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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

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  • Nikolaos Loukeris
  • Iordanis Eleftheriadis

Abstract

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.

Suggested Citation

  • Nikolaos Loukeris & Iordanis Eleftheriadis, 2015. "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," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 20(4), pages 341-361, October.
    • Handle: RePEc:wly:ijfiec:v:20:y:2015:i:4:p:341-361
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    Cited by:

    1. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    2. Stelios Bekiros & Nikolaos Loukeris & Iordanis Eleftheriadis & Christos Avdoulas, 2019. "Tail-Related Risk Measurement and Forecasting in Equity Markets," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 783-816, February.
    3. Samuel Egieyeh & James Syce & Sarel F Malan & Alan Christoffels, 2018. "Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    4. Stelios Bekiros & Nikolaos Loukeris & Nikolaos Matsatsinis & Frank Bezzina, 2019. "Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 647-667, August.

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