A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index

Livieris, Ioannis E.; Stavroyiannis, Stavros; Pintelas, Emmanuel G.; Kotsilieris, Theodore; Pintelas, Panayiotis E.

doi:10.1007/s12530-020-09361-2

Cited by 19 publications

(8 citation statements)

References 34 publications

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“…With evolution in cryptocurrency and advances in the creation of centralized and decentralized exchanges, accurate information on prices has become accessible and therefore studies are emerging in this line of research using Neural Networks and Deep Learning to analyze market volatility (Bu & Cho, 2018; Miura et al, 2019), forecast future prices (Betancourt & Chen, 2021b; Bu & Cho, 2018; Ji et al, 2019; Lahmiri & Bekiros, 2019, 2021; Lee, 2020; Li et al, 2020; Livieris et al, 2021; Loh & Ismail, 2020; Lucarelli & Borrotti, 2019; Miura et al, 2019; Nithyakani et al, 2021; Sattarov et al, 2020; Sun et al, 2021; Zanc et al, 2019), and managing portfolios with Bitcoin in an automated way (Betancourt & Chen, 2021a; Jiang & Liang, 2016; Ren et al, 2021; Shi et al, 2019; Sun et al, 2021).…”

Section: Systematic Reviewmentioning

confidence: 99%

Cryptocurrencies trading algorithms: A review

Silva

Hadad

Oliveira

2022

Journal of Forecasting

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This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.

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Section: Systematic Reviewmentioning

confidence: 99%

Cryptocurrencies trading algorithms: A review

Silva

Hadad

Oliveira

2022

Journal of Forecasting

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“…Yamashita et al 2018 experiment showed that a CNN is designed to learn automatically and is able to adapt on its own, but it experiences a data-overfitting problem, giving rise to less accurate results. In 2021, the experimental results of Livieris et al demonstrated that the use of dropout regularization to try to prevent the recurrent neural network from overfitting data positively affects the forecasting performance [11]. This paper's key contributions are as listed below:…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility

Wanjiru¹,

Waititu²,

Wanjoya³

2022

IJDSA

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Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout modelbased approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month's average tea price in Kenya.

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“…The input, hidden and output layers of MLPs are completely interconnected. The data is sent to the model via channels weighed by the nodes [48]. The weights between inputs and hidden layers can be computed using the activation function(sigmoid, Relu and tanh).…”

Section: Return Class Categorymentioning

confidence: 99%