A hybrid procedure with feature selection for resolving stock/futures price forecasting problems
A hybrid procedure with feature selection for resolving stock/futures price forecasting problems
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DOI：
10.1007/s00521-011-0721-4
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发表时间：
2013-03
影响因子：
6
通讯作者：
Chih-Ming Hsu
中科院分区：
文献类型：
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作者：
Chih-Ming Hsu
股票/期货价格预测是个人投资者、股票基金经理和金融分析师的重要金融话题，目前受到研究人员和从业人员的相当关注。然而，股票/期货价格的固有特性，即高波动性，复杂性和动荡性，使预测具有挑战性的奋进。过去，已经提出了各种方法来处理仅使用单一软计算技术难以解决的股票/期货价格预测问题。在这项研究中，提出了一个混合的过程的基础上的反向传播（BP）神经网络，特征选择技术，和遗传编程（GP），以解决股票/期货价格预测问题与技术指标的使用。最后，以台湾证券交易所市值加权股票指数期货现货月收盘价预测为例，验证此方法的可行性与有效性。实验结果表明，该方法是一种可行的和有效的工具，以提高股票/期货价格预测的性能。此外，最重要的技术指标可以通过应用基于所提出的模拟技术的特征选择方法来确定，或者仅基于初步GP预测模型来确定。
Stock/futures price forecasting is an important financial topic for individual investors, stock fund managers, and financial analysts and is currently receiving considerable attention from both researchers and practitioners. However, the inherent characteristics of stock/futures prices, namely, high volatility, complexity, and turbulence, make forecasting a challenging endeavor. In the past, various approaches have been proposed to deal with the problems of stock/futures price forecasting that are difficult to resolve by using only a single soft computing technique. In this study, a hybrid procedure based on a backpropagation (BP) neural network, a feature selection technique, and genetic programming (GP) is proposed to tackle stock/futures price forecasting problems with the use of technical indicators. The feasibility and effectiveness of this procedure are evaluated through a case study on forecasting the closing prices of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures of the spot month. Experimental results show that the proposed forecasting procedure is a feasible and effective tool for improving the performance of stock/futures price forecasting. Furthermore, the most important technical indicators can be determined by applying a feature selection method based on the proposed simulation technique, or solely on the preliminary GP forecast model.