Equity Premium Forecasting in Emerging Markets: A Comparative Analysis of KSA and UAE Using U.S. Predictors

Abstract

This dissertation investigates the application of U.S. predictor variables in forecasting equity premiums in two emerging markets, Saudi Arabia, and the UAE, from 2007 to 2023. Utilizing the methodology of Welch and Goyal (2008), the study evaluates the in-sample and out-of-sample performance of several predictors, such as dividend yields (D12), earnings-price ratios (E12), risk- free rates (Rfree), and corporate bond spreads (BAA). A key focus is on understanding how these predictors, traditionally used for U.S. markets, perform in markets characterized by structural factors such as oil dependence, government ownership, and geopolitical risks. The findings reveal that while some U.S. predictors exhibit moderate in-sample performance, their predictive power diminishes in the out-of-sample period, particularly during periods of heightened market volatility. Major events such as the Qatar diplomatic crisis (2017–2021), and the COVID- 19 pandemic profoundly impacted market dynamics, highlighting the limitations of these predictors during times of geopolitical and economic instability. Predictors tied to interest rates, such as Rfree and tbl, showed stronger out-of-sample performance during the pandemic, benefitting from aggressive monetary policies aimed at stabilizing financial markets. While U.S. predictors provide valuable insights, their limitations in emerging markets, particularly those affected by oil dependence and geopolitical risks, underscore the necessity of incorporating localized, region-specific variables to improve forecasting accuracy. The study suggests that future research should focus on integrating region-specific factors, such as oil price fluctuations and government interventions. Overall, this dissertation contributes to the growing body of literature on equity premium forecasting in emerging markets, offering insights into the application of global predictors in distinct economic environments.