Cross-Market Infection Research on Stock Herding Behavior Based on DGC-MSV Models and Bayesian Network

Abstract

This paper is concerned with the multivariate stochastic volatility modeling of the stock market. We investigate a DGC-t-MSV model to find the historical volatility spillovers between nine markets, including S&P, Nasdaq, SSE, SZSE, HSI, FTSE, CAC, DAX, and Nikkei indices. We use the Bayesian network to analyze the spreading of herd behavior between nine markets. The main results are as follows: (1) the DGC-t-MSV model we considered is a useful way to estimate the parameter and fit the data well in the stock market; (2) our computational analysis shows that the S&P and Nasdaq have higher volatility spillovers to the Shanghai and Shenzhen stock markets; (3) the results also show that there is a strong correlation between stock markets in the same region.

Data science and AI in FinTech: an overview

“…-generating algorithmic trading strategies such as analyzing relevant price-sensitive financial events from the news and announcements about the security or predicting security price movement [41]; -detecting illegal or incompliant behaviors and events such as discovering pool manipulation from collective investment accounts and investor behaviors [8]; -modeling the impact of external events in financial markets such as the impact of information security on stock market movement [53]; and -modeling cross-market investment strategies, investment herding behaviors, volatility spillover, and risk management [58,59].…”

Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting

“…Over the decades, many artificial intelligence algorithms have been developed and applied to financial market forecasting, for instance, artificial neural network [9]- [11], support vector machines [12]- [17], rough set theory [18]- [20], bayesian analysis [21]- [24] and evolutionary learning algorithms [25]- [28]. However, most of the past researches mainly focus on the accurate price forecast only.…”