Expansion of a novel bio-inspired supervised machine learning class applied to financial forecasting

Abstract

The aim presented in this research consists of applying the Artificial Organic Networks (AON), a nature-inspired, supervised, metaheuristic, machine learning framework, toward the defini- tion of a new algorithm based on this machine learning class, capable of employing it for computational finance purposes, specifically, for the modeling and prediction of a stock mar- ket, based on the Index Tracking Problem (ITP). The relevance of computational finance is discussed, pointing out that is an area that has developed significantly in the last decades with different applications, some of these are: rich portfolio optimization, index-tracking, credit risk, stock investment, among others. Specifically, the Index Tracking Problem (ITP) con- cerns the prediction of stock market prices, being this a complex problem of the kind NP-hard. In this regard, this work discusses the innovative approach to implement the AON method to tackle the ITP; thus, the concept of Artificial Halocarbon Compounds or AHC-algorithm is introduced as a supervised machine learning algorithm, and as a new topology based on the AON framework. Through the discussion we review some of the disadvantages that the origi- nal Artificial Hydrocarbon Networks (AHN) topology has; these disadvantages are considered regarding the definition of the new AHC-algorithm. The proficiencies of the AHC model ca- pabilities are measured by modeling the IPC Mexico stock market index, with the aid of other economic indicators, having obtained very promising results, with a computed R-square of 0.9919, and an 8e-4 mean relative error for the forecast; and as a main contribution, the new model is an adaptable, dynamic, and reconfigurable topology, that can be applied to different approaches or systems that require simulation analysis using time series.