Relationship Between Financial Risk And Financial Performance Of Listed Firms In The Manufacturing And Allied Sector Of The Nairobi Securities Exchange

Abstract

The study’s main objective of was to determine the relationship between financial risk and financial performance (FP) of manufacturing firms listed on the Nairobi Securities Exchange. The study’s specific objectives were to determine the relationships between solvency risk and FP, liquidity risk and FP, interest rate risk and FP and exchange rate risk and FP. Theories relied upon by the study include the financial distress theory, Modigliani and Miller capital structure theories, modern portfolio theory by Markowitz and the capital asset pricing model. The study’s research design was descriptive, and it used secondary data sourced from the audited accounts of the firms and from the Central Bank of Kenya’s official website. Data was analysed using panel data methods. The Breusch-Pagan LM test was used to compare the pooled OLS model and the random effects model, and upon detection of significant individual effects, the random effects model was selected. The Hausman test was then used to compare the random effects model and the fixed effects model, and the random effects model was selected as the true model for the data. The final model results showed that solvency risk (SR) had a negative effect that was significant on FP with a coefficient of -0.5529, liquidity risk (LR) had an insignificant positive effect on FP with a coefficient of 0.0022, interest rate risk (IR) had an insignificant negative effect on FP with a coefficient of -0.0372, and exchange rate risk (XR) had an insignificant positive effect on FP with a coefficient of 0.0085. The model’s y intercept of 0.3289 was also significant. Size of the firm had a significant positive effect on FP with a coefficient of 0.2217. The model’s chi square statistic of 258.391 on 5 degrees of freedom was also significant. The model had an adjusted R squared of 0.7860, meaning that 78.6% of the variation in FP could be explained by financial risk and size, the control variable, with 21.4% of the variation being explained by factors not included in the regression model. The researcher recommends that companies optimise their capital structures so as to avoid the negative effect that solvency risk has on their FP. Companies are also advised to take advantage of the positive effect that firm size has on performance by investing in assets and considering strategic mergers