We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.

I show that communication by asset managers can foster trust and encourage their investor clients to bear greater risk. Specifically, voluntary transparency about risk in discretionary investor letters leads to higher fund flows. To establish causality, I focus on index funds and exploit the presence of corner bunching using a control function approach. Various channels including learning, shrouding, and marketing cannot explain this result. Instead, the evidence supports a trust-building mechanism in which voluntary transparency about risk reduces investors' effective risk aversion, consistent with the "money doctors" theory proposed by Gennaioli, Shleifer, and Vishny (2015).

I examine how investors utilize data, exploiting a setting in which investors design machine-driven trading strategies under controlled yet realistic conditions. Investors disagree considerably in how they interpret identical information, leading to widely dispersed trading strategies and performance outcomes. Inexperienced investors underweight variables with predictive power for returns, and instead exhibit a bias towards variables with which they are more familiar. With experience, investors learn to overcome their bias, and benefit substantially from additional data availability. Investors' familiarity bias leads them to mis-specify their models of the world, and is encoded by the machine traders they design.

We develop an empirical model of how households form beliefs about stock market returns, utilizing advances in machine learning tools. Our approach estimates interpretable experience weights that vary across households while capturing a shared expectation formation process. These estimated experience effects explain close to one-fifth of the variation in subjective return expectations extracted from survey microdata. Interpreting the estimated weights reveals four stylized facts that offer new empirical foundations for theories of expectation formation. Most notably, we uncover a role for expected future states to influence the retrieval of past experiences, and illuminate households' perceived links between asset prices and the business cycle.

We examine how decision-makers' (DMs') ambiguity attitudes shape trust for two different sources of financial forecasting: human or machine learning (ML). In an incentivized laboratory experiment, we measure subjects' ambiguity attitudes and optimism regarding forecast accuracy for both sources. Our results reveal that DMs are similarly ambiguity-seeking and ambiguity-generated insensitive ("a-insensitive"; i.e., they insufficiently discriminate between changes in the likelihood of prediction accuracy), regardless of the analyst type. DMs hold more optimistic beliefs about the accuracy of ML analysts, which predicts higher trust in ML analysts over human analysts. However, DMs who are more a-insensitive are less likely to incorporate their beliefs into their trust. DMs' a-insensitivity increases with financial literacy, suggesting that financially literate DMs perceive greater ambiguity in prediction accuracy. Our findings demonstrate that a-insensitivity acts as a cognitive barrier between beliefs and trust.

We investigate whether competition between the fund companies that offer mutual funds constrains individual fund fees. We document that a substantial fraction of individual fund fee variation is explained by company-wide components. Moreover, we show using SEC prospectus download data that company-level attributes influence investors' consideration of companies. We connect these facts with a model of fee competition between co-considered fund companies, characterising the competitive landscape and associated equilibrium fees. Calibrating the model, we derive a testable prediction for competitively constrained fees. The prediction successfully explains cross-sectional variation in company-level average fees, identifying the influence of company competition on fees.