Prediction of stock prices using deep neural network models including an emotional predictor based on online news by industrial groups

被引：0

作者：

Lim, Jun Hyeong ^{[1
]}

Son, Young Sook ^{[1
]}

机构：

[1] Chonnam Natl Univ, Dept Stat, 77 Yongbong Ro, Gwangju 61186, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2020年 / 33卷 / 04期

关键词：

prediction of stock prices; deep neural network; social network analysis; online news; emotional variable;

D O I：

10.5351/KJAS.2020.33.4.483

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We used a deep neural network model for the prediction of the stock prices of Kia Motors and Shinsegae as listed in the KOSPI 100. We used an emotional variable derived from online news in addition to the various technical indicators most often used. The emotional variable used as a predictor variable was generated from the average of the emotional scores for companies in the industrial group after building an emotional dictionary specific to each industrial group classified in a social network analysis. The study was conducted with various combinations of predictors and confirmed that good predictive and profitable power could be expected when jointly using technical indicators and an emotional variable based on online news by industrial groups.

引用

页码：483 / 497

页数：15

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