Impact of Macroeconomic Indicators on Stock Market Predictions: A Cross Country Analysis

Abstract

This research explores the impact of macroeconomic variables on stock market forecasting across multiple countries, seeking to enhance the precision of predictive models by incorporating essential economic factors. The study utilizes a dataset spanning various economies with distinct financial structures to examine the roles of indicators such as economic growth, inflation, interest rates, unemployment, and currency exchange rates in shaping stock market dynamics. By applying machine learning algorithms and econometric techniques, the research assesses the relevance of these indicators for market predictions and identifies variations across different national and economic contexts. The cross-country approach provides valuable insights into how macroeconomic conditions influence market predictability, offering a comprehensive view on integrating economic variables into forecasting models. The findings contribute to the field by highlighting specific indicators with strong predictive power, enabling investors and policymakers to make more informed financial decisions and adjust their models based on macroeconomic trends. The study concludes by discussing implications for future research in multi-country stock market forecasting and the development of adaptive models that respond to evolving economic environments.