The Role of inflation in predicting stock market returns in Zimbabwe: a machine learning approach (2018-2022)
- Author
- Chirume, Tafadzwa
- Title
- The Role of inflation in predicting stock market returns in Zimbabwe: a machine learning approach (2018-2022)
- Abstract
- This study explores the impact of inflation on stock market returns in Zimbabwe during the economically challenging period from 2018 to 2022. It aims to investigate the relationship between inflation and stock returns and to compare the forecasting accuracy of Random Forest and XGBoost machine learning models. Analysing monthly macroeconomic data, both models were trained and evaluated for their ability to capture complex, non-linear relationships between inflation and market performance. The findings reveal a strong correlation between inflation and stock returns, indicating that inflation significantly influences market dynamics. The XGBoost slightly outperformed the Random Forest model, achieving a lower Mean Squared Error (0.00093), a lower Root Mean Squared Error (0.03042), and a higher R-squared value (0.97), which explains 97% of the variance in stock returns. The study suggests that Zimbabwean policymakers should monitor inflation trends to stabilize financial markets, and investors should consider inflation when making decisions, given its significant impact on market returns. It also recommends using advanced machine learning models, particularly XGBoost and Random Forest models, for future economic forecasting and investment strategies in volatile environments to enhance predictive accuracy and risk management. Overall, the research demonstrates the effectiveness of sophisticated machine learning techniques in modelling complex economic relationships, providing policymakers and investors in Zimbabwe’s evolving market with valuable insights.
- Date
- June 2025
- Publisher
- BUSE
- Keywords
- Inflation
- Stock Market Returns
- Machine Learning
- Supervisor
- Ms. P. Hlupo
- Media
-
Chirume, Tafadzwa.pdf