Publications

1. Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models, with Christian Julliard and Jiantao Huang, Journal of Finance (2023), vol. 78(1)
BayesianFactorZoo R package on CRAN

3. Forest through the Trees: Building Cross-Sections of Stock Returns, with Markus Pelger and Jason Zhu, forthcoming in Journal of Finance
[Online Appendix]
Best Paper in Asset Pricing Award at SFS Cavalcade, 2020

4. Missing Financial Data, with Sven Lerner, Martin Lettau, and Markus Pelger, forthcoming in Review of Financial Studies
Crowell Memorial First Prize 2022
Best Paper IQAM Research Award 2022
ICPM Research Award 2022

5. Consumption in Asset Returns, with Christian Julliard and Jiantao Huang, forthcoming in Journal of Finance
[subsumes and extends “Consumption Risk of Stocks and Bonds”]
Best Paper in Asset Pricing Award at Midwest Finance Association, 2016

Working papers

Strategic Arbitrage in Segmented Markets, with Anna Pavlova and Taisiya Sikorskaya, R&R at Journal of Financial Economics
(subsumes and extends “Profiting from Investor Mistakes: Evidence from Suboptimal Option Exercise”)
Best Paper Award at Colorado Finance Summit 2023

We propose a model in which arbitrageurs act strategically in markets with entry costs. In a repeated game, arbitrageurs choose to specialize in some markets, which leads to the highest combined profits. We present evidence consistent with our theory from the options market, in which suboptimally unexercised options create arbitrage opportunities for intermediaries. Using transaction-level data, we identify the corresponding arbitrage trades. Consistent with the model, only 57% of these opportunities attract entry by arbitrageurs. Of those that do, 50% attract only one arbitrageur. Finally, our paper details how market participants circumvent a regulation devised to curtail this arbitrage strategy.

(Almost) 200 Years of News-Based Economic Sentiment, with Jules H. van Binsbergen, Mayukh Mukhopadhyay, and Varun Sharma, R&R at Journal of Financial Economics
Media mention: Financial Times

Using the full text of 200 million pages of 13,000 US local newspapers and state-of-the-art machine learning methods, we construct a novel 170-year-long time series measure of economic sentiment at the country and state level, which expands the existing measures in both the time series (by over a century) and the cross section. We show that our measure predicts economic fundamentals such as GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly-used predictors. Our measure is distinct from the information in expert forecasts, and leads its consensus value. We use the text to isolate information about current and future events and show that it is the latter that drives our predictability results.

Asset-Pricing Factors with Economic Targets, with Victor DeMiguel, Sicong Li, and Markus Pelger
Best Paper at the 2023 annual meeting of the Society of Financial Econometrics (SOFIE) (Bates-White Prize)

We propose a method to estimate latent asset-pricing factors that incorporates economically motivated targets for both cross-sectional and time-series properties of the factors. Cross-sectional targets may capture the shape of loadings (monotonicity of expected returns across characteristic-sorted portfolios) or the pricing span of exogenous state variables (macroeconomic innovations or intermediary-based risk factors). Time-series targets may capture overall expected returns or mispricing relative to a benchmark reduced-form model. Using a large-scale set of assets, we show that these targets nudge risk factors to better span the pricing kernel, leading to substantially higher Sharpe ratios and lower pricing errors than conventional approaches.

Macro Strikes Back: Term Structure of Risk Premia and Market Segmentation, with Jiantao Huang and Christian Julliard

We develop a unified framework to study the term structure of risk premia of nontradable factors. Our method delivers level and time variation of risk premia, uncovers their propagation mechanism, is robust to misspecification and weak identification, and allows for segmented markets. Most macroeconomic factors are weakly identified at quarterly frequency, but have increasing (unconditional) term structures with large risk premia at business cycle horizons. Moreover, the slopes of their term structures are strongly procyclical. Most macroeconomic and intermediary-based factors command similar risk premia in equity and corporate bond markets, while we find strong evidence of segmentation for other factors.

Spurious Factors in Linear Asset Pricing Models, R&R at Review of Financial Studies
(a new version is coming soon)
Best Doctoral Student Conference Paper Award at European Finance Association, 2015.

When a risk factor has small covariance with asset returns, risk premia in the linear asset pricing models are no longer identified. Weak factors, similar to weak instruments, make the usual estimation techniques unreliable. When included in the model, they generate spuriously high significance levels of their own risk premia estimates, overall measures of fit and may crowd out the impact of the true sources of risk. I develop a new approach to the estimation of cross-sectional asset pricing models that: a) provides simultaneous model diagnostics and parameter estimates; b) automatically removes the effect of spurious factors; c) restores consistency and asymptotic normality of the parameter estimates, as well as the accuracy of standard measures of fit; d) performs well in both small and large samples. I provide new insights on the pricing ability of various factors proposed in the literature. In particular, I identify a set of robust factors (e.g. Fama-French ones, but not only), and those that suffer from severe identification problems that render the standard assessment of their pricing performance unreliable (e.g. consumption growth, human capital proxies and others).

Work in Progress

“Bayesian Fama-MacBeth Regressions”, with Christian Julliard and Jiantao Huang

“Simple Out-of-Sample Tests for Asset Pricing Models”, with Ashish Sahay