Optimizing Investments in the Portfolio Intelligence (PI) Model

Nikolaos Loukeris (), Lysimachos Maltoudoglou, Yannis Boutalis and Iordanis Eleftheriadis
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Nikolaos Loukeris: Department of Business Administration, University of West Attica, Petrou Ralli & Thivon Avenue, 12241 Athens, Greece
Lysimachos Maltoudoglou: Department of Production Engineers and Management, Democritus University of Thrace, 67100 Xanthi, Greece
Yannis Boutalis: Department of Electrical and Computer Engineers, Democritus University of Thrace, 67100 Xanthi, Greece
Iordanis Eleftheriadis: Department of Business Administration, University of Macedonia, Egnatias 156, 54636 Thessaloniki, Greece

JRFM, 2025, vol. 18, issue 9, 1-20

Abstract: A new methodology is introduced that incorporates advanced higher moment evaluation in a new approach to the Portfolio Selection problem, supported by effective Computational Intelligence models. The Portfolio Intelligence (PI) model extracts hidden patterns from numerous accounting data and financial statements, filtering misleading effects such as noise or fraud offering an optimal portfolio selection method. The chaotic reflections of speculative behaviors of investors are analyzed in fractal distributions, as higher moments with fundamentals clear the turbulence of noise while the PI model, under its robust AI classifiers, provides optimal investment support.

Keywords: integrated systems; radial basis functions; support vector machines; multi-layer perceptrons; neural networks; genetic algorithms; financial analysis; nonlinear regressions; financial wellfare; irrational investor behavior (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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