Effect of Liquidity Risk Hedging on Share Price Volatility of NSE-Listed Firms in Kenya

This study investigates the effect of liquidity risk hedging on share price volatility among NSE-listed firms in Kenya. Liquidity risk, the possibility that a firm may not be able to meet its short-term financial obligations, can significantly influence investor confidence and share price stability. The research examines whether firms that adopt liquidity risk hedging experience reduced volatility in their share prices compared to those that do not. Using a panel data approach, the study analyzes financial data from a representative sample of NSE-listed firms over a ten-year period. The study was anchored on liquidity preference theory and a mixed research design for the period between 2013 and 2022. Fifty-four NSE-listed firms were listed for the study. The findings indicate a p-value of 0.128, and the coefficient of liquidity risk hedging is -0.3386364. The results imply that a unit increase in liquidity risk hedging results in a decrease in the share price volatility of Kenyan firms listed on the NSE. The mean score of Capital Markets Authority (CMA), the moderating variable, is 3.95, indicating that CMA regulations have a moderating effect on the relationship between liquidity risk hedging and share price volatility of NSE-listed Firms in Kenya. The study accepted the alternative hypothesis and concluded that liquidity risk hedging statistically affects the share price volatility of listed firms in Kenya. The findings reveal a statistically significant negative relationship between effective liquidity risk hedging and share price volatility, suggesting that prudent liquidity management enhances market stability and investor confidence. The study contributes to financial risk management literature and provides practical insights for corporate managers and investors in emerging markets. The study recommends CMA and NSE to mandate more comprehensive disclosure of liquidity risk management strategies in financial reports, the listed firms to develop and enforce a risk disclosure index for listed firms to ensure consistency, comparability, and reliability of financial statements and CMA and NSE to support the development of local financial markets for hedging instruments to provide firms with accessible tools for managing liquidity risk. Future studies should analyze sector-specific impacts to identify whether liquidity risk hedging is more effective in certain industries than others, conduct comparative studies between the NSE and other stock exchanges to explore how market structure and regulation influence the hedging-volatility relationship and apply models such as GARCH or panel vector autoregression to better understand volatility behavior over time and across firms as the traditional linear regression may not capture dynamic or non-linear relationships.

Authors

Publication Details

Journal
The International Journal of Business & Management
Published
2025-09-09
DOI
https://doi.org/10.24940/theijbm/2025/v13/i6/bm2506-005
Primary Topic
Corporate Finance and Governance
Type
article
Field-Weighted Citation Impact
0.00
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article

Effect of Liquidity Risk Hedging on Share Price Volatility of NSE-Listed Firms in Kenya

Florence Kariuki, Paul Muoki Nzioki, Daniel Okello Omogi
The International Journal of Business & Management
Corporate Finance and Governance
article

Effect of Liquidity Risk Hedging on Share Price Volatility of NSE-Listed Firms in Kenya

Florence Kariuki, Paul Muoki Nzioki, Daniel Okello Omogi
article en

Abstract

This study investigates the effect of liquidity risk hedging on share price volatility among NSE-listed firms in Kenya. Liquidity risk, the possibility that a firm may not be able to meet its short-term financial obligations, can significantly influence investor confidence and share price stability. The research examines whether firms that adopt liquidity risk hedging experience reduced volatility in their share prices compared to those that do not. Using a panel data approach, the study analyzes financial data from a representative sample of NSE-listed firms over a ten-year period. The study was anchored on liquidity preference theory and a mixed research design for the period between 2013 and 2022. Fifty-four NSE-listed firms were listed for the study. The findings indicate a p-value of 0.128, and the coefficient of liquidity risk hedging is -0.3386364. The results imply that a unit increase in liquidity risk hedging results in a decrease in the share price volatility of Kenyan firms listed on the NSE. The mean score of Capital Markets Authority (CMA), the moderating variable, is 3.95, indicating that CMA regulations have a moderating effect on the relationship between liquidity risk hedging and share price volatility of NSE-listed Firms in Kenya. The study accepted the alternative hypothesis and concluded that liquidity risk hedging statistically affects the share price volatility of listed firms in Kenya. The findings reveal a statistically significant negative relationship between effective liquidity risk hedging and share price volatility, suggesting that prudent liquidity management enhances market stability and investor confidence. The study contributes to financial risk management literature and provides practical insights for corporate managers and investors in emerging markets. The study recommends CMA and NSE to mandate more comprehensive disclosure of liquidity risk management strategies in financial reports, the listed firms to develop and enforce a risk disclosure index for listed firms to ensure consistency, comparability, and reliability of financial statements and CMA and NSE to support the development of local financial markets for hedging instruments to provide firms with accessible tools for managing liquidity risk. Future studies should analyze sector-specific impacts to identify whether liquidity risk hedging is more effective in certain industries than others, conduct comparative studies between the NSE and other stock exchanges to explore how market structure and regulation influence the hedging-volatility relationship and apply models such as GARCH or panel vector autoregression to better understand volatility behavior over time and across firms as the traditional linear regression may not capture dynamic or non-linear relationships.

The International Journal of Business & Management
Openalex Percentile: Top 64%
Corporate Finance and Governance
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