一种参数化前馈多层感知器人工神经网络的方法,参考南非金融市场

IF 0.1 Q4 BUSINESS, FINANCE
M. L. Smith, F. Beyers, J. D. Villiers
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引用次数: 3

摘要

目前还没有分析程序来确定任何给定应用的最佳人工神经网络结构和参数。传统上,当人工神经网络被应用于金融建模问题时,结构和参数的选择往往是先验的,而没有充分考虑这些选择的影响。本研究的一个关键目的是通过探索模型结构和参数空间,开发一种可用于构建人工神经网络的通用方法,以便可以做出与模型设计相关的明智决策。在这项研究中,采用一种形式化的方法来确定具有单个隐藏层的前馈多层感知器人工神经网络的合适结构和参数。这种方法通过对四个南非经济变量,即货币、债券和股票市场的平均每月回报以及每月通货膨胀进行建模来证明。人工神经网络可以在上述变量上单独构建,也可以在一个集成模型中共同构建。将一系列传统的时间序列模型与人工神经网络模型的性能进行了比较。结果表明,在统计层面上,人工神经网络在预测金融市场回报方面的表现与时间序列模型一样好。将人工神经网络与时间序列模型相结合的混合模型,针对货币市场和通货膨胀率进行了构建、训练和测试。在预测通胀时,它们似乎为时间序列模型增加了价值,但对货币市场却没有作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets
No analytic procedures currently exist for determining optimal artificial neural network structures and parameters for any given application. Traditionally, when artificial neural networks have been applied to financial modelling problems, structure and parameter choices are often made a priori without sufficient consideration of the effect of such choices. A key aim of this study is to develop a general method that could be used to construct artificial neural networks by exploring the model structure and parameter space so that informed decisions could be made relating to the model design. In this study, a formal approach is followed to determine suitable structures and parameters for a Feed Forward Multi-layered Perceptron artificial neural network with a Resilient Propagation learning algorithm with a single hidden layer. This approach is demonstrated through the modelling of four South African economic variables, namely the average monthly returns on the money, bond and equity markets as well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables in isolation or, jointly, in an integrated model. The performance of a range of more traditional time series models is compared with that of the artificial neural network models. The results suggest that, on a statistical level, artificial neural networks perform as well as time series models at forecasting the returns for financial markets. Hybrid models, combining artificial neural networks with the time series models, are constructed, trained and tested for the money market and for the rate of inflation. They appear to add value to the time series models when forecasting inflation, but not for the money market.
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South African Actuarial Journal
South African Actuarial Journal BUSINESS, FINANCE-
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