Iranian Journal of Finance

Abstract This study aims to demonstrate the performance of algorithmic trading strategies compared to traditional trading methods in artificial financial markets. This research uses a hybrid model based on agent-based modeling and machine learning methods to simulate agents' behavior in an artificial financial market. This model includes two categories, traditional agents and intelligent agents. Traditional agents are divided into three groups: liquidity providers, liquidity consumers, and noise traders. Intelligent agents are trained using deep learning techniques and recurrent neural networks. Based on the developed algorithms, the agent-based model simulates both categories of traditional and trained agents in an artificial financial market. Sensitivity analysis tests were used to test the validity and reliability of the model, and the values of the fat-tailed distribution of returns, volatility clustering, autocorrelation of returns, long memory in order flow, concave price impact, and extreme price events are calculated in the model and compared with the standardized values. Historical data was used to predict stock prices, and model simulations were used to generate trading signals and update the limited order book. The results of executing the model show the ability of intelligent agents to trade in artificial financial markets compared to traditional agents.

Abstract The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.