Going-concern prediction using hybrid random forests and rough set approach
Introduction
The going-concern principle is one of the most important accounting assumptions in the preparation of financial statements. According to this principle, an entity or organization will continue its operations into the foreseeable future, at least, or in perpetuity. A going-concern opinion implies that the entity is not at risk of liquidation or even of reducing the scale of its operations substantially, whether voluntarily or involuntarily. Although auditors are not responsible for predicting bankruptcy or future events, according to Chen and Church [15], “going-concern opinions are useful in predicting bankruptcy and provide some explanatory power in predicting bankruptcy resolution.” Thus, going-concern prediction has been the focus of rigorous research efforts for decades. In particular, several researchers have suggested prediction models to aid auditors in conducting going-concern assessments of firms.
Prior studies on going-concern prediction are based primarily on conventional statistical techniques [22], [34] such as univariate statistical methods, multiple discriminant analysis (MDA), and logit and probit analyses. These conventional statistical methods, however, have some restrictive assumptions such as the linearity, normality, and independence of predictor or input variables. Considering that the violation of these assumptions occurs frequently within financial data [18], the methods have intrinsic limitations in terms of effectiveness and validity. Recently, Bellovary et al. [6] had undertaken an extensive review of going-concern prediction.
Artificial intelligence (AI) approaches such as inductive learning are less vulnerable to these violations. Moreover, AI aims to identify valid, novel, potentially useful, and understandable correlations and patterns in data [6]. AI can be an alternative solution to classification problems, given that data mining has shown to have better predictive capability than conventional statistical methods of going-concern prediction [2], [23], [26], [35], [37], [40]. Although the above-mentioned AI techniques have generally been shown to be effective in going-concern prediction, they are not without limitations.
First, since McKee [42] used financial ratios for going-concern prediction, most studies have primarily used financial ratios as independent variables. Financial ratios, originating in the financial statements of firms, can reflect some characteristics of a corporation. However, in the current knowledge era, the core competences of firms are derived from the knowledge and skills of their employees, and the value of intellectual capital (IC) now exceeds that of some tangible assets. Moreover, numerous researchers have recognized that the nature of a firm’s IC plays an important role in its risk of bankruptcy [57], [59]. Despite the growing importance of IC for firms in the knowledge era, it is usually excluded from early prediction models. Therefore, in this study, we believe that IC, which reflects the status of the corporation in going-concern predictions, will be a decisive factor that influences the predictive capability. Second, the studies mentioned above show that different researchers have used different independent variables as inputs for going-concern prediction. However, a few of the researchers used independent variables as a module of their going-concern prediction. These researchers did not pay much attention to finding and selecting important independent variables. Moreover, few studies employed these variables to generate the appropriate rules for going-concern decisions.
Recently, rough set theory (RST) [48], [49], [50], [51] is a relatively new approach in AI that has been extensively used for knowledge reasoning and knowledge acquisition. Using the concepts of lower and upper approximations in rough sets, the knowledge hidden in the information systems can be unraveled and expressed in the form of ‘‘if …, then …” decision rules [1], [47], [50], [51], [60], [62]. The extracted rules are easily interpretable, permitting complex relationships to be represented in an intuitive and comprehensible manner. The rules establish a relationship between descriptions of objects based on attributes and their assignment to specific classes. Moreover, the rules can be used for the classification of new objects [36]. Recently, it has found its application in a wide variety of fields including credit rating [17], [64], business failure [7], [61], knowledge acquisition [60], market decision-making [48], and early warning [7], [54], etc. Therefore, we attempt to investigate the effectiveness of RST approach in conducting the going-concern prediction tasks and to predict the characteristics of going-concern so decision-makers can understand the rules of going-concern.
Moreover, to determine and select important independent variables in the development of a going-concern prediction model, the random forest (RF) method is used in this study. RF is a relatively newer ensemble method that combines trees grown on bootstrap samples of data and a random subset bagging of predictor variables [10]. During the randomization of features, RF can provide an importance index of independent variables by calculating accuracy and the Gini index. Furthermore, the importance index captures the interactions among predictors through the randomizations of predictors [40]. In terms of robustness to outliers and noise, and calculation time, RF is superior to other machine learning methods such as bagging or boosting.
In order to make great use of the advantages of RF in preprocessing the business data, and further improve the classification accuracy of the RST predictor model, RF + RST is proposed to predict the going-concern in this work. Moreover, we examine whether an assessment of a corporation’s IC conveys any useful additional predictors in going-concern prediction. First, we use IC as a predictive (independent) variable. Second, RF is used to conduct variable selection because of its reliability in obtaining the significant independent variables. Third, the obtained significant independent variables from RF are used as inputs for the RST model. Fourth, we generate meaningful rules using RST for going-concern prediction. Fifth, to validate the effectiveness of our model, comparative experiments are conducted. Finally, to examine the effect of IC, we also compare the obtained results to see whether the model including IC gives better classification accuracy.
The remainder of this paper is organized as follows: In Section 2, we conduct a literature review about going-concern prediction and IC. In Section 3, we present the methodologies used in previous research, which are relevant to our paper for RF and RST. In Section 4, we describe the experimental design of this study. In Section 5, we summarize and discuss the empirical results. Finally, in Section 6, we present the conclusions of this study and discuss the future research directions.
Section snippets
Going-concern
Going-concern prediction as a concept remains one of the most controversial areas of the auditing profession and has received much criticism since its origin in the eighteenth century. Lenard et al. [37] state that the auditor must provide the annual audit report on the financial condition of a company, which is consolidated with the company’s financial statements. One of the important things that an audit report should address is the likelihood of the survival of the company (remain a
Random forest
RF is another advanced method of machine learning. The classification is achieved by constructing an ensemble of randomized classification and regression trees (CART) [10]. The RF algorithm uses a combination of independent decision trees to model data and measure variable importance [10]. Each decision tree in a forest is constructed using a bootstrap sample from the data. Approximately one-third of the data instances are not used to grow the tree; these instances are termed the out-of bag
Experimental process
In the study, we propose a hybrid model (RF + RST), which combines RF and RST to improve the accuracy rate of going-concern prediction. Moreover, we investigate whether IC is useful as an additional predictor in going-concern opinions. Initially, we incorporate IC as a potential predictive variable. Then, we use RF to perform variable selection because of its reliability in obtaining the relative importance of the predictive variables. Subsequently, we use the important predictive variables
Importance ranking of variables
To determine the relative importance of the potential predictive variables, we employed the RF available in the R package random forest [39] to calculate the value of variable importance. This implementation is based on the original Fortran code authored by Leo Breiman, the inventor of RF.
Following Liaw and Wiener [39], our preliminary tests indicated that the performance of RF barely depends on the actual value of its hyper-parameters within a large interval, which is consistent with the
Conclusions
Corporate going-concern prediction plays a significant role in accounting and audit decisions. In the current knowledge era, the core competences of firms are derived from the knowledge and skills of their employees, and the value of IC now exceeds that of some tangible assets. In current prediction models, many researchers have focused on the financial ratios rather than IC. In this study, we use IC as the predictive variable and propose a hybrid model that combines RF and RST (the LEM2
Acknowledgments
The authors would like to thank the Editor-in-Chief and reviewers for their useful comments and suggestions, which were very helpful in improving this manuscript.
References (67)
- et al.
A comparison of machine learning techniques with a qualitative response model for auditors’ going concern reporting
Expert Systems with Applications
(1999) - et al.
Variable precision rough set theory and data discrimination: an application to corporate failure prediction
Omega
(2001) - et al.
Business failure using rough sets
European Journal Operational Research
(1999) - et al.
Probabilistic neural networks for the identification of qualified audit opinions
Expert Systems with Applications
(2007) - et al.
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications
(2009) - et al.
Predicting going concern opinion with data mining
Decision Support Systems
(2008) - et al.
Rudiments of rough sets
Information Sciences
(2007) - et al.
Rough sets: some extensions
Information Sciences
(2007) Feature selection in bankruptcy prediction
Knowledge-Based Systems
(2009)- et al.
Neural networks in business: a survey of applications (1992–1998)
Expert Systems with Applications
(1999)
Employee well-being, firm leverage, and bankruptcy risk
Journal of Banking & Finance
A comparison of alternative bankruptcy prediction models
Journal of Contemporary Accounting & Economics
The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster– Shafer evidence theory
Knowledge-Based Systems
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
Rough sets data analysis in knowledge discovery: a case of Kuwaiti diabetic children patients
Advance in Fuzzy Systems
The auditor’s going concern decision: some UK evidence concerning independence and competence
Journal of Business Finance & Accounting
Further evidence on the auditor’s going-concern report: the influence of management plans
Auditing: A Journal of Practice & Theory
Empirical analysis of audit uncertainty qualifications
Journal of Accounting Research
A review of going concern prediction studies: 1976 to present
Journal of Business & Economic Research
Consistency of random forests and other averaging classifiers
Journal of Machine Learning Research
Intellectual capital: an exploratory study that develops measures and models
Management Decision
Random forests
Machine Learning
Predicting financial distress of Chinese listed companies using rough set theory and support vector machine
Asia-Pacific Journal of Operational Research
Audit committee composition and auditor reporting
Accounting Review
Modeling the audit opinions issued to bankrupt companies: a two-stage empirical
Journal of Business Finance & Accounting
Default on debt obligations and the issuance of going-concern opinions
Auditing: A Journal of Practice & Theory
The auditor’s consideration of the going concern assumption: a diagnostic model
Journal of Accounting, Auditing & Finance
A comparison of rough sets and recursive partitioning induction approaches: an application to commercial loans
International Transactions in Operational Research
A discriminant analysis of predictors of business failure
Journal of Accounting Research
A resource-based view of human resource management and organizational capability development
International Journal of Human Resource Management
Gene selection and classification of microarray data using random forest
BMC Bioinformatics
Cited by (101)
Bankruptcy prediction with low-quality financial information
2024, Expert Systems with ApplicationsThe evaluation of bankruptcy prediction models based on socio-economic costs
2023, Expert Systems with ApplicationsImpacts of crisis on SME bankruptcy prediction models’ performance
2023, Expert Systems with ApplicationsCitation Excerpt :Performance metrics of the developed models were better for a combination of qualitative indicators and financial ratios than only financial ratios. This study is in this meaning in line with existing studies (Bragoli et al., 2021; Tang et al., 2020; Xie et al., 2011; Yeh et al., 2014). However, only a minimum of these differences was statistically significant.
Prediction of environmental controversies and development of a corporate environmental performance rating methodology
2022, Journal of Cleaner ProductionAssessment of double materiality: The development of predictively valid materiality assessments with artificial intelligence
2023, Auditing Transformation: Regulation, Digitalisation and Sustainability