Financial Time Series Forecasting by Developing a Hybrid Intelligent System
AbstractThe design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and forecast time series, but designing a neural network model which provides a desirable forecasting is the main concern of researchers. For this purpose, the present study tries to examine the capabilities of two sets of models, i.e., those based on artificial intelligence and regressive models. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. Finally, in order to introduce a complete design of a neural network for modeling and forecasting of stock return series, the long memory feature and dynamic neural network model were combined. Our results showed that fractal markets hypothesis was confirmed in TSE; therefore, it can be concluded that the fractal structure exists in the return of the TSE series. The results further indicate that although dynamic artificial neural network model have a stronger performance compared to ARFIMA model, taking into consideration the inherent features of a market and combining it with neural network models can yield much better results.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 45860.
Date of creation: 17 Jan 2013
Date of revision:
Publication status: Published in European Journal of Scientific Research 4.98(2013): pp. 529-541
Stock Return; Long Memory; NNAR; ARFIMA; Hybrid Models;
Other versions of this item:
- Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45615, University Library of Munich, Germany.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-04-13 (All new papers)
- NEP-CMP-2013-04-13 (Computational Economics)
- NEP-ETS-2013-04-13 (Econometric Time Series)
- NEP-FOR-2013-04-13 (Forecasting)
- NEP-ORE-2013-04-13 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Granger, Clive & Timmermann, Allan G, 2002.
"Efficient Market Hypothesis and Forecasting,"
CEPR Discussion Papers
3593, C.E.P.R. Discussion Papers.
- Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
- Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2012. "Predicting BRICS Stock Returns Using ARFIMA Models," Working Papers 201235, University of Pretoria, Department of Economics.
- Hondroyiannis, George & Lolos, Sarantis & Papapetrou, Evangelia, 2005. "Financial markets and economic growth in Greece, 1986-1999," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(2), pages 173-188, April.
- Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992.
" Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,"
Journal of Finance,
American Finance Association, vol. 47(5), pages 1731-64, December.
- Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
- Andrew W. Lo & A. Craig MacKinlay, 1989.
"Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test,"
NBER Working Papers
2168, National Bureau of Economic Research, Inc.
- Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
- Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
- Kuswanto, Heri & Sibbertsen, Philipp, 2008. "A Study on "Spurious Long Memory in Nonlinear Time Series Models"," Diskussionspapiere der Wirtschaftswissenschaftlichen FakultÃ¤t der Leibniz UniversitÃ¤t Hannover dp-410, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
- Matkovskyy, Roman, 2012. "Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks," MPRA Paper 42153, University Library of Munich, Germany.
- Mohsin S. Khan & Abdelhak S. Senhadji, 2003. "Financial Development and Economic Growth: A Review and New Evidence," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 12(Supplemen), pages 89-110, September.
- Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
- Nazarian, Rafik & Gandali Alikhani, Nadiya & Naderi, Esmaeil & Amiri, Ashkan, 2013. "Forecasting Stock Market Volatility: A Forecast Combination Approach," MPRA Paper 46786, University Library of Munich, Germany.
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