Self-organizing neural networks for the analysis and representation of data: Some financial cases

Martin-del-Brio, B.; Serrano‐Cinca, Carlos

doi:10.1007/bf01414948

Cited by 83 publications

(21 citation statements)

References 13 publications

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“…For instance, the method has already been used for the analysis of states of banks. 14 The SOM formed a \solvency map," from which the state of the banks could beinferred at a glance. In time series analysis it is important that the nature of change in the state of the banks can be visualized on the map (e.g., as a s l o w shift toward the bankrupt region) even if the changes could not be predicted by the traditional methods.…”

Section: Discussionmentioning

confidence: 99%

Explorative Data Analysis Based on Self-organizing Maps and Automatic Map Analysis

Franzmeier

Witkowski

Rückert

2005

Computational Intelligence and Bioinspired Systems

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The self-organizing m ap (SOM) is a method that represents statistical data sets in an ordered fashion, as a natural groundwork on which t h e distributions of the individual indicators in the set can be displayed and analyzed. As a case study that instructs how to use the SOM to compare states of economic systems, the standard of living of different countries is analyzed using the SO M . B a s e d o n a g r eat number (39) of welfare indicators the SOM illustrates rather re ned relationships between the c o u n tries two-dimensionally. This method is directly applicable to the nancial grading o f c o m panies, too.

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Section: Discussionmentioning

confidence: 99%

Explorative Data Analysis Based on Self-organizing Maps and Automatic Map Analysis

Franzmeier

Witkowski

Rückert

2005

Computational Intelligence and Bioinspired Systems

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“…For example, the SOM has been used for financial benchmarking (Back et al 1998;Eklund et al 2003), macro-economic analysis (Kaski and Kohonen 1996;Länsiluoto 2007) , and bankruptcy prediction (Back et al 1995;Kiviluoto 1998;Martín-del-Brío and Serrano-Cinca 1993) . However, regardless of its obvious benefits, the SOM has not been widely applied in customer segmentation tasks.…”

Section: The Sommentioning

confidence: 99%

Customer portfolio analysis using the SOM

Holmbom

Eklund

Back

2011

IJBIS

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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 analyzing the profitability of customers. This study was made for a case organization, who wanted to identify their profitable and unprofitable customers, in order to gain knowledge on how to develop their marketing strategies.Data about the customers were gathered from the case organization's own database. The Self-Organizing Map (SOM) was used to divide the customers into segments, which were then analyzed in light of product sales information.

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“…Self-organizing maps (SOMs) [13]- [15] have been previously proposed to group stocks based on their financial ratios. SOM is a visualization tool which allows users to see how entities are clustered together, but it is hard for users to define clear clusters of entities because the boundaries of the clusters are difficult to distinguish.…”

Section: Self-organising Map (Som)mentioning

confidence: 99%

“…Fundamental analysts usually group companies (and consequently, stocks) that have similar financial health status by clustering them based on their financial ratios [13]- [15]. Once clusters are obtained, it is useful to understand which financial ratios the cluster of stocks have close similarities in, so that analysts can investigate the reasons behind it.…”

Section: Introductionmentioning

confidence: 99%

Mining maximal quasi‐bicliques: Novel algorithm and applications in the stock market and protein networks

Sim¹,

Li²,

Gopalkrishnan³

et al. 2009

Statistical Analysis

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Abstract-Several real world applications require mining of bicliques, as they represent correlated pairs of data clusters. However, the mining quality is adversely affected by missing and noisy data. Moreover, some applications only require strong interactions between data members of the pairs, but bicliques are pairs that display complete interactions. We address these two limitations by proposing maximal quasi-bicliques. Maximal quasibicliques tolerate erroneous and missing data, and also relax the interactions between the data members of their pairs. Besides, maximal quasi-bicliques do not suffer from skewed distribution of missing edges that prior quasi-bicliques have. We develop an algorithm MQBminer, which mines the complete set of maximal quasi-bicliques from either bipartite or non-bipartite graphs. We demonstrate the versatility and effectiveness of maximal quasibicliques to discover highly correlated pairs of data in two diverse real world datasets. Firstly, we propose to solve a novel financial stocks analysis problem by using maximal quasi-bicliques to cocluster stocks and financial ratios. Results show that the stocks in our co-clusters usually have significant correlations in their price performance. Secondly, we use maximal quasi-bicliques on a mining protein network problem and we show that pairs of protein groups mined by maximal quasi-bicliques are more significant than those mined by maximal bicliques.

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Self-organizing neural networks for the analysis and representation of data: Some financial cases

Cited by 83 publications

References 13 publications

Explorative Data Analysis Based on Self-organizing Maps and Automatic Map Analysis

Explorative Data Analysis Based on Self-organizing Maps and Automatic Map Analysis

Customer portfolio analysis using the SOM

Mining maximal quasi‐bicliques: Novel algorithm and applications in the stock market and protein networks

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