Expanding a machine learning class towards its application to the stock market forecast

• DOI: 10.1007/s10489-024-06018-4
• Publication Date: 2024-11-25T11:09:21Z
• Authors: Enrique González-...; Luis A. Trejo; Michael Kampouridis
• Keywords: Stock (firearms)

In this work, we present a new and efficient algorithm to perform short-term market trend forecast, based on the Artificial Organic Networks (AON) metaheuristic machine learning framework. Regarding goal, concept of Halocarbon Compounds (AHC) or AHC-algorithm as bio-inspired supervised AON Through our research, contrast forecast acquired with proposed AHC model, previously reported outcomes using Hydrocarbon (AHN) in similar tasks. The AHN is first formally defined topology AON, making vital benchmark contemplate. After comparing original AHN-algorithm, found out that due high computational complexity latter, more convenient when modeling complex systems; being characteristic main contribution AHC-algorithm, allowing it be adaptable, dynamic, reconfigurable topology. Likewise, compared results against derived from an ARIMA model; also made cross-reference concerning prediction other stock indices former state-of-the-art methods. proficiency assessed by doing IPC Mexico index obtaining good results, achieving computed R-square 0.9919, $$8\times 10^{-4}$$ mean relative error for forecast.