A new SAX-GA methodology applied to investment strategies optimization
A new SAX-GA methodology applied to investment strategies optimization
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DOI：
10.1145/2330163.2330310
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
A. Canelas;R. Neves;N. Horta
中科院分区：
文献类型：
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作者：
A. Canelas;R. Neves;N. Horta
本文提出了一种新的计算金融方法，将符号聚合近似(SAX)技术与基于遗传算法(GA)的优化核相结合。使用SAX表示法来描述金融时间序列，从而可以有效地识别相关模式。进化优化内核在这里被用来识别最相关的模式并生成投资规则。利用S 500指数的真实数据对所提出的方法进行了检验。结果表明，该方法的性能优于B&H和其他最先进的解决方案。
This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.