portfolio allocation using xcs experts in technical analysis, market conditions and options market
Schulenburg (2000) first proposed the idea to model different trader types by supplying different input information sets to a group of homogenous LCS agent. Gershoff (2006) investigated this idea further with XCS agent. This paper takes an extra step to build a trading system that not only adopts the multi-XCS agent idea, but also utilizes knowledge from discretization theory, modern portfolio theory, options theory and methods of combining multiple models. In comparison to previous work, a wider range of input data were used including technical analysis, general market conditions and options market conditions. Secondly, quantization of continuous financial series was achieved using entropy-based discretization and histogram equalization. Thirdly, subtle investment strategies can now be generated as a result of taking stock price magnitude into account. Finally, multiple agents' predictions were combined using a variant of stacking. Empirical results show the best-performing XCS agents always outclass benchmark agents in every stock examined. Variance is reduced after combining predictions from multiple models. The technical analysis XCS agent was able to replicate a well known technical trading rule widely used in the 60s.
xcs options computational finance learning classifier system reinforcement learning finance economics