Data and dimension reduction for visual financial performance analysis

Sarlin, Peter

doi:10.1177/1473871613504102

Cited by 18 publications

(13 citation statements)

References 73 publications

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“…We think that this has restricted researchers from acquiring detailed information for researching and evaluating the performance of the applied algorithms on various Financial data sets. In this context, it is worth mentioning that Sarlin [Sar15] provides good experimental comparison results among dimensional and data reduction algorithms applied to Financial data.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Visual Analysis Approaches for Financial Data

Cho

Afzal

et al. 2016

Computer Graphics Forum

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Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision-making processes based on financial data have been a popular topic in industries. However, analyzing financial data is a non-trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for financial data exploration. Often, the development of such systems requires researchers to collaborate with financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in financial domains with respect to real-world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

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Section: Discussionmentioning

confidence: 99%

A Survey on Visual Analysis Approaches for Financial Data

Cho

Afzal

et al. 2016

Computer Graphics Forum

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“…There are a number of different clustering techniques available, including partitioning techniques (e.g., k-means), modelbased techniques (e.g., SOMs), hierarchical techniques (e.g., Ward's method), density-based and grid-based techniques (Han and Kamber, 2001;Wu et al, 2008;Cavélius, 2011). In this study, we adopt the SOM for the following reasons: 1) the SOM is a very visual and managerially-oriented method for the analysis of multidimensional data (Eklund et al, 2008;Länsiluoto and Eklund, 2008), 2) compared to many multidimensional visualization methods (e.g., Multidimensional Scaling, MDS), the SOM is unique in performing both projection and clustering (Vesanto, 1999;Sarlin, 2012), 3) as opposed to many traditional statistical clustering methods (such as k-means), an SOM does not require the user to specify the number of clusters beforehand, making it an ideal tool for exploratory data analysis (Wang, 2001;Wu et al, 2008), and finally, 4) an SOM is very tolerant of most forms of problematic data, including linear and non-linear relationships, skewed distributions, and erroneous or missing data (Bishop, 1995;Kohonen, 2001;Wang, 2001).…”

Section: Self-organizing Maps (Soms)mentioning

confidence: 99%

Internal control effectiveness – a clustering approach

Länsiluoto

Jokipii

2016

Managerial Auditing Journal

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 Purpose: Internal control frameworks provide only broad guidance concerning internal control concepts, leaving the details to the adopting firms. In this study the adopted internal control structure and effectiveness in firms is examined and visualized, and a typology of firms is presented. Control structure and effectiveness are measured based on the assessment of management, rather than using reported material weaknesses as most studies do. This type of evaluation is more purposeful for firms that do not apply the Sarbanes-Oxley Act.  Design/methodology/approach: The survey data (from 741 CEOs) are clustered using the self-organizing map (SOM), a visual artificial neural network approach. A threedimensional effectiveness proxy is used.  Findings: The analysis reveals four alternative types of internal control effectiveness in firms and visually presents how the components of the internal control structure (COSO) are associated with each one. A typology of internal control structure and effectiveness is then created.  Practical implications: The findings suggest that there are interrelated, but not straightforward, relationships between internal control variables, and that there is a link between some of them and higher internal control effectiveness in practice. These findings have important implications for those responsible for improving or assessing internal control, such as management, personnel, and internal and external auditors.  Originality/value: This article uses a clustering approach to create a typology for alternative types of internal control structure and effectiveness, based on data from actual firms. Instead of using material weaknesses as a measure, this study uses managers' own assessments of internal control effectiveness.

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“…In this paper, we use the Self-Organizing Map (SOM) [24,25] for its dual capabilities for visual clustering; projection via neighborhood preservation and clustering via vector quantization. In [26], the SOM was shown to have a number of advantages over alternative methods for visual financial performance analysis. Conceptually, what the SOM performs is similar to what is achieved using stand-alone data and dimension reduction methods.…”

Section: Methodsmentioning

confidence: 99%

Financial performance analysis of European banks using a fuzzified Self-Organizing Map

Sarlin

Eklund

2013

KES

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Due to the recent wave of bank failures, stress tests have been conducted on banks within the European Union. The stress tests, however, only consider the adequacy of a bank's capital ratios, whereas the general financial performance of individual banks is disregarded. In this paper, we use the Self-Organizing Map (SOM) to perform a visual multidimensional and temporal financial performance analysis of European banks. We address several problems concerning financial performance analysis. We deal with the problem of selecting suitable financial ratios by performing dimensionality reduction using Principal Component Analysis. We also deal with difficult data using outlier trimming and normalization techniques, and use the SOM for imputing missing values. We use a decision-framework for choosing the final model, based upon a set of map and clustering quality measures. In addition, we implement a second-level fuzzified Ward clustering for visualization purposes and for assessing the crispness of the solution. The result is a visual SOM model for financial performance analysis of European banks.

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Data and dimension reduction for visual financial performance analysis

Cited by 18 publications

References 73 publications

A Survey on Visual Analysis Approaches for Financial Data

A Survey on Visual Analysis Approaches for Financial Data

Internal control effectiveness – a clustering approach

Financial performance analysis of European banks using a fuzzified Self-Organizing Map

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