Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

We survey recent methodological contributions in asset pricing using factor models and
machine learning. We organize these results based on their primary objectives: estimating …

We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

Existing studies rely on exogenous drivers to improve the accuracy of the volatility
forecasting of the least polluting fossil fuels, natural gas. However, the academic literature …

Motivated by recent investigations on the connections between geopolitical risk and crude
oil prices, we implement a moving average strategy using the geopolitical risk index to …

We perform a comparative analysis of machine learning methods for the canonical problem
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic …

In the investment theory, firms with high expected investment growth earn higher expected
returns than firms with low expected investment growth, holding investment and expected …

We examine whether climate change news risk is priced in corporate bonds. We estimate
bond covariance with a climate change news index and find that bonds with a higher climate …

We use deep neural networks to estimate an asset pricing model for individual stock returns
that takes advantage of the vast amount of conditioning information, keeps a fully flexible …

We show that machine learning methods, in particular, extreme trees and neural networks
(NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts …