Expanding a Machine Learning Class Towards its Application to the Stock Market Forecast

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