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Author: Klaus-Peter Hellwig Publisher: International Monetary Fund ISBN: 1513573586 Category : Business & Economics Languages : en Pages : 66
Book Description
In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.
Author: Klaus-Peter Hellwig Publisher: International Monetary Fund ISBN: 1513573586 Category : Business & Economics Languages : en Pages : 66
Book Description
In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.
Author: Mr.Andrew J Tiffin Publisher: International Monetary Fund ISBN: 1513519514 Category : Computers Languages : en Pages : 30
Book Description
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Author: Mr. Jorge A Chan-Lau Publisher: International Monetary Fund ISBN: Category : Business & Economics Languages : en Pages : 31
Book Description
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.
Author: Julia Markhovski Publisher: GRIN Verlag ISBN: 3389003649 Category : Computers Languages : en Pages : 114
Book Description
Bachelor Thesis from the year 2024 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Frankfurt School of Finance & Management, language: English, abstract: In a world characterized by increasingly complex financial markets, the prediction of financial crises is a constant challenge. This bachelor thesis investigates the use of machine learning, in particular regression algorithms, to analyze and predict financial crises based on macroeconomic data. By building six different regression models and optimizing them using cross-validation and GridSearch, the feasibility of using these technologies for accurate predictions is discussed. Although traditional models show limited effectiveness, the integration of machine learning, especially kNN algorithms, reveals significant potential for improving prediction accuracy. The paper highlights the importance of classification algorithms and provides crucial insights for application in real-world scenarios to provide valuable tools for policy and business decision makers.
Author: Tsendsuren Batsuuri Publisher: International Monetary Fund ISBN: Category : Business & Economics Languages : en Pages : 48
Book Description
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.
Author: Ms.Svetlana Cerovic Publisher: International Monetary Fund ISBN: 1484372913 Category : Business & Economics Languages : en Pages : 42
Book Description
This paper identifies leading indicators of fiscal crises based on a large sample of countries at different stages of development over 1970-2015. Our results are robust to different methodologies and sample periods. Previous literature on early warning sistems (EWS) for fiscal crises is scarce and based on small samples of advanced and emerging markets, raising doubts about the robustness of the results. Using a larger sample, our analysis shows that both nonfiscal (external and internal imbalances) and fiscal variables help predict crises among advanced and emerging economies. Our models performed well in out-of-sample forecasting and in predicting the most recent crises, a weakness of EWS in general. We also build EWS for low income countries, which had been overlooked in the literature.
Author: Jeremy Fouliard Publisher: ISBN: Category : Financial crises Languages : en Pages : 0
Book Description
Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary and "fiscal policy. We use the general framework of sequential predictions, also called online machine learning, to forecast crises out-of-sample. Our methodology is based on model aggregation and is “meta-statistical”, since we can incorporate any predictive model of crises in our analysis and test its ability to add information, without making any assumption on the data generating process. We predict systemic "financial crises twelve quarters ahead out-of-sample with high signal-to-noise ratio. Our approach guarantees that picking certain time dependent sets of weights will be asymptotically similar for out-of-sample forecasts to the best ex post combination of models; it also guarantees that we outperform any individual forecasting model asymptotically. We analyse which models provide the most information for our predictions at each point in time and for each country, allowing us to gain some insights into economic mechanisms underlying the building of risk in economies.
Author: Publisher: ISBN: 9789289948678 Category : Languages : en Pages :
Book Description
We develop early warning models for financial crisis prediction by applying machine learning techniques to macro-financial data for 17 countries over 1870-2016. Most nonlinear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering nonlinear relationships between the predictors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A at or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.
Author: Emile du Plessis Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper develops new forecasting methods for an old problem by employing 13 machine learning algorithms to study 147 year of systemic financial crises across 17 countries. It entails 12 leading indicators comprising real, banking and external sectors. Four modelling dimensions encompassing a contemporaneous pooled format through an expanding window, transformations with a lag structure and 20-year rolling window as well as individual format are implemented to assess performance through recursive out-of-sample forecasts. Findings suggest fixed capital formation is the most important variable. GDP per capita and consumer inflation have increased in prominence whereas debt-to-GDP, stock market and consumption were dominant at the turn of the 20th century. Through a lag structure, banking sector predictors on average describe 28 percent of the variation in crisis prevalence, real sector 64 percent and external sector 8 percent. A lag structure and rolling window both improve on optimised contemporaneous and individual country formats. Nearly half of all algorithms reach peak performance through a lag structure. As measured through AUC, F1 and Brier scores, top performing machine learning methods consistently produce high accuracy rates, with both random forests and gradient boosting in front with 77 percent correct forecasts. Top models contribute added value above 20 percentage points in most instances and deals with a high degree of complexity across several countries.
Author: Wayne Ferson Publisher: MIT Press ISBN: 0262039370 Category : Business & Economics Languages : en Pages : 497
Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Author: Klaus-Peter Hellwig
Author: Klaus-Peter Hellwig
Author: Mr.Andrew J Tiffin
Author: Mr. Jorge A Chan-Lau
Author: Julia Markhovski
Author: Tsendsuren Batsuuri
Author: Ms.Svetlana Cerovic
Author: Jeremy Fouliard
Author: 
Author: Emile du Plessis
Author: Wayne Ferson