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Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector

The present study examines the impact of short-term public opinion sentiment on the secondary market, with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk. The quantification of investment sentiment indicators and the persistent ana...

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Main Authors: Zhao, Cheng, Peng, Zhe, Lan, Xuefeng, Cen, Yuefeng, Wang, Zuxin
Format: Article
Published: Tech Science Press 2024
Online Access:http://psasir.upm.edu.my/id/eprint/106200/
https://www.techscience.com/cmc/v78n1/55362
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spelling my.upm.eprints.1062002024-05-24T02:16:32Z http://psasir.upm.edu.my/id/eprint/106200/ Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector Zhao, Cheng Peng, Zhe Lan, Xuefeng Cen, Yuefeng Wang, Zuxin The present study examines the impact of short-term public opinion sentiment on the secondary market, with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk. The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research. In this paper, a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed. The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments. It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism. The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis. Furthermore, the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately. Themean absolute percentage error (MAPE) of the proposedmethod is 0.463, a reduction of 0.294 compared to the benchmark attention algorithm. Additionally, the market backtesting results indicate that the return was 24.560, an improvement of 8.202 compared to the benchmark algorithm. These results suggest that the market trading strategy based on this method has the potential to improve trading profits. Tech Science Press 2024 Article PeerReviewed Zhao, Cheng and Peng, Zhe and Lan, Xuefeng and Cen, Yuefeng and Wang, Zuxin (2024) Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector. Computers, Materials and Continua, 78 (1). pp. 1503-1523. ISSN 1546-2218; ESSN: 1546-2226 https://www.techscience.com/cmc/v78n1/55362 10.32604/cmc.2024.039232
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The present study examines the impact of short-term public opinion sentiment on the secondary market, with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk. The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research. In this paper, a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed. The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments. It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism. The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis. Furthermore, the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately. Themean absolute percentage error (MAPE) of the proposedmethod is 0.463, a reduction of 0.294 compared to the benchmark attention algorithm. Additionally, the market backtesting results indicate that the return was 24.560, an improvement of 8.202 compared to the benchmark algorithm. These results suggest that the market trading strategy based on this method has the potential to improve trading profits.
format Article
author Zhao, Cheng
Peng, Zhe
Lan, Xuefeng
Cen, Yuefeng
Wang, Zuxin
spellingShingle Zhao, Cheng
Peng, Zhe
Lan, Xuefeng
Cen, Yuefeng
Wang, Zuxin
Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
author_facet Zhao, Cheng
Peng, Zhe
Lan, Xuefeng
Cen, Yuefeng
Wang, Zuxin
author_sort Zhao, Cheng
title Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
title_short Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
title_full Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
title_fullStr Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
title_full_unstemmed Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
title_sort structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector
publisher Tech Science Press
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/106200/
https://www.techscience.com/cmc/v78n1/55362
_version_ 1800093847102947328
score 13.211869

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