Adapting Altman's bankruptcy prediction model to the compositional data methodology

Introduction to the show: ident: Quantitative Finance. The day in quantitative finance: models of markets, risk and prices.

Ruth: We're looking at the paper: "Adapting Altman's bankruptcy prediction model to the compositional data methodology".

Warren: Here's the gist of the paper. Adapting Altman’s bankruptcy prediction model to the compositional data methodology explores how using log-ratios derived from compositional data can improve financial distress prediction compared to standard financial ratios.

Ruth: The results show that compositional methods are better in terms of sensitivity, with mixed results regarding specificity, compositional random forests and compositional logistic regression behaving the best.

Warren: Pruning redundant reasoning tokens The paper addresses the problems associated with using standard financial ratios in statistical models, such as extreme outliers, asymmetry, non-normality, non-linearity, and dependence on arbitrary decisions regarding accounting figures.

Ruth: The Compositional Data (CoDa) methodology uses log-ratio transformations to solve these problems.

Title and authors: Iris: To build on that, we have to talk about what this paper actually suggests as improvements over the existing approach. The authors are trying to show how this works in practice.

Ruth: They focus on using pairwise log-ratios to address the problems with standard ratios, and they also look at how different machine learning tools handle those compositional log-ratios.

Warren: So they’re suggesting a way to improve the prediction by focusing on these compositional features rather than just the raw financial figures.

Iris: They did look at logistic regression, and with standard ratios, they found predictors like (NCA+CA−NCL−CL)/(NCL+CL), (OR−OE)/CL, and CA/CL were relevant predictors with five percent significance.

Ruth: But when they use the compositional ratios, those specific problems disappear entirely. Instead, the statistically significant predictors become log(OR/CA) and log(RE/NCL).

Warren: That’s a big shift because it means the relationship between the variables changes fundamentally when you switch from standard ratios to these compositional ones.

Iris: And they also look at k-nearest neighbour method, and they found that this method didn't produce any measure of variable importance at all.

Ruth: So, in terms of improving the model structure itself, they are showing that the compositional approach handles the underlying statistical issues much better than standard ratios do.

Warren: What does this imply for a quantitative trader like me? It means if I use standard ratios, I’m introducing a lot of noise and bias into my signal compared to using these log-ratios.

The paper's summary: Ruth: So, to summarize what this paper is trying to say about the compositional data methodology adaptation of Altman's model, it’s that we need to move away from standard financial ratios in statistical models because they introduce severe violations of assumptions like non-normality and outliers.

Warren: And the core idea is that the Compositional Data methodology, by using log-ratio transformations, solves those problems and yields substantially different results when compared to those standard ratios.

Iris: They show how this method has been successfully applied to financial ratios over the last ten years specifically to solve those problems in connection with financial ratios Coenders two thousand twenty-five; Coenders and Arimany-Serrat 2025a; two thousand eighteen <ref:2603.24215#pg1,has been successfully applied to>.

Ruth: They also bring in machine learning tools and show how these compositional log-ratios can be used in combination with them, which is an under-researched area.

Warren: The practical application they use is on firms from the Spanish economy, specifically those with a large number of bankrupt firms based on the first two digits of their NACE code forty-sixXX.

Iris: They set up their data by downsampling the training dataset to one healthy firm for each bankrupt firm, while keeping the validation subset entirely intact.

Ruth: The key finding is that composition methods are superior in sensitivity, but they have mixed results on specificity across different models like compositional random forests and compositional logistic regression.

Warren: So, we’re seeing better recall when using these methods, but we need to be careful about how well the system actually classifies correctly.

The paper's improvements: Iris: The authors are suggesting specific improvements that address the limitations they found in their initial setup. They focus on making the predictor set more robust.

Ruth: They look at standard logistic regression first, and with standard ratios, they found relevant predictors with five percent significance, which were (NCA+CA−NCL−CL)/(NCL+CL), (OR−OE)/CL, and CA/CL.

Warren: But once they switch to compositional ratios, those specific problems are gone because the statistically significant predictors change entirely to log(OR/CA) and log(RE/NCL).

Iris: They also point out that the k-nearest neighbour method doesn't give any measure of variable importance at all, so that’s a limitation they identified.

Ruth: The suggested improvements are about using composition methods to avoid those issues altogether, which is what they are emphasizing when they say it leads to very substantial differences in the analysis results whenever it has been compared with standard financial ratios.

Warren: They also mention that the main advantage of using pairwise log-ratios is that there’s only one way of selecting all possible D(D-one)/two log-ratios, which makes the results less arbitrary and always replicable <ref:2603.24215#pg1>.

Conclusion: Ruth: So, wrapping up this discussion on "Adapting Altman's bankruptcy prediction model to the compositional data methodology," the main implication is that using compositional methods is a better way to predict financial distress than relying solely on standard financial ratios because of those statistical issues.

Warren: They found composition methods offer better sensitivity, and compositional random forests and compositional logistic regression showed the best performance in terms of balanced accuracy according to their assessment.

Iris: I just want to add that they note a major limitation is that their sample size had a very small number of bankrupt firms, which means the results might not be generalizable beyond what they observed.

Ruth: So, the final word is that while the compositional approach seems statistically sound and gives better sensitivity, we should use it when we’re predicting bankruptcy or default instead of just standard financial ratios.

Warren: The main thing to remember is that this paper shows how using log-ratios from the compositional data methodology can improve financial distress prediction by focusing on relative information among accounting figures.

Iris: That’s what we're talking about today, adapting Altman's bankruptcy prediction model to the compositional data methodology.