1,313 research outputs found
Corporate Bankruptcy Prediction
Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy
Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks
The paper aims to develop an early warning model that separates previously
rated banks (337 Fitch-rated banks from OECD) into three classes, based on their
financial health and using a one-year window. The early warning system is based
on a classification model which estimates the Fitch ratings using Bankscope bankspecific data, regulatory and macroeconomic data as input variables. The authors
propose a “hybridization technique” that combines the Extreme learning machine
and the Synthetic Minority Over-sampling Technique. Due to the imbalanced nature
of the problem, the authors apply an oversampling technique on the data aiming to
improve the classification results on the minority groups. The methodology proposed
outperforms other existing classification techniques used to predict bank solvency. It
proved essential in improving average accuracy and especially the performance of the
minority groups
ISBIS 2016: Meeting on Statistics in Business and Industry
This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647.
The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by:
David Banks, Duke University
Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL
Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL
Nalini Ravishankar, University of Connecticut
Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH
Martina Vandebroek, KU Leuven
Vincenzo Esposito Vinzi, ESSEC Business Schoo
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Estimating bank default with generalised extreme value regression models
The paper proposes a novel model for the prediction of bank failures, on the basis of both macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the paper we apply a regression method for binary data based on extreme value theory, which turns out to be more effective than classical logistic regression models, as it better leverages the information in the tail of the default distribution. The application of this model to the occurrence of bank defaults in a highly bank dependent economy (Italy) shows that, while microeconomic factors as well as regulatory capital are significant to explain proper failures, macroeconomic factors are relevant only when failures are defined not only in terms of actual defaults but also in terms of mergers and acquisitions. In terms of predictive accuracy, the model based on extreme value theory outperforms classical logistic regression models
SME default prediction: A systematic methodology-focused review
This study reviews the methodologies used in the literature to predict failure in small and medium-sized enterprises (SMEs). We identified 145 SMEs’ default prediction studies from 1972 to early 2023. We summarized the methods used in each study. The focus points are estimation methods, sample re-balancing methods, variable selection techniques, validation methods, and variables included in the literature. More than 1,200 factors used in failure prediction models have been identified, along with 54 unique feature selection techniques and 80 unique estimation methods. Over one-third of the studies do not use any feature selection method, and more than one-quarter use only in-sample validation. Our main recommendation for researchers is to use feature selection and validate results using hold-out samples or cross-validation. As an avenue for further research, we suggest in-depth empirical comparisons of estimation methods, feature selection techniques, and sample re-balancing methods based on some large and commonly used datasets.publishedVersio
Predicting Exporters with Machine Learning
In this contribution, we exploit machine learning techniques to predict
out-of-sample firms' ability to export based on the financial accounts of both
exporters and non-exporters. Therefore, we show how forecasts can be used as
exporting scores, i.e., to measure the distance of non-exporters from export
status. For our purpose, we train and test various algorithms on the financial
reports of 57,021 manufacturing firms in France in 2010-2018. We find that a
Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA)
performs better than other techniques with a prediction accuracy of up to
. Predictions are robust to changes in definitions of exporters and in
the presence of discontinuous exporters. Eventually, we argue that exporting
scores can be helpful for trade promotion, trade credit, and to assess firms'
competitiveness. For example, back-of-the-envelope estimates show that a
representative firm with just below-average exporting scores needs up to
more cash resources and up to times more capital expenses to reach full
export status.Comment: 40 pages, 10 figure
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