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Investor sentiment identification based on the universum SVM

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Abstract

Universum refers to additional samples which contain priori knowledge for classification but belonging to none of the class. It has been proved that universum positioned “in between” the two classes obtain better results. Since opinions on stock market defined as investor sentiment involve quite a number of neutral views, these neutral views can be used as universum samples to better identify investor sentiment. With universum samples, this paper uses support vector machine (SVM) to classify posts on stock forum. We define bullish views as positive samples, define bearish views as negative samples, and also further discuss the situation of a 3-class problem with neutral views. Compared with standard SVM, the empirical studies with universum samples in this paper show better performance for both 2- and 3-class classifications.

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Acknowledgments

This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61472390, 71101146, 11271361, 71331005, and 11226089), Major International (Regional) Joint Research Project (No. 71110107026) and the Beijing Natural Science Foundation (No. 1162005).

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Authors and Affiliations

  1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Wen Long, Ye-ran Tang & Ying-jie Tian

  2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Wen Long, Ye-ran Tang & Ying-jie Tian

  3. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Wen Long, Ye-ran Tang & Ying-jie Tian

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  1. Wen Long
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  2. Ye-ran Tang
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Correspondence to Ying-jie Tian.

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Long, W., Tang, Yr. & Tian, Yj. Investor sentiment identification based on the universum SVM. Neural Comput & Applic 30, 661–670 (2018). https://doi.org/10.1007/s00521-016-2684-y

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