基于金融指标的国家分类智能代理

P. S. D. M. Neto, Rosilda B. Souza, George D. C. Cavalcanti, T. Ferreira
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引用次数: 0

摘要

传统上,国家分类是根据几个特征进行的,这些特征与经济和社会因素有关。然而,由于难以获得这些特征和需要人类专业知识的干预,分类过程成本很高。本文提出了一种基于金融指标对国家进行分类的智能代理。人工智能体计算金融指标收益序列的概率密度函数(pdf)。这个图表描述了市场波动的特征。基于收益序列和pdf,估计了描述世界市场行为的指数函数的波动率和B系数。然后,智能体使用自组织地图(SOM)神经网络对发达国家和发展中国家的指标进行分类。结果表明,所提出的智能代理是传统组织提供分类的一种准确、快速、廉价的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Agent to Classify Countries Based on Financial Indices
Traditionally, the countries classification is performed based on several features, that are related to economic and social factors. However, the classification process is costly due to the difficulty of obtaining those features and the need for intervention of human expertise. In this paper, we propose an intelligent agent that classifies countries based on financial indices. The artificial agent calculates the probability density function (pdf) of the return series of financial indices. This pdf characterizes the fluctuation of a market. Based on the return series and pdf, the volatility and the B coefficient of the exponential function, that describe the behavior of world markets, are estimated. Then, the intelligent agent classifies the indices from developed and developing countries using a Self-Organizing Map (SOM) neural network. The results show that the proposed intelligent agent is an accurate, fast and cheap alternative to the classifications provided by traditional organizations.
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