Strongly-typed genetic programming and fuzzy inference system: An embedded approach to model and generate trading rules

Abstract Generating trading signals is an interesting topic and a hard problem to solve. This work uses fuzzy inference system (FIS) and strongly typed genetic programming (STGP) to generate trading rules for the US stock market, a framework that we call FISTGP. The two embedded models have not been widely evaluated in financial applications, and according to the literature, their combination could improve forecasting performance. The fitness function used to train the STGP model is based on accuracy, optimizing the buy and sell signals, taking a different approach to the classic optimization of return–risk ratio. The rules are generated in a FIS framework, and the final signal depends on the amount of information that the investor relies on. The model is suited to each investor as a recommendation of when to change portfolio composition according to his or her particular criteria. Ternary rules are generated based on an economic interpretation, considering the risk-free rate as a part of more demanding rules. The model is applied to 90 of the most traded and active stocks in the US stock market. This approach generates important recommendations and delivers useful information to investors. The results show that the proposed model outperforms the Buy and Hold (B&H) strategy by 28.62% in the test period, considering excesses of return, with almost the same risk (1.28% higher). The other base models underperform in comparison to the B&H, with the proposed model also outperforming them.