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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 14 items for :

  • Type: Journal Issue x
  • Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill x
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Cover IMF Working Papers
Predicting IMF-Supported Programs: A Machine Learning Approach
Tsendsuren Batsuuri
,
Shan He
,
Ruofei Hu
,
Jonathan Leslie
, and
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.
Keywords:
Early warning systems; IMF Lending; Machine Learning; IMF arrangement; ML model; machine learning approach; state-of-the-art machine learning; machine learning model; Emergency assistance; Data processing; Global financial crisis of 2008-2009; Global
Cover IMF Working Papers
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models
Mr. Jorge A Chan-Lau
,
Ruofei Hu
,
Maksym Ivanyna
,
Ritong Qu
, and
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.
Keywords:
Crisis prediction; machine learning; surrogates; explainable models; IMF ML crisis; ML crisis prediction; Annex I. IMF ML; surrogate data models; model interpretability; Early warning systems; Yield curve; Deposit rates; Global
Cover Departmental Papers
Assessing Banking Sector Vulnerabilities in the Gulf Cooperation Council in the Wake of COVID-19
Abdullah Al-Hassan
,
Imen Benmohamed
,
Aidyn Bibolov
,
Giovanni Ugazio
, and
Ms. Tian Zhang
The Gulf Cooperation Council region faced a significant economic toll from the COVID-19 pandemic and oil price shocks in 2020. Policymakers responded to the pandemic with decisive and broad measures to support households and businesses and mitigate the long-term impact on the economy. Financial vulnerabilities have been generally contained, reflecting ongoing policy support and the rebound in economic activity and oil prices, as well as banks entering the COVID-19 crisis with strong capital, liquidity, and profitability. The banking systems remained well-capitalized, but profitability and asset quality were adversely affected. Ongoing COVID-19 policy support could also obscure deterioration in asset quality. Policymakers need to continue to strike a balance between supporting recovery and mitigating risks to financial stability, including ensuring that banks’ buffers are adequate to withstand prolonged pandemic and withdrawal of COVID-related policy support measures. Addressing data gaps would help policymakers to further assess vulnerabilities and mitigate sectoral risks.
Keywords:
COVID-19; banking performance; corporates performance; sovereign-banks-economy nexus; bank asset quality; oil price shock; policy support; assessing banking sector vulnerability; C. bank assets quality; Financial statements; Commercial banks; Loans; Credit; Global; Middle East
Cover Technical Notes and Manuals
How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning
International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.
Keywords:
Risk Assessment; Supervised Machine Learning; Prediction; Sudden Stop; Exchange Market Pressure; Fiscal Crisis; Debt; Financial Crisis; Economic Crisis; Economic Growth; VE modeling toolkit; ML technique; Vulnerability Exercise approach using machine learning; IMF's Vulnerability Exercise; ML tool; crisis risk indices; Early warning systems; Global financial crisis of 2008-2009; Sudden stops; Banking crises; Global
Cover IMF Working Papers
UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification
Mr. Jorge A Chan-Lau
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.
Keywords:
clustering; unsupervised feature extraction; autoencoder; deep learning; biased label problem; crisis prediction; WP; crisis frequency; crisis observation; crisis risk; crisis data points; machine learning; Early warning systems; Global
Cover IMF Working Papers
Fintech Credit Risk Assessment for SMEs: Evidence from China
Yiping Huang
,
Ms. Longmei Zhang
,
Zhenhua Li
,
Han Qiu
,
Tao Sun
, and
Xue Wang
Promoting credit services to small and medium-size enterprises (SMEs) has been a perennial challenge for policy makers globally due to high information costs. Recent fintech developments may be able to mitigate this problem. By leveraging big data or digital footprints on existing platforms, some big technology (BigTech) firms have extended short-term loans to millions of small firms. By analyzing 1.8 million loan transactions of a leading Chinese online bank, this paper compares the fintech approach to assessing credit risk using big data and machine learning models with the bank approach using traditional financial data and scorecard models. The study shows that the fintech approach yields better prediction of loan defaults during normal times and periods of large exogenous shocks, reflecting information and modeling advantages. BigTech’s proprietary information can complement or, where necessary, substitute credit history in risk assessment, allowing unbanked firms to borrow. Furthermore, the fintech approach benefits SMEs that are smaller and in smaller cities, hence complementing the role of banks by reaching underserved customers. With more effective and balanced policy support, BigTech lenders could help promote financial inclusion worldwide.
Keywords:
WP; credit history; Fintech firm; house ownership; internet company; real-time customer rating; Fintech lending; Credit risk assessment; Big data; Machine learning; micro firm; firms' access; medium-size enterprise; firm size; adverse selection; firm's real-time customer rating; fixed cost; information advantage; BigTech company; MYbank loan; Fintech; Credit risk; Loans; Bank credit; b. firm location; fintech approach; cash flows
Cover IMF Working Papers
Completing the Market: Generating Shadow CDS Spreads by Machine Learning
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.
Keywords:
WP; firm; default probability; failure intensity; firm size proxy; Credit default swaps; Prediction; Machine Learning methods; input variable; contracts data; recovery rate; importance probability matrix; credit measure Distance to default; coverage ratio; equity market variable; macroeconomic variable; market perception; machine learning method; Credit default swap; Machine learning; Credit risk; Credit ratings; Stock markets; North America
Cover IMF Working Papers
FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk
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.
Keywords:
WP; ML model; bears risk; machine learning technique; ML analysis; ML evaluation; FinTech Credit; Financial Inclusion; Machine Learning; Credit Risk Assessment; ML analyst; credit risk driver; FinTech credit company; credit scoring; supervised machine learning model; capital structure; borrower default; neural network; Credit risk; Credit; Credit ratings; Loans; Global
Cover IMF Staff Country Reports
People’s Republic of China-Hong Kong Special Administrative Region: Financial Sector Assessment Program-Basel Core Principles for Effective Banking Supervision-Detailed Assessment of Observance
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.
Keywords:
ISCR; CR; risk management; senior management; holding company; banking group; risk profile; chief executive; incorporated AIs; credit risk; interest rate; financial system; capital base; corporate governance; audit program; Artificial intelligence; Market risk; Credit risk; External audit; Capital adequacy requirements; Global
Cover IMF Staff Country Reports
Qatar: Selected Issues
International Monetary Fund. Middle East and Central Asia Dept.
Qatar: Selected Issues
Keywords:
ISCR; CR; Qatar; risk; cost; investment; asset