How adaptive agents in stock market perform in the presence of random news: A genetic algorithm approach

Abstract

The effect of random news on the performance of adaptive agents as investors in stock market is modelled by genetic algorithm and measured by their portfolio values. The agents are defined by the rules evolved from a simple genetic algorithm, based on the rate of correct prediction on past data. The effects of random news are incorporated via a model of herd effect to characterize the human nature of the investors in changing their original plan of investment when the news contradicts their prediction. The random news is generated by white noise, with equal probability of being good and bad news. Several artificial time series with different memory factors in the time correlation function are used to measure the performance of the agents after the training and testing. A universal feature that greedy and confident investors outperform others emerges from this study.