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Data Analysis for Risk Management – Economics, Finance and Business...
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Data Analysis for Risk Management – Economics, Finance and Business II

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5495

Special Issue Editors

Prof. Dr. Krzysztof Jajuga

E-Mail Website
Guest Editor
Department of Financial Investments and Risk Management, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland
Interests: multivariate data analysis; classification; econometrics; financial markets; risk management; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Józef Dziechciarz

E-Mail Website
Guest Editor
Department of Econometrics, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland
Interests: econometrics; data analysis; marketing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit papers to be published in the Special Issue “Data Analysis for Risk Management – Economics, Finance and Business”. The main motivation for this volume is to provide recent results of the research in the area of data analysis to be applied in widely understood risk management.

We welcome papers which address two main directions that have been substantially explored in the last decade. The first is methodological development, leading to new proposals in classical multivariate data analysis and in the machine learning area. The second is the development in new types of data (in addition to numerical data), with new added opportunities in risk management through the exploration of alternative data such as symbolic data, text data, and spatial data, among other examples.

This Special Issue will contain both methodological and empirical papers. We encourage the sharing of the results of research based not only on data from economics, finance, and business, but – given the multidisciplinary approach – also on data from related areas such as social or natural sciences, since they can have an impact on economics, finance, or business.

Such a mix of theory and applications will add value for both scholars and practitioners in the various disciplines of science.

This Issue is a continuation of the previous successful Special Issue “Data Analysis for Risk Management – Economics, Finance and Business”.

Prof. Dr. Krzysztof Jajuga
Prof. Dr. Józef Dziechciarz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multivariate data analysis
  • classification and clustering
  • machine learning methods
  • natural language processing
  • risk management
  • financial data
  • macro- and microeconomic data

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Published Papers (1 paper)

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Research

17 pages, 709 KiB  
Article
Comparative Analysis of Machine Learning Models for Bankruptcy Prediction in the Context of Pakistani Companies
by Domicián Máté, Hassan Raza and Ishtiaq Ahmad
Risks 2023, 11(10), 176; https://doi.org/10.3390/risks11100176 - 10 Oct 2023
Cited by 5 | Viewed by 4935
Abstract
This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine learning techniques have advanced, there has been much interest in [...] Read more.
This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine learning techniques have advanced, there has been much interest in predicting bankruptcy due to their capacity to handle complex data patterns and boost prediction accuracy. In this study, we evaluated the performance of various machine learning algorithms. We collect comprehensive data comprising financial indicators and company-specific attributes relevant to the Pakistani business landscape from 2016 through 2021. The analysis includes AdaBoost, decision trees, gradient boosting, logistic regressions, naive Bayes, random forests, and support vector machines. This comparative analysis provides insights into the most suitable model for accurate bankruptcy prediction in Pakistani companies. The results contribute to the financial literature by comparing machine learning models tailored to anticipate Pakistani stock market insolvency. These findings can assist financial institutions, regulatory bodies, and investors in making more informed decisions and effectively mitigating financial risks. Full article
(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business II)
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