Knowledge that Transforms

We use close to 80 million daily returns for more than 19,000 CRSP listed firms to establish the best forecasting model for realized idiosyncratic variances. Comparing forecasts from multiple models, we find that the popular martingale model performs worst. Using the root-mean-squared-error (RMSE) to judge model performance, ARMA(1,1) models perform the best for about 46% of the firms in out-of-sample tests. The ARMA(1,1) model delivers an average RMSE that is statistically significantly lower than all alternative models, and also performs well when not the very best. Its forecasts reverse large, unexpected shocks to realized variances. When using this model to revisit the relation between idiosyncratic risk and returns (the IVOL puzzle), we fail to find a significant relation. The IVOL puzzle is closely connected to a very small set of observations where the martingale forecast over-predicts the future realized variance. These extreme observations are correlated with well-known firm characteristics associated with the IVOL puzzle such as poor liquidity as measured by high bid-ask spreads and the “MAX” effect.

Retail investors trade hard-to-value stocks. We document a large and persistent spread in the stock-level intensity of retail trading, even allowing for known biases in the attribution of retail trades. Stocks with a high share of retail-initiated trades exhibit higher shares of intangible capital, longer duration cash flows, and a higher likelihood of being mispriced. Consistent with retail-favored stocks being harder to value, we document that these stocks are less sensitive to earnings news and more sensitive to retail order imbalances. Such segmentation of trading intensity arises in a model where informed investors face a trade-off between the benefits of hiding their trades within noisy retail investor order flow and the costs of producing information about the fundamentals of hard-to-value stocks.

The literature has documented “reaching for yield”—the phenomenon of investing more in risky assets when interest rates drop—among institutional investors. We analyze detailed transaction data from a large brokerage firm to provide direct field evidence that individual investors also exhibit this behavior. Consistent with models of portfolio choice with labor income, reaching for yield is more pronounced among younger and less-wealthy individuals. Consistent with prospect theory, reaching for yield is more pronounced when investors are trading at a loss. Finally, we observe and discuss the phenomenon of “reverse reaching for yield.”

This article does three things: (1) it offers a slightly modernized treatment of the two well-known agency costs of external financing of Jensen and Meckling; (2) it provides a deeper exploration than they offer of the limited liability corporation, and of optimal control allocations when financial contracts are incomplete; and, (3) it assesses the lasting influence or their ideas, their multiple interpretations, as well as misinterpretations.

We study the aggregate consequences of dynamic lending relationships in a model of heterogeneous banks facing financial frictions. We estimate the model’s loan demand system on administrative loan-level data: the market power implied by the estimated strength and persistence of relationships yields a long run reduction in credit of 5.9%. Relationships amplify the negative real effects of credit supply shocks, but mute those of negative credit demand shocks. In a financial crisis which destroys 25% of bank net worth, for example, loan volume drops more than twice as much in our baseline model than in a competitive analog with no relationships, but banks recapitalize faster.

The regulatory framework for financial advisors is fragmented, with multiple state and federal regulators. Prior empirical literature on financial advisors has largely focused on a single subset of financial advisors, but we create a database containing brokers regulated primarily by FINRA, investment advisers regulated by the SEC or state securities regulators, and insurance producers regulated by state insurance regulators. There is significant overlap across the regimes; more than 40% of the advisors in our data are registered with more than one regulator. This overlap has implications for labor allocation and market discipline. For example, of the individuals who exit FINRA’s broker regime, 79% were jointly registered in insurance upon exiting FINRA’s regime. This could be efficient if it reflects bad actors who transition to lower risk work, but our evidence shows that these advisors continue to engage in financial planning after they move to the insurance side, as over 90% maintain licenses to sell annuities. Moreover, those who committed misconduct when regulated by FINRA continue to have heightened levels of misconduct in insurance. Our findings have additional implications for regulatory discipline. In 2018 and 2019, FINRA proposed rules designed to nudge “bad” brokers out of the industry. We show that these proposals caused thousands of high-risk brokers to exit the FINRA broker regime, but that the majority of these individuals did not leave financial services—98% are currently registered with state regulators as insurance producers.

We propose a latent-variables approach to recover extrapolative beliefs from asset prices. We estimate a present-value model of the price–dividend ratio of the market that embeds both return extrapolation and cash-flow extrapolation, alongside discount rates and rational expectations of dividend growth. This approach allows us to measure extrapolation bias without having to rely on survey data, and it inherently guarantees that the researcher focuses on a set of beliefs that matter for price formation. We show that extrapolative beliefs extracted from prices are highly correlated with surveys and that survey-based and price-based extrapolative beliefs share similar predictive properties for future returns, with the former improving upon the latter.

This paper examines the effects of targeted credit rationing by banks on firms likely to generate negative externalities. We exploit an initiative of the U.S. Department of Justice, labeled Operation Choke Point, which compelled banks to limit relationships with firms in controversial industries. Using supervisory loan-level data, we show that, as intended, targeted banks reduced lending and terminated relationships with affected firms. However, most of these firms fully substituted credit through nontargeted banks under similar terms. Overall, we find no significant shifts in the performance and investment of affected firms, suggesting that targeted credit rationing is widely ineffective in promoting change.

Several influential studies show that transformations of implied volatilities calculated from options prices predict stock returns. This predictability is puzzling because market participants readily observe options prices. We find that this predictability is consistent with implied volatilities reflecting stock borrow fees that are known to predict stock returns. We derive a formula relating the option-implied volatility spread to the borrow fee. Motivated by this relation, we show that the return predictability from implied volatility spread and skew decreases by at least two-thirds if high-fee stocks are excluded. The patterns for other predictors computed from option implied volatilities are similar.

Using financial account data linking small businesses to their owner households, we examine how business owners’ consumption responded to changes in business revenues during the COVID-19 crisis. In the first two months following the National Emergency, business revenues declined by 40 percent, largely driven by national factors rather than local infection rates or policies. However, the pass-through of revenue losses to owner consumption was limited: each dollar of revenue loss resulted in only a 1.6-cent decline in consumption. This muted pass-through persisted through 2021, even after the introduction of COVID-19 vaccines. Our findings suggest that federal subsidies and pandemic-induced reductions in spending opportunities explain the limited impact.