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Table 1 List of studies on machine learning applied to cryptocurrencies prices (organized by chronological and alphabetical order)

From: Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Article Dependent variable Frequency Sample period Models Type (classification/regression) Trading strategies (positions/trading costs) Input set Main findings
Madan et al. (2015) Bitcoin prices in USD from Coinbase 10-s, 10-min 5 years since the inception of Bitcoin Binomial logistic regressions (BLR) and random forest (RF) Classification Prices and 16 blockchain features 10-min data give a better sensitivity and specificity ratio than the 10-s data
Kim et al. (2016) Bitcoin, ethereum and ripple prices Daily Bitcoin: Dec-2013 to Feb-2016
Ethereum: Aug-2015 to Feb-2016
Ripple: Sept-2015 to Jan-2016
Averaged one-dependence estimators (AODE) Classification Long/no trading costs Trading information, and comments and replies posted in online communities Comments and replies are good predictors of Bitcoin prices
Żbikowski (2016) Bitcoin prices in USD from Bitstamp 15-min Jan-2015 to Feb-2015 Exponential moving average (EMA), box support vector machine (SVM) and volume weighted SVM (VW-SVM) Classification Long and short/trading costs of 0.2% 10 technical analysis indicators VW-SVM is the best model in terms of average return and maximum drawdown
Jiang and Liang (2017) Prices in USD of the 12 most traded cryptocurrencies at Poloniex 30-min Jun-2015 to Aug-2016 Convolutional neural networks (CNN) with deep reinforcement learning Regression Long and short/trading costs of 0.25% Returns Mixed results between CNN portfolio and Online Newton Step and Passive Aggressive Mean Reversion portfolios
Jang and Lee (2018) Bitcoin price index in USD Daily Sep-2011 to Aug-2017 Bayesian neural networks (BNN), linear regression and support vector regressions (SVM) Regression 26 blockchain features, trading information, exchange rates and macroeconomic variables The BNN is the best prediction model
McNally et al. (2018) Bitcoin prices in USD from CoinDesk Daily Aug-2013 to July-2016 Bayesian recurrent neural (RNN) and long short term memory (LSTM) Classification and Regression OHLC prices, difficulty, and hash rate of blockchain The best time lengths are 100 days for the LSTM and 20 days for the RNN
Nakano et al. (2018) Bitcoin returns in USD from Poloniex 15-min July- 2016 to Jan-2018 Artificial neural networks (ANN) Classification Long, and long and short/transaction costs of 0.025%,0.05% and 0.1% Returns and 4 technical analysis indicators Higher performance of the ANN strategy, except in the last month of data. Results are highly sensitive to the model specification and input data
Vo and Yost-Bremm (2018) Bitcoin prices in USD, CNY, JPY, EUR from 6 online exchanges 1-min Jan-2012 to Oct-2017 Random forests (RF) and a deep learning model Classification Long and short/no trading costs 5 technical analysis indicators RF is the best model for a frequency of 15-min
Alessandretti et al. (2019) Price indexes of 1681 cryptocurrencies in USD Daily Nov-2015 to Apr-2018 Ensemble of regression trees built by XGboost and long short term memory network Regression Long/transaction costs of 0,1%, 0,2%, 0,5% and 1% Price, market capitalization, market share, rank, volume, and age All strategies, produce a significant profit (expressed in bitcoin) even with transaction fees up to 0.2%
Atsalakis et al. (2019) Bitcoin ethereum, litecoin and ripple returns Daily Sep-2011 to Oct-2017 PATSOS—a hybrid neuro-fuzzy model Classification and regression Long and short/no transaction costs Returns and prices PATSOS outperforms other competing methods and produces a return significantly higher than the Buy-and-Hold (B&H) strategy
Catania et al. (2019) Bitcoin, ethereum, litecoin and ripple returns in USD Daily Aug-2015 to Dec-2017 Linear univariate and multivariate regression models, and selections and combinations of those models Regression Returns and several exogenous financial variables Statistically significant improvements in forecasting returns when using combinations of univariate models
de Souza et al. (2019) Bitcoin prices in USD Daily May-2012 to May-2017 Artificial neural network (ANN) and support vector machine (SVM) Classification Long and short/5 USD OHLC prices SVM provides conservative returns on the risk adjusted basis, and ANN generates abnormal profits during short run bull trends
Han et al. (2019) Bitcoin returns in USD Daily April-2013 to Mar-2018 NARX Neural Network Regression Returns NARX is effective in predicting the tendency but not the jumps
Huang et al. (2019) Bitcoin returns in USD Daily Jan-2012 to Dec-2017 Trees Classification Long and short/no trading costs 124 technical indicators computed from the OHLC prices Lower volatility, higher win-to-loss ratio and information ratio than those of every simple cut-off strategy or the B&H strategy
Ji et al. (2019b) Bitcoin returns in USD from Bitstamp Daily Nov.-2011 to Dec.-2018 Deep Neural Network (DNN), Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), Deep Residual Network (ResNet), combination of CNNs and RNNs (CRNN) and their combinations Classification and regression Long/no transaction costs Prices and 17 blockchain features Performances of the prediction models were comparable, LSTM is the best prediction model, DNN models are the best classification models, classification models were more effective for trading
Lahmiri and Bekiros (2019) Bitcoin, digital cash and ripple prices in USD Daily Bitcoin: July-2010 to Oct-2018
Digital Cash: Feb-2010 to Oct-2018
Ripple: Jan-2015 to Oct-2018
Long Short Term Memory (LSTM) and Generalized Regression Neural Networks (GRNN) Regression Prices Predictability of LSTM is significantly higher than of GRNN
Mallqui and Fernandes (2019) Bitcoin prices in USD Daily Apr-2013 to Apr-2017 Artificial neural networks (ANN), support vector machine (SVM) and ensembles Classification and Regression OHLC prices, Blockchain information and several exogenous financial variables Ensemble of recurrent neural networks and a Tree classifier is the best classification model, while SVM is the best regression model
Shintate and Pichl (2019) Bitcoin returns in CNY and USD from OkCoin 1-min Jun-2013 to Mar-2017 Random sampling method (RSM) Classification Long and short/No transaction costs OHLC prices The proposed RSM outperforms several alternatives, but the profit rates do not exceed those of the B&H strategy
Smuts (2019) Bitcoin and ethereum prices in USD 1-h Dec-2017 to Jun-2018 Long short term memory recurrent neural network (LSTM) Classification Prices, volumes, Google trends, and Telegram chat groups dedicated to bitcoin and ethereum trading Telegram data is a better predictor of bitcoin, while GoThe ensemble, by unweighted average of the four trading signals from the four models, after resampling the data, gives the best results.ogle Trends is a better predictor of ethereum, especially in one-week period
Borges and Neves (2020) Prices from Binance 100 cryptocurrencies pairs with the most traded volume in USD 1-min For each pair since beginning of trading at Binance until oct-2018 Logistic regression, random forest, support vector machine, and gradient tree boosting and an ensemble of these models Classification Long/transaction costs of 0.1% Returns, resampled returns, and 11 technical indicators  
Chen et al. (2020b) Bitcoin price index and trading prices from Binance in USD 5-min and daily July-2017 to Jan-2018 for 5-min and Feb-2017, to Feb-2019 for daily Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) Classification 5-min: OHLC prices and trading volume. Daily: 4 Blockchain features, 8 marketing and trading variables, Google trend search volume index, Baidu media search volume, and gold spot price For 5-min data machine learning models achieved better accuracy than LR and LDA, with LSTM achieving the best result (67% accuracy). For daily data, LR and LDA are better, with an average accuracy of 65%
Chu et al. (2020) Bitcoin, ethereum, dash, litecoin, MaidSafeCoin, monero and ripple from CryptoCompare in USD Hourly Feb-2017 to Aug-2017 Exponential Moving Averages (EMA) for time series and cross-sectional portfolios Classification and Regression Long and short/No transaction costs Trading prices Momentum trading does not beat the passive trading strategies
Sun et al. (2020) 42 cryptocurrencies Daily Jan-2018 to Jun-2018 LightGBM, SVM support vector machines (SVM) and Random Forests (RF) Classification Trading data and macroeconomic variables LightGBM outperforms SVM and RF, and the accuracy is higher for 2 weeks predictions