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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.
Making Sense of Chaos
‘Doyne Farmer is the world's leading thinker on technological change. For decades he has focused on the question of how we can make sense of the data of today to see where the world is going tomorrow. This wonderful book applies these insights to economics, addressing the big global issues of environmental sustainability, and the well-being and prosperity of people around the world’ Max Roser, Founder of Our World in Data We live in an age of increasing complexity, where accelerating technology and global interconnection hold more promise – and more peril – than any other time in human history. As well as financial crises, issues around climate change, automation, growing inequality ...
From Social Science to Data Science
From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding of computational techniques and seeing the bigger picture. With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis of key data collection and skills in Python.
Annual Report
None
The History and Geography of Human Genes
By mapping the worldwide geographic distribution of the genes, the scientists are now able to chart migrations and, in exploring genetic distance, devise a clock by which to date evolutionary history: the longer two populations are separated, the greater their genetic difference should be.
THE EMERGENCE AND NATURE OF HUMAN HISTORY Volume One
This book attempts to define the issues that face us in trying to understand the often-overwhelming complexity of the human experience. It is intellectually challenging, broad in its scope, richly detailed, and densely argued. It is the first in a projected series of five volumes in which the author will seek to touch on every aspect of human historical reality and all the multitudinous variables that have shaped it.
History of Azuki Beans Worldwide (300 BCE to 2021)
One of the world's most comprehensive, well documented, and well illustrated book on this subject. With extensive subject and geographic index. 104 photographs and illustrations - mostly color. Free of charge in digital PDF format.
