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. 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 Review, Quarterly Journal of Economics, Journal of Political Economy, Journal of Finance, Journal of Financial Economics, and Review of Financial Studies. He has served as co-editor of the Journal of Financial Econometrics and associate editor of Journal of Finance and 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 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.
GLOBAL FACTOR DATA
The factor portfolio return data below is for the 153 factors in 93 countries studied in "Is There A Replication Crisis In Finance?" by Jensen, Kelly, and Pedersen (2021). For researchers with a WRDS license, we provide SAS code to run on WRDS that produces all stock-level signals for a larger collection of 406 characteristics (and the associated factor portfolios) in 93 countries.
Factor Portfolio Returns (153 characteristic factors in 93 countries, .csv format, 235MB)
Github Code Repository (produces 406 stock-level characteristics and associated factor returns in 93 countries)
Request Additional Data (email me and describe request)
This is a website analyzing results and providing data based on "The Structure of Economic News" by Bybee, Kelly, Manela, and Xiu (2020).
INTERMEDIARY ASSET PRICING
Intermediary capital risk factor, 1970Q1–2018Q3 based on "Intermediary Asset Pricing: New Evidence From Many Asset Classes" by He, Kelly, and Manela (2017). Quarterly, monthly, and starting 2000-01-01 daily too. Also includes portfolio returns used in our cross-sectional tests. See readme.txt inside for details and replication code. Courtesy of Asaf Manela. Some of these series are updated more frequently by Zhiguo He and are available here.
Journal of Finance, Cond. Accepted (with T. Jensen and L. Pedersen)
Journal of Business and Economic Statistics, Invited Paper (with A. Manela and A. Moreira)
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, 2021 (with S. Pruitt and T. Moskowitz)
American Economic Review, Insights, Forthcoming (with D. Papanikolaou, A. Seru and M. Taddy)
Journal of Political Economy, 2021 (with B. Herskovic, H. Lustig and S. Van Nieuwerburgh)
Journal of Investment Management, 2020 (with R. Israel and T. Moskowitz)
Journal of Financial Economics, 2020 (with Y. Chen and W. Wu)
Review of Financial Studies, 2020 (with S. Gu and D. Xiu)
Review of Financial Studies, 2020 (with R. Engle, S. Giglio, H. Lee and J. Stroebel)
Journal of Financial Economics, 2019 (with S. Pruitt and Y. Su)
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)
Journal of Econometrics, 2015 (with S. Pruitt)
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)
(with J. Jiang and D. Xiu)
We reconsider the idea of trend-based predictability using methods that flexibly learn price patterns that are most predictive of future returns, rather than testing hypothesized or pre-specified patterns (e.g., momentum and reversal). Our raw predictor data are images—stock-level price charts—from which we elicit the price patterns that best predict returns using machine learning image analysis methods. The predictive patterns we identify are largely distinct from trend signals commonly analyzed in the literature, give more accurate return predictions, translate into more profitable investment strategies, and are robust to a battery of specification variations. They also appear context-independent: Predictive patterns estimated at short time scales (e.g., daily data) give similarly strong predictions when applied at longer time scales (e.g., monthly), and patterns learned from US stocks predict equally well in international markets.
(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.
(with Z. Ke and D. Xiu)
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. Giglio and S. Kozak)
We use a large cross-section of equity returns to estimate a rich affine model of equity prices, dividends, returns and their dynamics. Using the model, we price dividend strips of the aggregate market index, as well as any other well-diversified equity portfolio. We do not use any dividend strips data in the estimation of the model; however, model-implied equity yields generated by the model match closely the equity yields from the traded dividend forwards reported in the literature. Our model can therefore be used to extend the data on the term structure of discount rates in three dimensions: (i) over time, back to the 1970s; (ii) across maturities, since we are not limited by the maturities of actually traded dividend claims; and most importantly, (iii) across portfolios, since we generate a term structure for any portfolio of stocks (e.g., small or value stocks). The new term structure data generated by our model (e.g., separate term structures for value, growth, investment and other portfolios, observed over a span of 45 years that covers several recessions) represent new empirical moments that can be used to guide and evaluate asset pricing models.
(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.