We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.
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.
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.
International Monetary Fund. Monetary and Capital Markets Department
This Basel Core Principles (BCP) for Effective Banking Supervision Detailed Assessment Report has been prepared in the context of the Financial Sector Assessment Program for the People’s Republic of China–Hong Kong Special Administrative Region (HKSAR). The Hong Kong Monetary Authority (HKMA) supervises a major international financial center which was affected, though not significantly so, by the financial crisis. The HKMA is maintaining its commitment to the international regulatory reform agenda and is an early adopter of many standards. Supervisory practices, standards, and approaches are well integrated, risk based and of very high quality. There is one area in relation to the overarching legislative framework and powers which warrants further attention. The HKMA enjoys clear de facto but not de jure operational independence. There are two important cross border dimensions for Hong Kong as an international financial center. One is related to HKSAR’s significant position as a host supervisor. The second is the increasing importance of Mainland China in the current portfolios and prospects of the locally incorporated institutions, and indeed in the choice of HKSAR as a platform for overseas institutions to establish relationships with Mainland China.
International Monetary Fund. Middle East and Central Asia Dept.
This Selected Issues paper assesses efficiency of Qatar public investment. It discusses the trends in public capital spending and the rationale for improving public investment efficiency. The paper outlines three alternative methods for analyzing efficiency, and presents the main results. The results suggest that the efficiency of Qatar public investment spending is broadly comparable to GCC peers, but could be improved further. It is also concluded that strengthening fiscal institutions, particularly with an integrated public investment management process and a medium-term fiscal policy framework, is the key for improving public investment efficiency in Qatar.
Mr. Raphael A Espinoza and Mr. Ananthakrishnan Prasad
According to a dynamic panel estimated over 1995 - 2008 on around 80 banks in the GCC region, the NPL ratio worsens as economic growth becomes lower and interest rates and risk aversion increase. Our model implies that the cumulative effect of macroeconomic shocks over a three year horizon is indeed large. Firm-specific factors related to risk-taking and efficiency are also related to future NPLs. The paper finally investigates the feedback effect of increasing NPLs on growth using a VAR model. According to the panel VAR, there could be a strong, albeit short-lived feedback effect from losses in banks’ balance sheets on economic activity, with a semi-elasticity of around 0.4.
This paper examines the performance of Islamic banks (IBs) and conventional banks (CBs) during the recent global crisis by looking at the impact of the crisis on profitability, credit and asset growth, and external ratings in a group of countries where the two types of banks have significant market share. Our analysis suggests that IBs have been affected differently than CBs. Factors related to IBs‘ business model helped limit the adverse impact on profitability in 2008, while weaknesses in risk management practices in some IBs led to a larger decline in profitability in 2009 compared to CBs. IBs‘ credit and asset growth performed better than did that of CBs in 2008-09, contributing to financial and economic stability. External rating agencies‘ re-assessment of IBs‘ risk was generally more favorable.
This paper analyzes the relationship between oil price shocks and bank profitability. Using data on 145 banks in 11 oil-exporting MENA countries for 1994-2008, we test hypotheses of direct and indirect effects of oil price shocks on bank profitability. Our results indicate that oil price shocks have indirect effect on bank profitability, channeled through country-specific macroeconomic and institutional variables, while the direct effect is insignificant. Investment banks appear to be the most affected ones compared to Islamic and commercial banks. Our findings highlight systemic implications of oil price shocks on bank performance and underscore their importance for macroprudential regulation purposes in MENA countries.