Adapting to uncertainty: A quantitative investment decision model with investor sentiment and attention analysis
Abstract
In the face of global uncertainties, including pandemics, economic fluctuations, disruptions in supply chains, major disasters, wars, and impending economic crises, the financial landscape and the impact of investor sentiment on the return of stock index futures can be significantly altered. Understanding the relationship between investor sentiment, attention, and stock index futures returns in the face of these diverse challenges has become particularly critical. However, existing research does not adequately consider the effect of these unexpected events on the market and the shifts in investor attention. Using the COVID-19 pandemic as a case study, this research proposes a dynamic quantitative investment decision-making model that considers the influence of investors’ attention and emotional characteristics, aiming to adapt to the financial market under these global changes and improve the accuracy of quantitative investment forecasting. Initially, the Bidirectional Encoder Representations from Transformers model is employed to analyze investor comment data, extract information on investor attention and emotional characteristics, and construct investor sentiment indicators. Subsequently, a stock index futures forecasting method based on Variational Mode Decomposition algorithm and Support Vector Regression (SVR) model is constructed, and the grey wolf optimization algorithm is introduced to optimize the parameters of the SVR model. Guided by investor sentiment indicators, different market states are further distinguished, and appropriate investment strategies are implemented to effectively enhance the returns of quantitative investment. When compared with models that neglect investor attention and emotional characteristics, the results show that considering investor sentiment indicators not only improves the predictive ability of the model, but also reduces cognitive bias and market risk.
First published online 6 December 2024
Keyword : investment decision-making, emotional characteristics, investor attention, support vector regression, variational model decomposition
How to Cite
Gao, J., Bai, X., Tan, H., Fan, C., Mao, Y., & Xu, Z. (2024). Adapting to uncertainty: A quantitative investment decision model with investor sentiment and attention analysis. Technological and Economic Development of Economy, 1-33. https://doi.org/10.3846/tede.2024.21961
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Fan, F., Weng, Z., & Tian, J. (2024). Impact of government support policies on regional economic resilience under the COVID-19 outbreak. Technological and Economic Development of Economy, 30(1), 74–106. https://doi.org/10.3846/tede.2024.20397
Gao, Y., Zhao, C., Sun, B., & Zhao, W. (2022). Effects of investor sentiment on stock volatility: New evidences from multi-source data in China’s green stock markets. Financial Innovation, 8(77). https://doi.org/10.1186/s40854-022-00381-2
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Gupte, A., Joshi, S., Gadgul, P., & Kadam, A. (2014). Comparative study of classification algorithms used in sentiment analysis. International Journal of Computer Science and Information Technologies, 5(5), 6261–6264.
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Hong, W.-C., Dong, Y., Zheng, F., & Wei, S. Y. (2011b). Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Applied Mathematics and Computation, 217(15), 6733–6747. https://doi.org/10.1016/j.amc.2011.01.073
Hou, Y., Li, S., & Wen, F. (2019). Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach. Energy Economics, 83, 119–143. https://doi.org/10.1016/j.eneco.2019.06.020
Hu, H., Zhang, J., & Li, T. (2020). A comparative study of VMD-based hybrid forecasting model for nonstationary daily streamflow time series. Complexity, 2020, 1–21. https://doi.org/10.1155/2020/4064851
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Lan, Q., Xiong, Q., He, L., & Ma, C. (2018). Individual investment decision behaviors based on demographic characteristics: Case from China. PLOS ONE, 13(8), 1–16. https://doi.org/10.1371/journal.pone.0201916
Lee, C. M. C., & Swaminathan, B. (2000). Price momentum and trading volume. The Journal of Finance, 55(5), 2017–2069. https://doi.org/10.1111/0022-1082.00280
Li, F., Li, R., Tian, L., Chen, L., & Liu, J. (2019). Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions. Mechanical Systems and Signal Processing, 116, 462–479. https://doi.org/10.1016/j.ymssp.2018.06.055
Li, Y., Bu, H., Li, J., & Wu, J. (2020a). The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning. International Journal of Forecasting, 36(4), 1541–1562. https://doi.org/10.1016/j.ijforecast.2020.05.001
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