Shihao Gu

TL;DR: Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.

TL;DR: The authors performed a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia, and demonstrated large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature.

TL;DR: This model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature – autoencoder neural networks – to incorporate information from covariates along with returns themselves, and delivers estimates of nonlinear conditional exposures and the associated latent factors.

TL;DR: This model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature – autoencoder neural networks – to incorporate information from covariates along with returns themselves, and delivers estimates of nonlinear conditional exposures and the associated latent factors.

TL;DR: This article performed a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia, and demonstrated large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature.