Assessment of a failure prediction model in the energy sector: a multicriteria discrimination approach with Promethee based classification

TL;DR

This paper evaluates a non-parametric multi-criteria decision model for predicting company failure in the energy sector, comparing its performance with a PROMETHEE-based benchmark to assess accuracy and applicability.

Contribution

It introduces the M.H.DIS model for failure prediction and compares its effectiveness with PROMETHEE, providing insights into their relative performance in energy sector applications.

Findings

M.H.DIS model achieved limited prediction accuracy.

PROMETHEE provided a benchmark sorting method.

Model performance varied across datasets.

Abstract

This study presents the implementation of a non-parametric multiple criteria decision aiding (MCDA) model, the Multi-group Hierarchy Discrimination (M.H.DIS) model, with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), on a dataset of 114 European unlisted companies operating in the energy sector. Firstly, the M.H.DIS model has been developed following a five-fold cross validation procedure to analyze whether the model explains and replicates a two-group pre-defined classification of companies in the considered sample, provided by Bureau van Dijk's Amadeus database. Since the M.H.DIS method achieves a quite limited satisfactory accuracy in predicting the considered Amadeus classification in the holdout sample, the PROMETHEE method has been performed then to provide a benchmark sorting procedure useful for comparison purposes.