Communication can build trust, not just transmit information: I show that asset managers' transparency about risk fosters trust and thereby encourages investors to bear risk. Among quasi-identical S&P 500 index mutual funds, investor letters with a greater share of voluntary risk discussion predict higher next-month flows, especially in high risk aversion states. A randomized experiment shows that transparency raises trust in management even as perceived risk rises, and that most subjects prefer a transparent letter to a baseline one. During the Covid stock market crash, equity funds with more transparent prior records of communication experienced a smaller rise in redemptions.

If learning from data were a purely statistical problem, use of an identical dataset would not differ systematically across investors. Yet it does: in trading contests that restrict investors to a common dataset, experienced participants respond more strongly to informative signals and enjoy higher performance. Holding informativeness fixed, participants also exhibit a familiarity bias toward recognizable signals. Beyond contests, mutual fund managers' portfolio tilts toward predictive signals exhibit similar systematic patterns. A model capturing investors' aversion to adopting signals through penalization unifies these findings; estimation reveals investor penalties vary with experience. Investors' aversion to adopting signals sustains equilibrium return predictability.

We show how investors' local bias shapes both the demand for mutual funds and their supply. We identify investor locations using the in-state tax advantage of single-state municipal funds. Funds offered by families located in their investors' states are more than twice as large as funds offered by out-of-state families and attract significantly higher annual flows. Fees, maturity, credit quality, and returns do not explain the size differential, which is largest in direct-sold share classes, consistent with familiarity rather than an information advantage or local distribution networks. Supply mirrors this demand: families are far more likely to offer funds for their home state and offer more of them there across a broader range of product types. Families that sell directly to investors serve fewer states than those that sell through brokers, suggesting that intermediated distribution partly substitutes for familiarity. Beyond municipal funds, self-directed households at a large discount brokerage are more likely to hold mutual funds offered by nearby families. Finally, the growth rate of fund families from new entrants in a metropolitan area increases with its population and decreases with its number of existing families, consistent with local demand driving the geographic diffusion of the fund industry over the past century.

We show that the positioning of fund families influences price competition between individual mutual funds. We document that investors consider a limited set of families, and model the equilibrium consequences: a competition network arises, and funds have the incentive to internalize this market structure when setting fees. Calibrating our model with prospectus download data, we confirm that fees charged by seemingly homogeneous S&P 500 index funds are predicted by their families' positions in the consideration-based competition network. Counterfactual market structure analyses reveal that fees are one-third the level of monopoly fees but three times higher than competitive benchmarks.

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 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.

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.