scholarly journals A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Xu ◽  
Hongyong Fu ◽  
Yuchen Pan
Keyword(s):  
Financial Distress ◽  
Soft Set ◽  
Optimal Decision ◽  
Ensemble Model ◽  
Sample Sizes ◽  
Data Set ◽  
Financial Distress Prediction ◽  
Predicting Performance ◽  
Distress Prediction ◽  
Internet Searches

This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni-int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.

scholarly journals Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1886
Author(s):  
Michal Pavlicko ◽  
Marek Durica ◽  
Jaroslav Mazanec
Keyword(s):  
Prediction Model ◽  
Central Europe ◽  
Financial Distress ◽  
Prediction Models ◽  
Value Added ◽  
Ensemble Model ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction ◽  
Visegrad Group

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

scholarly journals Financial distress prediction of Islamic banks using tree-based stochastic techniques

2018 ◽  
Vol 44 (6) ◽  
pp. 759-773 ◽  
Author(s):  
Khaled Halteh ◽  
Kuldeep Kumar ◽  
Adrian Gepp
Keyword(s):  
Financial Distress ◽  
Gradient Boosting ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Z Score ◽  
Data Set ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Stochastic Gradient Boosting ◽  
Distress Prediction

PurposeFinancial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues.Design/methodology/approachThis paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables.FindingsThe results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking.Originality/valueThese findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

Using Accounting Ratios in Predicting Financial Distress: An Empirical Investigation in the Vietnam Stock Market

2015 ◽  
pp. 41-49
Author(s):  
Vinh Vo Xuan
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Financial Ratios ◽  
Ho Chi Minh City ◽  
Data Set ◽  
The Past ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Practical Research ◽  
Accounting Ratios

Financial distress prediction is an important and practical research topic for many stakeholders and has attracted extensive studies over the past decades. This paper investigates the challenging issue of financial distress in Vietnam by distinguishing “healthy” companies from “financially distressed” companies using a data sample of firms listed on the Ho Chi Minh City Stock Exchange. Employing the logistic regression model to predict financial distress with a unique data set, we characterize the determinants of financial distress in terms of firm accounting and financial ratios over the period from 2007 to 2012. The results indicate that financial ratios can be employed as an early warning of financial distress as financial ratios are significantly correlated with the probability of firm financial distress.

scholarly journals Predicting financial distress of agriculture companies in EU

2017 ◽  
Vol 63 (No. 8) ◽  
pp. 347-355 ◽  
Author(s):  
Klepac Václav ◽  
Hampel David
Keyword(s):  
Decision Trees ◽  
Financial Distress ◽  
Feature Selection Method ◽  
Support Vector ◽  
Full Data ◽  
Data Set ◽  
Adaptive Boosting ◽  
Financial Distress Prediction ◽  
Average Accuracy ◽  
Distress Prediction

The objective of this paper is the prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in the EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models, there was chosen the Logistic regression, the Support vector machines method with the RBF ANOVA kernel, the Decision Trees and the Adaptive Boosting based on the decision trees to acquire the best results. From the results, it is obvious that with the increasing distance to the bankruptcy, there decreases the average accuracy of the financial distress prediction and there is a greater difference between the active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive Boosting offer a better accuracy for the distress prediction than the SVM and logit methods, what is comparable to the previous studies. From the total of 15 accounting variables, there were constructed classification trees by the Decision Trees with the inner feature selection method for the better visualization, what reduces the full data set only to 1 or 2 attributes: ROA and Long-term Debt to Total Assets Ratio in 2011, ROA and Current Ratio in 2012, ROA in 2013 for the discrimination of the distressed companies.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

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