The Effects of Financial Metrics on Stock Price Volatility of Listed Stocks at Nairobi Securities Exchange Market

Abstract

Globalization has driven transformation across every sector, fundamentally altering how businesses operate and interact within the global economy. In today’s fast-paced business environment, organizations are navigating an increasingly dynamic and complex landscape, where the focus is on optimizing assets and equity to secure a competitive advantage and ensure sustained relevance. One of the most visible indicators of this shifting landscape is stock price volatility, which serves as a crucial signal of market sentiment, company performance, and broader economic trends. This study sought to examine the financial metrics contributing to stock price fluctuations among commercial banks listed at NSE. Employing a descriptive design with a census approach, the research analyzed data from 11 listed banks, ultimately collecting data from 9 over the period from 2010 to 2023. Secondary data, including Dividend Yield, Earnings Per Share, Return on Equity, and Market Capitalization, were collected to explore their relationship with share prices. Descriptive statistics revealed minimal variability in the data, confirming the dataset’s appropriateness for further analysis. Diagnostic tests further validated the statistical assumptions, while Analysis of Variance confirmed the significance of the dataset. The regression model analysis uncovered valuable insights into the relationships between stock price volatility/share price and the independent variables under consideration. The R-squared value of 0.63 indicated that 63% of the variations in stock price volatility were explained by the variables studied. The unstandardized coefficient for the constant term was 1.236, which represented the predicted value of stock price volatility when all independent variables were set to zero. This constant term was statistically significant, with a t-value of 4.414 and a significance level of 0.000, confirming the reliability of the prediction (β = 1.236, P = 0.000 < 0.05). The unstandardized coefficient for ROI was 1.498, indicating that a one-unit increase in ROI was associated with a 1.498-unit increase in stock price volatility/share price. The Beta value of 0.139 (β = 0.139, P = 0.038 < 0.05) highlighted a moderate positive relationship between ROI and stock price volatility. Unstandardized coefficient for Dividend Yield was 0.092, suggesting that for each unit increase in dividend yield, stock price volatility/share price increased by 0.092 units. The Beta value of 0.265 (β = 0.265, P = 0.002 < 0.05) indicated a stronger positive relationship between dividend yield and stock price volatility. Unstandardized coefficient for market capitalization (KES Billions) was 0.049, meaning that for every one-unit increase in market capitalization, stock price volatility/share price rose by 0.049 units. Beta value of 0.285 (β = 0.285, P = 0.002 < 0.05) demonstrated a relatively strong positive relationship between market capitalization and stock price volatility. The unstandardized coefficient for EPS was 1.81, meaning that a one-unit increase in EPS resulted in a 1.81-unit increase in stock price volatility/share price. The standardized Beta value of 0.249 (β = 0.249, P = 0.005 < 0.05) suggested a moderate positive relationship between EPS and stock price volatility. Despite these significant findings, it is important to note that the study’s sample, limited to commercial banks listed at the NSE, may not fully represent the entire stock market, as dynamics in other sectors could differ. The study does not capture the qualitative factors that may also influence stock price volatility, such as investor sentiment, market rumors, and geopolitical events. As such, further research may explore these qualitative dimensions to provide a more comprehensive understanding of the factors driving stock price fluctuations in the Kenyan market