Real stock trading using soft computing models

Brent Doeksen, A. Abraham, Johnson P. Thomas, M. Paprzycki
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引用次数: 40

Abstract

The main focus of this study is to compare different performances of soft computing paradigms for predicting the direction of individuals stocks. Three different artificial intelligence techniques were used to predict the direction of both Microsoft and Intel stock prices over a period of thirteen years. We explore the performance of artificial neural networks trained using backpropagation and conjugate gradient algorithm and a Mamdani and Takagi Sugeno fuzzy inference system learned using neural learning and genetic algorithm. Once all the different models were built the last part of the experiment was to determine how much profit can be made using these methods versus a simple buy and hold technique.
真实股票交易使用软计算模型
本研究的主要重点是比较软计算范式在预测个股方向方面的不同表现。研究人员使用了三种不同的人工智能技术来预测微软和英特尔在13年期间的股价走势。我们研究了使用反向传播和共轭梯度算法训练的人工神经网络的性能,以及使用神经学习和遗传算法学习的Mamdani和Takagi Sugeno模糊推理系统的性能。一旦建立了所有不同的模型,实验的最后一部分是确定使用这些方法与简单的买入并持有技术相比可以赚取多少利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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