Agent-Based Models Simulations for High Frequency Trading

Abstract

High frequency computer-based trading (HFT) represents a challenging topic nowadays, mainly due to the controversy it creates among investors on the financial market. The hereto paper compares two types of agent-based models, one with zero-intelligence traders and the other with intelligent traders in order to simulate the tick-by-tick high frequency trades on the stock market for the selected U.S. stocks. The simulations of the agent-based models are done with the help of Adaptive Modeler software application which uses the interaction of 2,000 heterogeneous agents to create a virtual stock market for the selected stock with the scope of forecasting the price. Within the intelligent agent-based model the population of agents is continuously adapting and evolving by using genetic programming in forming new agents by using the trading strategies of the best performing agents and replacing the worst performing agents in a process called breeding, while the zero-intelligence agent based model does not evolve, agents do not breed, and they trade in a random manner. After comparing the fitting of the two models with the real data, the results show that in almost all the cases the intelligent agent-based model performed better when compared to the zero-intelligence agent-based model, which could be interpreted as lower market efficiency, allowing for predictions of the stock market price, or even stock market manipulation. Also, the zero-intelligent agent-based model generates more trades and lower wealth for the population, compared to the intelligent agent-based model. The high-frequency data turns out to be very hard to simulate and analyse due to its particularities which differentiate them from daily data, as price changes are discrete, being multiples of the minimum price increment, the price changes not being independent.