Bankruptcy Prediction: A Model Based on Cash Flow Ratios: Evidence From Selected European Countries
Bibliographic record
Abstract
The importance of assessing the financial distress risk of a company is a topic that has been of central value in many different economic fields and since a long time. Until the twenty-first century, most of the studies were concentrated primarily on using mathematical and statistical methods to assess the health of businesses. Many of these studies employed either accounting-based ratios or cash flow-based ratios; even if there is not a unique conclusion, the use of cash flows seems to improve the predictive capacity of the models significantly. Especially in the last twenty-five years, methods derived from different fields started to be applied in forecasting corporate failures, such as artificial neural networks, genetic algorithms, and fuzzy logic.The objective of this study was to test the goodness of the discriminatory power of ratios based only on cash flows using a model that employs genetic algorithms and fuzzy logic. Five countries (Germany, Spain, France, Great Britain, Italy) and five Nace macro sectors (Agriculture, Industry, Services, Construction, Commerce and Food) have been considered in the analysis for a total of around 719-thousand companies. The model has proven to be well-performing on most of the countries and sectors that have been tested. The results obtained are almost all adequate; in particular, in Germany and Spain, results have been particularly good.The main weaknesses of this work are the limited availability of financial data in some countries and the time delay from the reporting of financial statement to the availability of the data through web services. It means that a large-scale risk assessment requires – being useful for the public and the private sectors – greater and faster disclosure of information at European level, and standardization of financial information transparency among countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".