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Nan Hu, Jian Li, and Alexis Meyer-Cirkel
We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
Majid Bazarbash
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Jihad Dagher
Financial crises are traditionally analyzed as purely economic phenomena. The political economy of financial booms and busts remains both under-emphasized and limited to isolated episodes. This paper examines the political economy of financial policy during ten of the most infamous financial booms and busts since the 18th century, and presents consistent evidence of pro-cyclical regulatory policies by governments. Financial booms, and risk-taking during these episodes, were often amplified by political regulatory stimuli, credit subsidies, and an increasing light-touch approach to financial supervision. The regulatory backlash that ensues from financial crises can only be understood in the context of the deep political ramifications of these crises. Post-crisis regulations do not always survive the following boom. The interplay between politics and financial policy over these cycles deserves further attention. History suggests that politics can be the undoing of macro-prudential regulations.
Mr. Luc Laeven
Capital markets can improve risk sharing and the efficiency with which capital is allocated to the real economy, boosting economic growth and welfare. However, despite these potential benefits, not all countries have well developed capital markets. Moreover, government-led initiatives to develop local capital markets have had mixed success. This paper reviews the literature on the benefits and costs of developing local capital markets, and describes the challenges faced in the development of such markets. The paper concludes with a set of policy recommendations emerging from this literature.
International Monetary Fund. Monetary and Capital Markets Department


Despite ongoing economic recovery and improvements in global financial stability, structural weaknesses and vulnerabilities remain in some important financial systems. The April 2011 Global Financial Stability Report highlights how risks have changed over the past six months, traces the sources and channels of financial distress with an emphasis on sovereign risk, notes the pressures arising from capital inflows in emerging economies, and discusses policy proposals under consideration to mend the global financial system.

Mr. Lev Ratnovski and Mr. Aditya Narain
While public financial institutions (such as public development banks) are commonly associated with developing countries, in fact they are prevalent in the developed world as well. We study a sample of public financial institutions in industrialized countries and identify dominant trends in their organization and oversight. While practices in developed countries may be a useful reference point, a more nuanced approach, accounting for the disparity of institutional environment, regulatory capacity, and government accountability and effectiveness, may be required in developing countries. Further investment in the accumulation of evidence and formulation of best practices in the organization and oversight of public financial institutions seems warranted and necessary. This paper was prepared while Mr. Ratnovski was working in the Financial Supervision and Regulation Division during January-April 2006. The authors are grateful to Jonathan Fiechter, David Marston, and participants of an MCM seminar in April 2006 for their helpful comments.
Mr. John R. Garrett, Hans-Joachim Beyer, and Ms. Claudia H Dziobek
Successful privatization must be accompanied by the complete removal of privileges and any public policy mission. Bank behavior changes rapidly as profit maximation replaces the bureaucratic objective function. Once privileges are granted, they are difficult to remove. Therefore, privatization is a one-time (nonreversible) operation. The German mortgage bank, DePfa, went through a carefully planned and lengthy privatization process that was successful. Fannie Mae, the U.S. mortgage firm, became a privately owned institution endowed with special privileges, which led to a quasi-monopoly position. This resulted in suboptimal financial sector performance. Fannie Mae’s special privileges have proven resistant to reform efforts.