Knowledge that Transforms

We study the impact of disclosure and inattention on the decision to retain fee-based financial advice using a two-tiered natural regulatory experiment. Increased salience in fee disclosure raises the drop rate for advice, implying improved attention — particularly for relatively sophisticated investors. However, a novel auto-drop requirement for inattentive investors generates far more drops, implying limited attention despite salient disclosure — particularly for the unsophisticated. Contrary to studies of commission-based advice, we find that investors benefit from fee-based advice. Benefits are higher for less sophisticated investors, who tend to be detrimentally auto-dropped. Drops triggered by salient disclosure tend to be beneficial.

We show how to measure the welfare effects arising from increased data availability. When lenders have more data on prospective borrower costs, they can charge prices that are more aligned with these costs. This increases total social welfare and transfers surplus across borrower types. We show that under certain assumptions the magnitudes of these welfare changes can be estimated using only quantity and price data. Applying our methodology to bankruptcy flag removal, we find that in a counterfactual world where bankruptcy flags are never removed from credit reports, previously-bankrupt borrowers’ surplus decreases substantially, whereas efficiency increases only modestly.

Errors in survey expectations display waves of pessimism and optimism. This paper develops a novel theoretical framework of time-varying beliefs capturing this fact. In our model, dynamic beliefs arise endogenously due to agents’ attitude towards alternative models. Decision-maker’s distorted beliefs generate countercyclical risk aversion, procyclical portfolio weights, and countercyclical equilibrium asset returns. A calibrated version of our model is shown to jointly match salient features in survey data and equity markets.

We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns , generalizing high-dimensional sorting with economic guidance and interpretability. Under the mean–variance efficient framework, P-Trees construct test assets that significantly advance the efficient frontier compared to commonly used test assets, with alphas unexplained by benchmark pricing models. P-Tree tangency portfolios also constitute traded factors, recovering the pricing kernel and outperforming popular observable and latent factor models for investments and cross-sectional pricing. Finally, P-Trees capture the complexity of asset returns with sparsity, achieving out-of-sample Sharpe ratios close to those attained only by over-parameterized large models.

Our paper analyzes whether a planner should design a taxonomy for sustainable investment products when conventional tools for environmental regulation can also be used to address externalities arising from firm production. We first show that the private market provision of ESG funds marketed to retail investors involves greenwashing , so that a mandatory taxonomy is necessary to generate real effects of sustainable finance . However, the introduction of such a taxonomy can only improve welfare, on top of optimally chosen environmental regulation, if financial frictions constrain socially valuable economic activity. Otherwise, environmental policy alone is sufficient to optimally address externalities.

This paper analyzes the reputational effects of forced CEO turnovers on outside directors. We find that directors interlocked to a forced CEO turnover experience large and persistent increases in withheld votes at subsequent re-elections relative to non-turnover-interlocked directors. Directors are not penalized for an involvement in a turnover per se but for forced CEO turnovers that are related to governance failures by the board. Our results challenge the widespread view that forcing out a CEO can generally be understood as a sign of a well-functioning corporate governance.

We model how investor learning about monetary-policy transmission impacts asset prices. In an asset-pricing model, investors learn from realized inflation surprises how effectively monetary policy steers future inflation. Downward revisions in perceived effectiveness raise expected inflation persistence, increasing return volatility and risk premia. These effects intensify when policy deviates significantly from neutral or monetary-transmission uncertainty is high. We estimate the model using U.S. macro and policy data from 1954 to 2023. The resulting dynamics align with observed patterns in equity returns and volatility. Empirical tests support the model’s core prediction: investor learning turns central-bank credibility into a priced risk factor.

Short sellers are widely known to be informed, which would typically suggest that they demand liquidity. We obtain comprehensive transaction-level data to decompose daily short volume into liquidity-demanding and liquidity-supplying components. Contrary to conventional wisdom, we show that the most informed short sellers are actually liquidity suppliers, not liquidity demanders. They are particularly informative about future returns on news days and trade on prominent cross-sectional return anomalies. Our analysis suggests that market making and opportunistic risk-bearing are unlikely to explain these findings. Instead, our results align with recent market microstructure theory, pointing to strategic liquidity provision by informed traders.

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in credit markets. The empirical setting exploits the outbreak of the Wells Fargo scandal as a negative shock to borrowers’ trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political affiliation to proxy for the magnitude of trust erosion in banks in a triple-differences specification, I find that, conditional on the same exposure to the scandal, a county experiencing a greater erosion of trust has a larger increase in FinTech share relative to a county experiencing less of an erosion of trust. Estimating treatment effect heterogeneity using generic machine learning inference suggests that borrowers with the greatest decrease in trust in banks and the greatest increase in FinTech adoption have similar characteristics.

The correlation between uncertainty shocks, as measured by changes in the VIX, and changes in break-even inflation rates declined and turned negative after the Great Recession. This estimated time-varying correlation is shown to be consistent with the predictions of a standard New Keynesian model with a lower bound on interest rates and a trend decline in the natural rate of interest. In one equilibrium of the model, higher uncertainty raises the probability of large shocks that leave the central bank constrained by the lower bound and unable to offset negative shocks. Resulting inflation shortfalls lower average inflation rates.