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Exploring the impact of investor’s sentiment tendency in varying input window length for stock price prediction

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Abstract

Stock price prediction is one of the most important aspects of business investment plans, and has been an attractive research topic for both researchers and financial analysts. Many previous studies indicated the effectiveness of social media sentiment in stock price predictions through time series modelling. However, the time series information hidden in consecutive trading days has not been fully explored. In this paper, we build a stock price prediction model based on attention-based Long Short Term Memory (ALSTM) network using price data, technical indicators and sentiment information from social media. We employed a novel method to feed the deep network with long time series data to learn the deep sequential information of stock price movement. A fine-tuned BERT sentiment classification model and a sentiment lexicon are proposed to extract deep sentiment tendency of social media posts. We conducted experiments on 28 stocks within three years’ transaction period, and the results show that: (1) evaluated by the indicators of the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the accuracy, our proposed method outperforms the baseline models in both validation and test data sets; (2) models incorporating stock prices, technical indicators and sentiment features perform better than models that only use partial data source; (3) the fine-tuned BERT model performs better in sentiment classification task, and the exploitation of the sentiment features computed with the use of BERT model also led to higher predicting accuracy compared with the features calculated using sentiment lexicon; and (4) setting the input window length to 5-day achieves the best performance in average prediction accuracy.

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Data availability

The datasets analysed during the current study are not publicly available due to data privacy policy but are available from the corresponding author on reasonable request.

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China (project numbers are 72274096, 72174087, 71774084 and 71874082 ), the National Social Science Fund of China (project number is 17ZDA291), program for Jiangsu Excellent Scientific and Technological Innovation Team (project number is [2020]10).

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  1. School of Economics and Management, Nanjing University of Science &Technology, 200. Xiaolinwei road., Nanjing, 210094, Jiangsu, China

    Zhongtian Ji, Chen Ling & Peng Zhu

  2. School of Intelligent Manufacturing, Nanjing University of Science &Technology, 200. Xiaolinwei road., Nanjing, 210094, Jiangsu, China

    Peng Wu

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Zhongtian Ji: Conceptualization, Methodology, Investigation, Writing - original draft. Peng Wu: Project administration, Supervision, Writing - review & editing, Funding acquisition. Chen Ling: Formal analysis, Writing - review & editing, Data curation. Peng Zhu: Writing - review & editing.

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Ji, Z., Wu, P., Ling, C. et al. Exploring the impact of investor’s sentiment tendency in varying input window length for stock price prediction. Multimed Tools Appl 82, 27415–27449 (2023). https://doi.org/10.1007/s11042-023-14587-8

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