01738nas a2200253   4500000000100000000000100001008004100002260001200043653002100055653002900076653001200105653002600117653002200143653001200165100001900177700001900196700001600215245008100231856008100312300001200393490000600405520105900411022001401470       2021                            d  c06/202110aDynamic Strategy10aEvolutionary Computation10aFinance10aGrammatical Evolution10aStructural Change10aTrading1 aCarlos Martín1 aDavid Quintana1 aPedro Isasi00aDynamic Generation of Investment Recommendations Using Grammatical Evolution  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_11.pdf  a104-1110 v63 aThe 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.  a1989-1660