Testing leverage-based trading strategies under an adaptive-expectations agent-based model

In this paper we introduce a novel trading strategy for trading in continuous-double auctions for risky assets. The principle underlying our strategy is an optimising of the level of leverage using numerical methods. Previous studies have shown that similar strategies can perform well when tested against theoretical models of asset price processes such as geometric Brownian motion, or back-tested against historical empirical price data. However, the former approach fails to account for phenomena such as non-Gaussian return distributions which are observed in real markets, and the latter cannot take into account how other participants in the market would likely respond to a newly introduced trading strategy. In order to account for both of these issues, we test our strategy using an existing agent-based model of financial markets which has previously been shown to replicate many of the statistical features observed in empirical financial time-series data. We analyse our strategy by simulating its behaviour under this model, and find that the key variable which influences its performance is the size of the market.