How do investors learn as data becomes bigger? Evidence from a FinTech platform

Prior findings suggest that investors learn with experience. We study the complementary channel of learning from data, particularly the effects of making additional predictive signals available to investors. We analyse a panel of systematic traders' investment outcomes, sourced from a FinTech platform that organises trading contests under highly-controlled conditions that allow us to identify learning effects. Investor outcomes improve with experience, and this is also apparent when counterfactually assessing their trading decisions on historical data, suggesting that they make use of historical data to attain their objectives. Importantly, when additional predictive variables are added to the common part of investors' information sets, the individual-level dispersions of investors' performance outcomes narrow, while their relative performance outcomes improve at higher experience levels. To explain why this widening of their common dataset benefits experienced investors, we model an investor as choosing a portfolio by learning from historical data while also taking model uncertainty into account. The robust learner therefore ignores predictive signals with historical predictive contributions below a subjective model uncertainty threshold; we conjecture this threshold varies with experience.