Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model
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.
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models
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.
Predicting IMF-Supported Programs: A Machine Learning Approach
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...
Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach
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.
Handbook of Industrial Chemistry and Biotechnology
Substantially revising and updating the classic reference in the field, this handbook offers a valuable overview and myriad details on current chemical processes, products, and practices. No other source offers as much data on the chemistry, engineering, economics, and infrastructure of the industry. The Handbook serves a spectrum of individuals, from those who are directly involved in the chemical industry to others in related industries and activities. It provides not only the underlying science and technology for important industry sectors, but also broad coverage of critical supporting topics. Industrial processes and products can be much enhanced through observing the tenets and applyin...
Application of Chemical Engineering
This book aims to correlate the latest advances in, and applications of, biomass and resource utilization technology, energy chemical industry, biochemical industry, paper and fiber engineering, chemical process machinery, nano-biochemistry and nano-materials, medicine and biomedical engineering, etc. It will serve as a useful handbook guide to those fields. Volume is indexed by Thomson Reuters CPCI-S (WoS).
Marriage Debt is Hard to Pay
With her broken family, she signed an indenture contract for her boyfriend, only to find out that her boyfriend had gone to bed with his' soulmate '. Despite her heart falling into despair, she still had to fulfill the contract and marry the CEO of the' nemesis'! Conspiracy came one after another, disaster came, she fought alone suddenly in his arms, he said: I am here. However, what she sincerely entrusted in return was his betrayal. She fled with a body full of wounds and wounds: Gu Chengze, I have already repaid the debt I owe you. He held her in his arms, his voice cold.
Review of the Adequacy of the Fund’s Precautionary Balances
On March 20, 2024, the IMF’s Executive Board reviewed the adequacy of the Fund’s precautionary balances. The review took place somewhat ahead of the standard two-year cycle, in view of the imminent attainment of the current indicative medium-term indicative target of SDR 25 billion for the first time. Precautionary balances comprise the Fund’s general and special reserves. They are a key element of the IMF’s multi-layered framework for managing financial risks. Precautionary balances provide a buffer to protect the Fund against potential losses, resulting from credit, income, and other financial risks. The review was based on the assessment framework established in 2010, which uses a...
