根据非银行金融机构的财务业绩对其进行分类

A. Costea
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引用次数: 0

摘要

摘要本文基于Kohonen自组织地图算法,利用无监督神经网络对罗马尼亚非银行金融机构的绩效进行了比较评价。我们以二维地图(自组织地图)的形式创建了一个基准模型,可用于根据不同的绩效维度(如资本充足率、资产质量和盈利能力)直观地评估非银行金融机构的绩效。我们使用以下指标:股本比率(杠杆)作为资本充足率维度,向客户发放的贷款(净值)/总资产(净值)作为资产质量维度,资产收益率(ROA)作为盈利能力维度。我们从分析中排除了用于评估银行绩效的其他三个维度,因为缺乏数据(用于两个定性维度:所有权和管理质量)以及与nfi部门(流动性)无关。提出的模型基于自组织地图算法,该算法从p维输入数据创建二维地图(例如6x4 = 24个神经元)。本研究收集了11家非银行金融机构2007-2010年四年的数据,共进行了44次观察。利用自组织地图模型的可视化功能和轨迹,我们展示了表现最差的三家非银行金融机构的动向:2007年至2010年间表现最差的最大表现不佳的机构用X表示,第二大表现不佳的机构用Y表示,第三大表现不佳的机构用Z表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classifying non-banking financial institutions based on their financial performance
Abstract In this paper we evaluate comparatively the performance of non-banking financial institutions in Romania by the means of unsupervised neural networks in terms of Kohonen’ Self-Organizing Maps algorithm. We create a benchmarking model in the form of a two-dimensional map (a self-organizing map) that can be used to assess visually the performance of non-banking financial institutions based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. We use the following indicators: Equity ratio (Leverage) for the capital adequacy dimension, Loans granted to clients (net value) / total assets (net value) for the assets’ quality dimension and Return on assets (ROA) for the profitability dimension. We have excluded from our analysis the other three dimensions used in evaluating the performance of banks, due to lack of data (for the two qualitative dimensions: quality of ownership and management) and irrelevance with the NFIs’ sector (liquidity). The proposed model is based on the Self-Organising Map algorithm which creates a two-dimensional map (e.g. 6x4 = 24 neurons) from p-dimensional input data. The data were collected for eleven non-banking financial institutions for four years 2007-2010, in total 44 observations. Using the visualization capabilities of the Self-Organising Map model and the trajectories we show the movements of the three non-banking financial institutions with the worst performance: the largest underperformer denoted with X, the second largest underperformer denoted with Y and the third largest underperformer denoted with Z between 2007 and 2010.
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