no code implementations • 21 Aug 2023 • Andrii Babii, Eric Ghysels, Jonas Striaukas
This paper surveys the recent advances in machine learning method for economic forecasting.
no code implementations • 5 Jul 2023 • Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies.
no code implementations • 26 Dec 2022 • Andrii Babii, Eric Ghysels, Junsu Pan
A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data.
no code implementations • 16 Oct 2020 • Andrii Babii, Xi Chen, Eric Ghysels, Rohit Kumar
We study the binary choice problem in a data-rich environment with asymmetric loss functions.
1 code implementation • 8 Aug 2020 • Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies.
2 code implementations • 28 May 2020 • Andrii Babii, Eric Ghysels, Jonas Striaukas
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies.
1 code implementation • 13 Dec 2019 • Andrii Babii, Eric Ghysels, Jonas Striaukas
We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals.
no code implementations • 8 Jul 2019 • Catherine D'Hondt, Rudy De Winne, Eric Ghysels, Steve Raymond
We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the benefits of robo-investing.