Forecasting gold price changes: Rolling and recursive neural network models

Journal of Multinational Financial Management

Volumn 18, Issue 5, 2008, Pages 477-487

  Parisi, Antonino ^a   Parisi, Franco ^a   Díaz, David ^a,b  

^a UNIVERSITY OF CHILE   (Chile)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Artificial neural networks; Recursive operation; Rolling operation

Indexed keywords

EID: 50949119959     PISSN: 1042444X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mulfin.2007.12.002     Document Type: Article

References (16)

1. Chen A., and Leung M.T. Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations. Journal of Forecasting 24 6 (2005) 403-420
2. Estrella A., and Mishkin F.S. Predicting US recessions: financial variables as leading indicators. The Review of Economics and Statistics 80 (1998) 45-61
3. Hellström, T., Hollström, K., 1998. Predicting the Stock Market. Research and Reports Opuscula ISRN HEV-BIB-OP-26SE Mälardalen University, Västeras Sweden.
4. Herbrich R., Keilbach M., Graepel T., Bollmann-Sdorra P., and Obermayer K. Neural networks in economics: background, applications and new developments. In: Brenner T. (Ed). Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics vol. 11 (2000), Kluwer Academics 169-196
5. Hodgson A., and Nicholls D. The impact of index futures markets on Australian share market volatility. Journal of Business Finance and Accounting 18 (1991) 267-280
6. Lai, K.K., Yu, L., Wang, S., Huang, W., 2006. Hybridizing exponential smoothing and neural network for financial time series predication. Lecture Notes in Computer Science. Springer Berlin/Heidelberg, ISBN 978-3-540-34385-1, pp. 493-500.
7. Leung M., Daouk H., and Chen A. Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting 16 (2000) 173-190
8. McCann, P., Kalman, B., 1994. A Neural Network Model for the Gold Market. Washington University in St. Louis, Part of the Philosophy Neuroscience and Psychological Technical Report Series.
9. McMillan D. Non-linear dynamics in international stock market returns. Review of Financial Economics 14 (2005) 81-91
10. Mirmirani S., and Li H.C. Gold price, neural networks and genetic algorithm. Computational Economics 23 (2004) 193-200
11. O'Connor M., Remus W., and Griggs K. Going up-going down: how good are people at forecasting trends and changes in trends?. Journal of Forecasting 16 (1997) 165-176
12. Parisi A., Parisi F., and Guerrero J.L. Modelos predictivos de redes neuronales en índices bursátiles. El Trimestre Económico 279 (2004) 721-744
13. Pesaran M.H., and Timmermann A. A simple nonparametric test of predictive performance. Journal of Business and Economic Statistics 10 (1992) 461-465
14. Qi M. Predicting US recessions with leading indicators via neural network models. International Journal of Forecasting 17 (2001) 383-401
15. Tsibouris G., and Zeidenberg M. In: A.P. (Ed). Testing the efficient markets hypothesis with gradient descent algorithms. Neural Networks in the Capital Markets (1995) 127-136
16. White H. Economic prediction using neural networks: the case of IBM daily stock returns. In: Trippi R.R., and Turban E. (Eds). Neural Networks in Finance and Investing (1993), Probus Publishing 315-328