SPEED UP OF THE MAJORITY VOTING ENSEMBLE METHOD FOR THE PREDICTION OF STOCK PRICE DIRECTIONS
SPEED UP OF THE MAJORITY VOTING ENSEMBLE METHOD FOR THE PREDICTION OF STOCK PRICE DIRECTIONS
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DOI：
10.24818/18423264/52.1.18.13
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发表时间：
2018-01-01
影响因子：
0.9
通讯作者：
Kim, Hongjoong
中科院分区：
文献类型：
--
作者：
Moon, Kyoung-Sook;Jun, Sookyung;Kim, Hongjoong
股票价格走势的预测在金融学中具有重要意义。多数投票包围法在预测精度上上级单分类器模型，包括Logistic回归、决策树、K-最近邻和支持向量机，但由于它考虑了单分类器模型的所有超参数，因此计算成本非常昂贵。目前的研究提出了一种修正的多数表决法，以提高计算效率。所提出的方法让每个单个分类器模型找到自己的超参数值，与标准的多数表决方法相比，这种修改将计算速度提高了500倍，同时保持了准确性。数值实验表明，建议的多数表决，标准的多数表决，然后其他单分类器模型，包括支持向量机的顺序的分类器模型的排名。这一改进将使多数表决集成方法在实际金融市场中得到应用。在过去的3年中，该算法在来自3大洲的7个国家指数上进行了测试，并以两个标准来衡量性能，即接收器操作特征曲线下的面积和正确分类的百分比。
The prediction of stock price directions is important in finance. The Majority Voting Ensemble method is superior in prediction accuracy to single classifier models including Logistic Regression, Decision Tree, K-Nearest Neighbors and Support Vector Machine, but the computational cost is very expensive since it considers all the hyperparameters of single classifier models. The current study proposes a revision of the majority voting method to improve the computational efficiency. The proposed method lets each single classifier model find its own hyperparameter values and this modification speeds up the computation by 500 times compared to the standard majority voting method while maintaining the accuracy. The numerical experiments show the ranking of the classifier models in the order of the proposed majority voting, the standard majority voting, and then other single classifier models including the support vector machine. This improvement will allow the majority voting ensemble method to be applied in the financial market in practice. The algorithms are tested on 7 national indices from 3 continents for the past 3 years, and the performance is measured in two criteria, the area under the receiver operating characteristic curve and the percent correctly classified.