  Building on similarities between earthquakes and extreme financial events, we
use a self-organized 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.
