Stock trading rule discovery with an evolutionary trend following model
Stock trading rule discovery with an evolutionary trend following model
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通过进化趋势跟踪模型发现股票交易规则
DOI：
10.1016/j.eswa.2014.07.059
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发表时间：
2015-01-01
影响因子：
8.5
通讯作者：
Liu, Mei
中科院分区：
文献类型：
--
作者：
Hu, Yong;Feng, Bin;Liu, Mei
进化学习是设计量化投资（QI）产品最流行的技术之一。趋势跟踪（TF）策略由于其准确性和有效性而被投资者广泛接受。令人惊讶的是，据我们所知，没有相关的研究调查TF投资策略的进化学习模型。提出了一种结合TF投资策略和扩展分类器系统（XCS）的混合长期和短期演化趋势跟踪算法（eTrend）。提出的eTrend算法有两个优点：（1）股票投资策略的组合（即，TF）和进化学习（即，（2）XCS能够自动适应市场方向，发现合理的、可理解的交易规则并进行进一步分析，有助于避免普通投资者的非理性交易行为。为了对eTrend进行评价，本文利用上海证券交易所三个著名指数的日交易数据流进行了实验。实验结果表明，eTrend在扣除交易成本后，在高Sortino比率下优于买入并持有策略。其性能也上级决策树和人工神经网络交易模型。此外，由于股票市场中普遍存在概念漂移现象，本文还对熊市和牛市阶段的交易规律进行了探索性的概念漂移分析。分析显示了有趣和合理的结果。总之，本文提出了令人信服的证据，建议的混合趋势跟踪模型确实可以产生有效的交易指导投资者。(C)2014爱思唯尔有限公司版权所有。
Evolutionary learning is one of the most popular techniques for designing quantitative investment (QI) products. Trend following (TF) strategies, owing to their briefness and efficiency, are widely accepted by investors. Surprisingly, to the best of our knowledge, no related research has investigated TF investment strategies within an evolutionary learning model. This paper proposes a hybrid long-term and short-term evolutionary trend following algorithm (eTrend) that combines TF investment strategies with the eXtended Classifier Systems (XCS). The proposed eTrend algorithm has two advantages: (1) the combination of stock investment strategies (i.e., TF) and evolutionary learning (i.e., XCS) can significantly improve computation effectiveness and model practicability, and (2) XCS can automatically adapt to market directions and uncover reasonable and understandable trading rules for further analysis, which can help avoid the irrational trading behaviors of common investors. To evaluate eTrend, experiments are carried out using the daily trading data stream of three famous indexes in the Shanghai Stock Exchange. Experimental results indicate that eTrend outperforms the buy-and-hold strategy with high Sortino ratio after the transaction cost. Its performance is also superior to the decision tree and artificial neural network trading models. Furthermore, as the concept drift phenomenon is common in the stock market, an exploratory concept drift analysis is conducted on the trading rules discovered in bear and bull market phases. The analysis revealed interesting and rational results. In conclusion, this paper presents convincing evidence that the proposed hybrid trend following model can indeed generate effective trading guidance for investors. (C) 2014 Elsevier Ltd. All rights reserved.