Integrating Artificial Neural Network Models By Markov Chain Process: Forecasting The Movement Direction Of Turkish Lira Us Dollar Exchange Rate Returns

Yıl 2013, Cilt: 22 Sayı: 2, 97 - 110, 29.12.2013

Doç.dr. Süleyman Bilgin Kılıç

Öz

In this study we first modeled daily US dollar returns as the discrete state Markov chain process and second we trained an Artificial Neural Network ANN model in order to estimate direction of dollar return The trained model provides valuable information about the direction of next day return Keywords: Artificial Neural Networks Markov chains Conditional probability Exchange rate returns Jel: C45 C53 F31

Anahtar Kelimeler

Artificial Neural Networks, Markov chains, Conditional probability, Exchange rate returns

Integrating Artificial Neural Network Models By Markov Chain Process: Forecasting The Movement Direction Of Turkish Lira Us Dollar Exchange Rate Returns

Öz

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Anahtar Kelimeler

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Kaynakça

• Anastasakis, L. & Mort N.(2009). Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach , Expert Systems with Applications, Volume 36, Issue 10, pp 12001-12011.
• Hussain, A.J., Knowles, A., Lisboa, P.J.G., El-Deredy, W. (2008). Financial time series prediction using polynomial pipelined neural networks, Expert Systems with Applications, Volume 35, Issue 3, pp 1186-1199.
• Kadılar, C., Şimşek, M., Aladağ, Ç.H. (2009). Forecasting the exchange rate series with ANN: the case of Turkey , İstanbul Üniversitesi İktisat Fakültesi Ekonometri ve İstatistik Dergisi, Volume 9, pp 17-29.
• Kayacan, E., Ulutas, B., Kaynak, O. (2010). Grey system theory-based models in time series prediction , Expert Systems with Applications, Volume 37, Issue, 2, pp 1784-1789.
• Kempa, B. & Riedel, J. (2013). Nonlinearities in exchange rate determination in a small open economy: Some evidence for Canada, The North American Journal of Economics and Finance, Volume 24, Issue January, pp 268-278.
• Leung M.T., Chen, A.S., Dauk, H. (2000). Forecasting exchange rates using general regression neural networks, Computers & Operations Research, Volume 27, Issue 11-12, pp 1093-1110.
• Panda, C. & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network, Journal of Policy Modeling, Volume 29, Issue March–April, pp 2272
• Sermpinis, G., Dunis, C., Laws, J., Stasinakis, C. (2012). Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and timevarying leverage, Decision Support Systems, Volume 54, Issue 1, pp 316-329
• Shin, T. & Han, I. (2000). Optimal signal multi-resolution by genetic algorithms to support artificial neural networks forexchange-rate forecasting, Expert Systems with Applications, Volume 18, Issue 4, pp 257-269.
• Qi, M., & Wu, Y.(2003). Nonlinear prediction of exchange rates with monetary fundamentals, Journal of Empirical Finance, Volume 10, Issue 5, pp 623-640
• Yubo, Y. (2013). Forecasting the movement direction of exchange rate with polynomial smooth support vectormachine , Mathematical and Computer Modelling, Volume 57, Issue 3-4, 932-944.
• Zhang G, B. & Michael Y.H. (1998). Neural network forecasting of the British Pound/US Dollar exchange rate, Omega, Volume 26, Issue 4, pp 495-506.