LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Zhong, Cheng; Du, Wei; Wang, Xu; Huang, Qizhao; Zhao, Yinuo; Wang, Mingming

doi:10.1016/j.dss.2023.113955

Cited by 32 publications

(13 citation statements)

References 38 publications

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“…The proposed model proved to be a high performer illustrating the training and testing accuracies of 96% and 98% respectively [28] . Likewise, another pertinent study [34] proposed a hybrid model of LSTM and relationwise graph attention network (ReGAT) for the trend prediction of cryptocurrencies' prices. The sequential patterns of individual cryptocurrency features were profiled by LSTM while ReGAT was used to extract the network features.…”

Section: Related Workmentioning

confidence: 99%

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Latif,

Javaid,

Aslam

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Latif,

Javaid,

Aslam

et al. 2024

Heliyon

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“…The application of machine learning techniques, including regression models, SVMs and random forests, has been used to analyze historical data and identify patterns or indicators that can signal price movements of cryptocurrencies (Jaquart et al, 2022;Smales, 2022). Additionally, deep learning methods, particularly recurrent neural networks, convolutional SEF 41,2 neural networks (CNNs) and LSTM networks, have gained popularity because of their ability to capture temporal dependencies in sequential data (Lahmiri and Bekiros, 2019;Alonso-Monsalve et al, 2020;Zhong et al, 2023;Oyedele et al, 2023). However, deep learning methods might be limited by the non-stationarity potentially exhibited by cryptocurrency markets, possible overfitting and substantially higher computational requirements.…”

Section: Machine Learning and Cryptocurrencymentioning

confidence: 99%

Drivers of the next-minute Bitcoin price using sparse regressions

Gurrib,

Kamalov,

Starkova

et al. 2023

SEF

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Purpose This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading? Design/methodology/approach Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted. Findings Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information. Originality/value To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

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“…The effectiveness of predictive models can significantly impact profitability by generating high cumulative returns. Presently, state-of-the-art methods can be classified into two categories: traditional machine learning techniques, as seen in the works of P Arora et al [1], MN Ashtiani et al [2], and deep learning-based methods presented in recent research such as work done by GI Kim et al [3], HV Dudukcu et al [4], and C Zhong et al [5]. The Long-Short Term Memory (LSTM) algorithm has been developed and utilized to forecast financial market movements with remarkable success.…”

Section: Introductionmentioning

confidence: 99%

Prediction of US 30-years-treasury-bonds mouvement and trading entry point using Robust 1DCNN-BiLSTM-XGBoost algorithm

Zaar

Benaya

Bakir

et al. 2023

Preprint

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This paper proposes a novel algorithm that accurately predicts market trends and trading entry points for US 30-year-treasury bonds using a hybrid approach of 1-Dimensional Convolutional Neural Network (1DCNN), Long-Short Term Memory (LSTM), and XGBoost algorithms. We compared the performance of various strategies using 1DCNN and LSTM and found that existing state-of-the-art methods based on LSTM have excellent results in market movement prediction tasks, but the effectiveness of 1DCNN and LSTM in terms of trading entry point and market perturbations has not been studied thoroughly. We demonstrate, through experiments that our proposed 1DCNN-BiLSTM-XGBoost algorithm combined with moving averages crossover effectively mitigates noise and market perturbations, leading to high accuracy in spotting trading entry points and trend signals for US 30-year-treasury-bonds. Our experimental study shows that the proposed approach achieves an average of 0.0001% Root Mean Squared Error and 100% R-Square, making it a promising method for predicting the market trends and trading entry points.

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LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Cited by 32 publications

References 38 publications

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Drivers of the next-minute Bitcoin price using sparse regressions

Prediction of US 30-years-treasury-bonds mouvement and trading entry point using Robust 1DCNN-BiLSTM-XGBoost algorithm

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