Forecasting stock market trends using support vector regression and perceptually important points

Mojtaba Azimifar, Babak Nadjar Araabi, Hadi Moradi
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引用次数: 1

Abstract

Intelligent stock trading systems use soft computing techniques in order to make trading decisions in the stock market. However, the fluctuations of the stock price make it difficult for the trading system to discover the underlying trends. In order to enable the trading system for trend prediction, this paper suggests using perceptually important points as a turning point prediction framework. Perceptually important points are utilized as a high-level representation for the stock price time series to decompose the price into several segments of uptrends and downtrends and define a trading signal which is an indicator of the current trend. A support vector regression model is trained on this high-level data to make trading decisions based on predicted trading signal. The performance of the proposed trading system is compared with three other trading systems on five of the top performing stocks in Tehran Stock Exchange, and obtained results show a significant improvement.
使用支持向量回归和感知重要点预测股票市场趋势
智能股票交易系统利用软计算技术在股票市场上进行交易决策。然而,股票价格的波动使得交易系统很难发现潜在的趋势。为了使交易系统能够进行趋势预测,本文建议使用感知重要点作为拐点预测框架。感知上重要的点被用作股票价格时间序列的高级表示,将价格分解为上升趋势和下降趋势的几个部分,并定义一个交易信号,这是当前趋势的一个指标。在这些高级数据上训练支持向量回归模型,根据预测的交易信号做出交易决策。在德黑兰证券交易所的5只表现最好的股票上,将所提出的交易系统的性能与其他三种交易系统进行了比较,得到的结果显示显着改善。
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