No abstract

Financial crises have repeatedly been coined as a potential application area in the recent literature on constructing early warning signals through identifying characteristics of critical slowing down on the basis of time series observations. To test this idea, we consider four historical financial crises-Black Monday 1987, the 1997 Asian Crisis, the 2000 Dot-com bubble burst, and the 2008 Financial Crisis-and investigate whether there is evidence for critical slowing down prior to these market collapses. We find statistical evidence for critical slowing down before Black Monday 1987, while the results are mixed or insignificant for the more recent financial crises. Keywords Time series • Bifurcations • Nonlinear dynamical systems • Critical slowing down • Early warning signal • Financial instability JEL Classification C14 • C53 • G01 B Juanxi Wang

This paper examines the dynamic effects of Social Influence on asset prices in the presence of heterogeneous expectations among investors. In our model, the choices of investors’ trading strategies are influenced not only by past payoffs but also by their neighbors’ choices in the social network. To obtain tractable results with generic implications for social structure, we use a mean-field approximation approach rather than specifying the exact structure of social network. Analytical conditions for the existence and local stability of equilibria of price dynamics are established and validated through numerical simulations. Our analysis shows that social influence increases the dimension of the dynamical system and that equilibria can only be expressed implicitly as solutions of certain equations. We also investigate the long-run behavior of price and fraction of trading strategies using numerical simulation under a scale-free network and a power function type social influence factor. Our results suggest that the system tends to be stable when social influence is small but exhibit complex periodic orbits and even chaos when social influence is large. These findings yield valuable insights into the role of social influence in financial markets.