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Small Business Economics

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08.11.2018

Combining Kohonen maps and prior payment behavior for small enterprise default prediction

verfasst von: Francesco Ciampi, Valentina Cillo, Fabio Fiano

Erschienen in: Small Business Economics | Ausgabe 4/2020

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Abstract

This study aims to verify the potential of combining corporate prior payment behavior and Kohonen maps for small enterprise default prediction. Logistic regression, discrete-time hazard models, and Kohonen maps were applied to a sample of 1200 Italian small enterprises, and two categories of prediction models were calculated: one exclusively based on financial ratios and the other based also on payment behavior-related variables. The main findings are as follows: (1) Kohonen map-based trajectories give significantly higher prediction accuracy rates compared to both logistic and hazard models; (2) the longer the forecast horizon and/or the smaller the firm’s size, the greater are the improvements in prediction accuracy obtainable through Kohonen maps; (3) accuracy rates are higher when company payment behavior-related variables are added to financial ratios as default predictors; and (4) the smaller a firm, the greater is the increase in accuracy obtainable by adding payment behavior-related variables.

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A Kohonen map, also known as a self-organizing map, is a type of artificial neural network using unsupervised learning to build a two-dimensional map of a problem space. The key distinctive feature of a Kohonen map compared to other approaches to problem solving is that it uses competitive learning rather than error-correction learning such as backpropagation with gradient descent. A Kohonen map can generate a visual representation of data on a hexagonal or rectangular grid. Applications include meteorology, oceanography, project prioritization, and oil and gas exploration.

In this paper, ‘cooperating banking group’ refers to the banking group (whose name is not disclosed for reasons of confidentiality) which collaborated with this research project by making data available in relation to company prior payment behavior variables, as described in Section 4.2.

Proportional stratified random sampling is a type of probability sampling wherein the entire population is branched off into multiple non-overlapping, homogeneous groups (strata), and final members of the sample are randomly and proportionally chosen from the various strata. Members in each stratum should be distinct so that every member of all groups has an equal opportunity of being selected using simple probability. This sampling method is also called random quota sampling. This study used the following stratification variables:

2015 turnover: below 1.0 million, 1.0–2.5 million, 2.5–4.0 million, 4.0–5.0 million

Business sector: food, clothing, wood products, chemical products, metallurgy, mechanical machines, electronic and optical machines and tools

Geographical location (region of central Italy in which the firm was located): Abruzzo, Emilia Romagna, Lazio, Liguria, Marche, Toscana, or Umbria.

The Central Credit Register is described in footnote number 7.

I would like to thank the anonymous reviewer who suggested the use of a hazard model as a second benchmark of self-organizing map-based trajectories, thus allowing for more striking and valuable results.

The results obtained through discrete-time hazard prediction models are very similar to those of logistic regression models. This is because all the default events in our analysis happened in 2015, with the result that the status of the firms (in the sample) associated with the independent variables relating to each of the 8 years under assessment is always a non-default status, with the exception of the last one.

For example, in Italy, there is the Central Credit Register, an information system on the debt of the customers of the banks and financial companies supervised by the Bank of Italy, which contains information on customers’ borrowings from the intermediaries and allows lenders to obtain a wide range of information on the risk position of each customer vis-à-vis the banking system, including past due and/or overdrawn exposures for more than 30, 60, 90, and 180 days.

A second-order method, based on second derivatives of network parameters, was not used in order to look into an equivalent number of points of comparison.

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Titel: Combining Kohonen maps and prior payment behavior for small enterprise default prediction
verfasst von: Francesco Ciampi
Valentina Cillo
Fabio Fiano
Publikationsdatum: 08.11.2018
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 4/2020
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-018-0117-2

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