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We present a new Hawkes-Contact model that combines a Hawkes process and a finite-range contact process to model the stock price movements, especially under the impact of news and other information flows that could lead to contagious effects. To fully capture the underlying price process, we take the Hawkes process to track the full pathway of historical prices on their future movements and the contact process to capture the impact from news/investment sentiment. We compare this full model to a univariate Hawkes process that works as a benchmark model through analyzing their statistical properties using both simulated returns and the real 5-min returns of the crude oil index (Wind CZCE-TA). The statistical properties include probability density function, complementary cumulative distribution function, and Lempel-Ziv Complex. Our results show that the real returns' distribution is often far from normal, but the simulated returns through the Hawkes or Hawke-Contact model can achieve close fit to the real returns and exhibit similar statistical properties. More importantly, the Hawkes-Contact model performs better than the simple Hawkes model in capturing characteristics in the return movements, which indicates that the price evolution is also driven by the news sentiment created after them. [ABSTRACT FROM AUTHOR]

This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simulation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the systemic risk of China's stock market during crashes in 2008 and 2015 using the 5-minute intraday transaction data. The results show that liquidity contracted significantly after the downtrend. The systemic risk was magnified during the crash in 2008 while the system risk increased to an abnormal level before the crash in 2015. The volatility of systemic risk rose in 2015 compared to the one in 2008. Moreover, the Johansen co-integration test proves that there is a long-run equilibrium relationship between security margin trading and systemic risk volatility. Granger causality test indicates that margin financing is the Granger cause of the volatility of systemic risk in a bear market. This shows that the government response may impose negative effects on the systemic risk of China's stock market. It helps us better to understand features of systemic risk in China's stock market, and offer new ideas on how to reduce and stabilize the systemic risk. Highlights • The 5-minute intraday transaction data is used to study the systemic risk of China's stock market. • The system risk increased to an abnormal level before the crash in 2015. • There is a long-run equilibrium relationship between systemic risk volatility and margin trading. • The most risky industry changed from manufacturing to finance & insurance. • Government response imposes negative effects on the systemic risk of China's stock market. [ABSTRACT FROM AUTHOR]