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An Analysis of the Applications of Neural Networks in Finance

Author

Listed:
  • Adam Fadlalla

    (Department of Computer and Information Science, Cleveland State University,1860 E18th Street, Cleveland, Ohio 44114)

  • Chien-Hua Lin

    (Department of Computer and Information Science, Cleveland State University)

Abstract

Over the last 10 years, neural networks have been increasingly applied to various areas of finance. Neural networks are more often applied on the assets side than on the liabilities side of the balance sheet. Some major characteristics of the areas of these applications are their data intensity, unstructured nature, high degree of uncertainty, and hidden relationships. Most of the applications use the backpropagation model with one hidden layer. In most of these applications, neural networks out-performed traditional statistical models, such as discriminant and regression analysis. Furthermore, these applications have shown significant success in financial practice, for example, in forecasting T-bills, in asset management, in portfolio selection, and in fraud detection.

Suggested Citation

  • Adam Fadlalla & Chien-Hua Lin, 2001. "An Analysis of the Applications of Neural Networks in Finance," Interfaces, INFORMS, vol. 31(4), pages 112-122, August.
    • Handle: RePEc:inm:orinte:v:31:y:2001:i:4:p:112-122
    DOI: 10.1287/inte.31.4.112.9662
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    References listed on IDEAS

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    Cited by:

    1. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
    2. Hoi-Ming Chi & Okan K. Ersoy & Herbert Moskowitz & Kemal Altinkemer, 2007. "Toward Automated Intelligent Manufacturing Systems (AIMS)," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 302-312, May.
    3. Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.
    4. Adam Fadlalla & Farzaneh Amani, 2014. "Predicting Next Trading Day Closing Price Of Qatar Exchange Index Using Technical Indicators And Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 209-223, October.
    5. Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.
    6. Waychal, Nachiketas & Laha, Arnab Kumar & Sinha, Ankur, 2022. "Customized forecasting with Adaptive Ensemble Generator," IIMA Working Papers WP 2022-06-04, Indian Institute of Management Ahmedabad, Research and Publication Department.

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