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Modification of hybrid RNN-HMM model in asset pricing: univariate and multivariate cases

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Abstract

Hidden Markov Model (HMM) which is frequently used in time series modeling with satisfactory results is commonly used for predicting stock prices in many studies. Due to its more transparent structure than most of the current Neural Network (NN) models and its sensitivity to the initial parameter settings, we propose a hybrid model that combines Recurrent NN (RNN) and HMM in modeling stock prices to eliminate initial parameter influence. Despite its common application to speech recognition data with categorical variables, we reconstruct RNN and HMM for financial data. RNN is used as a solution to the probability of not reaching the global maximum based on the HMM’s initial parameters selection. To do so, we improve the classification power of HMM to achieve the hidden states that do not get stuck at a local maximum but provide the global maximum. In addition, unlike the literature, the loss function is not chosen as the maximum likelihood but is defined directly over the prices. Thus, the model does not only detect the states appropriately, yet the predictions can get closer to the actual prices. Besides, a multivariate comparison is performed to determine the effect of different numbers and types of variables through bivariate and trivariate models. The application is made on S &P 500, Nasdaq daily closing prices and daily EUR/USD exchange rates data from 2000 to 2021. It is shown that the accuracy is increased significantly compared to the implementations of HMM and RNN methods separately.

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Availability of data and materials

The datasets analysed during the current study are openly available in the public domain resource [Yahoo Finance] at https://finance.yahoo.com.

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Authors and Affiliations

  1. Department of Materials and Production, Aalborg University, Fibigerstræde 10, Aalborg, 9220, Denmark

    Dilek Aydogan-Kilic

  2. Institute Of Applied Mathematics, Middle East Technical University, Dumlupınar Bulvarı, Ankara, 06800, Turkey

    Dilek Aydogan-Kilic & A. Sevtap Selcuk-Kestel

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  1. Dilek Aydogan-Kilic
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  2. A. Sevtap Selcuk-Kestel
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Correspondence to Dilek Aydogan-Kilic or A. Sevtap Selcuk-Kestel.

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Appendix: Parameters of the RNN, RNN-HMM, LSTM and GRU experiments

Appendix: Parameters of the RNN, RNN-HMM, LSTM and GRU experiments

The common parameters of each RNN, RNN-HMM, LSTM and GRU experiment are mentioned in Section 3.2. Moreover, the remaining parameters of learning rate and epoch numbers of RNN and RNN-HMM are presented in Table 15, and the unmentioned parameters of LSTM and GRU are shown in Table 16 and 17, respectively.

Table 15 Learning Rates and Epoch Numbers of the RNN and RNN-HMM Experiments
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Table 16 Parameters of the LSTM Experiments
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Table 17 Parameters of the GRU Experiments
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Aydogan-Kilic, D., Selcuk-Kestel, A.S. Modification of hybrid RNN-HMM model in asset pricing: univariate and multivariate cases. Appl Intell 53, 23812–23833 (2023). https://doi.org/10.1007/s10489-023-04762-7

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