A Novel Correlation Model between Investor Sentiment and Trading Behavior Based on Attention Mechanism with Time-Varying Information

DOI

10.2991/978-94-6463-572-0_7How to use a DOI?

Keywords

investor sentiment; trading behavior; correlation analysis; Copula; Encoder

Abstract

The security market has the dual characteristics of emerging and transforming, and its stability has a far-reaching impact on the healthy development of social economy. At present, there are few researches on the correlation analysis between investor sentiment and trading behavior in securities market, and there is a lack of systematic and in-depth theoretical analysis. In this paper, statistical model (ARMA-GARCH-Copula) and deep learning model (Encoder+CNN) are combined to accurately describe and study the correlation between investor sentiment and transaction behavior from multiple perspectives. A novel correlation model between investor sentiment and trading behavior based on attention mechanism with time-varying information (CMISTB) is proposed. The CMISTB model can capture the nonlinear and volatility characteristics of time series data, flexibly analyze the dependence between investor sentiment and trading behavior time series data, and reduce the prediction bias caused by the limitation of a single method. The CMISTB model can deal with multi-variable time series data effectively, which provides a powerful tool for complex financial risk management. This work contributes to the in-depth study of irrational fluctuations in the market, provides theoretical and empirical support for maintaining the steady development of the financial market, and provides an important reference and guidance for further exploring the market behavior.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Shan Li
AU  - Mengxiang Sun
AU  - Xinge Liu
AU  - Li Zeng
PY  - 2024
DA  - 2024/11/19
TI  - A Novel Correlation Model between Investor Sentiment and Trading Behavior Based on Attention Mechanism with Time-Varying Information
BT  - Proceedings of the 3rd International Conference on Financial Innovation, FinTech and Information Technology (FFIT 2024)
PB  - Atlantis Press
SP  - 57
EP  - 64
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-572-0_7
DO  - 10.2991/978-94-6463-572-0_7
ID  - Li2024
ER  -