Seth Pruitt

Market Expectations in the Cross Section of Present Values

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 R-squared as high as 13% at the annual frequency (0.9% monthly). We document similar out-of-sample predictability for returns on value, size, momentum and industry-sorted portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a dynamic latent factor system. We find that spreads in growth and value portfolios’ exposures to economic shocks are key to identifying predictability and are consistent with duration-based theories of the value premium. Our findings suggest that discount rates are far less persistent, and their shocks far more volatile, than implied by leading asset pricing models.

The Demand for Youth: Implications for the Hours Volatility Puzzle

The employment and hours worked of young individuals fluctuate much more over the business cycle than those of prime-aged individuals. Understanding the mechanism underlying this observation is key to explaining the volatility of aggregate hours over the cycle. We argue that the joint behavior of age-specific hours and wages in the U.S. data point to differences in the cyclical characteristics of labor demand. To articulate this view, we consider a production technology displaying capital-experience complementarity. We estimate the key parameters governing the degree of complementarity and show that the model can account for the behavior of age-specific hours and wages while generating a series of aggregate hours that is nearly as volatile as output.

The Market-Perceived Monetary Policy Rule

We introduce a novel method for estimating a monetary policy rule using macroeconomic news. Market forecasts of both economic conditions and monetary policy are affected by news, and our estimation links the two effects. This enables us to estimate directly the policy rule agents use to form their expectations, and in so doing flexibly capture the particular dynamics of policy response. We find evidence that between 1994 and 2007 the market-perceived Federal Reserve policy rule changed: the output response vanished, and the inflation response path became more gradual but larger in long-run magnitude. In a standard model we show that output smoothing caused by a larger inflation response magnitude is offset by the more measured pace of response. Our response coefficient estimates are robust to measurement and theoretical issues with both potential output and the inflation target.

Markup Variation and Endogenous Fluctuations in the Price of Investment Goods

The two sector model presented in this note suggests a simple structural decomposition of movements in the price of investment goods into exogenous and endogenous sources. The endogenous fluctuations arise in the presence of countercyclical markups which vary differently across the consumption and investment sectors. In turn, the movements in the markups are due to endogenous procyclical net business formation. The model, while being consistent with the countercyclicality of the price of investment goods, suggests that about a quarter of the movement in the price series can be attributed to this endogenous mechanism.

Instrumented Principal Component Analysis

We propose a new approach of latent factor analysis that, in addition to the main panel of interest, introduces other relevant data that serve as instruments for dynamic factor loadings. The method, called IPCA, provides a parsimonious means of incorporating vast conditioning information into factor model estimates. This improves the efficiency of estimates for the latent factors and their loadings, and helps to ascertain the economic relationships among factors and individuals via the observable instruments. The estimation is fast to calculate and accommodates unbalanced panels. We show consistency and asymptotic normality under general panel data generating processes. We demonstrate the advantages of IPCA in simulated data and in applications to equity asset pricing and international macroeconomics.

Characteristics Are Covariances: A Unified Model of Risk and Return

We use a new method to estimate common risk factors and loadings in the cross section of asset returns. The method, Instrumented Principal Components Analysis (IPCA), allows for time-varying loadings in a latent factor return model by introducing observable characteristics that instrument for the unobservable dynamic loadings. If the characteristics’expected return relationship is driven by compensation for exposure to latent risk factors, IPCA will identify the corresponding latent factors. If no such factors exist, IPCA infers that the characteristic effect is compensation without risk and allocates it to an “anomaly” intercept. Studying returns and characteristics at the stock-level, we find that three IPCA factors explain the cross section of average returns significantly more accurately than existing factor models and produce characteristic-associated anomaly intercepts that are small and statistically insignificant. Furthermore, among a large collection of characteristics explored in the literature, only seven are statistically significant in the IPCA specification and are responsible for nearly 100% of the model’s accuracy. ∗We thank Svetlana Bryzgalova (discussant), John Cochrane, Stefano Giglio, Lars Hansen, Serhiy Kozak, Toby Moskowitz, Andreas Neuhierl, Dacheng Xiu and audience participants at AQR, ASU, Chicago, Duke, Minnesota, SoFiE, and the St. Louis Federal Reserve for helpful comments. We are grateful to Andreas Neuhierl for generously sharing data with us. Corresponding author contact information: bryan.kelly@chicagobooth.edu, (773) 702-8359.

Macroeconomic News in the Cross Section of Asset Growth

Firms make forward-looking decisions. We provide evidence that firms’ investment decisions contain news about future aggregate conditions. This information is best extracted by dimension-reduction techniques. The investment-based signal improves upon the widely-used GDP forecasts found in the Survey of Professional Forecasters. We appeal to news-driven business cycle theory to explain our result, suggesting that these investment decisions contain firms’ information about future productivity shocks. This theory also helps us to understand why accounting-based measures of investment reveal the news while market-based measures of value do not.

The Pseudo-Information Filter

The pseudo-information filter is defined in order to solve a linear state state model. The filter offers significant computational advantages over the Kalman filter when the data are high-dimensional.