Building on similarities between earthquakes and extreme financial events, we use a URLanized criticality-generating model to study herding and avalanches dynamics in financial markets. We consider a community of interacting investors, distributed on a small world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market compared to the day before, following the S&P500 historical time series. Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of trader s , randomly distributed inside the network, who adopt a random investment strategy. These results suggest a promising strategy to limit the size of financial bubbles and crashes. We also find that the final wealth distribution of all traders corresponds to the well-known Pareto power law, while that one of random traders only is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders.
Building on similarities between earthquakes and extreme financial events, we use a URLanized criticality-generating model to study herding and avalanche dynamics in financial markets. We consider a community of interacting investors, distributed on a small-world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market which has been specified according to the S&P500 historical time series. Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of traders , randomly distributed inside the network, who adopt a random investment strategy. Our findings suggest a promising strategy to limit the size of financial bubbles and crashes. We also obtain that the resulting wealth distribution of all traders corresponds to the well-known Pareto power law, while the one of random traders is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders.
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