An artificial neural network approach for assigning rating judgements to Italian Small Firms

Based on new regulations of Basel II Accord in 2004, banks and financial nstitutions have now the possibility to develop internal rating systems with the aim of correctly udging financial health status of firms. This study analyses the situation of Italian small firms that are difficult to judge because their economic and financial data are often not available. The intend of this work is to propose a simulation framework to give a rating judgements to firms presenting poor financial information. The model assigns a rating judgement that is a simulated counterpart of that done by Bureau van Dijk-K Finance (BvD). Assigning rating score to small firms with problem of poor availability of financial data is really problematic. Nevertheless, in Italy the majority of firms are small and there is not a law that requires to firms to deposit balance-sheet in a detailed form. For this reason the model proposed in this work is a three-layer framework that allows us to assign ating judgements to small enterprises using simple balance-sheet data.rating judgements, artificial neural networks, feature selection

A Framework for Enterprise Knowledge Discovery from Databases

Knowledge discovery from large databases has become an emerging research topic and application area in recent years primarily because of the successful introduction of large business information systems to enterprises in the electronic business era. However, transferring subjects/problems from managerial perspective to data mining tasks from information technology perspective requires multidisciplinary domain knowledge. This paper proposes a practical framework for enterprise knowledge discovery in a systematical manner. The six-step framework employs the cause-andeffect diagram to model enterprise processes, tasks and attributes corresponding diagram to define data mining tasks, and multi-criteria method to assess the mined results in the form of association rules. This research also applied the proposed framework to a real case study of knowledge discovery from service records. The mining results have been proven useful in product design and quality improvement and the framework has demonstrated its applicability of guiding an enterprise to discover knowledge from historical data to tackle existing problems

Deploying data mining techniques to gain deeper insight into Nigerian customers' financial activities

Presently, Nigerian banks issue account statements in a tabular flat form. These statements mainly show basic logs of credit and debit transactions. They do not offer a deeper insight into the pure nature of transactions. Moreover, they lack rich mine-able data, and rather contain basic data tables that do not provide enough insights into customers' monthly/weekly/yearly expenses and earnings. In today’s fast-paced digital world, where information processing methods are rapidly changing, customers need not just a basic table of transactions but deeper analysis and detail report of their finances. This paper aims at identifying and addressing these problems by deploying data mining techniques and practices in building an application that helps customers gain a deeper insight and understanding of their spending and earnings over a particular period. Some of the techniques used are classification, statistical analysis, visualization, report generation and summarization. Keywords: Data mining, API, Anomaly Detection, GTBank, CBN, Bank statements, Nigeri

Prediction of Corporate Bankruptcy using Financial Ratios and News

A corporate’s insolvency can have catastrophic effects on not only the corporate but also on the returns of its lenders and investors. Predicting bankruptcy has been one of the most sought-after areas for researchers for many decades. This study involves predicting the bankruptcy of the United States corporates using financial ratios and news data. The financial ratios of the companies were extracted from yearly financial reports of the companies, and the news data of the companies was scrapped from online newspapers, reports and articles using Google News. The news data was analyzed for negative and positive sentiments. The sentiment scores, along with the financial ratios of the companies, were given as features to the machine learning models. Various models were analyzed for their results such as Random Forest, Logistic Regression and Support Vector Machines (SVM). The study finds the best results from the random forest model with an accuracy of 90%. Moreover, the significant feature importance of the sentiment score given by the model proves that unstructured data, such as news, can play a crucial part in predicting bankruptcy in conjunction with the structured data, such as financial ratios

Classifying Firms’ Performance Using Data Mining Approaches

Superior prediction and classification in determining company’s performance are major concern for practitioners and academic research in providing useful or important information to the shareholders and potential investors for investment decision. Generally, the normal practice to analysed firm’s performance are based on financial indicators reported in the company’s annual report including the balance sheet, income and cash flow statements. In this work, a few popular and important benchmarking machine learning techniques for the data mining including neural networks, support vector machine, rough set theory, discriminant analysis, logistic regression, decision table, sequential minimal optimization and decision tree have been tested as to classify firm’s performance. The data mining techniques produce high classification rate that is more than 92%. This work also has reduced total number of ratios to be evaluated due to long processing time and large processing resources. Finally, the CA/TA, S/TA, E/TA, GM, FC, PBT/TA, and EPS have been considered for of the final reduced financial ratios. The results show that the 7 reduced ratios are comparable as the common 24 ratios. And to the still produce high classification rate and able classify the firm’s performance

Predicting Corporate Bankruptcy: Lessons from the Past

The need for corporate bankruptcy prediction models arises in 1960 after the increase in incidence of some major bankruptcies. Over the years, the episodes of financial turmoil increase in number and so does these bankruptcy prediction models. Existing reviews of bankruptcy models are either narrowly focused or outdated. Current study aims to provide an overview of the existing models for predicting bankruptcy and review the significance of these models. Furthermore, it highlights the problems and issues in the existing models which hinders the accuracy in predicting bankruptcy

Predicting financial distress of agriculture companies in EU

The objective of this paper is prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models we chose Logistic regression, Support vector machines method with RBF ANOVA kernel, Decision trees and Adaptive boosting based on decision trees to acquire the best results. From the results it is obvious that with the rising distance to the bankruptcy there drops average accuracy of financial distress prediction and there is a greater difference between active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive boosting offer better accuracy for distress prediction than SVM and logit methods, what is comparable to previous studies. From overall of 15 accounting variables, we construct classification trees by Decision trees with inner feature selection method for better vizualization, what reduce 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 discrimination of distressed companies.O

Bankruptcy prediction of engineering companies in the EU using classification methods

This article focuses on the problem of binary classification of 902 small- and medium-sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.O

Failure prediction of European high-tech companies

The aim of this thesis is to develop a model for predicting the failure of high-tech and mediumhigh tech companies from different European countries. This study uses firm-level data from the Bureau van Dijk’s Amadeus database and includes the financial information of 32,929 firms. The data were collected from the financial statements of the companies for the period 2012–2017 and logistic regression was used as the analysis method. Findings indicate that the accuracies of individual variables across countries are not very high and there are large differences in the accuracies of individual ratios when comparing non-failed and failed firms. Aggregate accuracies for all ratios within country and across countries show that the most accurate predictions are obtained for non-failed firms using the ratios for the preceding two years combined. The practical value of this work lies in the knowledge of the relevant variables, which allows companies to focus in a timely manner on aspects that have determined failure in the past. Subsequent works should attempt to use a larger sample of European countries and include other variables in addition to financial ratios