Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning

Company bankruptcy is often a very big problem for companies. The impact of bankruptcy can cause losses to elements of the company such as owners, investors, employees, and consumers. One way to prevent bankruptcy is to predict the possibility of bankruptcy based on the company's financial data. Therefore, this study aims to find the best predictive model or method to predict company bankruptcy using the dataset from Polish companies bankruptcy. The prediction analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using feature importance to XGBoost with a weight value filter of 10. The ensemble learning method used is stacking. Stacking is composed of the base model and meta learner. The base model consists of K-nearest neighbor, decision tree, SVM, and random forest, while the meta learner used is LightGBM. The stacking model accuracy results can outperform the base model accuracy with an accuracy rate of 97%

Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model

Student satisfaction with courses in academic institutions is an important issue and is recognized as a form of support in ensuring effective and quality education, as well as enhancing student course experience. This paper investigates whether there is a connection between student satisfaction with courses and log data on student courses in a virtual learning environment. Furthermore, it explores whether a successful classification model for predicting student satisfaction with course can be developed based on course log data and compares the results obtained from implemented methods. The research was conducted at the Faculty of Education in Osijek and included analysis of log data and course satisfaction on a sample of third and fourth year students. Multilayer Perceptron (MLP) with different activation functions and Radial Basis Function (RBF) neural networks as well as classification tree models were developed, trained and tested in order to classify students into one of two categories of course satisfaction. Type I and type II errors, and input variable importance were used for model comparison and classification accuracy. The results indicate that a successful classification model using tested methods can be created. The MLP model provides the highest average classification accuracy and the lowest preference in misclassification of students with a low level of course satisfaction, although a t-test for the difference in proportions showed that the difference in performance between the compared models is not statistically significant. Student involvement in forum discussions is recognized as a valuable predictor of student satisfaction with courses in all observed models

Modelling bankruptcy prediction models in Slovak companies

An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks), there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future develop-ment becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice.Purpose of the article: This study aims to predict the bankruptcy of companies in the engineer-ing and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engi-neering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regres-sion to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bank-ruptcy using six of these indicators. Almost all sampled industries are privatised, and most com-panies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct com-parative analyses of their own model with ours to reveal areas of model improvements.KEGA [001PU-4/2022]; Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic; Slovak Academy Sciences [1/0590/22]1/0590/22; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR, KEGA: 001PU-4/202

Gestão de resultados como preditor de insolvências: evidência nas empresas portuguesas

As demonstrações financeiras reportadas pelas empresas nem sempre transparecem a verdadeira “saúde” das mesmas, visto recorrerem a mecanismos de Earnings Management com o intuito de alterar os resultados para os desejados nesse momento. Assim sendo, a insustentabilidade destas práticas de gestão de resultados, em virtude da obtenção de benefícios, pode originar situações de insolvência inesperadas. A presente dissertação pretende avaliar o impacto da existência de Earnings Management na previsão das insolvências, alvo de diversas investigações científicas ao longo dos últimos anos. Para tal, procedemos à análise de informações financeiras, compreendidas entre 2010 e 2018, para 17.665 empresas portuguesas, retiradas da Base de Dados Bureau van Djik’s Amadeus. Adicionalmente, foram calculados quatro modelos que se debruçam sobre o estudo da gestão de resultados assentes em accruals, o Original Jones, o Modified Jones, o Cash-flow Jones e Performance Jones, assim como quatro variáveis de controlo consideradas relevantes. Deste modo, suportámos esta investigação em testes aos modelos estatísticos de previsão existentes na literatura, que visaram o Modelo Logístico e posteriormente o recurso às Árvores de Decisão, através dos softwares SPSS Statistics e SPSS Modeler. Por fim, os resultados obtidos indiciaram a existência de uma relação entre os Earnings Management (em conjunto com as variáveis de controlo) e as insolvências das empresas, prevendo uma percentagem de exemplos corretamente classificados de 96% (para empresas insolventes), comprovada para uma amostra com uma abrangência mais alargada, quando comparada com os restantes estudos presentes na literatura.The financial statements reported by the companies do not always show their true "health", since they resort to Earnings Management mechanisms in order to change the results to those desired at that time. Therefore, the unsustainability of these Earnings Management practices, linked with the obtaining of benefits, can emerge in unexpected situations of insolvency. This dissertation aims to evaluate the impact of the existence of Earnings Management in the insolvency forecasts, target of several scientific investigations over the last years. To this end, we have analyzed financial information, between 2010 and 2018, for 17.665 portuguese companies, through the Bureau van Djik's Amadeus Database. In addition, we calculated four models that focus on the study of Earnings Management based on accruals, Original Jones, Modified Jones, Cash-flow Jones and Performance Jones, as well as four control variables considered relevant. Consequently, we supported this research in tests of statistical prediction models in the literature, which targeted the logistic model and later the use of Decision Trees, through the software SPSS Statistics and SPSS Modeler. Finally, the results obtained indicated the existence of a relationship between Earnings Management (along with the control variables) and company insolvencies, achieving an accuracy of 96% (for insolvent companies), proven for a sample with a wider scope, when compared with other studies present in the literature

Predicting business failure using artificial intelligence system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information for decision making for financial institutions, investors, creditors, and other participants in the business market. Financial reporting systems provide relevant information that can be used to assess the financial position of firms. It is crucial to have classification and prediction models that can analyse this financial information and provide accurate assurance for users about business health. Recent studies have explored the use of machine learning tools as substitute for traditional statistical methods to develop classification models to classify firm insolvency according to financial statement information. However, these models have no ideal classifier, since each provides a certain percentage of wrong outputs, which is a crucial consideration; every percentage of wrong response can mean massive financial losses for stakeholders. Therefore, this study proposes new insolvency classification and perdition models based on machine learning modelling techniques to develop an improved classifier. Individual modelling techniques using statistical methods and machine learning were used to develop the classification model of business insolvency. The results showed that machine learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest performance based on all performance measurements used in the study, and it was the best individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble- Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it has been proven that DPL modelling approach is useful for business insolvency classification. A key contribution in enhancing individual classifier outputs is the use of traditional combining methods with two new aggregation methods in business insolvency (Fuzzy Logic and Consensus Approach). The Consensus Approach showed the best improvement in the results of all individual classifiers with average accuracy of 97.7%, and it is considered the best classification method not only in comparison with individual classifiers, but also with traditional combiners. This study pioneers the development of a time series business insolvency prediction model with Big Data for UK businesses. The aim of the model is to provide early prediction about a business health. Three prediction models were developed based on Nonlinear Autoregressive with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR), and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of 83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in comparison with the best individual classifier (deep learning)