Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Cancho-Rodriguez, Ernesto David
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Novel framework and reference architecture for artificial intelligence models for stock markets Cancho-Rodriguez, Ernesto David; Cano Lengua, Miguel Angel
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11171

Abstract

This original research article proposes, as its contribution, a novel unified framework and reference architecture for stock market prediction in postCOVID financial markets, which exhibit unprecedented volatility and nonlinear dynamics, demanding more robust predictive approaches than traditional models can provide. This original framework integrates artificial intelligence (AI) and machine learning (ML) models, ranging from classical techniques support vector machine (SVM) to deep learning (DL) architectures such as long short-term memory (LSTM) neural networks and gated recurrent unit (GRU) models, within a modular system encompassing data ingestion, sentiment processing, predictive optimization, reinforcement learning (RL), and cloud-based portfolio management. Another key original contribution is the synthesis of standards (ISO 23053, ISO 38505, ISO 20546) with big data methodological frameworks (REBD and Biggy), forming a unified meta-framework that orchestrates predictive signals from sentiment analysis (SA) and macroeconomic indicators. Experimental realworld stock market validation on mining-sector stocks demonstrated, with a 100% success rate, consistent investment outperformance over passive Buy Hold baselines, yielding investment optimizations of up to +11.11 pp: the evaluated portfolios achieved 23.57% and 8.25% returns versus their 19.94% and 5.41% baselines, respectively. These results confirm the validity of the proposed novel framework as a reproducible reference architecture, an original contribution empirically grounded and experimentally validated for the development of future financial AI systems.
Co-Authors Cano Lengua, Miguel Angel