The Lasso and the Factor Zoo-Predicting Expected Returns in the Cross-Section

Autor: Marcial Messmer, Francesco Audrino

Publikováno v: Forecasting, Vol 4, Iss 4, Pp 969-1003 (2022)

We investigate whether Lasso-type linear methods are able to improve the predictive accuracy of OLS in selecting relevant firm characteristics for forecasting the future cross-section of stock returns. Through extensive Monte Carlo simulations, we sh

Externí odkaz: https://doaj.org/article/8e880db27c8a432a85c5d995ff85f561

Testing the Lag Structure of Assets’ Realized Volatility Dynamics

Autor: Francesco Audrino, Lorenzo Camponovo, Constantin Roth

Publikováno v: Quantitative Finance and Economics, Vol 1, Iss 4, Pp 363-387 (2017)

A (conservative) test is applied to investigate the optimal lag structure for modelingrealized volatility dynamics. The testing procedure relies on the recent theoretical results that showthe ability of the adaptive least absolute shrinkage and selec

Externí odkaz: https://doaj.org/article/65729ecb7e8e43ca94f88f28472dd74d

How Does Post-Earnings Announcement Sentiment Affect Firms’ Dynamics? New Evidence from Causal Machine Learning

Autor: Francesco Audrino, Jonathan Chassot, Chen Huang, Michael Knaus, Michael Lechner, Juan-Pablo Ortega

Publikováno v: Audrino, F, Chassot, J, Huang, C, Knaus, M, Lechner, M & Ortega, J-P 2022, ' How Does Post-Earnings Announcement Sentiment Affect Firms’ Dynamics? New Evidence from Causal Machine Learning ', Journal of Financial Econometrics . https://doi.org/10.1093/jjfinec/nbac018

We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms’ return, volatility, and trade volume dynamics. To this end, we apply causal machine learning on the earnings announcements

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff625b46cde3f3e734b738c03c6cfd10
https://doi.org/10.1093/jjfinec/nbac018

Predicting U.S. Bank Failures with MIDAS Logit Models

Autor: Alexander Kostrov, Juan-Pablo Ortega, Francesco Audrino

Publikováno v: Journal of Financial and Quantitative Analysis. 54:2575-2603

We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance probl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d6b731ff3a7aa6d6f7003af120900de1
https://doi.org/10.1017/s0022109018001308

Oracle Properties, Bias Correction, and Bootstrap Inference for Adaptive Lasso for Time SeriesM-Estimators

Autor: Lorenzo Camponovo, Francesco Audrino

Publikováno v: Journal of Time Series Analysis. 39:111-128

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for possibly nonlinear time series models. In particular, we investigate the question of how to conduct inference on

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9b1f79064705ab7478e162dad8966bb1
https://doi.org/10.1111/jtsa.12270