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.
References
[1]
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O. et al. (2018). Systematic Review of Bankruptcy Prediction Models: Towards a Framework for Tool Selection. Expert Systems with Applications, 94, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040
[2]
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23, 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
[3]
Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
[4]
Dechow, P. M. (1994). Accounting Earnings and Cash Flows as Measures of Firm Performance. Journal of Accounting and Economics, 18, 3-42. https://doi.org/10.1016/0165-4101(94)90016-7
[5]
Gentry, J. A., Newbold, P., & Whitford, D. T. (1987). Funds Flow Components, Financial Ratios, and Bankruptcy. Journal of Business Finance & Accounting, 14, 595-606. https://doi.org/10.1111/j.1468-5957.1987.tb00114.x
[6]
Gombola, M. J., & Ketz, J. E. (1983). Financial Ratio Patterns in Retail and Manufacturing Organizations. Financial Management, 12, 45-56. https://doi.org/10.2307/3665210
[7]
Laitinen, E. K. (1994). Traditional Versus Operating Cash Flow in Failure Prediction. Journal of Business Finance & Accounting, 21, 195-217. https://doi.org/10.1111/j.1468-5957.1994.tb00313.x
[8]
Laitinen, E. K. (1980). Financial Ratios and the Basic Economic Factors of the Firm: A Steady State Approach. Doctoral Dissertation, University of Jyv？skyl？. https://jyx.jyu.fi/jyx/Record/jyx_123456789_103777
[9]
Magrini, A. (2025). Bankruptcy Risk Prediction: A New Approach Based on Compositional Analysis of Financial Statements. Big Data Research, 41, Article 100537. https://doi.org/10.1016/j.bdr.2025.100537
[10]
McPhee, R. D., & Seibold, D. R. (1979). Rationale, Procedures, and Applications for Decomposition of Explained Variance in Multiple Regression Analyses. Communication Research, 6, 345-384. https://doi.org/10.1177/009365027900600305
[11]
Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18, 109-131. https://doi.org/10.2307/2490395
[12]
Pereira, J. M., Basto, M., & Silva, A. F. D. (2016). The Logistic Lasso and Ridge Regression in Predicting Corporate Failure. Procedia Economics and Finance, 39, 634-641. https://doi.org/10.1016/s2212-5671(16)30310-0
[13]
Pinches, G. E., Mingo, K. A., & Caruthers, J. K. (1973). The Stability of Financial Patterns in Industrial Organizations. The Journal of Finance, 28, 389-396. https://doi.org/10.1111/j.1540-6261.1973.tb01782.x
[14]
Seibold, D. R., & McPhee, R. D. (1979). Commonality Analysis: A Method for Decomposing Explained Variance in Multiple Regression Analyses. Human Communication Research, 5, 355-365. https://doi.org/10.1111/j.1468-2958.1979.tb00649.x
[15]
Shumway, T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. The Journal of Business, 74, 101-124. https://doi.org/10.1086/209665
[16]
Sloan, R. G. (1996). Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings? The Accounting Review, 71, 289-315. https://doi.org/10.2308/tar-9608042309
[17]
Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting Financial Distress and Corporate Failure: A Review from the State-of-the-Art Definitions, Modeling, Sampling, and Featuring Approaches. Knowledge-Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006
[18]
Zhao, J., Ouenniche, J., & De Smedt, J. (2024). Survey, Classification and Critical Analysis of the Literature on Corporate Bankruptcy and Financial Distress Prediction. Machine Learning with Applications, 15, Article 100527. https://doi.org/10.1016/j.mlwa.2024.100527
[19]
Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59-82. https://doi.org/10.2307/2490859
