Ruofei Hu's Books

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

Predicting IMF-Supported Programs: A Machine Learning Approach

By: Tsendsuren Batsuuri,Shan He,Ruofei Hu,Jonathan Leslie,Flora Lutz

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Predicting IMF-Supported Programs: A Machine Learning Approach

By: Tsendsuren Batsuuri,Shan He,Ruofei Hu,Jonathan Leslie,Flora Lutz

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.

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International Monetary Fund

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ISBN 13

9798400269363

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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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

Average ratings

N/A

Write a review

Solve jigsaw puzzle

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Publisher

International Monetary Fund

Published Date

ISBN 13

9798400278396

Book's by Ruofei Hu

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

Predicting IMF-Supported Programs: A Machine Learning Approach

Predicting IMF-Supported Programs: A Machine Learning Approach

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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