Artificial Organic Networks Approach Applied to the Index Tracking Problem

González, N Enrique; Trejo, Luis A.

doi:10.1007/978-3-030-89817-5_2

Cited by 2 publications

(6 citation statements)

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“…Distinct experiments have been conducted to validate the efficacy of the AHC algorithm, put forth as a suggested supervised machine learning algorithm, and can successfully conduct short-term forecasts of market price trends, as initially specified in [2]. In this context, Section 3.1 details the utilized data and outlines the preprocessing steps undertaken for the experiments.…”

Section: Methodsmentioning

confidence: 99%

“…This selection is based on the cost-energy function, ensuring the overall low error of the produced models. These dynamic options involve decisions such as substituting the type of curve or choosing among different fitting methods, including the multiple nonlinear regressive (MNLR) model [2], among others. This involves replacing the method used to characterize each molecule.…”

Section: Artificial Halocarbon Compounds Approachmentioning

confidence: 99%

“…The time period is from the 1st of June 2006 to the 31st of May 2023. We chose this time period to ensure that at least one short economic cycle was used for the analysis and prediction [2]. The indices and the FX data were sourced from Yahoo Finance, and the rest of the variables were retrieved from the OECD.…”

Section: Datamentioning

confidence: 99%

“…For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7 × 10 −4 . From this perspective, the objective is aligned with the aim previously outlined in [2], centered on crafting a novel, efficient algorithm based on the AON framework that is capable of producing short-term forecasts for market trends.…”

Section: Introductionmentioning

confidence: 99%

“…Find the energy level of the subset Υ i with each halogen τ j , considering λ. Update the final behavior of the molecule in Υ i , by selecting the best halogenation τ j , following the ENTROPY-RULE. Update the error function ε using the true fractional relative error defined in [2]. 14: end while 15: return C, and τ…”

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confidence: 99%

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A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

González-Núñez,

Trejo,

Kampouridis

2024

BDCC

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This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7×10−4. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting.

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Section: Methodsmentioning

confidence: 99%

Section: Artificial Halocarbon Compounds Approachmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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