Blockchain Papers

Sarfaraz Hashemkhani Zolfani, Hassan Mehtari Taheri, Mahmoud Gharehgozlou, Alireza Farahani

No abstract is available for this record.

Ditdit Nugeraha Utama

Evaluation of cryptocurrency’s performance is performed questionably. There is no role for computer-model, make such an evaluation process does not have guidance. In this study, a simple decision support model (DSM) based on fuzzy logic was academically constructed to observe the cryptocurrency’s performance. By operating the primary method of fuzzy logic and taking into account three types of parameters (i.e. time-series close price data, daily max-min price, and transaction number), a novel DSM for evaluating the cryptocurrency’s performance was fruitfully executed. Based on three types of real cryptocurrency six-month data (i.e. Bitcoin Ethereum, and Dogecoin), the model could irreversibly expose that Bitcoin has the best performance with 36.39 performance points.

Chanapon Phasook, Jantima Polpinij, Bancha Luaphol

This work aims to present a comparative study method of closed-price prediction for cryptocurrencies namely machine learning-based and deep learning-based methods. Three data normalization techniques in the data pre-processing stage were also compared. They are log scaling, min-max, and z-score normalization. In the machine learning-based method, support vector regression (SVR) was used to develop the predictive model, whereas long-short term memory (LSTM) was used in the deep learning-based method to develop the predictive model. In addition, three datasets are used in this study namely Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The results of evaluating the predictive models using RMSE and MAPE revealed that SRV with RBF kernel produced slightly better results than LSTM. Compared to other data normalization methods, log scaling normalization produced outcomes that are more satisfactory.

Haruna Umar Yahaya, John Sunday Oyinloye, Samuel Olorunfemi Adams

The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criteria. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.

Andrei-Alexandru Encean, Daniel Zinca

cryptocurrencies are increasingly used by investors as assets, mainly for short term. Therefore, in the financial sector, prediction is important and can be done using Deep Learning techniques. This paper presents results on the prediction of three cryptocurrencies that see high volumes but are discussed in very few papers. In this paper we used LSTM (Long Short-Term Memory) models and also GRU (Gated Recurrent Unit). These are applied to three important cryptocurrencies, namely DOGECoin, Ripple (XRP) and OMG Network. The results presented prove that LSTM and GRU can be successful in predicting the daily closing values for these cryptocurrencies.

Changqing Luo, Lurun Pan, Binwei Chen, Huiru Xu

In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.

Azeez A. Oyedele, Anuoluwapo Ajayi, Lukumon O. Oyedele, Sururah A. Bello · 5 authors

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks.

Shalini Sharma, Angshul Majumdar

Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological side, a new dynamical modeling approach is proposed. Our approach keeps the probabilistic formulation of the state-space model, which provides uncertainty quantification on the estimates, and the function approximation ability of deep neural networks. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy.

Wei Yin, Ziling Chen, Xinxin Luo, Berna Kirkulak-Uludag

This article proposes a graph neural network strategy (GNN), in which the long short-term memory (LSTM) and graph convolution network (GCN) are applied to capture both temporal and spatial features to forecast the price of Bitcoin, Litecoin, Ethereum, and Dash Coin with the ‘stable-coin’ Tether (USDT) and financial stress index (FSI). The main results show that the GNN strategy has better performance than univariate LSTM and multivariate LSTM in all of the seven steps forward forecasting. A sensitivity check shows that USDT and FSI/sub-FSI are important factors in the construction of the graphs and they verify the validity of the results.

Yongjun Kim, Yung-Cheol Byun

This study uses the API of Upbit, one of Korea’s cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction.

Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Mansur Masih

No abstract is available for this record.

David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Juliana Ngozi Ndunagu

Cryptocurrency is an advanced digital currency that is secured by encryption, making it nearly impossible to forge or duplicate. Many cryptocurrencies are blockchain-based with decentralized networks. The prediction of cryptocurrency prices is a very difficult task because of the absence of an appropriate analytical basis to substantiate their claims. Cryptocurrencies are also dependent on several variables, such as technical advancement, internal competition, market pressure, economic concerns, security, and political considerations. This paper proposed the hybrid walk-forward ensemble optimization technique and applied it to predict the daily prices of fifteen cryptocurrencies, such as Cardano (ADA-USD), Bitcoin (BTC-USD), Dogecoin (DOGE-USD), Ethereum Classic (ETC-USD), Chainlink (LINK-USD), Litecoin (LTC-USD), NEO (NEO-USD), Tron (TRX-USD), Tether (USDT-USD), NEM (XEM-USD), Stellar (XLM-USD), Ripple (XRP-USD), and Tezos (XTZ-USD). A performance comparison of these cryptocurrencies was done using classical statistical models, machine learning algorithms, and deep learning algorithms on different cryptocurrency time series. Simulation results show that our proposed model performed better in terms of cryptocurrency prediction accuracy compared to the classical statistical model and machine and deep learning algorithms used in this paper.

Patrick Jaquart, Sven Köpke, Christof Weinhardt

We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest cryptocurrencies. Our results show that all employed models make statistically viable predictions, whereby the average accuracy values calculated on all cryptocurrencies range from 52.9% to 54.1%. These accuracy values increase to a range from 57.5% to 59.5% when calculated on the subset of predictions with the 10% highest model confidences per class and day. We find that a long-short portfolio strategy based on the predictions of the employed LSTM and GRU ensemble models yields an annualized out-of-sample Sharpe ratio after transaction costs of 3.23 and 3.12, respectively. In comparison, the buy-and-hold benchmark market portfolio strategy only yields a Sharpe ratio of 1.33. These results indicate a challenge to weak form cryptocurrency market efficiency, albeit the influence of certain limits to arbitrage cannot be entirely ruled out.

Apriliyanus Rakhmadi Pratama

The rising of bitcoin’s user as a digital currency and investments causing an instability and an uncertainty in price movement and increasing the risk of trading, therefore in this study we try to forecast the future value of bitcoin price using ARIMA Models. 2 candidate models are selected by the lowest value of AIC and using the performance indicators ME, RSME, MAE, MPE, and MAPE conclude ARIMA (1,1,0) are the best ARIMA model, then the next 5 months future price forecasted using the best model. While ARIMA (1,1,0) is the best model, the model failed to follow price movement as shown in the forecasted price.

Chuen Yik Kang, Chin Poo Lee, Kian Ming Lim

Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.

Ozan Kaymak, Selahattin Koç

Cryptocurrencies emerged with the invention of Bitcoin in 2008 and in a short time they managed to attract the attention of a significant number of investors. Since 2012, cryptocurrency markets have become markets where unpredictable transaction volumes take place around the world. Bitcoin is the most recognized asset which has the highest trading volume in cryptocurrency markets today. This is due to the dominance effect of Bitcoin on cryptocurrency markets. It is a matter of curiosity in the world and in our country whether savings are transferred from traditional financial markets to cryptocurrency markets. In this study, the possible relationships between Bitcoin trading volumes and Bitcoin price changes between 2017 and 2021, and the trading volumes realized in Borsa Istanbul (BIST) and the BIST 100 index were examined with the Granger causality approach and the direction of these relationships was tried to be determined using the Toda Yamamoto causality model. As a result of the examination, it has been determined that there is no causal relationship between Bitcoin trading volumes and Borsa Istanbul trading volumes according to the Toda Yamamoto approach.

Ferdiansyah Ferdiansyah, Siti Hajar Othman, Raja Zahilah Md Radzi, Deris Stiawan · 5 authors

<span lang="EN-US">Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional</span><span lang="EN-US">–</span><span lang="EN-US">LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin, Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below <10%.</span>

Abdul Mannan Kanji, Ishita Chaudhary, Rithika Lakshmi Shankar, Gowri Srinivasa

Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin.

Bhaskar Tripathi, Rakesh Kumar Sharma

No abstract is available for this record.

Trần Kim Toại, Thanh Thi Tuyet Le, Thinh Tien Bui, Vắng Quang Đàng · 5 authors

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies.

Mohammad Samin-Al-Wasee, Promee Shankar Kundu, Israt Mahzabeen, Tasnim Tamim · 5 authors

The cryptocurrency, ether (ETH), often sees its price go through rapid fluctuations due to its popularity for being the foundation for various decentralized applications and the fuel of the Ethereum network which has simplified commerce and trade between both anonymous and recognized parties, unfavourable circumstances like political conflicts, natural disasters, and so on, causing the market to become extremely volatile and risky for the crypto investors and the developers. So, from the urge to have a specialized ether price forecasting system, this research aimed to find an accurate price prediction model for ether using the long short-term memory (LSTM) network. For this, ether time-series price data were fitted into multiple basic and hybrid LSTM network variants, with future prices predicted using both univariate and multivariate time-series analysis. Furthermore, a comparative analysis was conducted among the models and also some popular existing forecasting techniques like autoregressive integrated moving average (ARIMA) as the baseline forecast to understand the effectiveness of the LSTM networks, especially, the hybrid variant, in the prediction of future market behaviour.

Müslüm Polat, Oktay Karakaya

Çalışmanın temel amacı; son on yıla damga vuran kripto paralar arasındaki getiri ve risk açısından nedensellik ilişkisini tespit etmektir. Bu amaçla piyasa değeri en yüksek 10 kripto paradan en fazla verisi bulunan Bitcoin, Ethereum, Litecoin, Stellar, Ripple arasında 10 model oluşturulmuş ve her model, Granger nedensellik ve Hafner-Herwatz varyansta nedensellik testleri test edilmiştir. Çalışmada 23 Şubat 2017 ile 18 Haziran 2021 tarihleri arasındaki günlük verilerden oluşan 1577 gözlem kullanılmıştır. Nedensellik analizi sonuçlarına göre seçili kripto paralar arasında ortalamada Ethereum - Litecoin hariç diğer değişkenler arasında Granger nedensellik ilişkisi, varyansta ise Bitcoin - Ethereum ve Bitcoin - Litecoin hariç diğer değişkenler arasında varyansta nedensellik ilişkisi tespit edilmiştir.

Sean Foley, William Krekel, Vito Mollica, Jiří Švec

No abstract is available for this record.

Abtin Ijadi Maghsoodi

No abstract is available for this record.