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

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 architecture...

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Bibliographic Details
Published in:Journal of risk and financial management 2024-05, Vol.17 (5), p.200
Main Authors: Dip Das, Joy, Thulasiram, Ruppa K., Henry, Christopher, Thavaneswaran, Aerambamoorthy
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Banking industry
Computational linguistics
Crypto-currencies
Digital currencies
Forecasting
Forecasts and trends
Hybridization
Language processing
Machine learning
Natural language interfaces
Neural networks
Pharmaceutical industry
Securities markets
Stock prices
Stocks
Trends
Volatility
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Summary: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.
ISSN:1911-8074
1911-8066
1911-8074
DOI:10.3390/jrfm17050200