Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm

Hyper-heuristics have successfully been applied to a vast number of search and optimization problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analyzed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.