Human-Centered Financial Signal Analysis Based on Visual Patterns in Stock Charts

The study adopted a human-centered perspective to research the financial markets, focusing on identifying variations in eye movement patterns between professional and non-professional traders as they analyze a series of stock charts. Eye movement data was selected as the analysis target based on the hypothesis that it represents a behavioral phenotype indicative of stock analysts' cognitive processes during market analysis. Disparities were identified by conducting variance analysis and the Wilcoxon signed-rank test on statistical metrics derived from eye fixations and saccades. Psychological and behavioral economic interpretations were provided to understand the underlying reasons for these observed patterns. To showcase the practical application potential of the human-centered perspective, eye movement data and human visual characteristics were used to construct visual saliency prediction models of professional stock analysts. Leveraging this human-centered model, we developed two practical application demonstrations specifically designed to support and instruct novice traders. Based on the above demonstrations, a training program was designed that demonstrates how, with ongoing training, the non-professional traders' ability to observe stock charts improves progressively.