This paper adopts a novel approach to studying the evolution of interest rate term structure over the U.S. business cycles and to predicting recessions. Applying an effective algorithm, I classify the Treasury yield curve into distinct shapes and find the less frequent shapes intrinsically...

The Baker and Wurgler (2006) sentiment index purports to measure irrational investor sentiment, while the University of Michigan Consumer Sentiment Index is designed to largely reflect fundamentals. Removing this fundamental component from the Baker and Wurgler index creates an index of investor...

Average skewness, which is defined as the average of monthly skewness values across firms, performs well at predicting future market returns. This result still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. We also find that average...

We study the role of analysts and options traders in the information transmission between options and stock markets. We first show that the predictive power of option implied volatilities (IVs) on stock returns more than doubles around analyst-related events, indicating that a significant...

We analyze U.S. stock return predictability using a measure of credit standards (Standards) derived from the Federal Reserve Board's Senior Loan Officer Opinion Survey on Bank Lending Practices. Standards is a strong predictor of stock returns at a business cycle frequency, especially in the...

Bond market order flow contains information about future yield changes that is not incorporated into the current yield curve. Daily and monthly forecasts based on models including interdealer order flow outperform forecasts based on traditional term structure models and the random walk. A new...

Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term...

As some recent studies have shown empirically, future gold price fluctuations are especially difficult to forecast. Against this background, this study evaluates the forecasting power of three methods that have been applied successfully in a stock market prediction context: 1) technical...

You're probably familiar, at least in passing, with the 'convexity' of long-term bonds - i.e. that yields dropping 1% produce a bigger price move than yields rising 1%. A significant amount of brainpower has gone into understanding all the ramifications of this convexity in the fixed income...

We use learning in an equilibrium model to explain the puzzling predictive power of the volatility risk premium (VRP) for option returns. In the model, a representative agent follows a rational Bayesian learning process in an economy under incomplete information with the objective of pricing...