PUBLISHED ARTICLES

ABOUT ME

Bryan Kelly is Professor of Finance at the Yale School of Management, a Research Fellow at the National Bureau of Economic Research, Associate Director of SOM’s International Center for Finance, and is the head of machine learning at AQR Capital Management, LLC. Professor Kelly’s primary research fields are asset pricing and financial econometrics. He is interested in issues related to financial machine learning; volatility, tail risk, and correlation modeling in financial markets; banking sector systemic risk; financial intermediation; and financial networks. His papers in these areas have been published in American Economic ReviewQuarterly Journal of Economics, Journal of Political EconomyJournal of Finance, Journal of Financial Economics, and Review of Financial Studies. He is co-editor of the Journal of Financial Econometrics and associate editor of the Journal of Finance and the Journal of Financial Economics. Before joining Yale, Kelly was a tenured professor of finance at the University of Chicago Booth School of Business.  He earned a bachelor’s degree in economics from the University of Chicago, a master’s degree in economics from University of California San Diego, and a PhD in finance from New York University’s Stern School of Business. Kelly worked in investment banking at Morgan Stanley prior to pursuing his PhD.

Annual Review of Financial Economics, In Process (with S. Giglio and J. Stroebel)

Journal of Financial Economics, Forthcoming (with I. Dew-Becker and S. Giglio)

Journal of Financial Economics, Forthcoming (with S. Pruitt and T. Moskowitz)

American Economic Review, Insights, Forthcoming (with D. Papanikolaou, A. Seru and M. Taddy)

Journal of Political Economy, Forthcoming (with B. Herskovic, H. Lustig and S. Van Nieuwerburgh)

Journal of Investment Management, Forthcoming (with R. Israel and T. Moskowitz)

Journal of Financial Economics, Forthcoming (with Y. Chen and W. Wu)

Journal of Portfolio Management (with T. Gupta)

Journal of Econometrics, Forthcoming (with S. Gu and D. Xiu)

Review of Financial Studies, Forthcoming (with S. Gu and D. Xiu)

Review of Financial Studies, Forthcoming (with R. Engle, S. Giglio, H. Lee and J. Stroebel)

Journal of Financial Economics, Forthcoming (with S. Pruitt and Y. Su)

Journal of Economic Literature, Forthcoming (with M. Gentzkow and M. Taddy)

Quarterly Journal of Economics, 2018 (with S. Giglio)

Journal of Financial Economics, 2017 (with Z. He and A. Manela)

American Economic Review, 2016 (with H. Lustig and S. Van Nieuwerburgh)

Journal of Finance, 2016 (with L. Pastor and P. Veronesi)

Journal of Financial Economics, 2016 (with B. Herskovic, H. Lustig and S. Van Nieuwerburgh)

Journal of Financial Economics, 2016 (with S. Giglio and S. Pruitt)

Review of Financial Studies, 2014 (with H. Jiang)

Journal of Finance, 2014 (with K. Balakrishnan, M. Billings and A. Ljungqvist)

Journal of Finance, 2013 (with S. Pruitt)

Review of Financial Studies, 2012 (with A. Ljungqvist)

Journal of Business and Economic Statistics, 2012 (with R. Engle)

Journal of Risk, 2011 (with C. Brownless and R. Engle)

WORKING PAPERS

 

(with D. Palhares and S. Pruitt)

We propose a new conditional factor model for returns on corporate bonds.  The model has four factors with time-varying factor loadings that are instrumented by observable bond characteristics.  We have three main empirical findings.  The first is that our factor model excels in describing the risks and returns of corporate bonds, improving over previously proposed models in the literature by a large margin.  Second, using bond characteristics to instrument evolving bond risk exposures significantly improves not only our model, but also previously proposed models of observable corporate bond factors. Third, our no-arbitrage model recommends a systematic bond investment portfolio that significantly outperforms leading corporate credit investment strategies.  However, also we find that a ``pure alpha'' bond portfolio---which is orthogonal to factor risk---is incrementally profitable when combined with the no-arbitrage strategy.

(with S. Malamud and L. Pedersen)

We propose a new asset-pricing framework in which all securities’ signals are used to predict each individual return. While the literature focuses on each security’s own-signal predictability, assuming an equal strength across securities, our framework is flexible and includes cross-predictability—leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out-of-sample alphas to standard factors in several data sets.

(with L. Bybee, A. Manela, and D. Xiu)

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984–2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the proportion of news attention allocated to each theme at each point in time. We then use our news attention estimates as inputs into statistical models of numerical economic time series. We demonstrate that these text-based inputs accurately track a wide range of economic activity measures and that they have incremental forecasting power for macroeconomic outcomes, above and beyond standard numerical predictors. Finally, we use our model to retrieve the news-based narratives that underly “shocks” in numerical economic data.

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a sentiment score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of sentiment terms via predictive screening, 2) assigning sentiment weights to these words via topic modeling, and 3) aggregating terms into an article-level sentiment score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we text-mine one of the most actively monitored streams of news articles in the financial system—the Dow Jones Newswires—and show that our supervised sentiment model excels at extracting return-predictive signals in this context.

(with S. Pruitt and Y. Su)

Econometric development of the IPCA method used in ''Characteristics Are Covariances: A Unified Model of Risk and Return ''

(with R. Israelov)

Uncertainty about the future option return has two sources: Changes in the position and shape of the implied volatility surface that shift option values (holding moneyness and maturity fixed), and changes in the underlying price which alter an option's location on the surface and thus its value (holding the surface fixed). We estimate a joint time series model of the spot price and volatility surface and use this to construct an ex ante characterization of the option return distribution via bootstrap. Our ''ORB'' (option return bootstrap) model accurately forecasts means, variances, and extreme quantiles of S&P 500 index conditional option return distributions across a wide range of strikes and maturities.

(with A. Manela and A. Moreira)

Text data is inherently ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text also tends to be a highly selected outcome—journalists, speechwriters, and others carefully craft messages to target the limited attention of their audi- ences. We develop an economically motivated high dimensional selection model that improves machine learning from text (and from sparse counts data more generally). Our model is especially useful in cases where the cover/no-cover choice is separate or more interesting than the coverage quantity choice. 

CONTACT

Bryan Kelly

Yale School of Management

165 Whitney Ave. 

New Haven, CT 06511

bryan.kelly@yale.edu

203-432-2221

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Assistant: Lauren Cable

lauren.cable@yale.edu

203-436-9604

 

©2019 by Bryan Kelly.