TITLE:
Unique and Common Effects of Financial Ratios on Financial Risk Estimation
AUTHORS:
Erkki K. Laitinen
KEYWORDS:
Financial Risk, Financial Ratios, Communality Analysis, Unique Effects, Common Effects, Regression Analysis, Finnish Firms
JOURNAL NAME:
Theoretical Economics Letters,
Vol.16 No.3,
June
8,
2026
ABSTRACT: This study examines the determinants of financial risk using both a binary bankruptcy indicator and an ordinal credit rating measure. The empirical analysis is based on a large sample of Finnish firms and employs linear regression combined with commonality analysis to decompose the coefficient of determination (R2) into unique and shared contributions of key financial ratios, including solvency (Equity ratio), profitability (Return on assets), liquidity (Quick ratio), and cash flow measures (Traditional and Operating cash flows). The regression results indicate that solvency is the most important individual predictor of financial risk, followed by profitability, while liquidity exhibits weaker and partly counterintuitive effects. Traditional (accrual-based) cash flow consistently outperforms operating (cash-based) cash flow in explaining both bankruptcy risk and credit ratings. The explanatory power is substantially higher for the credit rating model than for the binary bankruptcy model. Commonality analysis reveals that the majority of explained variance arises from shared effects among the financial ratios rather than from their unique contributions. Negative lower-order communalities indicate substantial overlap and redundancy among predictors, while large higher-order shared components suggest that financial risk is best understood as a multidimensional construct captured by the joint interaction of financial indicators. Overall, the findings highlight the importance of considering both individual and joint effects of financial ratios and suggest that accrual-based measures provide more comprehensive information about financial risk. The results contribute to the literature by integrating regression analysis with variance decomposition techniques to provide a more nuanced understanding of financial risk prediction.