To confront the challenge that disaster risk is “dark matter” in finance, we construct an objective measure of disaster risk, which is able to predict half of GDP crashes in a sample of 20 advanced economies between 1870 and 2021. Despite this significant predictability, we find no...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

We analyze the announcement risk premia on the US market between September 1987 and March 2023 and find that the market index exhibits average excess returns of 8.3 bps for macroeconomic announcement days. This strongly contrasts with 1.4 bps returns for non-announcement days. We further measure...

Hierarchical analysis is considered and a multilevel model is presented in order to explore causality, chance and complexity in financial economics. A coupled system of models is used to describe multilevel interactions, consistent with market data: the lowest level is occupied by agents...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

We show that the pattern of positive pre-announcement market drift is present not only for FOMC announcements, as documented by Lucca and Moench (2015), but also for other major macroeconomic announcements such as Nonfarm Payroll, ISM and GDP. This commonality in pre-announcement returns leads...

This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high...

The purpose of this paper is to determine the factors that shape the liquidity levels of euro area sovereign bonds. The values of liquidity measure and explanatory variables were calculated from the limitorder book dataset for almost five hundred bonds from six largest euro area sovereign bond...