A novel text mining approach to financial time series forecasting
A novel text mining approach to financial time series forecasting
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一种新颖的文本挖掘金融时间序列预测方法
DOI：
10.1016/j.neucom.2011.12.013
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发表时间：
2012-04-15
期刊：
影响因子：
6
通讯作者：
Wang, Xiaolong
中科院分区：
文献类型：
--
作者：
Wang, Baohua;Huang, Hejiao;Wang, Xiaolong
金融时间序列具有噪声、非平稳、混沌等特点，其预测成为一个挑战。现有的预测模型大多没有考虑市场情绪。为了克服这一局限性，本文利用市场情绪中包含的有用预测信息，将文本信息用于金融时间序列预测，提出了一种结合ARIMA和SVR（Support Vector Regression）的文本挖掘方法。该方法包括三个步骤：将文本数据表示为特征向量，使用ARIMA分析线性部分，并开发一个只基于文本特征向量的SVR模型来建模非线性部分。为了验证所提出的方法的有效性，选择了六家证券公司的季度净资产收益率（ROE）作为预测目标。与现有的一些国家的最先进的模型相比，所提出的方法给出了上级的结果。这表明，所提出的模型，使用额外的市场情绪提供了一个有前途的替代金融时间序列预测。(C)2011 Elsevier B.V.保留所有权利。
Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. Most of the existing forecasting models for this problem do not take market sentiment into consideration. To overcome this limitation, motivated by the fact that market sentiment contains some useful forecasting information, this paper uses textual information to aid the financial time series forecasting and presents a novel text mining approach via combining ARIMA and SVR (Support Vector Regression) to forecasting. The approach contains three steps: representing textual data as feature vectors, using ARIMA to analyze the linear part and developing a SVR model based only on textual feature vector to model the nonlinear part. To verify the effectiveness of the proposed approach, quarterly ROEs (Return of Equity) of six security companies are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model that uses additional market sentiment provides a promising alternative to financial time series prediction. (C) 2011 Elsevier B.V. All rights reserved.