Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets
Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets
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DOI：
10.1016/j.physa.2015.08.042
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发表时间：
2016-01
影响因子：
3.3
通讯作者：
Tomoya Suzuki;Yuushi Ohkura
中科院分区：
文献类型：
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作者：
Tomoya Suzuki;Yuushi Ohkura
为了检验金融市场的可预测性和盈利能力，我们引入了三个思想来改进传统的技术分析，以更快地检测投资时机。首先，通过学习隐藏在金融市场中的复杂行为模式，非线性预测模型被认为是提高这种检测能力的有效方法。其次，套袋算法可以量化预测的置信度，形成新的技术指标。第三，我们还介绍了如何通过两步选择来选择更有利可图的股票以提高投资绩效：第一步在学习期间选择更可预测的股票，然后第二步自适应动态地选择在每次投资中显示最显著技术信号的最自信的股票。最后，一些基于真实金融数据的投资模拟表明，这些想法在克服复杂的金融市场方面是成功的。
In order to examine the predictability and profitability of financial markets, we introduce three ideas to improve the traditional technical analysis to detect investment timings more quickly. Firstly, a nonlinear prediction model is considered as an effective way to enhance this detection power by learning complex behavioral patterns hidden in financial markets. Secondly, the bagging algorithm can be applied to quantify the confidence in predictions and compose new technical indicators. Thirdly, we also introduce how to select more profitable stocks to improve investment performance by the two-step selection: the first step selects more predictable stocks during the learning period, and then the second step adaptively and dynamically selects the most confident stock showing the most significant technical signal in each investment. Finally, some investment simulations based on real financial data show that these ideas are successful in overcoming complex financial markets.