Using Self Organizing Maps for Banking Oversight

2016 ◽  
pp. 1306-1332 ◽  
Author(s):  
Felix Lopez-Iturriaga ◽  
Iván Pastor-Sanz
Keyword(s):  
Neural Networks ◽  
Financial Stability ◽  
Bankruptcy Prediction ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Savings Banks ◽  
Visual Tool ◽  
The Government ◽  
Over Time ◽  
Self Organizing

This chapter combines two methods based on neural networks (trait recognition and self-organizing maps) to develop a model of bankruptcy prediction. The authors apply the method to the Spanish savings banks, most of them rescued by the Government between 2008 and 2013 in a costly massive process. First, the authors detect the combinations of variables (performance, asset structure, and capitalization) that best describe the profile of the rescued savings banks. Then, the authors use these combinations on a yearly basis to generate bi-dimensional maps in which banks are placed according to their risk and similarities. This method provides a visual tool that can improve the oversight of policy makers on the whole financial system and enable time pertinent answers to some threatens to the country financial stability. The maps are useful means to detect and understand how the financial threats emerge over time too.

Using Self Organizing Maps for Banking Oversight

2016 ◽  
pp. 116-140
Author(s):  
Felix Lopez-Iturriaga ◽  
Iván Pastor-Sanz
Keyword(s):  
Neural Networks ◽  
Financial Stability ◽  
Bankruptcy Prediction ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Savings Banks ◽  
Visual Tool ◽  
The Government ◽  
Over Time ◽  
Self Organizing

This chapter combines two methods based on neural networks (trait recognition and self-organizing maps) to develop a model of bankruptcy prediction. The authors apply the method to the Spanish savings banks, most of them rescued by the Government between 2008 and 2013 in a costly massive process. First, the authors detect the combinations of variables (performance, asset structure, and capitalization) that best describe the profile of the rescued savings banks. Then, the authors use these combinations on a yearly basis to generate bi-dimensional maps in which banks are placed according to their risk and similarities. This method provides a visual tool that can improve the oversight of policy makers on the whole financial system and enable time pertinent answers to some threatens to the country financial stability. The maps are useful means to detect and understand how the financial threats emerge over time too.

Intra-sectoral and Intersectoral Parity Issues in Pricing of Agricultural Crops: A Preliminary Analysis

1987 ◽  
Vol 26 (4) ◽  
pp. 401-417
Author(s):  
Sarfraz K. Qureshi
Keyword(s):  
Preliminary Analysis ◽  
Agricultural Sector ◽  
Terms Of Trade ◽  
Agricultural Crops ◽  
Allocation Of Resources ◽  
Price Determination ◽  
Policy Makers ◽  
The Government ◽  
Distribution Of Income ◽  
Over Time

Intersectoral terms of trade play a cruc1al role in determining the sectoral distribution of income and resource allocation in the developing countries. The significance of intra-sectoral terms of trade for the allocation of resources within the agricultural sector is also widely accepted by research scholars and policy-makers. In the context of planned development, the government specifies production targets for the agricultural sector and for different crops. The intervention of government in the field of price determination has important implications for the achievement of planned targets. In Pakistan, there is a feeling among many groups including farmers and politicians with a rural background that prices of agricultural crops have not kept their parities intact over time and that prices generally do not cover the costs of production. The feeling that production incentives for agriculture have been eroded is especially strong for the period since the early 1970s. It is argued that strong inflationary pressures supported by a policy of withdrawal of government subsidies on agricultural inputs have resulted in rapid increases in the prices paid by agriculturists and that increases in the prices received by farmers were not enough to compensate them for the rising prices of agricultural inputs and consumption goods.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

scholarly journals Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
Times Series ◽  
The Times ◽  
Self Organizing

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

scholarly journals Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

2017 ◽  
Vol 9 (4) ◽  
Author(s):  
Aline Regina Walkoff ◽  
Sandra Regina Masetto Antunes ◽  
Maria Elena Payret Arrúa ◽  
Lívia Ramazzoti Chanan Silva ◽  
Dionisio Borsato ◽  
...  
Keyword(s):  
Neural Networks ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks

2019 ◽  
Vol 14 (3) ◽  
pp. 86-98 ◽  
Author(s):  
Oleksii Mints ◽  
Viktoriya Marhasova ◽  
Hanna Hlukha ◽  
Roman Kurok ◽  
Tetiana Kolodizieva
Keyword(s):  
Neural Networks ◽  
Financial Stability ◽  
Banking System ◽  
Bank Loans ◽  
Resource Base ◽  
Systemic Crisis ◽  
The Stability ◽  
Bankruptcy Rate ◽  
Legal Entities ◽  
Self Organizing

The article proposes an approach to analyzing reliability factors of commercial banks during the 2014–2017 systemic crisis in the Ukrainian banking system, using the Kohonen self-organizing neural networks and maps. As a result of an experimental study, data were obtained on financial factors affecting the stability of a commercial bank in a crisis period. It has been concluded that during the banking crisis in Ukraine in 2014–2017, the resource base of a bank was the main factor of this bank stability. The most preferred sources of resources were funds from other banks (bankruptcy rate of 5.7%) and legal entities (bankruptcy rate of 8%), and the least stable were funds from individuals (bankruptcy rate of 28.5%). The relationship between financial stability and the amount of capital and the structure of bank loans is less pronounced. However, one can say that banks that focused on lending to individuals experienced a worse crisis than banks whose main borrowers were legal entities. The tools considered in the article (the Kohonen self-organizing neural networks and maps) allow for efficiently segmenting data samples according to various criteria, including bank solvency. The “hazardous” zones with a high bankruptcy rate (up to 49.2%) and the “safe” zone with a low rate of bankruptcy (6.3%) were highlighted on the map constructed. These results are of practical value and can be used in analyzing and selecting counterparties in the banking system during a downturn.

scholarly journals Drivers’ Growth of Islamic Banking Industry: Lesson from Malaysia

2014 ◽  
Vol 1 (4) ◽  
pp. 122-128
Author(s):  
Yagoub Elryah
Keyword(s):  
Financial Services ◽  
Financial Stability ◽  
Banking Industry ◽  
Foreign Banks ◽  
Islamic Banking ◽  
Master Plan ◽  
Policy Makers ◽  
Innovative Products ◽  
The Government

Numerous studies focus on the Islamic banking performance, banks’ growth. There are, however, very little is known about the drivers’ growth of Islamic banking. The paper attempted to fill this gap. To achieve the objectives of the study, we consider government financial strategies for Islamic banking in Malaysia (Master Plan financial services 2000-2010 and Blueprint financial sector plan 2011-2020) and interviews the policy makers and regulators from BNM and selected banks. In this context, we explored the drivers’ growth Islamic banking industry in Malaysia for the period 2002-2012. The findings of the study revealed that the government strategies, high skilled banker’s human resources, financial stability, foreign banks, innovative products, awareness of the customers and quality of the financial and regulatory reforms were main drivers’ growth of Islamic banking in Malaysia. DOI: http://dx.doi.org/10.3126/ijssm.v1i4.10626 Int. J. Soc. Sci. Manage. Vol-1, issue-4: 122-128 

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