipline would normally produce: the "I'm hot, this is working" sizing creep. Over time, the oversized trades compound into outsized losses that erase the prior wins.

Self-awareness and trading performance → the entire Kyra thesis

FindingLo, Repin, and Steenbarger combined physiological measurement (skin conductance, heart rate) with trading-outcome data from day traders and found that traders with more intense emotional reactions to gains and losses had significantly lower P&L. Crucially, the study did not advocate suppressing emotion; it argued for awareness and structured handling of emotional states as they arose.

In tradingThis is the closest published research to Kyra's product thesis. The paper makes the case that measuring emotion alongside outcome produces actionable insight; Kyra is the operational implementation of that case for retail traders.

Guide: How emotions affect trading performance →

Cortisol and risk-taking → after-loss caution

FindingCoates and Herbert measured salivary cortisol and testosterone in male professional traders over an 8-day window and found that cortisol rose with market volatility and uncertainty; the authors argued sustained elevation would bias traders toward risk aversion — a prediction later work bore out. The neurochemical signature is the same one observed in stress responses generally: cortisol elevation, narrowing of focus, conservative-decision bias.

In tradingA trader who has just absorbed a significant loss is, neurochemically, in a state that biases against further risk. This is sometimes useful (it prevents the immediate revenge trade) and sometimes harmful (it makes the trader pass on the next setup that would have worked). The state is real; the question is how to handle it deliberately.

Guide: How to stop revenge trading →

Time-of-day and decision quality → the afternoon-loss pattern

FindingWhile not a trading study, this paper documented that judicial rulings followed a strong time-of-day pattern: favorable rulings dropped from ~65% at the start of a session to nearly 0% just before a break, then recovered after the break. The proposed mechanism, decision fatigue, is still debated — later critiques point to how the cases were ordered — which is why Kyra treats time-of-day patterns as something to examine in your own data, not as settled law.

In tradingDecision quality is not constant throughout a trading day. A trader who is sharp at 9:31am may be running on fumes at 2:31pm without consciously noticing. Time-of-day P&L patterns are often the most surprising patterns Kyra surfaces because traders rarely track them manually.

Sample size and inference → why we wait

FindingThe methodological backbone of why Kyra has tier labels. Schönbrodt and Perugini quantified how unstable correlation estimates are at small sample sizes (n < 250) and how the instability decays as n grows. Their finding generalizes to any rate estimate: small samples produce wide confidence intervals, and acting on small-sample point estimates as if they were precise leads to systematic overinterpretation.

Guide: How many trades before patterns emerge →