What causes deep recessions and slow recovery? I revisit this question and develop a macro-finance model that quantitatively matches the salient empirical features of financial crises such as a large drop in the output, a high risk premium, reduced financial intermediation, and a long duration...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse...

Policymakers fear artificial intelligence (AI) will disrupt labor markets, especially for high-skilled workers. We investigate this concern using novel, task-specific data for security analysts. Exploiting variation in AI's power across stocks, we show analysts with portfolios that are more...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

Using the introduction of high-speed rail (HSR) as an exogenous shock to costs of information acquisition, we show that reductions in information-acquisition costs lead to (i) a significant increase in information production, evidenced by a higher frequency of analysts visiting portfolio firms,...

Using the introduction of high-speed rail as exogenous shocks to costs of information acquisition, we show that reductions in information-acquisition costs lead to a significant increase in information production and improvement in output quality, evidenced by higher frequency of analysts...

A growing literature uses the Russell 1000/2000 reconstitution event as an identification strategy to investigate corporate finance and asset pricing questions. To implement this identification strategy, researchers need to approximate the ranking variable used to assign stocks to indexes. We...

Purpose – We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.Design/methodology/approach – We apply six...

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual...