The Journal of Finance

Market Expectations in the Cross‐Section of Present Values

Published: 05/13/2013   |   DOI: 10.1111/jofi.12060

BRYAN KELLY, SETH PRUITT

Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross‐section of book‐to‐market ratios, we find an out‐of‐sample return forecasting R2 of 13% at the annual frequency (0.9% monthly). We document similar out‐of‐sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios’ exposures to economic shocks are key to identifying predictability and are consistent with duration‐based theories of the value premium.

Modeling Corporate Bond Returns

Published: 04/24/2023   |   DOI: 10.1111/jofi.13233

BRYAN KELLY, DIOGO PALHARES, SETH PRUITT

We propose a conditional factor model for corporate bond returns with five factors and time‐varying factor loadings. We have three main empirical findings. First, 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, our model recommends a systematic bond investment portfolio whose high out‐of‐sample Sharpe ratio suggests that the credit risk premium is notably larger than previously estimated. Third, we find closer integration between debt and equity markets than found in prior literature.

The Virtue of Complexity in Return Prediction

Published: 12/08/2023   |   DOI: 10.1111/jofi.13298

BRYAN KELLY, SEMYON MALAMUD, KANGYING ZHOU

Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

Equity Term Structures without Dividend Strips Data

Published: 10/24/2024   |   DOI: 10.1111/jofi.13394

STEFANO GIGLIO, BRYAN KELLY, SERHIY KOZAK

We use a large cross section of equity returns to estimate a rich affine model of equity prices, dividends, returns, and their dynamics. Our model prices dividend strips of the market and equity portfolios without using strips data in the estimation. Yet model‐implied equity yields closely match yields on traded strips. Our model extends equity term‐structure data over time (to the 1970s) and across maturities, and generates term structures for various equity portfolios. The novel cross section of term structures from our model covers 45 years and includes several recessions, providing a novel set of empirical moments to discipline asset pricing models.

The Price of Political Uncertainty: Theory and Evidence from the Option Market

Published: 03/01/2016   |   DOI: 10.1111/jofi.12406

BRYAN KELLY, ĽUBOŠ PÁSTOR, PIETRO VERONESI

We empirically analyze the pricing of political uncertainty, guided by a theoretical model of government policy choice. To isolate political uncertainty, we exploit its variation around national elections and global summits. We find that political uncertainty is priced in the equity option market as predicted by theory. Options whose lives span political events tend to be more expensive. Such options provide valuable protection against the price, variance, and tail risks associated with political events. This protection is more valuable in a weaker economy and amid higher political uncertainty. The effects of political uncertainty spill over across countries.

(Re‐)Imag(in)ing Price Trends

Published: 08/02/2023   |   DOI: 10.1111/jofi.13268

JINGWEN JIANG, BRYAN KELLY, DACHENG XIU

We reconsider trend‐based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock‐level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short‐term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.

Principal Portfolios

Published: 12/14/2022   |   DOI: 10.1111/jofi.13199

BRYAN KELLY, SEMYON MALAMUD, LASSE HEJE PEDERSEN

We propose a new asset pricing framework in which all securities' signals predict each individual return. While the literature focuses on securities' own‐signal predictability, assuming equal strength across securities, our framework 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.

Is There a Replication Crisis in Finance?

Published: 05/26/2023   |   DOI: 10.1111/jofi.13249

THEIS INGERSLEV JENSEN, BRYAN KELLY, LASSE HEJE PEDERSEN

Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication that leads to different conclusions. The majority of asset pricing factors (i) can be replicated; (ii) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio; (iii) work out‐of‐sample in a new large data set covering 93 countries; and (iv) have evidence that is strengthened (not weakened) by the large number of observed factors.

Business News and Business Cycles

Published: 08/09/2024   |   DOI: 10.1111/jofi.13377

LELAND BYBEE, BRYAN KELLY, ASAF MANELA, DACHENG XIU

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns. A text‐augmented vector autoregression demonstrates the large incremental role of news text in forecasting macroeconomic dynamics. We retrieve the narratives that underlie these improvements in market and business cycle forecasts.

Shaping Liquidity: On the Causal Effects of Voluntary Disclosure

Published: 05/28/2014   |   DOI: 10.1111/jofi.12180

KARTHIK BALAKRISHNAN, MARY BROOKE BILLINGS, BRYAN KELLY, ALEXANDER LJUNGQVIST

Can managers influence the liquidity of their firms’ shares? We use plausibly exogenous variation in the supply of public information to show that firms actively shape their information environments by voluntarily disclosing more information than regulations mandate and that such efforts improve liquidity. Firms respond to an exogenous loss of public information by providing more timely and informative earnings guidance. Responses appear motivated by a desire to reduce information asymmetries between retail and institutional investors. Liquidity improves as a result and in turn increases firm value. This suggests that managers can causally influence their cost of capital via voluntary disclosure.