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Neural Networks In Finance And Economics Forecasting

Author

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  • WEI HUANG

    (Institute of Intelligent Management and Complex System, School of Management, Huazhong University of Science and Technology, WuHan, 430074, China;
    Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China)

  • KIN KEUNG LAI

    (Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China;
    College of Business Administration, Hunan University, Changsha 410082, China)

  • YOSHITERU NAKAMORI

    (School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1, Asahidai, Ishikawa 923-1292, Japan)

  • SHOUYANG WANG

    (Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China;
    College of Business Administration, Hunan University, Changsha 410082, China)

  • LEAN YU

    (Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China;
    Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China)

Abstract

Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijitdm:v:06:y:2007:i:01:n:s021962200700237x
    DOI: 10.1142/S021962200700237X
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    References listed on IDEAS

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    1. Simon van Norden & Huntley Schaller, 2002. "Fads or bubbles?," Empirical Economics, Springer, vol. 27(2), pages 335-362.
    2. William A. Brock, 1993. "Pathways to randomness in the economy: Emergent nonlinearity and chaos in economics and finance," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 8(1), pages 3-55.
    3. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
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    Citations

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    Cited by:

    1. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
    2. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
    3. E. Martinez-De-Pison & J. Fernandez-Ceniceros & A. V. Pernia-Espinoza & F. J. Martinez-De-Pison & Andres Sanz-Garcia, 2016. "Hotel Reservation Forecasting Using Flexible Soft Computing Techniques: A Case of Study in a Spanish Hotel," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1211-1234, September.
    4. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.
    5. Ricardo Najarro Chuchón, 2019. "Indicador líder de la inversión privada: metodología de redes neuronales," Revista de Análisis Económico y Financiero, Universidad de San Martín de Porres, vol. 1(03), pages 40-47.
    6. Wei-Chang Yeh & Yu-Hsin Hsieh & Chia-Ling Huang, 2022. "Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm," Papers 2211.12839, arXiv.org.
    7. Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020. "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers 2005.02217, arXiv.org.
    8. Taufiq Choudhry & Frank McGroarty & Ke Peng & Shiyun Wang, 2012. "High‐Frequency Exchange‐Rate Prediction With An Artificial Neural Network," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 170-178, July.

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