Assessment Of Artificial Neural Network To Improve Hidden Markov Model For Financial Data

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2022-7-27

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Aydoğan Kılıç, Dilek

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The aim of this thesis is to eliminate the possible weaknesses of HMMs, which is a successful statistical model that is frequently used in time series modeling. Depending on the selection of the initial parameters of the HMMs, RNN is used as a solution to the failure to reach the global maximum, and it is aimed to benefit from the classification power of this method. The hybrid model, which is developed with this motivation, is built in a way that is suitable for use in non-categorical data, contrary to the version generally used in the literature. In this thesis, the hybrid model, which is effective in the development of speech recognition in the literature, is reconstructed and applied to financial data. Additionally, a multivariate comparison is conducted in order to identify the effect of the other variables in the model. Therefore, apart from univariate models, bivariate and trivariate models are also constructed. Moreover, classical HMM and RNN are applied and compared with the Hybrid model results. The applications use daily closing prices for the S&P 500 and Nasdaq and daily EUR/USD exchange rates from 2000 to 2021. In comparison to the single HMM and RNN methods, the accuracy in forecasting is significantly increased.

Subject Keywords

Hidden Markov Model, Neural Network, stock prices, forecasting, EUR/USD exchange rates

URI

https://hdl.handle.net/11511/98552

Collections

Graduate School of Applied Mathematics, Thesis

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Citation Formats

D. Aydoğan Kılıç, “Assessment Of Artificial Neural Network To Improve Hidden Markov Model For Financial Data,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.