Essays on SMEs insolvency risk

In light of the new Basel Capital Accord, Small and medium size enterprises (SMEs) play a fundamental role in the economic performance of major economies. Several lending communities proposed to treat SMEs as retail clients to optimize capital requirements and profitability. In this context, it is becoming critically important to have a detailed understanding of its risk behavior for appropriate pricing of credit risk. Thus, this thesis presents four essays on SMEs insolvency risk starting from chapter 3 through chapter 6 that investigates different dimensions of their default risk. My first essay makes distinction among SMEs that report operating cash flow and those which do not while modeling their default risk. However, I do not report any significant improvement in model’s classification performance when operating cash flow information is made available. Similarly, my second essay considers domestic and international SMEs separately while modelling their default risk and report almost identical classifications performance of the models’ developed for both the groups. The third essay compares the default risk attributes of micro, small and medium-sized firms respectively with SMEs. Test results suggest significant difference in the default risk attributes of only micro firms and SMEs. On a different line, my fourth essay deals with the methodological issues that have been witnessed recently in the bankruptcy literature that use hazard models for making bankruptcy predictions. This essay highlights the critical issues and provides appropriate guidance for the correct use of hazard models in making bankruptcy predictions. Here, I also propose a default definition for SMEs which considers both legal bankruptcy laws and firms’ financial health while defining the default event. Empirical results show that my default definition performs significantly better than its respective counterparts in identifying distressed firms with superior goodness of fit measures across all econometric specifications. Detailed abstract of respective essays are as follows.Evidence pertaining to SMEs financing strongly motivates me to believe that firms which are unable to generate sufficient operating cash flow (OCF) are more susceptible to bankruptcy. However, the role of OCF in bankruptcy of SMEs lacks empirical validation. Thus, my first essay (chapter 3) investigates the role of operating cash flow information as predictors in assessing the creditworthiness of SMEs. One-year distress prediction model developed using significant financial information of United Kingdom SMEs over a period of 2000 to 2009 confirm that the presence of operating cash flow information does not improve the prediction accuracy of the distress prediction model.My second essay (chapter 4) considers domestic and international small and medium-sized enterprises (SMEs) of the United Kingdom separately while modelling their default risk. To establish the empirical validation, separate one-year default prediction models are developed using dynamic logistic regression technique that encapsulates significant financial information over an analysis period of 2000 to 2009. Almost an identical set of explanatory variables affect the default probability of domestic and international SMEs, which contradicts the need for separate default risk models. However, the lower predictive accuracy measures of the model developed for international SMEs motivate me to compare the weights of regression coefficients of the models developed for domestic and international firms. Test results confirm that four out of the nine common predictors display significant statistical differences in their weights. However, these differences do not contribute to the discriminatory performance of the default prediction models, given that I report very little difference in each model’s classification performance.A huge diversity exists within the broad category of Small and medium size enterprises (SMEs). They differ widely in their capital structure, firm size, access to external finance, management style, numbers of employees etc. Thus, my third essay (chapter 5) contributes to the literature by acknowledging this diversity while modeling credit risk for them, using a relatively large UK database, covering the analysis period between 2000 and 2009. My analysis partially employs the definition provided by the European Union to distinguish between ‘micro’, ‘small’, and ‘medium’ sized firms. I use both financial and non-financial information to predict firms’ failure hazard. I estimate separate hazard models for each sub-category of SMEs, and compare their performance with a SMEs hazard model including all the three sub-categories. I test my hypotheses using discrete-time duration-dependent hazard rate modelling techniques, which controls for both macro-economic conditions and survival time. My test results strongly highlight the differences in the credit risk attributes of ‘micro’ firms and SMEs, while it does not support the need to consider ‘small’ and ‘medium’ firms’ category separately while modelling credit risk for them, as almost the same sets of explanatory variables affect the failure hazard of SMEs, ‘small’ and ‘medium’ firms.My fourth essay (chapter 6) considers all serious and neglected concerns while developing discrete and continuous time duration dependent hazard models for predicting failure of US SMEs. I compare theoretical and classification performance aspects of three popular hazard models, namely discrete hazard models with logit and clog-log links and the extended Cox model. I report that discrete hazard models are superior to extended Cox models in making default predictions. I also propose a default definition for SMEs which considers both legal bankruptcy laws and firms’ financial health while defining the default event. My empirical results show that my default definition performs significantly better than the default definitions which are only based on legal consequence or firms’ financial health in identifying distressed firms. In addition, my default definition also shows superior goodness of fit measures across all econometric specifications

Three essays on SMEs credit risk and capital structure

The main purpose of this study is to provide further insight into the SMEs’ credit risk and capital structure. Thus, this thesis presents three essays on SMEs probability of bankruptcy and capital structure in chapter 3 to chapter 5. The first empirical chapter investigates the extent to which size affects the SMEs probabilities of bankruptcy. I use a dataset of (11,117) US non-financial firms, of which (465) filed for insolvency under chapters 7/11 between 1980 and 2013. I forecast the bankruptcy probabilities by developing four discrete-time duration-dependant hazard models for SMEs, Micro, Small, and Medium firms. A comparison of the default prediction models for medium firms and SMEs suggest that an almost identical set of explanatory variables affect the default probabilities leading us to believe that treating each of these groups separately has no material impact on the decision making process. However, comparisons of the micro and small firms with the SMEs firms strongly suggest that they need to be considered separately when modelling credit risk for them.The second empirical chapter investigates the reasons for SMEs’ choice of being debt-free in their capital structure. Furthermore, I study to what extent different SME size segments (namely micro, small, and medium) affect the debt-free decision. I use a dataset of 95,450 firm-year observations of which there are 18,764 debt-free firm-year observations. I find that borrowing constraints and financing activities play a significant role in the debt-free capital structure decisions of the SMEs. A surprising result is that a large number of debt-free SMEs pay significantly higher dividends than their counterparts with debt. Finally, I find that pension obligations, and lease commitments do not play a significant role in explaining the debt-free policy. However, when conducting the logit regressions on entry and exit decisions of the debt-free SMEs I find that the NDTS plays a significant role in explaining the firm’s decision whether to enter or exit the debt-free status.According to the capital structure hypothesis, if firms deviate too far from their optimum capital structure they will not maximize their value. However, an increasing number of firms across different countries follow a debt-free policy, preferring to have no leverage compared to that which would maximize the firm value. In line with the above statement, the third chapter tries to address the question of what is the impact of a debt-free decision on the default risk of SMEs in the US market and how this substantial deviation from the optimal capital structure affects the SMEs’ probabilities of failure compared to their leveraged counterparts. I forecast the bankruptcy probabilities by developing two discrete-time duration-dependant hazard models for debt and debt-free models. A comparison between the models shows that four explanatory variables: the research and development ratio, tangible assets, abnormal capital expenditure, and asset sales affect the probability of bankruptcy differently for each model, thus suggesting a potential need to treat debt and debt-free SMEs separately when modelling credit risk

Asset securitization for small and medium-sized real estate enterprises in China

Based on the economic background of strict regulation and control of real estate bank credit and on the background of vigorous development of the financial system of direct financing in China, this thesis analyzes the necessity and feasibility of asset securitization financing for small and medium-sized real estate cmpanies. It shows that this type of financing is not only in line with the direction of the dual economic and financial reforms, but also that small and medium-sized real estate companies can meet their financing needs by relying on their specific high-quality assets rather than the main body credit financing. Using Company A as a typical case, we carried out design of the securitization program for the underlying asset pool construction, choice of bankruptcy remote and credit enhancement instruments, and product tranches, based on the characteristics of the asset composition status and low credit rating of the main body. Not only does it meet financing needs, but it also improves other financial situations. Therefore, this thesis has reference value, promotion, and practical significance for small and medium-sized real estate companies.Este trabalho analisa a necessidade e a viabilidade do financiamento de pequenas e médias empresas imobiliárias por via da securitização de ativos, no contexto económico da rigorosa regulamentação do crédito imobiliário e do vigoroso desenvolvimento do sistema de financiamento direto na China. Demonstra-se que este tipo de financiamento não só está de acordo com a direção da dupla reforma no âmbito da economia e das finanças, mas também que as pequenas e médias empresas do setor imobiliário podem satisfazer as suas necessidades de financiamento por via dos seus ativos específicos de alta qualidade como alternativa ao crédito por financiamento bancário. Utilizando a empresa A como um caso típico, elaborou-se o desenho do programa de securitização para uma pool de ativos subjacentes, a seleção de instrumentos de isolamento de insolvência e de melhoria do nível do crédito, e o desenho do programa de securitização com tranches de produtos, com base nas características dos ativos e na avaliação de crédito da estrutura principal. Isto não só satisfez as necessidades de financiamento, como também melhorou outras condições financeiras. Por conseguinte, este trabalho tem valor de demonstração, de divulgação e significado prático para as pequenas e médias empresas imobiliárias chinesas

Predicting Business Distress Using Neural Network in SME-Arab Region

The paper analyzes the financial and operational measures for Small and medium-sized enterprises (SME) business distress for predicting credit worthiness by using panel data of 110 observations from 22 SME companies for a period of 5 years (2009 – 2013). Panel logistic and Neural Network (NN) models are developed as alternative techniques for predicting the business distress.  The result suggests that cash cycle, net fixed assets, and leverage ratio are key factors in making credit decisions by lenders. The logistic model overall correctly classified 70 percent while NN framework outperformed the logistic model with 93 percent overall correct classification in training phase, and 83 percent in testing phase. The study opens up potential opportunities for the lending firms to adopt advanced analytical frameworks for predicting distress behavior of business firms. Keywords: SME, Business distress, Arab region, Petrochemical sub-sectors, Logit Model, Neural Network.   JEL codes: G29, G3

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

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

A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models

This paper presents a cross-country comparison of significant predictors of small business failure between Italy and the UK. Financial measures of profitability, leverage, coverage, liquidity, scale and non-financial information are explored, some commonalities and differences are highlighted. Several models are considered, starting with the logistic regression which is a standard approach in credit risk modelling. Some important improvements are investigated. Generalised Extreme Value (GEV) regression is applied in contrast to the logistic regression in order to produce more conservative estimates of default probability. The assumption of non-linearity is relaxed through application of BGEVA, non-parametric additive model based on the GEV link function. Two methods of handling missing values are compared: multiple imputation and Weights of Evidence (WoE) transformation. The results suggest that the best predictive performance is obtained by BGEVA, thus implying the necessity of taking into account the low volume of defaults and non-linear patterns when modelling SME performance. WoE for the majority of models considered show better prediction as compared to multiple imputation, suggesting that missing values could be informative

Comparison between the financial structure of SMES and that of large enterprises (LES) using the BACH database.

This study examines the financial structures and the performances of small and medium-sized enterprises (SMEs) as opposed to large enterprises (LEs) on the basis of the BACH database, which is the most advanced publicly available database for comparisons in this field. It covers the period 1990-1996 and concerns 9 countries: Austria, Belgium, France, Germany, Italy, Portugal, Spain, Japan and the United States. It deals with manufacturing only since this industry provides the best-quality data.SMES, LES, BACH Database, capitcal structures, financial structure and capital, profitability