The forecast of crude oil prices has always been important for investors and scholars and
has drawn more attention to applying deep learning techniques in recent years. Under this …

This study demonstrates the major role played by the healthcare and pharmaceutical
industries during the COVID-19 pandemic. For this purpose, it provides evidence of a better …

This paper provides empirical evidence on predictable time variations in out-of-sample bond
return predictability. Bond return predictability is associated with periods of high (low) …

Integrating complicated travel behaviour mechanisms into transportation studies is
necessary for understanding and modelling urban mobility. However, insufficient research …

We investigate the predictability of bond returns using real-time macro variables and
consider the possibility of a nonlinear predictive relationship and the presence of weak …

We explore the statistical and economic importance of restrictions on the dynamics of risk
compensation from the perspective of a real-time Bayesian learner who predicts bond …

Relying on a weighted group neural network model, this paper reexamines whether treasury
bond returns are predictable when real-time, instead of fully-revised, macro information is …

We examine the importance of volatility and jump risk in the time‐series prediction of S&P
500 index option returns. The empirical analysis provides a different result between call and …

The importance of unspanned macroeconomic variables for Dynamic Term Structure Models
has been intensively discussed in the literature. To our best knowledge the earlier studies …

In this dissertation, I investigate three applications of machine learning in financial
forecasting. The first study investigates the best techniques for forecasting corporate …