Merging public opinion information and stock numerical data for stock trend prediction based on deep learning

Geng Lv; Jianjiang Cui

doi:10.1117/12.2691661

21 August 2023 Merging public opinion information and stock numerical data for stock trend prediction based on deep learning

Geng Lv, Jianjiang Cui

Author Affiliations +

Proceedings Volume 12783, International Conference on Images, Signals, and Computing (ICISC 2023); 127830J (2023) https://doi.org/10.1117/12.2691661
Event: International Conference on Images, Signals, and Computing (ICISC2023), 2023, Chengdu, China

Abstract

Unlike other stock markets participants, the participants in China mainland are composed of individual investors, which account for 82% of the trading volume of the stock market. The decision-making basis of individual investors is mainly public opinion and recent stock prices. Therefore, the public opinion on professional stock social sites has an important impact on the decision of individual investors, which in turn affects the trend of the stock market. However, the previous stock market forecasting methods mostly ignored the influence of public opinion information on the market. For this reason, this paper proposes a novel framework to predict the stock trend by using both public opinion and stock numerical data. The original contributions of this paper include stock commentary word embedding model based on the stock comment text data crawled from https://xueqiu.com through two-stage training and LSTM-CNN layered model based on the improved self-attention mechanism. Two main experiments are conducted: the first experiment extract stock commentary word embedding, and the second experiment forecasts the stock price trends of Shanghai and Shenzhen A-share market. Results show that: 1)LSTM-CNN layered model is better than previous methods; 2)The combination of public opinion information and numerical data can improve the performance of the model; 3)Stock commentary word embedding model is better than pre-training word embedding model; 4) The longer the data span, the better the stock forecasting model will perform

Citation Download Citation

Geng Lv and Jianjiang Cui "Merging public opinion information and stock numerical data for stock trend prediction based on deep learning", Proc. SPIE 12783, International Conference on Images, Signals, and Computing (ICISC 2023), 127830J (21 August 2023); https://doi.org/10.1117/12.2691661

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KEYWORDS

Data modeling

Education and training

Performance modeling

Neural networks

Deep learning

Tunable filters

Data processing

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