Financial Benchmarking Using Self-Organizing Maps - Studying the International Pulp and Paper Industry

Data Mining ◽  
2011 ◽  
pp. 323-349 ◽  
Author(s):  
Tomas Eklund ◽  
Barbro Back ◽  
Hannu Vanharanta ◽  
Ari Visa
Keyword(s):  
Paper Industry ◽  
Primary Source ◽  
Time Frame ◽  
Pulp And Paper ◽  
Financial Data ◽  
Annual Reports ◽  
The Internet ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Self Organizing

Performing financial benchmarks in today’s information-rich society can be a daunting task. With the evolution of the Internet, access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders are in need of a tool that allows them to quickly and accurately analyze these data. An emerging technique that may be suited for this application is the self-organizing map. The purpose of this study was to evaluate the performance of self-organizing maps for the purpose of financial benchmarking of international pulp and paper companies. For the study, financial data in the form of seven financial ratios were collected, using the Internet as the primary source of information. A total of 77 companies and six regional averages were included in the study. The time frame of the study was the period 1995-2000. A number of benchmarks were performed, and the results were analyzed based on information contained in the annual reports. The results of the study indicate that self-organizing maps can be feasible tools for the financial benchmarking of large amounts of financial data.

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

2003 ◽  
Vol 2 (3) ◽  
pp. 171-181 ◽  
Author(s):  
Tomas Eklund ◽  
Barbro Back ◽  
Hannu Vanharanta ◽  
Ari Visa
Keyword(s):  
Performance Analysis ◽  
Pulp And Paper ◽  
The Self ◽  
Financial Data ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Period ◽  
The World ◽  
Self Organizing

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.

Visualization of the Internet News Based on Efficient Self-Organizing Map Using Restricted Region Search and Dimensionality Reduction

2012 ◽  
Vol 16 (2) ◽  
pp. 219-226 ◽  
Author(s):  
Tetsuya Toyota ◽  
◽  
Hajime Nobuhara ◽  
Keyword(s):  
Morphological Analysis ◽  
Computation Time ◽  
The Internet ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Internet News ◽  
Restricted Region ◽  
Region Search ◽  
Reduce Computation Time ◽  
Self Organizing

In this paper, we propose a system to visualize the relationships in huge quantities of Internet news by twodimensional self-organizing maps instead of the conventional methods of listing Internet news. In the proposed method, morphological analysis is conducted on the texts of Internet news to generate input vectors with elements of keywords. The characteristics specific to Internet news that many of the vector elements become sparse allows dimensional reductions as well as speeding up of self-organizing mapping with restricted search regions in learning. We verify through evaluation experiments with the data of 80 pieces of news that the proposed system can reduce computation time by 75% to 99% and can create more efficient SOM compared with the generally available SOM.

scholarly journals Horizontal Temperature Fluxes in the Arctic in CMIP5 Model Results Analyzed with Self-Organizing Maps

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 251 ◽  
Author(s):  
Daniel Mewes ◽  
Christoph Jacobi
Keyword(s):  
Climate Model ◽  
General Structure ◽  
Time Frame ◽  
The Arctic ◽  
Self Organizing Map ◽  
Cmip5 Model ◽  
Self Organizing Maps ◽  
Horizontal Flux ◽  
Heat And Moisture ◽  
Self Organizing

The meridional temperature gradient between mid and high latitudes decreases by Arctic amplification. Following this decrease, the circulation in the mid latitudes may change and, therefore, the meridional flux of heat and moisture increases. This might increase the Arctic temperatures even further. A proxy for the vertically integrated atmospheric horizontal energy flux was analyzed using the self-organizing-map (SOM) method. Climate Model Intercomparison Project Phase 5 (CMIP5) model data of the historical and Representative Concentration Pathway 8.5 (RCP8.5) experiments were analyzed to extract horizontal flux patterns. These patterns were analyzed for changes between and within the respective experiments. It was found that the general horizontal flux patterns are reproduced by all models and in all experiments in comparison with reanalyses. By comparing the reanalysis time frame with the respective historical experiments, we found that the general occurrence frequencies of the patterns differ substantially. The results show that the general structure of the flux patterns is not changed when comparing the historical and RCP8.5 results. However, the amplitudes of the fluxes are decreasing. It is suggested that the amplitudes are smaller in the RCP8.5 results compared to the historical results, following a greater meandering of the jet stream, which yields smaller flux amplitudes of the cluster mean.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

A New Method for Emulating Self-Organizing Maps for Visualization of Datasets

2018 ◽  
Vol 17 (03) ◽  
pp. 1850014 ◽  
Author(s):  
Macario O. Cordel ◽  
Arnulfo P. Azcarraga
Keyword(s):  
Disaster Response ◽  
Dimensional Space ◽  
Main Idea ◽  
Cost Effective ◽  
Fine Tuning ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Text Complexity ◽  
Self Organizing Maps ◽  
Self Organizing

Several time-critical problems relying on large amount of data, e.g., business trends, disaster response and disease outbreak, require cost-effective, timely and accurate data summary and visualization, in order to come up with an efficient and effective decision. Self-organizing map (SOM) is a very effective data clustering and visualization tool as it provides intuitive display of data in lower-dimensional space. However, with [Formula: see text] complexity, SOM becomes inappropriate for large datasets. In this paper, we propose a force-directed visualization method that emulates SOMs capability to display the data clusters with [Formula: see text] complexity. The main idea is to perform a force-directed fine-tuning of the 2D representation of data. To demonstrate the efficiency and the vast potential of the proposed method as a fast visualization tool, the methodology is used to do a 2D-projection of the MNIST handwritten digits dataset.

scholarly journals On building early-warning systems for preventing the deterioration of financial institutions’ performance

2019 ◽  
Vol 1 (1) ◽  
pp. 194-202
Author(s):  
Adrian Costea
Keyword(s):  
Financial Institutions ◽  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Capital Adequacy ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Performance Dimensions ◽  
Som Model ◽  
Self Organizing

Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would accurately forecast the NFIs future performance (whether they would stay or be eliminated from the NFI’s Special Register three quarters into the future). The results are promising: the model is able to correctly predict NFIs’ performance movements. Finally, we compared the results of our SOM-based model with those obtained by applying a multivariate logit-based model. The SOM model performed worse in discriminating the NFIs’ performance: the performance classes were not clearly defined and the model lacked the interpretability of the results. In the contrary, the multivariate logit coefficients have nice interpretability and an individual default probability estimate is obtained for each new observation. However, we can benefit from the results of both techniques: the visualization capabilities of the SOM model and the interpretability of multivariate logit-based model.

DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES

2009 ◽  
Vol 18 (04) ◽  
pp. 603-611 ◽  
Author(s):  
CHIH-FONG TSAI ◽  
YUAH-CHIAO LIN ◽  
YI-TING WANG
Keyword(s):  
Individual Investors ◽  
Self Organizing Map ◽  
Tree Model ◽  
Stock Trading ◽  
Self Organizing Maps ◽  
Two Factors ◽  
Group A ◽  
Trading Activities ◽  
High Level ◽  
Self Organizing

Stock trading activities are always very popular in many countries. Generally, investors with various backgrounds have different preferences over the stocks they trade. In literature, a number of studies examine the institutions' holding preferences for certain stock characteristics when choosing the security portfolio. However, very few studies investigate the stock trading preferences of individual investors. In this paper, we focus on two factors which affect the portfolio choices of investors, which are stock characteristics and investor features. In particular, a self-organizing map (SOM) is used to group a certain number of clusters based on a chosen dataset. Then, the decision tree model is used to extract useful rules from the clusters which contain the most trading records in the sample. We find that if the investors are females, less wealthy, and make stock trades with lower frequencies, they will be more careful and conservative. On the other hand, if the investors are males, having a high level of wealth, and make stock trades very often, they tend to choose stocks with high EPS, high market-to-book, and high prices.

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