Short-term Debt, Profitability and Stock Market Volatility at the Nairobi Securities Exchange, Kenya

Abstract: This study examines the relationship between short-term debt and stock market volatility among firms listed on the Nairobi Securities Exchange (NSE) in Kenya. Acknowledging the increased sensitivity of emerging markets to external financial shocks, the research aims to clarify how short-term financing affects market dynamics. Using secondary data from the NSE and company financial reports covering the period from 2013 to 2022, the study employs a quantitative approach that incorporates multiple linear regression, Pearson correlation analysis, and panel random effects models to capture both cross-sectional and time-series variations. The findings reveal a cyclical pattern in short-term borrowing and a strong positive relationship between short-term debt and market volatility. Regression analysis, which considers firm size and profitability, further confirms that short-term debt has a statistically significant positive impact on volatility. This suggests that short-term financing contributes to market instability when firm-specific factors are taken into account. The persistent presence of short-term debt in corporate capital structures underscores its strategic importance. These results highlight the need for investors and policymakers to carefully monitor corporate debt profiles to mitigate volatility risks in emerging financial markets.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-20
DOI
https://doi.org/10.5281/zenodo.17740809
Primary Topic
Corporate Finance and Governance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Short-term Debt, Profitability and Stock Market Volatility at the Nairobi Securities Exchange, Kenya

Zenodo (CERN European Organization for Nuclear Research)
Corporate Finance and Governance
article

Short-term Debt, Profitability and Stock Market Volatility at the Nairobi Securities Exchange, Kenya

article en

Abstract

Abstract: This study examines the relationship between short-term debt and stock market volatility among firms listed on the Nairobi Securities Exchange (NSE) in Kenya. Acknowledging the increased sensitivity of emerging markets to external financial shocks, the research aims to clarify how short-term financing affects market dynamics. Using secondary data from the NSE and company financial reports covering the period from 2013 to 2022, the study employs a quantitative approach that incorporates multiple linear regression, Pearson correlation analysis, and panel random effects models to capture both cross-sectional and time-series variations. The findings reveal a cyclical pattern in short-term borrowing and a strong positive relationship between short-term debt and market volatility. Regression analysis, which considers firm size and profitability, further confirms that short-term debt has a statistically significant positive impact on volatility. This suggests that short-term financing contributes to market instability when firm-specific factors are taken into account. The persistent presence of short-term debt in corporate capital structures underscores its strategic importance. These results highlight the need for investors and policymakers to carefully monitor corporate debt profiles to mitigate volatility risks in emerging financial markets.

Zenodo (CERN European Organization for Nuclear Research)
Jomo Kenyatta University of Agriculture and Technology (KE)
Openalex Percentile: Top 31%
Corporate Finance and Governance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.