Forecasting Stock Market Returns in Developed and Emerging Markets

Abstract

Stock market forecasting represents a cornerstone of modern finance. In this field, the ability to predict even marginal improvements in forecasting accuracy translates to billions in enhanced returns and reduced losses. Recently, the emergence of machine learning techniques has challenged the Efficient Market Hypothesis (EMH). This hypothesis predicts that the developed markets should demonstrate a lower predictability than emerging markets due to superior information efficiency. While research related to market forecasting using novel models has proliferated, the existing literature lacks exploration of its effectiveness in comparison with other statistical methods and across different economic entities. To address these gaps, this paper presents an empirical framework that systematically evaluates forecasting performance across eight major economies representing both developed and emerging markets using a rolling window cross-validation approach. Using World Bank Global Economic Monitor data from the 21st century, this research tested seven distinct models, ranging from Random Walk benchmarks to traditional econometric approaches and advanced machine learning techniques. As a result, the implementation reveals a paradoxical finding: emerging markets demonstrate systematically higher forecasting errors compared to developed markets, contradicting theoretical predictions that less efficient markets should be more predictable.