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

Multi-agent hybrid mechanism for financial risk management

Purpose: The goal of this study was to propose the multi-agent mechanism to forecast the corporate financial distress. Design/methodology/approach: This study utilized numerous methods, namely random subspace method, discriminant analysis and decision tree to construct the multi-agent forecasting model. Findings: The study shows a superior forecasting performance. Originality/value: The use of multi-agent model to predict the corporate financial distress.Peer Reviewe

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

Automotive manufacturing technologies – an international viewpoint

The automotive industry can be described as a backbone in many developed countries such as Japan, Korea, USA, and Germany, while being an enabler for economic prosperity in developing countries like China, Brazil, Eastern Europe, and Russia at the same time. However, the dynamics and uncertainty are increasing heavily by market changes, regulations, customer behavior, and new product technologies. Manufacturing research has to find answers to increase quality of products, flexibility of plants, and supply chain networks, to manage complexity in technologies and variants and overall to stay competitive even in high wage countries. In this paper, major technological challenges are discussed and the current state of manufacturing technology and research is presented. Moreover, for each technological and organizational area, future industrial, and research challenges are highlighted

A survey of multiple classifier systems as hybrid systems

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed

COMPARING VARIOUS MACHINE LEARNING METHODS FOR PREDICTION OF PATIENT REVISIT INTENTION: A CASE STUDY

Many techniques have been proposed for analysis of costumer intention, from surveys to statistical models. During the last few years, different machine learning approaches have successfully been applied to costumer-centric decision-making problems. In this study, we conduct a comparative assessment of the performance of ten widely used machine learning methods, (i.e., logistic regression, multilayer perceptron, support vector machines,  IBk linear NN search, KStar, locally weighted learning, decisionstump, C4.5., randomtree and  reduced error pruning tree) for the aim of suggesting appropriate machine learning techniques in the context of patient revisit intention prediction problem. Experimental results reveal that the C4.5 decision tree demonstrates to be the best predictive model since it has the highest overall average accuracy and a very low percentage error on both Type I and Type II errors, closely followed by the locally weighted learning and decisionstump, whereas the logistic regression and the IBk linear NN search algorithms appear to be the worst in terms of average accuracy and type II error. Besides the randomtree and the IBk linear NN search algorithms appear to be the worst in terms of type I error

ECONOMIC PERSPECTIVE ON ALGORITHM SELECTION FOR PREDICTIVE MAINTENANCE

The increasing availability of data and computing capacity drives optimization potential. In the industrial context, predictive maintenance is particularly promising and various algorithms are available for implementation. For the evaluation and selection of predictive maintenance algorithms, hitherto, statistical measures such as absolute and relative prediction errors are considered. However, algorithm selection from a purely statistical perspective may not necessarily lead to the optimal economic outcome as the two types of prediction errors (i.e., alpha error ignoring system failures versus beta error falsely indicating system failures) are negatively correlated, thus, cannot be jointly optimized and are associated with different costs. Therefore, we compare the prediction performance of three types of algorithms from an economic perspective, namely Artificial Neural Networks, Support Vector Machines, and Hotelling T² Control Charts. We show that the translation of statistical measures into a single cost-based objective function allows optimizing the individual algorithm parametrization as well as the un-ambiguous comparison among algorithms. In a real-life scenario of an industrial full-service provider we derive cost advantages of more than 17% compared to an algorithm selection based on purely statistical measures. This work contributes to the theoretical and practical knowledge on predictive maintenance algorithms and supports predictive maintenance investment decisions