Ritong Qu's Books

Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach

By: Ms. Burcu Hacibedel,Ritong Qu

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Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach

By: Ms. Burcu Hacibedel,Ritong Qu

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.

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International Monetary Fund

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ISBN 13

9798400216299

Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

By: Mr. Jorge A Chan-Lau,Ruofei Hu,Luca Mungo,Ritong Qu,Weining Xin,Cheng Zhong

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Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

By: Mr. Jorge A Chan-Lau,Ruofei Hu,Luca Mungo,Ritong Qu,Weining Xin,Cheng Zhong

We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function. This function integrates high-frequency, market-based, aggregate distress signals with low-frequency, firm-level financial ratios, and macroeconomic indicators. When provided with private firms' financial ratios, the model, which we name signal-knowledge transfer learning model (SKTL), transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones and performs well as an ordinal measure of privately-held firms' default risk.

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International Monetary Fund

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ISBN 13

9798400278396

Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

By: Mr. Jorge A Chan-Lau,Ruofei Hu,Maksym Ivanyna,Ritong Qu,Cheng Zhong

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Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

By: Mr. Jorge A Chan-Lau,Ruofei Hu,Maksym Ivanyna,Ritong Qu,Cheng Zhong

Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.

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International Monetary Fund

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ISBN 13

9798400234828

Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

By: Mr. Jorge A Chan-Lau,Ruofei Hu,Luca Mungo,Ritong Qu,Weining Xin,Cheng Zhong

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Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

By: Mr. Jorge A Chan-Lau,Ruofei Hu,Luca Mungo,Ritong Qu,Weining Xin,Cheng Zhong

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International Monetary Fund

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ISBN 13

9798400278396

Book's by Ritong Qu

Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach

Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach

Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

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