Many market participants continuously dole out advice that higher economic growth results in higher investment returns. This tendency persists even though there has been much investment research providing evidence to the contrary. With the help of some examples and data presented by others, the...

This paper examines the ability of bond and stock markets to predict subsequent GDP growth over a range of horizons for twelve international countries. The results, using linear, probit, time- and regime-varying in-sample regressions and out-of-sample forecasting, confirm the view that both...

Testing for constant expected returns and forecasting future returns necessitate the information beyond a single predictor. We consider the predictive regression model with multiple predictors which are potentially strongly persistent and cointegrated. Instrumental variables based tests for...

The predictability of stock returns has always been one of the core research questions in finance. This paper attempts to introduce machine learning method to answer whether stock returns are predictable in China. With 108 characteristics data in Chinese stock market from January 1997 to...

In this paper, we explore how to build and test models for forecasting 1-year and 10-year returns for U.S. stocks as inputs to a strategic asset allocation process. We find that a model that forecasts value and growth indices separately best serves as a predictor for returns in the U.S. equity...

We analyse the importance of jumps and the leverage effect on forecasts of realized volatility in a large cross-section of 18 international equity markets, using daily realized measures data from the Oxford-Man Realized Library, and two widely employed empirical models for realized volatility...

We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions...