ICARE 2023

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/27631

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The Impact of Capital Adequacy Ratio on Bank Risk-Taking Behavior: Evidence from Local Commercial Banks in Sri Lanka
(Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya Sri Lanka, 2023) Thilakarathne, U.R.S.; Abeywardhana, D.K.Y.
This study investigates the relationship between capital adequacy ratio (CAR) and default risk (DR) among local commercial banks in Sri Lanka. Utilizing a panel dataset spanning from 2012 to 2022, the study employs a random effects regression model to analyze the impact of CAR on DR, controlling for bank profitability (BP), bank size (BS), and bank interest rate (I). The findings reveal a complex relationship between CAR and DR, suggesting that a higher CAR may not always lead to a lower level of default risk. This counterintuitive finding challenges the conventional understanding of CAR as a standalone measure for mitigating risk. The study also identifies a positive and statistically significant relationship between BS and DR, emphasizing the need for enhanced risk management practices, particularly for larger banks.
Predicting Corporate Failure in License Finance Companies in Sri Lanka
(Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya Sri Lanka, 2023) Pabasara, H.G.R.; Abeywardhana, D.K.Y.
The license finance industry in Sri Lanka plays a vital role in providing credit to businesses and individuals. However, the industry is not without risk, as evidenced by the number of LFCs that have failed in recent years. Corporate failure can have a significant impact on the economy, as it can lead to various kinds of negative consequences to the economy as well as to the whole nation. This study aims to develop a model to predict corporate failure in licensed finance companies (LFCs) in Sri Lanka. The Altman Z-Score, a widely used financial distress prediction model, was employed to identify distressed LFCs. To enhance the reliability of the model, the CAMELS rating system, a supervisory rating system for financial institutions, was also used to evaluate the performance of the identified distressed LFCs. The study found that the Altman Z-Score and CAMELS rating systems identified LFCs that were in corporate failure or had lower performance. The findings suggest that both models can be used as early warning systems for identifying distressed LFCs in Sri Lanka.
The Relationship between Business Life Cycle and Capital Structure: Evidence from Listed Companies in Sri Lanka
(Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya Sri Lanka, 2023) Edirisingha, K.P.S.D.; Abeywardhana, D.K.Y.
The determination of the optimal capital structure needs to be done by each company. Capital structure is the balance or ratio between debt and equity capital. One proxy for capital structure is leverage. The well-known theory for determining leverage or capital structure is the pecking order theory. There are many variables that affect the determination of a company’s leverage, so there is no single standard model for determining the leverage or capital structure of the company. One variable that adds to the explanation of the determination of a company’s capital structure is the life cycle, as proposed by (Dickinson, 2011). The company's life cycle is differentiated by its cash flow, including cash flow from operating, financing, and investment. This study aims to determine whether the company life cycle can explain the determination of the leverage or capital structure of the company and find out the influence of other variables such as age of business, growth, tangibility, size, and liquidity on the leverage or capital structure of the company. This paper uses a panel data approach and data collected from listed companies in Sri Lanka between 2012/13 and 2021/22 to investigate the impacts of business life cycle stages on capital structure. This study conducted an analysis using descriptive statistics, correlation, and regression analysis. The results showed that in 2012/13 and 2021/22, the variable of the life cycle can be one of the variables that can explain the company’s decision. The control variables that affect leverage are the age of the business, tangibility, size, and liquidity. The variable that does not affect leverage is the growth of the company.