scholarly journals Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling

2018 ◽  
Vol 13 (4) ◽  
pp. 57
Author(s):  
Francesco Ciampi
Keyword(s):  
Logistic Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
The Other ◽  
Financial Ratios ◽  
Small Enterprise ◽  
Forecast Horizon ◽  
Default Prediction ◽  
Size Groups ◽  
Prediction Modelling

This study aims to verify the potential of combining prior payment behavior variables and financial ratios for SE default prediction modelling. Logistic regression was applied to a sample of 980 Italian SEs in order to calculate and compare two categories of default prediction models, one exclusively based on financial ratios and the other based also on company payment behavior related variables. The main findings are: 1) using prior payment behavior variables significantly improves the effectiveness of SE default prediction modelling; ii) the longer the forecast horizon and/or the smaller the size of the firms which are the object of analysis, the higher  the improvements in prediction accuracy that can be obtained by using also prior payment behavior variables as default predictors; iii) SE default prediction modelling should be separately implemented for different size groups of firms.

scholarly journals Empirics of Korean Shipping Companies’ Default Predictions

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 159
Author(s):  
Sunghwa Park ◽  
Hyunsok Kim ◽  
Janghan Kwon ◽  
Taeil Kim
Keyword(s):  
Logit Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Financial Ratios ◽  
Probability Of Default ◽  
Shipping Industry ◽  
Market Participants ◽  
Korean Economy ◽  
Default Prediction ◽  
Financial Characteristics

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

scholarly journals The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

2017 ◽  
Vol 12 (12) ◽  
pp. 251 ◽  
Author(s):  
Francesco Ciampi
Keyword(s):  
Central Italy ◽  
Manufacturing Firms ◽  
Financial Ratios ◽  
Small Enterprise ◽  
Small Enterprises ◽  
Units Of Analysis ◽  
Default Prediction ◽  
Multivariate Discriminant ◽  
Industry Sectors ◽  
Accuracy Rates

The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs’ risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.

scholarly journals Failure prediction of government funded start-up firms

2017 ◽  
Vol 14 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Oliver Lukason ◽  
Kaspar Käsper
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Financial Development ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Development Patterns ◽  
Start Up

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.

scholarly journals The usage of logistic regression and artificial neural networks for evaluation and predicting property-liability insurers' solvency in Egypt

2021 ◽  
Vol 1 (3) ◽  
pp. 215-234
Author(s):  
Eid Elghaly Hassan ◽  
◽  
Diping Zhang ◽  
Keyword(s):  
Logistic Regression ◽  
Financial Performance ◽  
Prediction Models ◽  
Retention Rate ◽  
Financial Ratios ◽  
Insurance Companies ◽  
Fiscal Year ◽  
Stepwise Logistic Regression ◽  
Favorable Result ◽  
Artificial Neural

<abstract> <p>Unlike prior solvency prediction studies conducted in Egypt, this study aims to set up a real picture of companies' financial performance in the Egyptian insurance market. Therefore, 11 financial ratios commonly used by NAIC, AM BEST Company, and S &amp; P Global Ratings were calculated for all property-liability insurance companies in Egypt from 2010 to 2020. They have been used to measure those companies' financial performance efficiency levels by comparing these ratios with the international standard limits. The financial analysis results for those companies revealed that property-liability insurers in Egypt do not have the same level of financial performance efficiency where those companies are classified into three groups: excellent, good, and poor. Furthermore, this paper investigates using the stepwise logistic regression model to determine the most factors among these selected financial ratios that influence those companies' financial performance. The results suggest that only three ratios were statistically significant predictors: "Risk retention rate", "Insurance account receivable to total assets", and "Net profit after tax to total assets". Finally, this paper presents the multi-layers artificial neural network with a backpropagation algorithm as a new solvency prediction model with perfect classifying accuracy of 100%. The trained ANN could predict the next fiscal year with a prediction accuracy of 91.67%, and this percent is a good and favorable result comparing to other solvency prediction models used in Egypt.</p> </abstract>

scholarly journals PREDICTIVE POTENTIAL AND RISKS OF SELECTED BANKRUPTCY PREDICTION MODELS IN THE SLOVAK BUSINESS ENVIRONMENT

2017 ◽  
Vol 18 (6) ◽  
pp. 1156-1173 ◽  
Author(s):  
Beata GAVUROVA ◽  
Miroslava PACKOVA ◽  
Maria MISANKOVA ◽  
Lubos SMRCKA
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
The Other ◽  
Prediction Ability ◽  
Other Hand ◽  
Analytical Structure ◽  
Ohlson Model ◽  
Altman Model

In our study, we focused on the assessment of four bankruptcy prediction models, to figure out which model is most appropriate in the conditions of the Slovak business environment. Based on the previous research within the Slovak conditions, we set a portfolio of 4 models to be assessed: Altman model (1984), Ohlson model (1980), indexes IN01 and IN05 that were validated on the sample of 700 Slovak companies. Based on previous studies we expected that IN indexes are superior to Ohlson and Altman model. The excellency of our research lies in validation and assessing the accuracy of bankruptcy prediction models at three levels: the overall accuracy, accuracy of the bankruptcy prediction, and the non-bankruptcy prediction accuracy. This analytical structure enables to look at the topic more complexly and to increase the objectification of accuracy of analysed models. Based on the results, we showed that Ohlson model is not applicable to predict bankruptcy in the Slovak conditions as reached the lowest bankruptcy prediction ability even if has high non bankruptcy prediction ability. On the other hand, we have confirmed our expectation about the bankruptcy prediction ability of index IN05, that is proven to be superior to Ohlson and Altman model and so is the most appropriate model for Slovak business environment.

A territorial perspective of SME’s default prediction models

2018 ◽  
Vol 35 (4) ◽  
pp. 542-563 ◽  
Author(s):  
Linda Gabbianelli
Keyword(s):  
Financial Institutions ◽  
Prediction Models ◽  
Financial Ratios ◽  
Business Failure ◽  
Competitive Strategies ◽  
Content Type ◽  
Default Prediction ◽  
Qualitative Variables ◽  
The Relationship ◽  
Accuracy Rates

Purpose The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models. Design/methodology/approach The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory. Findings Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved. Research limitations/implications The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included. Practical implications The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit. Originality/value The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.

scholarly journals Tax Arrears Versus Financial Ratios in Bankruptcy Prediction

2019 ◽  
Vol 12 (4) ◽  
pp. 187 ◽  
Author(s):  
Oliver Lukason ◽  
Art Andresson
Keyword(s):  
Logistic Regression ◽  
Multilayer Perceptron ◽  
Prediction Accuracy ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Prediction Methods ◽  
Combined Model ◽  
The Individual ◽  
Practical Implications

This paper aims to compare the usefulness of tax arrears and financial ratios in bankruptcy prediction. The analysis is based on the whole population of Estonian bankrupted and survived SMEs from 2013 to 2017. Logistic regression and multilayer perceptron are used as the prediction methods. The results indicate that closer to bankruptcy, tax arrears’ information yields a higher prediction accuracy than financial ratios. A combined model of tax arrears and financial ratios is more useful than the individual models. The results enable us to outline several theoretical and practical implications.

Evaluating Aging Pedestrian Crash Severity with Bayesian Complementary Log–Log Model for Improved Prediction Accuracy

2017 ◽  
Vol 2659 (1) ◽  
pp. 155-163 ◽  
Author(s):  
Angela E. Kitali ◽  
Emmanuel Kidando ◽  
Thobias Sando ◽  
Ren Moses ◽  
Eren Erman Ozguven
Keyword(s):  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Injury Severity ◽  
Prediction Models ◽  
Information Criteria ◽  
The Other ◽  
Severe Injury ◽  
Logit Models ◽  
Operating Characteristics ◽  
Severe Injuries

Reliable prediction accuracy is an essential attribute for crash prediction models. Generally, more severe injury outcomes, such as fatalities, are rarer than less severe crashes, such as property damage only or minor injury crashes. The complementary log–log (cloglog) model, commonly used in epidemiological research, is known for its accuracy in predicting rare events. This study implemented the cloglog model in analyzing pedestrian injury severity and compared its performance with the two conventional models used in injury severity research: the probit and logit models. The three models were developed with data from 1,397 crashes involving aging pedestrians that occurred in Florida from 2009 through 2013. The response variable, injury severity level, was binary and categorized as either fatal or severe injury or minor or no injury. The study used three accuracy metrics (deviance information criteria, prediction accuracy, and receiver operating characteristics curves) to compare the performance of the models. The cloglog model outperformed the probit and logit models in overall goodness of fit and prediction accuracy. More important, the cloglog model outperformed the other two models considerably for predicting fatal and severe crashes according to the recall metric (72% accuracy versus 43% and 41% for probit and logit models, respectively). However, the other two models outperformed the cloglog model in predicting crashes with no or minor injuries. Of predictor variables included in the model, six were found to significantly influence fatal or severe injuries for aging pedestrians at 95% Bayesian credible interval. These variables included pedestrian age, alcohol involvement, first harmful event, vehicle movement, shoulder type, and posted speed.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

scholarly journals Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

2021 ◽  
Vol 13 (7) ◽  
pp. 3870
Author(s):  
Mehrbakhsh Nilashi ◽  
Shahla Asadi ◽  
Rabab Ali Abumalloh ◽  
Sarminah Samad ◽  
Fahad Ghabban ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sustainability Assessment ◽  
Regression Trees ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Sustainability Performance ◽  
Learning Techniques ◽  
Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

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