Business Failure Prediction Research Articles

I Will Survive: Predicting Business Failures from Customer Ratings

This paper presents a novel method that uses online ratings to predict business failures.

The Business Failure Prediction Using Statistical Approach

The topic of business failure is important because it can be used as a basis for policy making by stakeholders in a company or government. The results of business failure predictions can be used as company managers to take preventive measures for business failure. This study aim is to study the literature regarding the methods and results of predicting business failure from various sectors and regions. We used PRISMA (preferred reporting items for systematic reviews and meta-analyses) for conducting this research. As the result, we found twelve statistical methods for business failure prediction, including Hybrid Failure Prediction (HFP), Altman’s Z-score Model, Data Envelopment Analysis (DEA), Logistic Regression (LR), Neural Networks (NN), Support Vector Machine (SVM), Kernel Fuzzy C-Means (KFCM), IN01, IN05, Ohlson Model, Cart-Based Model and Cash-Flow-Based Measures. The highest result obtained by using cart-based model for dataset of financial indicators of Slovak companies with 92,00% accuracy.

Predicting French SME failures: new evidence from machine learning techniques

ABSTRACT The aim of this study is to provide new insights into French small and medium-sized enterprises (SME) failure prediction using a unique database of French SMEs over the 2012–2018 period including both financial and nonfinancial variables. We also include text variables related to the type of activity. We compare the predictive performance of three estimation methods: a dynamic Probit model, logistic Lasso regression, and XGBoost algorithm. The results show that the XGBoost algorithm has the highest performance in predicting business failure from a broad dataset. We use SHAP values to interpret the results and identify the main factors of failure. Our analysis shows that both financial and nonfinancial variables are failure factors. Our results confirm the role of financial variables in predicting business failure, while self-employment is the factor that most strongly increases the probability of failure. The size of the SME is also a business failure factor. Our results show that a number of nonfinancial variables, such as localization and economic conditions, are drivers of SME failure. The results also show that certain activities are associated with a prediction of lower failure probability while some activities are associated with a prediction of higher failure.

Why Business Fail? A Thematic Review Analysis on SMEs

It is not easy to stand up again after any failure because the collapse may not only bind with the business organisation but it may root straight away to the individual or personality of the entrepreneur. The most important thing for any failure is to act fast and to ‘re-stand up again’ without any delay. However, most studies focused on the prediction of business failure which mostly concentrated on the sustainability of the distressed business. Therefore, this study was inspired by the idea that the recovery of the fail entrepreneur is much more straightforward as compared to the creation of a new entrepreneur. Thus, the aim of this systematic review is to synthesize the previous studies related to the reason and recovery of business failure among Small Medium Enterprises from 2014 to 2019 using ATLAS.ti 8. A thematic analysis approach; that is useful to code and deeply interpret the identified common themes; was being used upon these 42 articles has led into the generation of 21 initial codes characterizing the study of business failure, grouped in 4 clusters: (1) effect of business failure, (2) prediction of business failure, (3) reason of business failure and (4) solution of business failure. The results contribute to the future study on business failure among SMEs and can guide the educators, researchers and practitioners in this field.

A Comparative Analysis of Decision Trees Vis-`a-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

The Validity of Altman’s Models in Predicting Iraqi Private-Banks Soundness

Nowadays, banks play a significant role in the economy. Thus, its fail extends beyond stakeholders, companies and organization. But may reach to threaten the entire economy. Such an institution better to depend on an early warning system to prevent the costly fail of it. Financial distress prediction (FDP) refers to a method of utilizing statistical models to predict business failure or difficulties that may face banks. A famous earlier model suggested by Altman in which use by the current study to assess the soundness of Iraqi private banks and to find out if such a model could be appropriate to predict financial failure. The model applied to two groups of Iraqi private banks. The first group involves already failure banks; in contrast, the second group comprises Non-failure banks. The study finds that the Altman model unable to predict financial distress in Iraqi private bank with worthwhile accuracy. Therefore, it is inapplicable as applied in other business environments.

Majority voting ensemble with a decision trees for business failure prediction during economic downturns

Accurate business failure prediction represents an advantage for market players and is important for risk management. The purpose of this study is to develop a more accurate and stable business failure prediction model by using a majority voting ensemble method with a decision tree (DT) with experimental data on US restaurant between 1980 and 2017. According to the diversity principle and individual optimized principle, DT and logit were selected as basic learning algorithms for the voting ensemble of business failure prediction. Three models, including an entire period (EP) model, an economic downturn (ED) model, and an economic expansion (EE) model, were developed by using WEKA 3.9. The prediction accuracy of the models were 88.02% for the EP model, 80.81% for the ED model, and 87.02 % for the EE model. While the EE model revealed the market capitalization, operating cash flow after interest and dividends (OCFAID), cash conversion cycle (CCC), return on capital employed (ROCE), accumulated retained earnings, stock price, and Tobin’s Q as significant variables, the ED model exposed quite different variables such as OCFAID, KZ index, stock price, and CCC. The EP model combined most of the variables from two sub-divided models except for Tobin’s Q, stock price, and debt to equity (D/E) ratio. The contribution of the paper is twofold. First, this is the first study to comprehensively evaluate the financial and market-driven variables in the context of predicting restaurant failure, especially during economic recessions. This research has employed several accounting-based measures, market-based variables, and a macro-economic factor to improve the relevance and effectiveness of prediction models. And second, by using an ensemble model with a DT, it has improved both the interpretability of the results and the prediction accuracy.

How childhood ADHD-like symptoms predict selection into entrepreneurship and implications on entrepreneurial performance

This study advances research on mental health and entrepreneurship through the examination of Attention Deficit Hyperactivity Disorder (ADHD)-like symptoms, associated with hyperactivity/impulsivity and inattention. We examine the impact of these symptoms at age 10 on entrepreneurial performance as an adult. We find that while ADHD-like symptoms in childhood may have a positive impact on entrepreneurial selection, they negatively impact on survival and performance, with a variant effect by each symptom, predominantly among males. We find that high levels of inattention predict business failure and lower take-home income, while high levels of hyperactivity/impulsivity contribute to overall negative earnings' growth.

Prediction of Financial Health of Business Entities of Selected Sector Using Balance Analysis II. by Rudolf Doucha and Verification of Its Predictive Ability through ROC

Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. A healthy financial management of a business entity is very important for the proper operation of the business, and it is therefore very important to know how to assess financial health and to anticipate possible problems that will be easier to eliminate in advance. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the conditions of a particular country or even just for a specific sector of a national economy. Predictive models can indicate whether an entity tends to prosper or bankruptcy, and so we can assess the financial health of the business. This paper provides a description of the balance analysis II. by Rudolf Doucha, discusses its application to a sample of 266 Slovak subjects and points to its prediction in the given field. The verification of the ability to forecast bankruptcy or financial stability has been evaluated through ROC analysis.

The Usefulness of Operating Cash Flow for Predicting Business Bankruptcy in Medium-Sized Firms

Purpose – This paper proposes using operating cash flow (OCF) as an instrument to determine a firm’s likelihood of bankruptcy. It presents a new model capable of predicting business failure based on data derived from financial statements. Design/methodology/approach – In this study, logit analysis was employed because of its frequently-cited conceptual advantages relative to multiple discriminant analysis. Through the logistic regression, we identified an integrated function for several ratios, which is useful when assessing whether a firm can be classified as solvent or insolvent in the future. The sample consists of Spanish medium-sized enterprises (MEs) included in the SABI. Findings – We identified an integrated function for several ratios, including information derived from cash flow statements, which is useful when assessing whether a firm can be classified as solvent or insolvent in the future. The analysis suggests the usefulness of taking into account information provided by cash flow statements when making decisions regarding lending to firms and, also, in relation to the acceptable level of leverage that can be assumed by a firm. Originality/value – The model is capable of predicting the probability that a medium-sized firm might have financial problems three years before this occurs, increasing the capacity to take corrective measures.

Estudio de la insolvencia empresarial en las cooperativas mediante técnicas multivariantes

One of the consequences of the current economic crisis is a significant rise in business failures, which is fueling the interest of researchers and users for its analysis, review and update of the traditional models.In reviewing the financial literature on business failure in our country, we noticed that the are very few studies focusing exclusively on cooperative enterprises, although this kind of companies have a broad presence in our country. This significant lack of studies on this kind of business, the recent availability of large databases and the current economic circumstances have influenced us to undertake this work. The main paper objective focuses on the estimation of appropriate models to predict business failure in cooperative enterprises, using statistical techniques.

Business Failure Prediction for Slovak Small and Medium-Sized Companies

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.

Can the SOM Analysis Predict Business Failure Using Capital Structure Theory? Evidence from the Subprime Crisis in Spain

The paper aims to identify which variables related to capital structure theory predict business failure in the Spanish construction sector during the subprime crisis. An artificial neural network (ANN) approach based on Self-Organizing Maps (SOM) is proposed, which allows one to cluster between default and active firms’ groups. The similarities and differences between the main features in each group determine the variables that explain the capacities of failure of the analyzed firms. The network tests whether the factors that explain leverage, such as profitability, growth opportunities, size of the company, risk, asset structure, and age of the firm, can be suitable to predict business failure. The sample is formed by 152 construction firms (76 default and 76 active) in the Spanish market. The results show that the SOM correctly predicts 97.4% of firms in the construction sector and classifies the firms in five groups with clear similarities inside the clusters. The study proves the suitability of the SOM for predicting business bankruptcy situations using variables related to capital structure theory and financial crises.

Enhancing the Knowledge of Construction Business Failure: A Social Network Analysis Approach

The failure of construction companies is quite crucial because it results in unfinished projects and responsibilities that in turn result in many losses to governments, economies, owners, creditors, and surety companies. Previous research on the prediction of construction business failure utilized insolvency causes that were either arbitrary or based on the availability of data. Many scholars have shown the absence of, and need for, a holistic framework for the identification of the causes of construction business failure. As such, this paper reviews previous literature incorporating construction business failure applications, with the objective of identifying existing knowledge, current gaps, and needed future research directions on the different failure factors in a comprehensive approach. To this end, the authors (1) performed a meta-analysis of previous research work for a 30-year period spanning from 1988 to 2018; (2) identified and defined failure factors that impact the business operations of construction firms; and (3) utilized social network analysis to quantitatively identify the overlooked and missing construction business failure factors. Research results indicate that there are 20 factors that could collectively contribute to business failures of construction firms. It is also shown that there is a dire need for future research to better explore the impacts of some understudied critical factors, including the effect of inadequate company organizational structure and human capital on construction business failure. Another important finding is the absence of models that include a holistic incorporation of all 20 construction business failure factors. The findings herein are a significant contribution to the body of knowledge on construction business failure because they integrate the outcomes of previous works and use them to provide robust foundations for knowledge advancement. The presented guidelines are believed to close areas where an abundance of research work occurs and to unveil areas where additional research is necessary.

İŞLETME BAŞARISIZLIĞININ TAHMİN EDİLMESİNDE NAKİT AKIM TABLOSUNUN KULLANIMI: GELİŞMEKTE OLAN BİR PİYASADAN BULGULAR

In the competitive economic environment, cash flow statement reveals prominent information about firms. The aim of the present study is to examine the usefulness of cash flow statement in the prediction of business failure by using a sample including 66 firms listed on Borsa Istanbul from 2010 to 2018. A total of 33 non-failed firms are matched with 33 failed firms based on size and industry. In the present study, logistic regression models are constructed for each of three years prior to business failure. The empirical findings of this study indicate that cash flow measures are strong predictors of business failure.

Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

This paper focuses on business financial health evaluation with the use of selected mathematical and statistical methods. The issue of financial health assessment and prediction of business failure is a widely discussed topic across various industries in Slovakia and abroad. The aim of this paper was to formulate a data envelopment analysis (DEA) model and to verify the estimation accuracy of this model in comparison with the logit model. The research was carried out on a sample of companies operating in the field of heat supply in Slovakia. For this sample of businesses, we selected appropriate financial indicators as determinants of bankruptcy. The indicators were selected using related empirical studies, a univariate logit model, and a correlation matrix. In this paper, we applied two main models: the BCC DEA model, processed in DEAFrontier software; and the logit model, processed in Statistica software. We compared the estimation accuracy of the constructed models using error type I and error type II. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing the financial health of businesses from the analyzed sample. In contrast to the logit model, the results of this method are independent of any assumptions.

Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach

In order to assess risks associated with establishing relationships with corporate partners such as clients, suppliers, debtors or contractors, decision makers often turn to business failure prediction models. While a large body of literature has focused on optimizing and evaluating novel methods in terms of classification accuracy, recent research has acknowledged the existence of asymmetric misclassification costs associated with prediction errors and thus, advocates the usage of alternative evaluation metrics. However, these papers often assume a misclassification cost matrix to be known and fixed for both the training and the evaluation of models, whereas in reality these costs are often uncertain. This paper presents a methodological framework based upon heterogeneous ensemble selection and multi-objective optimization for cost-sensitive business failure prediction that accommodates uncertainty at the level of misclassification costs. The framework assumes unknown costs during model training and accommodates varying degrees of uncertainty during model deployment. Specifically, NSGA-II is deployed to optimize cost space resulting in a set of pareto-optimal ensemble classifiers where every learner minimizes expected misclassification cost for a specific range of cost ratios. An extensive set of experiments evaluates the method on multiple data sets and for different scenarios that reflect the extent to which cost ratios are known during model deployment. Results clearly demonstrate the ability of our method to minimize cost under the absence of exact knowledge of misclassification costs.

The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization

Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the conditions of a particular country or even just for a specific sector of a national economy. Field of financial predictions regarding to international view consists of elementary used models, for example, such as Altman´s Z-score or Beerman´s index, which are globally know and used as basic of many other modificated models. Following article deals with selected Slovak prediction models designed to Slovak conditions, states how these models stand in this global world, what is their international connection to the worldwide economies, and also states verification of their prediction ability in a specific sector. The verification of predictive ability of the models is defined by ROC analysis and through results the paper demonstrates the most suitable prediction models to use in the selected sector.

Does the macroeconomic context condition the prediction of business failure?

The objective of this study is to identify both micro and macroeconomic variables that allow us to analyze in advance the probabilities of business failure. The selected sample contains all the listed companies of the IPC index of Mexico, IBEX-35 of Spain and EURO STOXX50 of Europe for a time horizon of 5 years. Our contribution lies in the empirical testing of the results by two different techniques: general estimating equations (a parametric technique) and a decision tree (a non-parametric technique based on artificial intelligence). The obtained results show that the factors of liquidity, indebtedness and profitability are the ones that affect the prediction of corporatebankruptcy for listed companies, but not the macroeconomic ones, since the macroeconomic peculiarities of each country are diluted by the importance of the economic-financial structure of each company.

Logit business failure prediction in V4 countries

Abstract The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.