Dynamic asset allocation for stock trading optimized by evolutionary computation
Effective trading with given pattern-based multi-predictors of stock price needs an intelligent asset allocation strategy. In this paper, we study a method of dynamic asset allocation, called the meta policy, which decides how much the proportion of asset should be allocated to each recommendation for trade. The meta policy makes a decision considering both the recommending information of multi-predictors and the current ratio of stock funds over the total asset. we adopt evolutionary computation to optimize the meta policy. The experimental results on the Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods.
asset allocation evolutionary computation meta policy stock market multi-predictors approach