Dynamic Generation of Investment Recommendations Using Grammatical Evolution.

Carlos Martín; David Quintana; Pedro Isasi

doi:10.9781/ijimai.2021.04.007

Authors

Carlos Martín Universidad Carlos III de Madrid
David Quintana Universidad Carlos III de Madrid
Pedro Isasi Universidad Carlos III de Madrid

DOI:

https://doi.org/10.9781/ijimai.2021.04.007

Keywords:

Dynamic Strategy, Evolutionary Computation, Finance, Grammatical Evolution, Structural Change, Trading

Supporting Agencies

The authors would like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin). This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3MXX), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

Abstract

The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest.

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