Abstract
Financial forecasting is an important area in computational finance. Evolutionary Dynamic Data Investment Evaluator (EDDIE) is an established genetic programming (GP) financial forecasting algorithm, which has successfully been applied to a number of international financial datasets. The purpose of this paper is to further improve the algorithm’s predictive performance, by incorporating heuristics in the search. We propose the use of two heuristics: a sequential covering strategy to iteratively build a solution in combination with the GP search and the use of an entropy-based dynamic discretisation procedure of numeric values. To examine the effectiveness of the proposed improvements, we test the new EDDIE version (EDDIE 9) across 20 datasets and compare its predictive performance against three previous EDDIE algorithms. In addition, we also compare our new algorithm’s performance against C4.5 and RIPPER, two state-of-the-art classification algorithms. Results show that the introduction of heuristics is very successful, allowing the algorithm to outperform all previous EDDIE versions and the well-known C4.5 and RIPPER algorithms. Results also show that the algorithm is able to return significantly high rates of return across the majority of the datasets.
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Notes
We use these indicators because they have been proved to be quite useful in developing GDTs in previous works like Martinez-Jaramillo (2007), Allen and Karjalainen (1999) and Austin et al. (2004). Of course, there is no reason why not use other information like fundamentals or limit order book. However, the aim of this work is not to find the ultimate indicators for financial forecasting.
These are the 6 indicators mentioned earlier; each indicator has two different period lengths, 12 and 50 days, thus resulting to a total of 12 technical indicators.
As we have mentioned, each GDT makes recommendations of buy (1) or not-to-buy (0). The former denotes a positive signal and the latter a negative. Thus, within the range of the training period, which is \(t\) days, a GDT will have returned a number of positive signals.
To make this clearer, let us give an example: if a given GP tree can have a maximum of \(k\) indicators, then the permutations of the available 12 indicators (we are using 6 different indicators, with 2 periods each, thus \(6*2=12\)) under EDDIE 7 are \(12^k\); on the other hand, if EDDIE 8 is using the same 6 indicators with periods within the range of 2 to 65 days, then the permutations of the available 384 indicators (we are using 6 different indicators with 65\(-\)1=64 periods each, thus \(64*6=384\)) are \(384^k\). It is thus obvious that EDDIE 8’s search space is significantly larger, which can therefore explain the difficulties of EDDIE 8 of consistently finding good solutions.
The datasets used in our experiments can be downloaded from: http://www.cs.kent.ac.uk/people/staff/mk451/datasets.html.
Refer to Sect. 5.2 for the definition of best tree.
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Communicated by C.-S. Lee.
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Kampouridis, M., Otero, F.E.B. Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm. Soft Comput 21, 295–310 (2017). https://doi.org/10.1007/s00500-015-1614-8
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DOI: https://doi.org/10.1007/s00500-015-1614-8