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

Multi-agent hybrid mechanism for financial risk management

Purpose: The goal of this study was to propose the multi-agent mechanism to forecast the corporate financial distress. Design/methodology/approach: This study utilized numerous methods, namely random subspace method, discriminant analysis and decision tree to construct the multi-agent forecasting model. Findings: The study shows a superior forecasting performance. Originality/value: The use of multi-agent model to predict the corporate financial distress.Peer Reviewe

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Risk prediction of product-harm events using rough sets and multiple classifier fusion:an experimental study of listed companies in China

With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm)

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

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

An overview of bankruptcy prediction models for corporate firms: a systematic literature review

Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.Peer Reviewe

Integration of Social Media News Mining and Text Mining Techniques to Determine a Corporate’s Competitive Edge

Market globalization have triggered much more severe challenges for corporates than ever before. Thus, how to survive in this highly fluctuating economic atmosphere is an attractive topic for corporate managers, especially when an economy goes into a severe recession. One of the most consensus conclusions is to highly integrate a corporate’s supply chain network, as it can facilitate knowledge circulation, reduce transportation cost, increase market share, and sustain customer loyalty. However, a corporate’s supply chain relations are unapparent and opaque. To solve such an obstacle, this study integrates text mining (TM) and social network analysis (SNA) techniques to exploit the latent relation among corporates from social media news. Sequentially, this study examines its impact on corporate operating performance forecasting. The empirical result shows that the proposed mechanism is a promising alternative for performance forecasting. Public authorities and decision makers can thus consider the potential implications when forming a future policy

An Analysis of Assessment of Financial Distress in Textile Sector of Pakistan (2012-2018)

As business expand in modern global economy, cutthroat competition also becomes a reality. It becomes more important for businesses not only to diversify but also to break through with their respective products on different markets through cost cutting techniques and technologies. For survival and carrying on business activities, an organization should have to manage their finances on priority matters that need utmost care and attention. Therefore, company’s financial health and position can be monitored through their financial statement. Broad based expansion in private sector loans during Jul-Feb FY2018 makes financing requirement increased for working capital due to increase in input prices of cotton and coal. Large amount of loans are a burden on textile firms. More use of loans may results into the problem of financial distress that may lead to future bankruptcy.The objective of the study is to analyze the financial distress prediction in textile sector of Pakistan during the period 2012 to 2018. Dependent variable is financial distress prediction and independent variables are WC/TD, RE/TA, EBT/Equity, CF/TD, S/TA, interest rates, exchange rates, cotton prices and export prices. Researcher took secondary data of 120 textile-listed firms for the period of seven years from 2012 to 2018 derived from annual reports.Regression analysis, ANOVA and correlation were performed to check whether independent variables have significant relation with financial distress prediction. Results revealed that WC/TD, interest rates, cotton prices and export prices were found significant.RE/TA, EBT/Equity, CF/TD, and S/TA and exchanges rates have negative and insignificant relation with financial distress prediction. Keywords: Financial distress, bankruptcy prediction, Fulmer model, Pakistan Stock Exchange, Financial ratios, Macroeconomic indicators DOI: 10.7176/EJBM/11-19-01 Publication date:July 31st 201

Predicting Romanian Financial Distressed Companies

The study consisted in collecting financial information for a group of distressed and non-distressed Romanian listed companies during the period 2006–2008, in order to create early warning signals for financial distressed companies using the following methodologies: the Logistic and the Hazard model, the CHAID decision tree model and the Artificial Neural Network model (ANN). For each company a set of 14 financial ratios, that reflect the company’s profitability, solvency, asset utilization, growth ability and size, were calculated and then used in the study. A Principal Component Analysis was also used to reduce the dimensionality of the data space and to allow seeing that the 2 types of companies do form 2 distinct groups suggesting that the ratios used are useful enough to predict financial distress. The following 4 data sets were separately analyzed: first-year data to predict distress one year ahead, second-year data for a 2 year-ahead prediction, third-year data for a 3 year-ahead prediction, as well as cumulative three-year data to predict distress 1 year ahead by letting the ratios vary in time. For each data set, several prediction models were created using CHAID, the Logit and Hazard models as well as the ANN and the hybrid-ANN. The results are consistent with the theory and also to previous studies and the out-of-sample forecast accuracy of the estimated models of 73%-100% indicates that the proposed early warning models for the Romanian listed companies are quite efficient.early warning signals, CHAID, ANN