We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

We present a novel approach to analyzing stock return predictability that accommodates (i) arbitrary predictor persistence, (ii) panels with common factors, (iii) multiple predictors, (iv) short- and long-horizon analysis, and relies on standard inference from least-squares estimation of a...

This paper establishes a methodology to elicit and quantify narratives from survey data using textual analysis. We extract thirteen narratives from daily US stockholder questionnaires conducted during the first-wave COVID-19 period and measure their prevalence over time. Survey-based narratives...

We develop metrics based on Shapley values for interpreting time-series forecasting models, including “black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

We introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fashion. The extensions decompose conditional variance into a short-term and a long-term component. The...

The relationship between the level of stock market volatility and public information flow is non-linear, resembling a bell-shaped function. Medium levels of information flow generate heightened volatility, whereas weak and strong information flow do not, regardless of whether news are negative...

This paper provides empirical evidence on predictable shifts in the degree of bond return predictability. Bond returns are predictable in high (low) economic activity (uncertainty) states, which suggests that the expectations hypothesis of the term structure holds periodically. These...