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Neural networks and genetic algorithms for bankruptcy predictions

Abstract We are focusing on three alternative techniques-linear discriminant analysis, logit analysis and genetic algorithms-that can be used to empirically select predictors for neural networks in failure prediction. The selected techniques all have different assumptions about the relationships between the independent variables. Linear discriminant analysis is based on linear combination of independent variables, logit analysis uses the logistical cumulative function and genetic algorithms is a global search procedure based on the mechanics of natural selection and natural genetics. In an empirical test all three selection methods chose different bankruptcy prediction variables. The best prediction results were achieved when using genetic algorithms.

Combining Data and Text Mining Techniques for Analysing Financial Reports

There is a vast amount of financial information on companies’ financial performance available to investors today. While automatic analysis of financial figures is common, it has been difficult to automatically extract meaning from the textual part of financial reports. The textual part of an annual report contains richer information than the financial ratios. In this paper, we combine data mining methods for analyzing quantitative and qualitative data from financial reports, in order to see if the textual part of the report contains some indication about future financial performance. The quantitative analysis has been performed using selforganizing maps, and the qualitative analysis using prototype-matching text clustering. The analysis is performed on the quarterly reports of three leading companies in the telecommunications sector.

Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking:

In this paper, we illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995–2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. The results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.

Comparing numerical data and text information from annual reports using self-organizing maps

Abstract More and more companies provide their accounting information in electronic form today. The accounting information in electronic form can be found in large commercial databases or on the web. This information is of great interest for different stakeholders, i.e., stockholders, creditors, auditors, financial analysts, and management. For the stakeholders it is important to be able to extract both quantitative and qualitative information concerning the companies they are interested in. The annual reports contain information both in numerical and symbolic form. So far, only the numerical information has been analyzed with help of computers. However, technology has evolved and in particular neural networks in the form of self-organizing maps (SOMs) provide a new tool for analyzing also text information. In this paper, we compare results on quantitative data with results on qualitative data from annual reports. We use smart encoding, SOMs, and document histograms for comparing the performance of forest companies worldwide. Firstly, we cluster the companies according to, on the one hand, quantitative information, and on the other hand, qualitative information. Secondly, we compare the results produced by the clustering methods. Our results of the comparison show that there is a difference between the results.

The language of quarterly reports as an indicator of change in the company's financial status

This paper adopts a multi-methodological approach to information systems research in order to produce new information through data mining. This approach is particularly suitable for mining material that consists of both qualitative and quantitative information. The contents of quarterly reports from three telecommunications companies were compared. The study focused on the years 2000-2001, a period of economic decline for many IT companies. The central quantitative data, reflected by seven financial ratios, were visualised using self-organising maps. The qualitative data, consisting of the textual contents of the reports, were visualised using collocational networks; these showed the relationships between the central concepts in the texts. As the visualisations of the contents were compared, certain patterns could be found. The results seemed to suggest that changes in the networks indicated future changes in the self-organising maps. In the cases studied, a change in the textual data usually indicated a change in the financial data in the following quarter. This may be a consequence of the fact that the texts reflected the plans and future expectations of management, whereas the financial ratios reflected the current financial situation of the company.

Validating an expert system for financial statement planning

Validation is often considered the cornerstone of expert systems evaluation. Validating expert systems has, however, turned out to be a difficult task because an expert system is often both a piece of software and a model. The general procedures for validation of traditional software cannot, therefore, usually be followed when validating an expert system. This article shows how the validation of a concrete full-scale expert system for financial statement planning was conducted, utilizing guidelines on validation of expert systems given in literature and incorporating them into the classical spiral model used in developing the system.

Customer portfolio analysis using the SOM

In order to compete for profitable customers, companies are looking to add value using customer relationship management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analysing the profitability of customers. The purpose of this paper is to illustrate how the self-organising map (SOM) can be used for CPA. We segment, identify and analyse a case organisations profitable and unprofitable customers in order to gain knowledge for the organisation to develop its marketing strategies. The results are validated through cross and face validation. The SOM is able to segment the data in an innovative and reliable way and to provide new insights for decision makers.

Combining unsupervised and supervised data mining techniques for conducting customer portfolio analysis

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. In this study, we apply a two-level approach that combines SOM-Ward clustering and decision trees to conduct customer portfolio analysis for a case company. The created two-level model was then used to identify potential high-value customers from the customer base. It was found that this hybrid approach could provide more detailed and accurate information about the customer base for tailoring actionable marketing strategies.

Combining visual customer segmentation and response modeling

Customer relationship management is a central part of Business Intelligence, and sales campaigns are often used for improving customer relationships. This paper uses advanced analytics to explore customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer-segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment-migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment-migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than 1 million customers.

Industry‐specific cycles and companies' financial performance comparison using self‐organizing maps

Multilevel environment analysis is important for companies operating on the global market. Previous studies have in general focused on one level at a time, but the need to perform multilevel environment analysis has also been stressed. Multilevel analysis can partly explain the benchmarking gap between companies, as changing conditions in the upper environment levels affect lower levels. In todays information‐rich era, it is difficult to conduct multilevel analysis without suitable computational tools. This paper illustrates how the self‐organizing map can be used for the simultaneous comparison of industry‐level changes and financial performance of pulp and paper companies. The study shows the importance of simultaneous analysis, as some simultaneous changes were found at both industry and corporate levels. Also found were some industry‐specific explanatory factors for good (Scandinavian companies) and poor (Japanese companies) financial performance. The results indicate that the self‐organizing map could be a suitable tool when the purpose is to visualize large masses of multilevel data from high‐dimensional databases.