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Neural Networks in Finance

Author

Listed:
  • McNelis, Paul D.

    (Robert Bendheim Professor of International Economic and Financial Policy at Fordham University Graduate School of Business. Professor of Economics at Georgetown University until 2004.)

Abstract

This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Suggested Citation

  • McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    • Handle: RePEc:eee:monogr:9780124859678
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    Citations

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    Cited by:

    1. Manel Hamdi & Chaker Aloui & Santosh kumar Nanda, 2016. "Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices," Economics Bulletin, AccessEcon, vol. 36(4), pages 2430-2442.
    2. Wei Huang & Kin Keung Lai & Yoshiteru Nakamori & Shouyang Wang & Lean Yu, 2007. "Neural Networks In Finance And Economics Forecasting," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 113-140.
    3. Burka, David & Puppe, Clemens & Szepesvary, Laszlo & Tasnadi, Attila, 2016. "Neural networks would 'vote' according to Borda's rule," Working Paper Series in Economics 96, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    4. Mark T. Leung & An‐Sing Chen & Ruben Mancha, 2009. "Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(4), pages 257-277, October.
    5. Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.
    6. Craig Ellis & Patrick J. Wilson & Ralf Zurbruegg, 2007. "Real Estate ‘Value’ Stocks and International Diversification," Journal of Property Research, Taylor & Francis Journals, vol. 24(3), pages 265-287, September.
    7. Huck, Nicolas, 2009. "Pairs selection and outranking: An application to the S&P 100 index," European Journal of Operational Research, Elsevier, vol. 196(2), pages 819-825, July.
    8. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.
    9. Olcay Erdogan & Ali Goksu, 2014. "Forecasting Euro and Turkish Lira Exchange Rates with Artificial Neural Networks (ANN)," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(4), pages 307-316, October.
    10. Montero-Romero, Teresa & López-Martín, María del Carmen & Becerra-Alonso, David & Martínez-Estudillo, Francisco José, 2012. "Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 13(1), pages 3-23, June.
    11. M. Kanevski & M. Maignan & A. Pozdnoukhov & V. Timonin, 2007. "Interest rates mapping," Papers 0709.4361, arXiv.org.
    12. Jiří Trešl, 2011. "Srovnání vybraných metod predikce změn trendu indexu PX [Selected Methods of the Prediction of PX Index Trend Reversal]," Politická ekonomie, Prague University of Economics and Business, vol. 2011(2), pages 184-204.
    13. Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 6(2), pages 7-15, Diciembre.
    14. Kerem SENEL & A. Bulent PAMUKCU, 2012. "A Comparative Study For Multi-Period Asset Allocation Of Defined Contribution Schemes: Evidence From Turkey," Istanbul Commerce University Journal of Social Sciences, Istanbul Commerce University, vol. 21(1), pages 289-304.
    15. Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 58(3), pages 203-225, julio-sep.
    16. Cabrera Llanos Agustín Ignacio & Ortíz Arango Francisco, 2012. "Pronóstico del rendimiento del IPC (Índice de Precios y Cotizaciones)mediante el uso de redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 57(2), pages 63-81, abril-jun.

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