Published at : 07 Dec 2020
Volume : IJtech Vol 11, No 6 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i6.4424
|Sergei Grishunin||National Research University, Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia|
|Svetlana Suloeva||Peter the Great St. Petersburg Polytechnic University, 29 Politechnicheskaya Ulitsa, St. Petersburg, 195251, Russia|
|Alexandra Egorova||National Research University, Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia|
|Ekaterina Burova||Peter the Great St. Petersburg Polytechnic University, 29 Politechnicheskaya Ulitsa, St. Petersburg, 195251, Russia|
The quantitative assessment of the credit quality of manufacturing companies is a task of great interest to researchers and practitioners. This is underpinned by the elevated credit risk of these companies stemming from rapid technological changes. However, few studies have addressed this issue specifically for manufacturing companies. This study aimed to fill this research gap by comparing the predictive power of various methods in reproducing manufacturing companies’ public credit ratings from available financial and non-financial data. The sample included 109 manufacturing companies from developed and emerging markets over the period 2005–2016. The analysis included three methods: ordered logistic regression (OLR) and two machine learning techniques, random forest and gradient boosting. The results showed that machine learning techniques outperformed OLR in terms of predictive power. In the best specification model, random forest had an accuracy of 50%, followed by gradient boosting (47%) and OLR (25%). We also tested two types of sampling in the training and test sets: random and time-dependent. The results showed that the models’ predictive power was greater with random sampling. The inclusion of macroeconomic variables did not improve the models’ predictive power due to the rating agencies’ preferred through-the-cycle rating approach. The study’s findings have implications for the development of manufacturing firms’ internal credit ratings. They can also be useful for researchers exploring the accuracy of empirical models in predicting industrial firms’ insolvency and creditworthiness.
Credit rating; Machine learning; Manufacturing companies; Rating agencies; Risk management
Over the last decade, the manufacturing industry has been influenced by a new wave of scientific and technological progress, which has been called “the fourth industrial revolution” (Liao et al., 2017). However, these opportunities come with new threats that increase the credit risks of manufacturing firms. Thus, selecting the credit risk assessment model with the highest accuracy has become increasingly important. For manufacturing firms, these models are used by investors to make funding decisions. They are also needed for the calculation of bad debt provisions as required by Basel III (Basel Committee on Banking Supervision, 2017) or the international accounting standard IFRS9. Lastly, they are used by manufacturing firms’ stakeholders to control, diagnose, and monitor their credit quality (Cuny and Dube, 2017).
Public credit ratings (PCRs) are the most common measure of creditworthiness. These ratings are assigned by domestic and international rating agencies, such as Moody’s Investors Service, Fitch Ratings, and Standard & Poor’s Financial Services (Karminsky and Peresetsky, 2009). The ratings provide a consistent global framework for accurate assessment and comparison of companies’ and countries’ credit quality (Karminsky and Polozov, 2016). However, ratings are often assigned to large, diversified manufacturers mainly from developed markets. This is due to the high cost of the rating, the need to provide agencies with a large amount of information, and the reluctance of small companies to publish low ratings. Other limitations of PCRs are their long update intervals, as well as errors and inefficiencies of rating agencies which were found by various studies (Langohr and Langohr, 2008). The errors included maintenance ratings at the investment grade level for companies with high credit risks or slow response to the credit crisis. To address these issues, investors develop internal credit rating models (ICRs). A common approach is to reproduce the PCRs of unrated companies from available financial and non-financial data using empirical methods. ICRs have proven to be objective and low-cost. However, their predictive power varies significantly depending on the underlying models (Karminsky and Peresetsky, 2007). The task of comparing various models and selecting the optimal one has therefore become extremely important.
The aims of this study were: (1) to compare the predictive power of various empirical methods in reproducing Moody’s ratings specifically for manufacturing firms; and (2) to identify the optimal model in terms of data availability, forecast accuracy, and outcome interpretability. We also examined whether the addition of macroeconomic factors to the set of explanatory variables increases the models’ prediction accuracy. This study contributes to the literature in several ways. First, it focuses on manufacturing companies, which are not the focus of most studies. Second, it applies explanatory variables that match those used in Moody’s methodology. Third, it uses data spanning a long period (from 2005 to 2016). The study’s findings have implications for the development of manufacturing firms’ internal credit ratings. The results can be useful for researchers exploring the ability of empirical models to predict industrial firms’ insolvency and creditworthiness.
In this study, we compared the accuracy of OLR, RF,
and GB in reproducing global manufacturing companies’ PCRs. RF and GB
outperformed OLR by a factor of almost 2. Random sampling yielded higher
predictive power than time-dependent sampling. The inclusion of macroeconomic
variables did not improve the models’ predictive power. The predictive power of
our models is consistent with the literature. We conclude that ML techniques can
be effective in reproducing manufacturing companies’ PCRs. Future research should
examine how the addition of non-financial metrics can improve models’
predictive power. Such metrics include indicators of market and operational
performance, corporate governance and quality of management, and companies’
intellectual capital. Future studies should also determine and compare sets of
explanatory factors for reproducing credit ratings for diverse non-financial
industries, such as oil and gas, metal and mining, chemical, automotive, and fast-moving
consumer goods companies. Determining the best sets of explanatory factors for
each industry will help improve the models’ predictive power.
research was supported by the Academic Excellence Project 5-100 proposed by
Peter the Great St. Petersburg Polytechnic University.
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