The double-edged role of social learning: Flash crash and lower total volatility

In this paper, we study how agents’ social learning behavior influences market volatility and how the flash crash emerges. We build a model of order-driven market, in which agents use a combination of four components to form their return anticipations: a social learning component, a chartist component, a fundamentalist component and a noise induced component. By numerical simulations, we find that social learning plays a double-edged role in market volatilities. On the one hand, social learning plays a role in reducing total price volatilities and stabilizing the market. On the other hand, in some excitable regimes, social learning instead acts as the critical factor contributing to a flash crash. More interestingly, the lower volatility associated with social learning in the stable regime is crucial to give birth to a flash crash. In addition, we do some robust analyses on the roles of social learning by running the model under many different parameter settings. With the increase of the social learning innate parameter, both the average draw-down and draw-up, the average spread and the average gap show a downward trend. Meanwhile, the tail exponents for the draw-downs and draw-ups also show a downward trend, confirming that social learning plays a double-edged role. The chartist belief can stabilize the market when the social influence is not so trusted, while the chartist belief transfers to contribute to the market instability when the social influence is sufficiently trusted. The fundamentalist belief shows quite opposite impacts in respect to the chartist belief. Finally, we summarize typical return and volatility patterns before a flash crash, which will give some inspirations to regulators and investors.