The Relationship between Retained Earnings and Financial Performance of Listed Non-Financial Firms: An Econometrics Examination
DOI:
https://doi.org/10.53819/81018102t4285Abstract
Internal funding sources such as retained earnings provide minimize information asymmetry in addition to being cheap and convenient for ailing firms such as listed non-financial firms trading at the Nairobi Securities Exchange (NSE). Research has however failed to do document precisely how the potential inherent in retained earnings can be exploited to improve financial performance of such firms in the long run. Therefore, this research conducted among 42 non-financial firms listed on the exchange explores the relationship between retained earnings and financial performance from an econometrics perspective. The study is anchored on the Pecking Order Theory, and employs the panel data research design founded in the positivist research paradigm, with data covering the time interval 2016 to 2022 inclusive. Data is sourced purposively from the annual reports of the non-financial sector, including agricultural firms, automobiles and accessories, commercial & services, construction & allied, energy & petroleum, insurance, investment, investment services, manufacturing & allied, telecommunication, and real estate investment trust. The research uses the fixed effects model under panel regression to show a positive and significant relationship between retained earnings and financial performance, and particularly the ultimate direct influence that retained earnings have on financial performance of listed nonfinancial firms. Therefore nonfinancial sector stakeholders should seek to leverage retained earnings to enhance financial performance and achieve resilience in the volatile market conditions, while scholars should seek to enhance external validity of these findings by replicating them in other study contexts.
Keywords: Information asymmetry, retained earnings, financial performance, nonfinancial firms, Securities exchange, Econometrics, Panel data.
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