Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Joy Dip Das (), Ruppa K. Thulasiram (), Christopher Henry and Aerambamoorthy Thavaneswaran
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Joy Dip Das: Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Ruppa K. Thulasiram: Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Christopher Henry: Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Aerambamoorthy Thavaneswaran: Department of Statistics, University of Manitoba, Winnipeg, MB R3T 2N2, Canada

JRFM, 2024, vol. 17, issue 5, 1-23

Abstract: This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

Keywords: autoencoder; LSTM; GRU; hybridization; stocks; stock index; cryptocurrency (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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