Knowledge that Transforms

We use non-Gaussian features in U.S. macroeconomic data to identify aggregate supply and demand shocks while imposing minimal economic assumptions. Macro risks represent the variables that govern the time-varying variance, skewness, and higher-order moments of these two shocks, with ”good” (”bad”) variance associated with positive (negative) skewness. We document that macro risks significantly contribute to the variation of yields and risk premiums for nominal bonds. While overall bond risk premiums are countercyclical, an increase in aggregate demand variance significantly lowers risk premiums. Macro risks also significantly predict future realized bond return variances.

Stocks of firms with cash flows concentrated in the short term (i.e., short duration stocks) pay a large premium over long duration stocks. I empirically demonstrate that this premium (i) is long-lived and strong even among large firms, (ii) subsumes the value and profitability premia, and (iii) exposes investors to variation in expected returns, especially in times when the premium is high. These facts are consistent with an intertemporal model in which the marginal (long-term) investor dislikes expected return declines as they lead to lower expected wealth growth. The model also captures the positive relation between risk premia and bond duration.

We estimate a regime-switching model for the equity term structure with Bayesian methods. Our approach accounts for the data sample being unrepresentative of the population distribution of regimes. We find that (i) the term structure of expected equity dividend strip returns is downward sloping in recessions and upward sloping in expansions, and (ii) the unconditional term structure of expected equity returns is positively sloped. Our estimation shows that the sample unrepresentativeness induces a downward bias in the estimate of the equity term structure slope. We present a regime-switching consumption-based asset-pricing model that matches the empirical findings.

The constant maturity zero-coupon yield curve for the US Treasuries is one of the most studied datasets. We construct a new yield curve using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yields. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gürkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data.

We examine the effects of a 2012 regulatory reform that mandated fee and performance disclosures for the investment options in 401(k) plans. We show that participants became significantly more attentive to expense ratios and short-term performance after the reform. The disclosure effects are stronger among plans with large average contributions per participant and are weaker for plans with many investment options. Additionally, these results are not driven by secular changes in investor behavior or sponsor-initiated changes to the investment menus. Our findings suggest that providing salient fee and performance information can mitigate participants inertia in retirement plans.

The anomaly zoo has caused many to question whether researchers are using the right tests of statistical significance. But even if researchers are using the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors (i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly, the “anomaly base rate”). We propose a way to estimate it by combining two key insights: Empirical Bayes methods capture the implicit process by which researchers form priors about the likelihood that a new variable is a tradable anomaly based on their past experience, and under certain conditions, a one-to-one mapping exists between these prior beliefs and the best-fit tuning parameter in a penalized regression. The anomaly base rate varies substantially over time, and we study trading-strategy performance to verify our estimation results.

We investigate the relation between common institutional ownership of the firms in an industry and product market competition. We find that common ownership is neither robustly positively related with industry profitability or output prices nor is it robustly negatively related with measures of nonprice competition, as would be expected if common ownership reduces competition. This conclusion holds regardless of industry classification choice, common ownership measure, profitability measure, nonprice competition proxy, or model specification. Our point estimates are close to zero with tight bounds, rejecting even modestly sized economic effects. We conclude that antitrust restrictions seeking to limit intra-industry common ownership are not currently warranted.

In 2010, Google unexpectedly withdrew its searching business from China, reducing investors’ ability to find information online. The stock price crash risk for firms searched for more via Google before its withdrawal subsequently increases by 19%, suggesting that Internet searching facilitates investors’ information processing. The sensitivity of stock returns to negative Internet posts also rises by 36%. The increase in crash risk is more pronounced when firms are more likely to hide adverse information and when information intermediaries are less effective in assisting investors’ information processing. In addition, liquidity (price delay) decreases (increases) after Google's withdrawal.

We use a new data set on macroprudential foreign exchange (FX) regulations to evaluate their effectiveness and unintended consequences. Our results support the predictions of a model in which banks and markets lend in different currencies, but only banks can screen firm productivity. Regulations significantly reduce bank FX borrowing, and firms respond by increasing FX debt issuance. Moreover, regulations reduce bank sensitivity to exchange rates but are less effective at reducing the sensitivity of the broader economy. Therefore, FX regulations mitigate bank vulnerability to currency fluctuations and the global financial cycle, but appear to partially shift the snowbanks of vulnerability elsewhere.

Using comprehensive credit bureau data, we document that consumers who borrow from marketplace lending (MPL) platforms have lower credit scores and higher default rates in the long run relative to observably similar applicants for bank loans. The long-run credit scores and default rates of MPL borrowers are especially worse when the MPL platforms provide less information to MPL investors, when MPL borrowers are benchmarked against relationship bank borrowers, and for one-time MPL borrowers as compared to repeat MPL borrowers. Overall, our results suggest that MPL lenders face greater information asymmetries with respect to their borrowers than traditional banks.