Integrative Analysis of Traditional and Cash Flow Financial Ratios: Insights from a Systematic Comparative Review

Dimitra Seretidou (), Dimitrios Billios and Antonios Stavropoulos
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Dimitra Seretidou: Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece
Dimitrios Billios: Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece
Antonios Stavropoulos: Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece

Risks, 2025, vol. 13, issue 4, 1-28

Abstract: This systematic review analyzes and compares the predictive power between traditional financial ratios and cash flow-based ratios in estimating performance. Although traditional ratios of return on assets and debt to equity have received extensive application, cash flow ratios are increasingly valued by their dynamic insights into both liquidity and financial health. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this review systematically analyzes 21 studies spread across various industries and regions. The results reveal that cash flow ratios usually dominate the traditional metrics during forecasting financial performance, especially in the presence of the use of machine learning models. Among the identified variables of the logistic regression model and gradient boosting model predictors, key indicators are those showing the return on investment, the current ratio, and the debt-to-asset ratio. The bottom line of the findings is that a combination of cash flow and traditional ratios gives a better understanding of a company’s financial stability. These results may serve as a starting point for investors, regulators, and entrepreneurs and may further facilitate informed decisions with a reduced chance of miscalculations that enhance proactive financial planning. In addition, future prediction models should integrate non-financial factors such as governance quality and market conditions to enhance financial health assessments. Additionally, longitudinal studies examining the evolution of financial ratios over time, along with hybrid statistical and machine learning approaches, can improve forecasting accuracy. Integrating cutting-edge analytical tools with the strength of financial metrics gives this study actionable insights that allow stakeholders to understand financial performance in a more nuanced sense.

Keywords: financial ratio; cash flow analysis; financial distress prediction; PRISMA 2020; machine learning; systematic review; comparison (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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