Prediction of cryptocurrency returns using machine learning

Akyıldırım, Erdinç; Göncü, Ahmet; Şensoy, Ahmet

doi:10.1007/s10479-020-03575-y

Cited by 165 publications

(76 citation statements)

References 50 publications

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“…Statistical models, machine learning, and deep learning models have been used in the literature to predict financial asset prices (Abedin et al, 2020;Akyildirim et al, 2021;Cui et al, 2020;Fischer & Krauss, 2018;Guotai et al, 2017;Jiang et al, 2020;Kyriakou et al, 2021;Shajalal et al, 2021;Xia et al, 2020). We apply machine learning and deep learning algorithms to measure different types of errors and find the best model for the dataset to measure the prediction accuracy for each currency against USD.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based exchange rate prediction during the COVID-19 pandemic

et al. 2021

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This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based exchange rate prediction during the COVID-19 pandemic

et al. 2021

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“…Drawing on work by Kristoufek (2013), they show how Google searches for cryptocurrencies can be driven by recent price movements. Akyildirim et al (2020) use various machine learning methods to test the predictability of the most liquid twelve cryptocurrencies in circulation. This particular study concludes that machine learning can be used to potentially forecast cryptocurrencies, albeit this may be possible only at the intraday level when using past prices.…”

Section: Behavior Of Cryptocurrency Pricesmentioning

confidence: 99%

Herding and feedback trading in cryptocurrency markets

King

Koutmos

2021

Ann Oper Res

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This paper examines the extent to which herding and feedback trading behaviors drive price dynamics across nine major cryptocurrencies. Using sample price data from bitcoin, ethereum, XRP, bitcoin cash, EOS, litecoin, stellar, cardano and IOTA, respectively, we document heterogeneity in the types of feedback trading strategies investors utilize across markets. Whereas some cryptocurrency markets show evidence of herding, or, 'trend chasing', behaviors, in other markets we show evidence of contrarian-type behaviors. These findings are important because they elucidate upon, firstly, what forces drive cryptocurrency markets and, secondly, how this type of trading behavior affects autocorrelation patters for cryptocurrencies. Finally, and from our intertemporal asset pricing model, we shed new light on the observed nature of the risk-return tradeoffs for each of our sampled cryptocurrencies.

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“…This corpus uses the conventional time series models like the GARCH family models, which were extended recently in light of outliers that characterize cryptocurrency markets (Aslan & Sensoy, 2020 ; Charles & Darné, 2019 ; Catani et al, 2019 ; Trucíos, 2019 , among others). A second subset of the literature involves approaches inspired by operations research, such as neural networks (Adcock & Gradojevic, 2019 ; Jay et al, 2020 ; among others), machine learning, and deep learning (Lahmiri & Bekiros, 2019 ; Patel et al, 2020 ; Akyildirim et al, 2020 , 2021 ; Sensoy, 2019 ; among others).…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

2021

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This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

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Prediction of cryptocurrency returns using machine learning

Cited by 165 publications

References 50 publications

Deep learning-based exchange rate prediction during the COVID-19 pandemic

Deep learning-based exchange rate prediction during the COVID-19 pandemic

Herding and feedback trading in cryptocurrency markets

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

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