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.
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.
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.
Bank Profitability in Europe: Not Here to Stay
Slower passthrough of policy interest rate hikes to deposit rates relative to their loan rates has led to sharply wider bank net interest margins. Combined with resilient asset quality, wider net interest margins supported record profits for European banks in 2023. Drawing on historical data from the balance sheets and income statements of over 2,500 European banks, this paper shows that abnormally high profits are expected to fade soon as interest income will decline, once policy rates start being lowered, while higher impairment costs historically have weighed on profits with a lag. Moreover, a number of structural factors that have eroded the performance of European banks in the past two decades have largely remained unaddressed and will continue being a drag on profits and capital. Therefore, policymakers should encourage banks to preserve capital buffers and build resilience to future shocks, while exercising caution when considering taxes on profits or other measures that could divert potential sources of capital from banks.
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...
Political Fragility: Coups D’État and Their Drivers
The paper explores the drivers of political fragility by focusing on coups d’état as symptomatic of such fragility. It uses event studies to identify factors that exhibit significantly different dynamics in the runup to coups, and machine learning to identify these stressors and more structural determinants of fragility—as well as their nonlinear interactions—that create an environment propitious to coups. The paper finds that the destabilization of a country’s economic, political or security environment—such as low growth, high inflation, weak external positions, political instability and conflict—set the stage for a higher likelihood of coups, with overlapping stressors amplif...
An Evaluation of World Economic Outlook Growth Forecasts, 2004–17
This paper examines the performance of World Economic Outlook (WEO) growth forecasts for 2004-17. Short-term real GDP growth forecasts over that period exhibit little bias, and their accuracy is broadly similar to those of Consensus Economics forecasts. By contrast, two- to five-year ahead WEO growth forecasts in 2004-17 tend to be upward biased, and in up to half of countries less accurate than a naïve forecast given by the average growth rate in the recent past. The analysis suggests that a more efficient use of available information on internal and external factors—such as the estimated output gap, projected terms of trade, and the growth forecasts of major trading partners—can improve the accuracy of some economies’ growth forecasts.
Republic of San Marino
Context. The global financial crisis and international efforts to address preferential tax regimes exposed the vulnerabilities of San Marino’s oversized financial sector servicing nonresidents. While the banking system entered a deep crisis in 2008 and continues to struggle, the nonfinancial sector has experienced a recovery underpinned by cost-competitiveness and strong corporate balance sheets. More recently, prudent fiscal policies, access to international capital markets and favorable external conditions improved the public finances and boosted confidence. As a result, the economy has been remarkably resilient throughout the pandemic and Russia’s invasion of Ukraine. Despite volatile financial conditions, the government was able to rollover the Eurobond maturing next year. However, San Marino is a microstate subject to very high volatility and financial sector vulnerabilities remain, suggesting that larger-than-usual fiscal buffers are needed.
Risk and Liquidity
This book presents the Clarendon Lectures in Finance by one of the leading exponents of financial booms and crises. Hyun Song Shin's work has shed light on the global financial crisis and he has been a central figure in the policy debates. The paradox of the global financial crisis is that it erupted in an era when risk management was at the core of the management of the most sophisticated financial institutions. This book explains why. The severity of the crisis is explained by financial development that put marketable assets at the heart of the financial system, and the increased sophistication of financial institutions that held and traded the assets. Step by step, the lectures build an a...
