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Further Higher Moments in Portfolio Selection and A Priori Detection of Bankruptcy, Under Multi‐layer Perceptron Neural Networks, Hybrid Neuro‐genetic MLPs, and the Voted Perceptron
Author(s) -
Loukeris Nikolaos,
Eleftheriadis Iordanis
Publication year - 2015
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.1521
Subject(s) - perceptron , portfolio , artificial neural network , computer science , a priori and a posteriori , multilayer perceptron , artificial intelligence , economics , selection (genetic algorithm) , genetic algorithm , machine learning , bankruptcy , network topology , mathematical optimization , finance , mathematics , philosophy , epistemology , operating system
A novel approach on the portfolio selection theory is given with regard to advanced utility performance that incorporates more accurate investor patterns up to the fifth moment. Bankruptcy detection, a priori , on an investment portfolio of stocks is a significant process that can eliminate potential losses. Even in case of corporate fraud, efficient funds can maximize their net present value by reforming the assets. Multi‐layer perceptron neural networks are compared with hybrids of neuro‐genetic multi‐layer perceptrons and the voted‐perceptron algorithm to define the most efficient classification method into the perceptrons family, implementing extensive network topologies. Copyright © 2015 John Wiley & Sons, Ltd.