International Effect on Family SME Financial Distress Prediction

Understanding the reasons of default risk is crucial to avoid the firm's bankruptcy. The purpose of this work is to analyze the impact of internationalization on firm's probability of distress. For it, this chapter aims to propose a model to predict default specific to family SMEs (small and medium enterprises). An unbalanced panel of 10,832 firms over the period from 2012-2018 is analyzed. Ex-ante criteria to classify firms in default or compliant is used. International SMEs have lower probability of default than domestic firms, and compliant firms export more. Results show that export ratio is an important determinant of the probability of default. Moreover, the ratios of liquidity, profitability, size, leverage, efficiency, cash flow, and age are also relevant. Moreover, these ratios explain default risk of both groups international and domestic SMEs. The proposed model has an accuracy of 92.9%, which increases to 95.6% if only export SMEs are analyzed.

FINANCIAL DISTRESS PREDICTION: A NOVEL DATA SEGMENTATION RESEARCH ON CHINESE LISTED COMPANIES

In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the internal changes of each listing state. Considering that the requirement of the withdrawal of ST in the mechanism is relatively loose, we propose a new segmentation approach for Chinese listed companies, which are divided into negative companies and positive companies according to the number of times being labeled ST. Under the framework of data mining, we use financial indicators, non-financial indicators, and time series to build a financial distress prediction model of distinguishing the long-term development of different Chinese listed companies. Through data segmentation, we find that the negative samples have a huge destructive interference on the prediction effect of the total sample. On the contrary, positive companies improve the prediction accuracy in all aspects and the optimal feature set is also different from all companies. The main contribution of the paper is to analyze the internal impact of the deterioration of financial distress prediction in time series and construct an optimization model for positive companies.

Non-financial variables related to governance and financial distress prediction in SMEs–evidence from Egypt

PurposeThis study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.Design/methodology/approachThis study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.FindingsThe results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .Research limitations/implicationsAlthough the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.Practical implicationsThe proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.Originality/valueThis study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.

PENGARUH CORPORATE GOVERNANCE DAN FIRM SIZE TERHADAP FINANCIAL DISTRESS (Studi pada Perusahaan Perbankan yang terdaftar di Bursa Efek Indonesia periode 2017-2019)

This study aims to find empirical evidence of the influence of corporate governance and firm size on financial distress. The sample used in this study is a banking company listed on the Indonesia Stock Exchange (BEI) for the 2017-2019 period. The sampling technique used was purposive sampling and obtained a sample of 40 samples that met the criteria. The data analysis technique used is multiple regression analysis. The financial distress criteria in this study measured using the Z-score in Altman's financial distress prediction model. Based on the study results, it can be concluded that managerial ownership, the board of commissioners, and the audit committee have no effect on financial distress, while the board of directors has a positive and significant effect on financial distress and firm size has a negative and significant effect on financial distress.