Understanding And Predicting Systemic Corporate Distress A Machine Learning Approach
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Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach
Author: Ms. Burcu Hacibedel
language: en
Publisher: International Monetary Fund
Release Date: 2022-07-29
In this paper, we study systemic non-financial corporate sector distress using firm-level probabilities of default (PD), covering 55 economies, and spanning the last three decades. Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy. A machine-learning based early warning system is constructed to predict the onset of distress in one year’s time. Our results show that credit expansion, monetary policy tightening, overvalued stock prices, and debt-linked balance-sheet weaknesses predict corporate distress. We also find that systemic corporate distress events are associated with contractions in GDP and credit growth in advanced and emerging markets at different degrees and milder than financial crises.
Predicting IMF-Supported Programs: A Machine Learning Approach
Author: Tsendsuren Batsuuri
language: en
Publisher: International Monetary Fund
Release Date: 2024-03-08
This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
Applications and Innovations in Intelligent Systems VIII
Author: British Computer Society. Specialist Group on Expert Systems. International Conference
language: en
Publisher: Springer Science & Business Media
Release Date: 2001-01-10
The papers in this volume are the Applications papers presented at ES 2000, the Twentieth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, held in Cambridge in December 2000. The scope of the Application papers has expanded over recent years to cover not just innovative applications using traditional knowledge based systems, but also to include applications demonstrating the whole range of AI technologies. These papers continue to illustrate the maturity of AI as a commercially viable technology to solve real world problems. This is the eighth volume in the Applications and Innovations in Intelligent Systems series. The series serves as a key reference as to how AI technology has enabled organisations to solve complex problems and gain significant business benefits. The Technical Stream papers from ES 200 are published as a companion volume under the title Research and Development in Intelligent Systems XVII.