International Research Journal of Finance and Economics, Vol. Long-term Dependencies and the Profitability of Technical Analysis.
A MULTIFRACTAL WALK DOWN WALL STREET SERIES
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Predicting BRICS Stock Returns Using ARFIMA Models.
Ěye, G.C., Balcilar, M., Gupta, R., Kilimani, N., Nakumuryango, A., Redford, S. Effects of financial liberalization on financial market development and economic performance of the SSA region: An empirical assessment, Economic Modelling, Vol. G - Financial Economics > G1 - General Financial Markets > G10 - General Stock Return, Long Memory, NNAR, ARFIMA, Hybrid ModelsĬ - Mathematical and Quantitative Methods > C2 - Single Equation Models Single Variables > C22 - Time-Series Models Dynamic Quantile Regressions Dynamic Treatment Effect Models Diffusion ProcessesĬ - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related TopicsĬ - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods Simulation Methods Item Type:įinancial Time Series Forecasting by Developing a Hybrid Intelligent System 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. 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. 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. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. 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. 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. The design of models for time series forecasting has found a solid foundation on statistics and mathematics.
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