Relationship between macroeconomic factors and the stock market development in Kenya

Abstract

Stock markets constitute an essential component of modern economic systems, serving as mechanisms for capital allocation, savings mobilization, and the promotion of economic development. They offer corporations a structured avenue to raise capital for expansion, enhance market liquidity, facilitate price discovery, and contribute to both the creation and distribution of wealth. In Kenya, the interplay between macroeconomic indicators and stock market development warrants comprehensive investigation, particularly in light of the growing significance of financial markets in the country’s economic architecture. This study sought to examine the relationship between selected macroeconomic variables specifically Gross Domestic Product (GDP) growth, inflation, interest rates, and exchange rates and their influence on the development of the Kenyan stock market. The investigation was anchored in prominent theoretical models such as the Efficient Market Hypothesis (EMH), Arbitrage Pricing Theory (APT), and the Three-Factor Model. These frameworks offered foundational perspectives for interpreting the interactions between economic fundamentals and financial market performance. Using a mixed-methods approach, the study combined quantitative analysis with time series data from 2000 to 2023 on a quarterly basis. The statistical analysis was conducted using STATA 12, employing a multiple linear regression model under the ordinary least squares (OLS) methodology to identify significant relationships among the variables of interest. Primary data sources included financial disclosures from listed firms on the Nairobi Securities Exchange (NSE), macroeconomic data from the Central Bank of Kenya and the Kenya National Bureau of Statistics, as well as exchange rate information obtained from the International Monetary Fund (IMF) and the World Bank. The analytical process commenced with a descriptive statistical assessment, encompassing measures such as the mean, variance, skewness, and kurtosis. Thereafter, diagnostic tests were conducted to ensure the fulfillment of OLS assumptions, including tests for stationarity, multicollinearity, and serial correlation. To capture both short-term dynamics and long-term equilibrium relationships, the study applied advanced econometric techniques, including Vector Error Correction Model (VECM), the Johansen Cointegration Test, and Granger Causality analysis. Furthermore, the Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) were utilized to evaluate the dynamic temporal responses of the variables. The findings of the study yielded valuable insights for policymakers, investors, and financial analysts by elucidating the macroeconomic determinants of stock market performance in Kenya. The results also served to inform strategic financial decision-making, enhance risk management frameworks, and support the formulation of sound economic policies. Additionally, this study aimed to contribute to the broader academic discourse on financial market development within the context of emerging economies.