Are Spanish Ibex35 stock future index returns forecasted with non-linear models?

Applied Financial Economics

Volumn 15, Issue 14, 2005, Pages 963-975

  Pérez Rodríguez, Jorge V ^a   Torra, Salvador ^b   Andrada Félix, Julian ^a  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

^b UNIVERSITY OF BARCELONA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

FUTURE PROSPECT; MODEL; PRICE DETERMINATION; STOCK ASSESSMENT;

EASTERN HEMISPHERE; EURASIA; EUROPE; SOUTHERN EUROPE; SPAIN; WORLD;

EID: 27644509709     PISSN: 09603107     EISSN: None     Source Type: Journal    
DOI: 10.1080/09603100500108220     Document Type: Article

References (42)

1. Adya, M. and Collopy, F. (1998) How effective are neural networks at forecasting and prediction? A review and evaluation, Journal of Forecasting, 17, 481-95.
2. Alexander, S. (1961) Price movements in speculative markets: trends or random walks, Industrial Management Review, 2, 7-26.
3. Brock, W., Dechert, W. and Scheinkman, J. (1987) A test for independence based on the correlation dimension, University of Wisconsin, Dept. of Economics, Revised in Brock, W., Dechert, W., Scheinkman, J. and LeBaron, B. (1996), Econometric Reviews, 15, 197-235.
4. Casdagli, M. (1989) Nonlinear prediction of chaotic time series, Physica D, 35, 335-56.
5. Diebold, F. and Mariano, R. (1995) Comparing predictive accuracy, Journal of Business & Economic Statistics, 13, 253-63.
6. Eirtheim, O. and Teräsvirta, T. (1996) Testing the adequacy of smooth transition autoregressive models, Journal of Econometrics, 74, 59-75.
7. Elman, J. L. (1990) Finding structure in time, Cognitive Science, 14, 179-211.
8. Fama, E. (1965) The behaviour of stock market prices, Journal of Business, 38, 34-105.
9. Fama, E. and Blume, M. (1966) Filters rules and stock market trading profits, Journal of Business, 39, 226-41.
10. Farmer, D. and Sidorowich, J. (1987) Predicting chaotic time series, Physical Review Letters, 59, 845-8.
11. Fernández, F., García, D. and Sosvilla, S. (1999) Dancing with bulls and bears: nearest-neighbour forecast for the Nikkei index, Japan and the World Economy, 11, 395-413.
12. Fernández, F., González, Ch. and Sosvilla, S. (2000) On the profitability of technical trading rules based on artificial neural networks: evidence from the Madrid stock market, Economic Letters, 69, 89-94.
13. Fernández, F., Sosvilla, S. and Andrada, J. (2003) Technical analysis in foreign exchange markets: evidence from the EMS, Applied Financial Economics, 13, 113-22.
14. Fernández, F., Sosvilla, S. and García, D. (1997) Using nearest-neighbour predictors to forecast the Spanish stock market, Investigaciones Econömicas, 21, 75-91.
15. Fernández-Rodríguez, F. and Sosvilla-Rivero, S. (1998) Testing nonlinear forecastability in time series: theory and evidence from EMS, Economic Letters, 59, 49-63.
16. García, R. and Gengçay, R. (2000) Pricing and hedging derivative securities with neural networks and a homogeneity hint, Journal of Econometrics, 94, 93-115.
17. Gengçay, T. (1996) Nonlinear prediction of security return with moving average rules, Journal of Forecasting, 15, 165-74.
18. Gengçay, R. and Liu, T. (1997) Nonlinear modeling and prediction with feedforward and recurrent networks, Physica D, 108, 119-34.
19. Granger, C. W. J. and Terävirta, T. (1993) Modelling Nonlinear Economic Relationships, Oxford University Press, Oxford, pp. 1-198.
20. Hill, T., Marquez, L., O'Connor, L. and Remus, W. (1994) Artificial neural network models for forecasting and decision making, International Journal of Forecasting, 10, 5-15.
21. Hinich, M. and Paterson, D. (1985) Evidence of nonlinearity in stock returns, Journal of Business and Economic Statistics, 3, 69-77.
22. Hornik, K., Stinchcombe, M. and White, H. (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural Networks, 3, 551-60.
23. Hsieh, D. (1991) Chaos and nonlinear dynamics: applications to financial markets, Journal of Finance, 46, 1839-78.
24. Klimasauskas, C. (1991) Applying neural networks, Part 3: training a neural network, PC-AI, May-June, 20-4.
25. Kuan, C. and White, H. (1994) Artificial neural networks: an econometric perspective, Econometric Reviews, 13, 1-91.
26. Leitch, G. and Tanner, J. (1991) Economic forecast evaluation: profits versus the conditional error measures, American Economic Review, 81, 580-90.
27. Mizrach, B. (1993) Multivariate nearest-neighbour forecasts of EMS exchange rate, Journal of Applied Econometrics, 7 (supplement), 151-63.
28. Moody, J. and Utans, J. (1995) Architecture selection strategies for neural networks: application to bond rating prediction, in Neural Networks in the Capital Markets (Ed.) A.-P. N. Refenes, John Wiley, New York, pp. 1-27.
29. Pesaran, M. H. and Timmermann, P. (1992) A simple nonparametric test of predictive performance, Journal of Business and Economic Statistics, 10, 461-5.
30. Ratner, M. (1996) Investigating the behaviour and characteristics of the Madrid stock exchange, Journal of Banking and Finance, 20, 135-49.
31. Satchell, S. and Timmermann, A. (1995) An assessment of the economic value of nonlinear foreign exchange rate forecasts, Journal of Forecasting, 14, 477-97.
32. Scheinkman, J. and LeBaron, B. (1989) Nonlinear dynamics and stock returns, Journal of Business, 62, 311-38.
33. Sharda, R. and Patil, R. (1992) Connectionist approach to time series prediction: an empirical test, Journal of Intelligent Manufacturing, 3, 317-23.
34. Sharpe, W. (1966) Mutual fund performance, Journal of Finance, 39, 119-38.
35. Subrahmanyam, A. (1991) A theory of trading in stock index futures, The Review of Financial Studies, 4, 17-51.
36. Surkan, A. and Singleton, J. (1990) Neural networks for bond rating improved by multiple hidden layers, International Joint Conference on Neural Networks, 2, 157-62.
37. Swanson, N. R. and White, H. (1997) Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models, International Journal of Forecasting, 13, 439-61.
38. Sweeney, R. J. (1988) Some new filter rule tests: methods and results, Journal of Financial and Quantitative Analysis, 23, 285-300.
39. Tam, K. and Kiang, M. (1992) Managerial applications of neural networks: the case of bank failure predictions, Management Science, 38, 926-47.
40. Terävirta, T., Tjøtheim, D. and Granger, C. W. J. (1994) Aspects of modelling nonlinear time series, in Handbook of Econometrics, Vol. 4, Chap. 48 (Eds) R. F. Engle and D. L McFadden, Elsevier Science, Amsterdam, pp. 2919-57.
41. White, H. (1988) Economic prediction using neural networks: the case of IBM daily stock returns, Proceedings of the IEEE International Conference on Neural Networks, San Diego, California.
42. Zhang, G., Patuwo, E. and Hu, M. Y. (1998) Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting, 14, 35-62.