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A multiple support vector machine approach to stock index forecasting with mixed frequency sampling

机译：混合频率采样的多支持向量机股票指数预测方法

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摘要

The independent variables commonly used to predict the stock price index usually contain data sampled at different frequencies, and simultaneously, there exist multiple outputs. However, most current researches ignore different frequencies among independent variables and multi-output issues. This paper proposes a multiple output support vector machine unrestricted mixed data sampling (MSVM-UMIDAS) approach which can achieve multiple results for sequential points simultaneously by applying mixed frequency independent variables. We test the in-sample and out-of-sample performances of MSVM-UMIDAS for stock forecasting in terms of (t-1), (t-2) and (t-3) and then compare the performances of the proposed model with those of other models. The results indicate that our model performs better when assessed by four different measurements. Thus, our proposed model is more realistic in practice and an appropriate tool for multi-output and mixed frequency issues for stock price forecasting. (C) 2017 Elsevier B.V. All rights reserved.
机译：通常用于预测股票价格指数的自变量通常包含以不同频率采样的数据，并且同时存在多个输出。但是，当前大多数研究都忽略了自变量和多输出问题之间的不同频率。本文提出了一种多输出支持向量机无限制混合数据采样（MSVM-UMIDAS）方法，该方法可以通过应用混合频率独立变量来同时获得连续点的多个结果。我们根据（t-1），（t-2）和（t-3）测试了MSVM-UMIDAS的样本内和样本外预测性能，然后将建议模型的性能与其他型号的那些。结果表明，当通过四个不同的测量值评估时，我们的模型表现更好。因此，我们提出的模型在实践中更现实，并且是用于股票价格预测的多输出和混合频率问题的合适工具。 （C）2017 Elsevier B.V.保留所有权利。

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