标题: | 应用分类元股票交易系统于台湾加权指数趋势预测之研究 Applying XCS-based Security Trading System on Taiwan |
作者: | 苏俊辅 陈安斌 资讯管理研究所 |
关键字: | 分类元股票交易系统;延伸分类元系统;台湾加权指数(TAIEX);技术指标;XCS-based Security Trading System;Extended Classifier System;TAIEX;Technical Indicator |
公开日期: | 2004 |
摘要: | 人工智慧模型近年来在财金领域上的应用相当广泛,也常与传统的统计模型相互比较。传统时间序列数学模型存在许多假设与限制,但人工智慧模型较具有弹性,可解决非线性问题,较适合应用于像股票市场这种动态环境。 本研究为应用人工智慧方法之延伸分类元系统(Extended Classifier System, XCS)于台湾加权指数趋势之预测。分类元是一种以基因演算法为基础的学习模式,它拥有一个规则集,而且会动态对环境进行调整。本研究以民国七十八年九月二十六日至民国九十四年三月七日台湾加权指数4200笔日资料,运用不同天期移动平均线(MA)、随机指标(KD)、平滑异同移动平均线(MACD)等技术指标当作输入因子,加权指数之买卖讯号作为输出因子。实验期间前1500天为训练期,后2700天为测试期,经由分类元系统动态学习买卖规则,每次交易以加权指数为标的。实证结果显示十年测试期间分类元股票交易系统(CSTS)三十次模拟结果之平均报酬率为165.38%,平均交易胜率为60.31%。统计分析显示CSTS系统之交易报酬率及交易准确率皆显着优于传统回归模式及随机交易策略。本研究结论为分类元系统可较传统策略准确掌握加权指数之趋势,非常适合投资者作为交易决策系统。 Stock market is nonlinear and semi-structured. In other words, there are many different situations and the environment states are changing quickly. In Taiwan, stock market is always affected by political factors. So the fluctuation of stock price is always larger than other country. Therefore, to predict the trend of stock index is more important. Traditional trading strategy like regression model and random walk are limited in fixed time interval and can not perform well. Other learning models like genetic algorithm result in stable trading rules which are generated from specific training time period without being adapted when the environment state is changed. This paper adopts Extended Classifier System (XCS) technique to design an XCS-based Security Trading System (CSTS), which makes continuous on-line learning while making decision and generate trading rules to adapt environment state. The simulation results showed that this system could get an outstanding trading profit and accuracy rate. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009234520 http://hdl.handle.net/11536/77168 |
显示于类别: | Thesis |