用神经网络预测股票交易运动:一个案例研究

Hanif Tahersima, Mohammad H. Tahersima, M. Fesharaki, Navid Hamedi
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引用次数: 20

摘要

金融时间序列是非常复杂和动态的,因此被称为混沌时间序列。本研究的主要目的是利用2010年9月20日至2011年1月21日的每小时数据集预测欧元对日元(EURJPY)的收盘价变动。一个神经网络(MLP)被用来预测欧元日元的收盘价走势。本研究结果表明,神经计算模型是预测新兴市场股票交易走势的有用工具。这些结果也表明,噪声的过滤对预测的改进有巨大的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Stock Exchange Movements Using Neural Networks: A Case Study
Financial time series are very complex and dynamic, so they are characterized as chaotic time series. The major aim of this research is to forecast the stock exchange of Euro vs. Japanese Yen (EURJPY) closing price movements using hourly dataset from September 20, 2010 to January 21, 2011. One neural network (MLP) is used to predict the EURJPY closing price movements. The results of this study show that neuro-computational models are useful tools in forecasting stock exchange movements in emerging markets. These results also indicate that filtering of noises have an enormous effect on prediction improvements.
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