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

共 50 条

[31] Profit Prediction based on Financial Statements using Deep Neural Network
Montesinos, Alonso
2022 IEEE WORLD AI IOT CONGRESS (AIIOT), 2022, : 533 - 537
[32] Churn Prediction using Neural Network based Individual and Ensemble Models
Saghir, Mehpara
Bibi, Zeenat
Bashir, Saba
Khan, Farhan Hassan
PROCEEDINGS OF 2019 16TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGY (IBCAST), 2019, : 634 - 639
[33] Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
Li, Chengyu
Qian, Guoqi
APPLIED SCIENCES-BASEL, 2023, 13 (01):
[34] DEEP NEURAL NETWORK-BASED CLICK-THROUGH RATE PREDICTION USING MULTIMODAL FEATURES OF ONLINE BANNERS
Xia, Bohui
Wang, Xueting
Yamasaki, Toshihiko
Aizawa, Kiyoharu
Seshime, Hiroyuki
2019 IEEE FIFTH INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM 2019), 2019, : 162 - 170
[35] Prediction of particle-laden pipe flows using deep neural network models
Haghshenas, Armin
Hedayatpour, Shiva
Groll, Rodion
PHYSICS OF FLUIDS, 2023, 35 (08)
[36] Identification of Fake News Using Deep Neural Network-Based Hybrid Model
Gupta S.
Verma B.
Gupta P.
Goel L.
Yadav A.K.
Yadav D.
SN Computer Science, 4 (5)
[37] Efficient compressive strength prediction of concrete incorporating industrial wastes using deep neural network
Shubham K.
Rout M.K.D.
Sinha A.K.
Asian Journal of Civil Engineering, 2023, 24 (8) : 3473 - 3490
[38] A Deep Neural Network-Based Multisource Information Fusion Method for Stock Price Prediction of Enterprises
Quan, Li
Zheng, Dahuan
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2025, 34 (03)
[39] A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network
Zhao, Yangzi
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2021, 2021
[40] Prediction-Based Portfolio Optimization Models Using Deep Neural Networks
Ma, Yilin
Han, Ruizhu
Wang, Weizhong
IEEE ACCESS, 2020, 8 (08): : 115393 - 115405

← 1 2 3 4 5 →