Introduction

Trader decision quality depends on attention: traders focused on relevant information make better decisions than those distracted. Eye-tracking studies reveal what information traders actually attend to versus what they should attend to. Machine learning correlates attention patterns with trading outcomes, identifying attention mistakes and informing trader training and interface design.

Eye-Tracking Methodology

Equip traders with eye-tracking hardware. Record eye movements during trading sessions: which screens/charts attract attention, how long traders look at each area, etc. Analyze gaze patterns: do traders look at relevant fundamentals (earnings, cash flow) or just price momentum? Do traders ignore risk indicators (volatility, correlations)?

Attention-Performance Correlation

Correlate attention patterns with trading outcomes (profits/losses). Traders whose eye gaze tracks fundamental indicators show better risk-adjusted returns than those focused purely on price momentum. Machine learning builds predictive models of attention quality.

Conclusion

Eye-tracking studies reveal attention biases affecting trading decisions, informing interventions (training, interface redesign) to improve performance.