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Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
The development of Internet technologies makes it possible to obtain data in near real time about the financial state of companies. Moreover, tools such as XBRL have been developed to deal with the automatic generation of business reports. However the available tools are not suitable to support the current tendency towards the so called, Continuous Reporting. Here, for a specific purpose, the wealth...
The paper introduces decision tree algorithm and C5.0 algorithm in the data mining at first. Then it introduces financial analysis methods, the problems which need to pay attention to in application and the selection process of attributes. At last, we study the financial ratios of listed logistics companies through the application of SPSS Clenmentine12.0 software. The accuracy of this model is as...
Cost-sensitive learning is of critical importance in many domains including bankruptcy prediction where the costs of different errors are unequal. Most existing classification methods aim to minimize overall error based on the assumption that the costs are equal. This paper presents three cost-sensitive learning vector quantization (LVQ) approaches to incorporate cost matrix in classification. Experimental...
Summary form only given. In the past 20 years there has been an explosive growth of the variety of traded financial instruments, from European and American options to a more complex, alas ill-fated, credit derivatives. The rapid increase in computational power coupled with the use of mathematical tools for valuing these instruments and estimating the risk has given rise to the discipline of computational...
In this paper our method of discovering data sequences in the time series is presented. Two major approaches to this topic are considered. The first one, when we need to judge whether a given a series is similar to any of the known patterns and the second one when there is a necessity to find how many times within a long series a defined pattern occurs. In both cases the main problem is to recognize...
The paper built an analysis model of financial statements based on data mining methods, that is making data mining methods such as clustering, association rules and decision making tree work together to step by step go into deeper analysis of existing financial statements, during which a annual assets structure statement is worked out. The data used for research is from financial statements of electronic...
Credit-risk evaluation is a very challenging and important problem in the domain of financial analysis. Many classification methods have been proposed in the literature to tackle this problem. Statistical and neural network based approaches are among the most popular paradigms. However, most of these methods produce so called "hard" classifiers, those generate decisions without any accompanying...
The paper discusses how hypertext can be used to provide explanations in knowledge-based systems (KBS), from both conceptual and implementation perspectives. To this end, it proposes a generic approach to providing hypertext-based explanations, which is based on the functional match between hypertext and explanations in MPS. A simulated KBS for financial analysis (Hyper-FINALYZER) is also described...
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