Genetic Fuzzy Expert Trading System for Nasdaq Stock Market Timing

Abstract

Technical indicators are developed for monitoring the movement of stock prices from different perspective. They are widely used to define trading rules to assist investors to make the buy-sell-hold decision. Most of these trading rules are vague and fuzzy in nature. Since the stock prices are affected by the artificial news factors from time to time, investors cannot be the winner all the time on using the same set of trading rules. The weight (i.e., the significance) of a trading rule to investors is varying with time. The problem of determining the weight of the trading rules can be modelled as an optimization problem. In this paper, we propose a Genetic Fuzzy Expert Trading System (GFETS) for market timing. We apply the fuzzy expert system to simulate vague and fuzzy trading rules and give the buy-sell signal. The set of trading rules adopted in the system will vary with time and is optimized using Genetic Algorithm (GA). Two training approaches—incremental and dynamic—are designed and studied. The system was evaluated with the stocks in NASDAQ market. Experimental results showed that the system can give reliable buy-sell signals and using the system to perform buy-sell can produce significant profit.