In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

This article surveys recent research on the usefulness of the term spread (ie, the difference
between the yields on long-term and short-term Treasury securities) for predicting changes …

This article measures the trade interconnectedness among countries before and after the
COVID-19 outbreak, and forecasts the future direction of trade. Using Trade Network …

The ongoing COVID-19 pandemic has caused global health impacts, and governments
have restricted movements to a certain extent. Such restrictions have led to disruptions in …

We review the past 25 years of research into time series forecasting. In this silver jubilee
issue, we naturally highlight results published in journals managed by the International …

Are asset prices useful predictors of inflation and real output growth? After reviewing the
large literature on this topic, we undertake an empirical analysis of quarterly data for seven …

The Basel Committee on Banking Supervision proposes a capital adequacy framework that
allows banks to calculate capital requirement for their banking books using internal …

Evidence exists that emerging market stock returns are influenced by a different set of factors
than those that influence the returns for stocks traded in developed countries. This study …

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition
autoregressive (STAR), and neural network (NN) time series models for 47 monthly …

This study compares neural network forecasts of one-year-ahead Canadian stock returns
with the forecasts obtained using ordinary least squares (OLS) and logistic regression (logit) …