An initial investigation of choice function hyper-heuristics for the problem of financial forecasting

Financial forecasting is a vital area in computational finance. This importance is reflected in the literature by the continuous development of new algorithms. EDDIE is well-established genetic programming financial forecasting tool, which has successfully been applied to a variety of international datasets. Recently, we introduced hyper-heuristics to EDDIE. This was the first time in the literature that hyper-heuristics were used for financial forecasting. Results showed that this introduction significantly benefited the performance of the algorithm. However, an issue was encountered in the way that lowlevel heuristics were selected during the search process, because it was considered to be a static way. To address this issue, in this paper we further improve our algorithm by introducing a Choice Function, which is a score based technique that offers a more dynamic selection of the low-level heuristics. This paper presents preliminary results, after having tested the Choice Function approach with 10 datasets. These results show that the introduction of the Choice Function is beneficial to EDDIE, thus making it a very promising tool for future investigation on financial forecasting problems.