In this study, we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May 2021. We identify the leading role played by the US internationally by showing that the weekly returns of US industries, especially the US basic materials and energy industries, significantly Granger cause the returns of most other countries’ industries, suggesting that non-US industries react with some delay to new information. This delayed reaction is even more noticeable during periods of recession in the US when cross-country correlations are higher. This implies that the ability of the lagged returns of US industries to predict industries’ returns from other countries is even more pronounced when the US experienced an economic recession. A similar asymmetric relationship is found between the volatility of US industries and that of industries in other markets. The analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries, particularly in the presence of extreme negative shocks. Finally, we demonstrate that our predictions are valuable to real-world investors. Long-short strategies generate sizable and statistically significant alphas, and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

This paper examines stock returns and dividend growth predictability using dividend yields in seven developed markets: United States of America (US), United Kingdom (UK), Japan, France, Germany, Italy, and Spain. Altogether, these countries account for around 85% of the Morgan Stanley Capital International (MSCI) World Index. The use of the long time series with up-to-date data allows the comparison not only between countries, but also across periods, putting into perspective the existence or not of noticeable changes since the 1980’s. The majority of the literature on this topic is US-centered. This emphasis on the US is even more pronounced when it comes to examining the relationship between the dividend unpredictability and dividend smoothing. There is also the need to know if the relationships already documented for the post-Second World War (WWII) period still hold during the last three decades, when stock markets were subjected to a high level of turbulence worldwide. The relationship between dividend yields and returns and dividend growth is central to understand the functioning of capital markets, and has considerable implications for capital asset pricing and investment strategies. Overall, the results show that even for developed capital markets there is no clear pattern on the predictive ability of dividend yields on stock returns and dividend growth, instead these relationships seem to be time-dependent and country-specific. For each country, the predictive ability of the dividend yield is examined in a first-order structural VAR framework by applying bootstrap significance tests and the degree of dividend smoothing is assessed using four partial-adjustment models for the dividend behavior. Additionally, an out-of-sample analysis is conducted using pseudo-R2 and a normal mean squared prediction error (MSPE) adjusted statistic. For the post-WWII period, returns are predictable, but dividends are unpredictable in the US and the UK, while the opposite pattern is observed in Spain and Italy. In Germany, there is some evidence of short-term predictability for both returns and dividends, while in France only returns are predictable. In Japan, neither variable can be forecasted. The dividend smoothing results show that dividends are more persistent in the US and the UK, however, there is no clear connection between dividend smoothness and predictability for the other countries. An important conclusion to retain from the out-of-sample analysis is that the predictability of returns after the WWII, especially present in the US, appeared to have been missing in the last three decades, most probably due to the turmoil experienced by the stock markets during this last period.

Recognizing the profound influence of geopolitical risks and world uncertainty on financial investment behaviour, this study uses a comprehensive approach to assess the impact of rising geopolitical risk on sovereign debt holdings for a panel of 24 OECD economies for the period of Q1 2004 to Q4 2023. To do so, we employ ordinary least squares (OLS) fixed effects and quantile regression techniques within a panel data framework to capture the nuanced effects on both domestic and foreign entities holdings. We find that escalating geopolitical tension decreases government debt holdings among domestic entities, notably domestic Banks, while foreign investors increase their ownership. This phenomenon is more pronounced for high proportion levels of debt in investor’s portfolios. Further, our results allow us to conclude that while domestic economic agents display clearer risk aversion, foreign economic agents have a more risk-taking behaviour in what concerns the financial investment made on government debt.

This study employs dynamic model averaging and selection of Vector Autoregressive and Time-Varying Parameters Vector Autoregressive models to forecast out-of-sample monthly returns of US stocks, bonds, and Real Estate Investment Trusts (REITs) indexes from October 2006 to December 2021. The models were recursively estimated using 17 additional predictors chosen by a genetic algorithm applied to an initial list of 155 predictors. These forecasts were then used to dynamically choose portfolios formed by these assets and the riskless asset proxied by the 3-month US treasury bills. Although we did not find any predictability in the stock market, positive results were obtained for REITs and especially for bonds. The Bayesian-based approaches applied to just the returns of the three risky assets resulted in portfolios that remarkably outperform the portfolios based on the historical means and covariances and the equally weighted portfolio in terms of certainty equivalent return, Sharpe ratio, Sortino ratio and even Conditional Value-at-Risk at 5%. This study points out that Constant Relative Risk Averse investors should use Bayesian-based approaches to forecast and choose the investment portfolios, focusing their attention on different types of assets.