An improved genetic-based forecasting model for high-speed trading

The advancement of information technology and research in finance have recently led to flash decision making and actions by computer algorithms in order to respond to fast events occurring in the stock markets. This new area of technology involves the implementation of high-speed trading strategies which have generated significant amount of activity and information for financial research. In this study, we proposed an improved version of a computational intelligence model using genetic algorithms (GA) for the forecasting of stock price at the microscopic level. Our empirical results show that the new version of the GA-based system is able to improve the accuracy of the prediction significantly, which shall advance the current state of research for artificial intelligence and relevant financial applications.