Stock index return forecasting: Semantics-based genetic programming with local search optimiser

Making accurate stock price predictions is the pillar of effective decisions in high-velocity environments since the successful prediction of future prices could yield significant profit and reduce operational costs. Generally, solutions for this task are based on trend predictions and are driven by various factors. To add to the existing body of knowledge, we propose a semantics-based genetic programming framework. The proposed framework blends a recently developed version of genetic programming that uses semantic genetic operators with a local search method. To analyse the appropriateness of the proposed computational method for stock market price prediction, we analysed data related to the Dow Jones index and to the Istanbul Stock Index. Experimental results confirm the suitability of the proposed method for predicting stock market prices. In fact, the system produces lower errors with respect to the existing state-of-the art techniques, such as neural networks and support vector machines.