Social learning is a fundamental mechanism shaping decision-making across numerous social networks, including social trading platforms. In those platforms, investors combine traditional investing with copying the behavior of others. However, the underlying factors that drive mirroring decisions and their impact on performance remain poorly understood. Using high-resolution data on trades and social interactions from a large social trading platform, we uncover a fundamental tension between popularity and performance in shaping imitation behavior. Despite having access to performance data, people overwhelmingly choose whom to mirror based on social popularity, a signal poorly correlated with actual performance. This bias, reinforced by cognitive constraints and slow-changing popularity dynamics, results in widespread underperformance. However, traders who frequently revise their mirroring choices (trading explorers) consistently outperform those who maintain more static connections. Building an accurate model of social trading based on our findings, we show that prioritizing performance over popularity in social signals dramatically improves both individual and collective outcomes in trading platforms. These findings expose the hidden inefficiencies of social learning and suggest design principles for building more effective platforms.
