Market impact and performance of arbitrageurs of financial bubbles in an agent-based model

We analyse the consequences of predicting and exploiting financial bubbles in an agent-based model, with a risky and a risk-free asset and three different trader types: fundamentalists, noise traders and "dragon riders" (DR). The DR exploit their ability to diagnose financial bubbles from the endogenous price history to determine optimal entry and exit trading times. We study the DR market impact as a function of their wealth fraction. With a proportion of up to 10%, DR are found to have a beneficial effect, reducing the volatility, value-at-risk and average bubble peak amplitudes. They thus reduce inefficiencies and stabilise the market by arbitraging the bubbles. At larger proportions, DR tend to destabilise prices, as their diagnostics of bubbles become increasingly self-referencing, leading to volatility amplification by the noise traders, which destroys the bubble characteristics that would have allowed them to predict bubbles at lower fraction of wealth. Concomitantly, bubble-based arbitrage opportunities disappear with large fraction of DR in the population of traders