The Pricing of Earnings News (JMP) October 2024

How does earnings news get priced into stock returns? I use a demand system approach to show that this passthrough depends on investor responses to both earnings and prices and that these sensitivities are heterogeneous across investors. A key identification challenge is that earnings news is rapidly incorporated into prices; as a result, it is difficult to distinguish whether investors react to the earnings news itself or the concurrent price change. Using a two-step procedure to isolate price from earnings responses, I identify an average asset-weighted earnings elasticity of 3, i.e. for a stock that beats earnings expectations by 1%, the average investor would increase his number of shares held by 3% if prices were held fixed. These estimates vary across sectors, with most institutional investors more earnings elastic and price inelastic compared to the residual (“household”) sector. The stock-level sensitivities implied by their ownership account for heterogeneous earnings passthroughs, as stocks with higher earnings sensitivity and lower price sensitivity see larger return responses from the same earnings surprise. Extremes of price and earnings elasticities are also closely related to misreaction: a strategy that bets on subsequent reversal (momentum) in sensitive (insensitive) stocks in response to earnings news generates significant outperformance and alpha. These findings suggest that the pricing of earnings news is closely related to the ownership structure of stocks.

Real-Time Discovery and Tracking of Return-Based Anomalies October 2024
(with Stefan Nagel)

We explore the cross-sectional predictability of stock returns with lagged past returns through the lens of a hypothetical Bayesian researcher who begins with an initial prior that is neutral, showing no bias toward momentum, reversal, or other predictable patterns. This researcher considers a wide range of monthly lags as potential return predictors. By applying Gaussian process regression, which flexibly allows expected returns to depend on lagged returns, and using empirical Bayes shrinkage to guard against spurious anomaly discovery due to multiple testing and against mistaking the ex-post visible effects of investor learning as ex-ante expected returns, this researcher would have discovered prominent return-based anomalies—such as momentum and long-term reversal—well before the authors of the published studies analyzed the data. This suggests that these anomalies represented properties of ex-ante expected returns at the time of their academic discovery. However, tracking these anomalies in real-time, with posterior beliefs based on optimally weighted historical data, reveals that the ex-ante expected returns of many prominent anomalies diminish significantly around their publication dates. Based on these findings, there is little justification for viewing momentum, long-term reversal effects, or other return-based anomalies as permanent features of the cross-section of expected stock returns.

Inflation Expectations and Stock Returns July, 2022
(with Manav Chaudhary)

AQR Asset Management Institute Prize
Yiran Fan Memorial Fellowship (Best 3rd Year Paper in Finance)
Do stocks protect against rising inflation expectations? We directly measure investors’ expectations using traded inflation-indexed contracts and show that, post-2000, stocks offer positive returns in response to higher expected inflation: unconditionally, a 10 basis point increase in 10-year breakeven inflation is associated with a 1.1% increase in the value-weighted stock index. Using high-frequency identification around scheduled CPI releases, we show this relationship is likely causal. We provide evidence that the price increase is driven by lowering future expected excess returns rather than changing risk-free rates or cashflows: (1) in the cross-section, return responses are almost completely explained by CAPM beta but not by cashflow or leverage related variables, (2) VAR decompositions of returns as well as mediation regressions that directly control for alternate channels attribute nearly all the changes to expected excess returns. Finally, we show inflation expectations predict future output, suggesting that investors may use information about high future inflation as a signal for economic growth, thereby lowering risk premia.

Bank Relationships and the Pricing of Loans July, 2022
(with Brandon Zborowski)

In frictionless markets, interest rates across various loan products should not differ within borrower, at the same point in time. This paper documents the existence of persistent, loan-level discounts to firms, identified as the difference between spreads on institutional investor-held loans and loans held by banks. Within a loan package – loans offered to the same firm at the same time – institutional term loans command a spread 64 basis points above revolving loans and 61 basis points above term loans. We show the discounts are not driven solely by loan characteristics, bid ask spreads, or upward sloping supply curves. Instead we use our measure to test theories of banking relationships and loan pricing. Discounts are higher when no previous banking relationship exists with the borrowing firm, and public borrowers receive higher initial discounts and have steeper declines in the discount over the course of the banking relationship, relative to private borrowers. We propose and provide evidence for a cross-selling model of the pricing of banking services, where banks price services with the impacts on other lines of business in mind. We show that initial discounts are highest to public firms, consistent with greater competition for firms with high likelihood of other banking service needs, and that having previous banking relationships are associated with a greater likelihood of future hiring for all types of banking services.

The Past is Present: Optimal Monetary Policy at the Effective Lower Bound September, 2020
(with Fernando Duarte)

We use a New Keynesian model with an effective lower bound (ELB) and a general stochastic process for the natural rate to study optimal monetary policy. The central bank has perfect commitment and an interest rate smoothing term in its loss function. Despite the ELB binding occasionally and endogenously, we can derive a closed-form solution for the optimal interest rate: it is the maximum of zero and a weighted average of all past realizations of the output gap. This implies that the optimal interest rate (i) takes a simple form, (ii) is path dependent at all times, (iii) should be pre-emptively lowered when close to the ELB — or kept at zero if at the ELB — if and only if the weighted average of past output gaps is negative, and (iv) behaves very differently from the Taylor rule. We illustrate these insights by solving for key variables in the New Keynesian model using a neural network.