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: | , , , , |
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| Format: | Article |
| Published: |
Tech Science Press
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106200/ https://www.techscience.com/cmc/v78n1/55362 |
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| Summary: | 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. |
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