Blockchain Papers

A. Hachicha, Fatma Hachicha

No abstract is available for this record.

Authors unavailable

In this paper, we proposed to predict the Bitcoin price accurately taking into consideration various parameters that affect the Bitcoin value. For the first moment of our paper we gathered the data set in Quandl site. Which come with some pure dataset. The data set consist of various features related to the Bitcoin price and trading value. Then we have done the training and testing for dataset. At the second stage of our paper, we have applied some prediction techniques as Linear Regression model to perform the data set very accurately for getting the MAE, MSE and r-squared values. Then we move on to the next stage as AI algorithm. In this paper, we have used the LASSO (Least Absolute Shrinkage Selection Operator) machine learning algorithm. This helps to do the selection operator in large dataset and performing a fabulous for finding the maximum accuracy. These two algorithms are very helpful for finding the Bitcoin price very high accuracy. In the Finally stage of our investigation, using the available information we tested the results by real-time results. Then we get the approximately accuracy then the Real-time accuracy. The sign of the daily price change with highest possible accuracy will be predicted.

Majid Khedmati, Farid Seifi, Mohammad Javad Azizi

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The proposed models are applied on the Bitcoin price from December 18, 2019 to March 1, 2020 and their performances are compared in terms of the performance measures of RMSE and MAPE by Diebold-Mariano statistical test. Based on RMSE and MAPE measures, the results show that SVM provides the best performance among all the models. In addition, ARIMA and Bayesian approaches outperform other univariate models where they provide smaller values for RMSE and MAPE.

Giulia Serafini, Ping Yi, Qingquan Zhang, Marco Brambilla · 7 authors

Nowadays, Bitcoin has become the most popular cryptocurrency, which gains the attention of investors and speculators alike. Asset pricing is a risky and challenging activity that enchants lots of shareholders. Indeed, the difficulty in making predictions lies in understanding the multiple factors that affect the Bitcoin price trend. Modeling the market behavior and thus, the sentiment in the Bitcoin ecosystem provides an insight into the predictions of the Bitcoin price. While there are significant studies that investigate the token economics based on the Bitcoin network, limited research has been performed to analyze the network sentiment on the overall Bitcoin price. In this paper, we investigate the predictive power of network sentiments and explore statistical and deep-learning methods to predict Bitcoin future price. In particular, we analyze financial and sentiment features extracted from economic and crowd-sourced data respectively, and we show how the sentiment is the most significant factor in predicting Bitcoin market stocks. Next, we compare two models used for Bitcoin time-series predictions: the Auto-Regressive Integrated Moving Average with eXogenous input (ARIMAX) and the Recurrent Neural Network (RNN). We demonstrate that both models achieve optimal results on new predictions, with a mean squared error lower than 0.14%, due to the inclusion of the studied sentiment feature. Besides, since the ARIMAX achieves better predictions than the RNN, we also prove that, with just a linear model, we may obtain outstanding market forecasts in the Bitcoin scenario.

Yan Li, Wei Dai

In this study, aiming at the problem that the price of Bitcoin varies greatly and is difficult to predict, a hybrid neural network model based on convolutional neural network (CNN) and long short‐term memory (LSTM) neural network is proposed. The transaction data of Bitcoin itself, as well as external information, such as macroeconomic variables and investor attention, are taken as input. Firstly, CNN is used for feature extraction. Then the feature vectors are input into LSTM for training and forecasting the short‐term price of Bitcoin. The result shows that the CNN‐LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network. The finding has important implications for researchers and investors in the digital currencies market.

Ayten Yağmur, Fatih Mangır

İktisat bilimi, ekonomik aktörlerin tercihlerini ve bu tercihlerin ekonomik göstergelere olan etkisini incelemektedir. Özelde, davranışsal iktisat okulu bu tercihlerin zaman zaman rasyonaliteden ayrıldığını ve bu irrasyonalitenin yol açtığı ekonomik krizleri de modelleyen çıkarımlar yapmaktadır. 2008 yılının sonlarında kripto para olan Bitcoin deneysel olarak kullanılmış, bu tarihten sonra bu kripto finans aracı hızla işlem görmeye başlamıştır. Ancak daha sonraki yıllarda Bitcoin’nin değerindeki dalgalanmalar onun finansal bir araç olmaktan daha çok bir deney aracı olduğu yönünde eleştirilmesine neden olmuştur. Bu çalışmada etkin piyasa hipotezi ve davranışsal iktisat öğretilerinden yola çıkarak bitcoin piyasasının fiyat balonlarıyla spekülatif ve rassal hareketlere açık olup olmadığı (Supremum Augmented Dickey-Fuller ) SADF testi ile analiz edilmiştir. Ampirik bulgulara göre 2015-2019 dönemine ait Bitcoin fiyatlarında ortaya çıkan şokların etkisi kalıcıdır ve rassal yürüyüş hipotezi geçerlidir.

Ziyang Ji, Victor Chang, Hao Lan, Ching-Hsien Robert Hsu · 5 authors

As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.

Yakup Söylemez

Ama -Son on ylda finans alannda dijital inovasyonlar zellikle Blockchain teknolojisine bal olarak ortaya kmaktadr. Blockchain teknolojisinin tm dnyada en yaygn olarak kullanld rn ise kripto para birimleridir. Kripto para birimleri ierisinde Bitcoin gerek piyasa kapitalizasyonu gerekse ilem hacmi ile dikkat ekmektedir

S. M. Taslim Uddin Raju, Ali Mohammad Tarif

Bitcoin is the first digital decentralized cryptocurrency that has shown a\nsignificant increase in market capitalization in recent years. The objective of\nthis paper is to determine the predictable price direction of Bitcoin in USD by\nmachine learning techniques and sentiment analysis. Twitter and Reddit have\nattracted a great deal of attention from researchers to study public sentiment.\nWe have applied sentiment analysis and supervised machine learning principles\nto the extracted tweets from Twitter and Reddit posts, and we analyze the\ncorrelation between bitcoin price movements and sentiments in tweets. We\nexplored several algorithms of machine learning using supervised learning to\ndevelop a prediction model and provide informative analysis of future market\nprices. Due to the difficulty of evaluating the exact nature of a Time\nSeries(ARIMA) model, it is often very difficult to produce appropriate\nforecasts. Then we continue to implement Recurrent Neural Networks (RNN) with\nlong short-term memory cells (LSTM). Thus, we analyzed the time series model\nprediction of bitcoin prices with greater efficiency using long short-term\nmemory (LSTM) techniques and compared the predictability of bitcoin price and\nsentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE\n(Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515\n(multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM\nwith multi feature shows the more accurate result.\n

Chee Kean Chin, Nazlia Omar

Bitcoin is a digital currency and investment tool that has received worldwide attention recently. However, the fluctuation of Bitcoin price has been a concern to the users and investors. Forecasting the Bitcoin price can serve as a guideline for investor and user to make effective strategy in their investment or usage. With the rapid development of the Internet, online data including news article can facilitate forecasting Bitcoin price. This research aims to study the effect of news article sentiment towards Bitcoin price with study period from September 2017 to August 2019. Accordingly, this study introduces sentiment analysis to understand the relevant information of online news articles and use it as an input feature for Bitcoin price prediction. Two main phases are included in the study, which is sentiment analysis and price prediction. In sentiment analysis, the sentiment is extracted based on a lexicon-based approach to capture the relevant news articles information regarding cryptocurrency markets. In price prediction, the sentiment is used as an input feature and Long Short-Term Memory (LSTM) model is used in price prediction phase. With Bitcoin market data and news articles as samples, the empirical results show that news articles sentiment reduced the overall error in Bitcoin price predictions.

Feda Hassan Jahjah, Muhanad Rajab

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.

Reshma Sundari Gadey, Nikita Thakur, Naveen Charan, R. Obulakonda Reddy

With the development Machine learning and AI-assisted trading has gained interest in the past few years. To bring out the abnormal profits from the cryptocurrency market, we use this machine learning and AIassisted trading. We store the daily data for a certain period. With the strategies assisted by state-of-the-art algorithms we obtain great outcomes. With the help of simple algorithms and architecture, the outcomes made the growth in the cryptocurrency market. The cryptocurrency has become popular in 2017 because of the growth in market capitalization. More than 1500 crypto currencies are actively trading in today's scenario. The crypto currency can be generated and be used for online transactions. Bitcoin is a cryptocurrency technology. The value of Bitcoin keeps varying every second. Therefore, to predict the value of bitcoin price here, we use the LSTM Architecture. With the help of this architecture, we are trying to prove this LSTM architecture provides more accurate results than any other machine learning algorithms and architecture.

Ashrit Deebadi

‘‘Cryptocurrency trading was one of the most exciting jobs of 2017’’. ‘‘Bit- coin’’,‘‘Blockchain’’, ‘‘Bitcoin Trading’’ were the most searched words in Google during 2017. High return on investment has attracted many people towards this crypto market. Existing research has shown that the trading price is completely based on speculation, and its trading volume is highly impacted by news media. This paper discusses the existing work to evaluate the sentiment and price of the cryptocurrency, the issues with the current trading models. It builds possible solutions to understand better the semantic orientation of text by comparing different machine learning techniques and predicts Bitcoin trading price based on Twitter feed sentiment and additional Bitcoin metrics. We observe that the statistical machine learning model was able to better predict the sentiment of Twitter tweet feed compared to the advanced BERT model. Using Twitter feed sentiment and additional Bitcoin metrics, we were able to improve the prediction of bitcoin price compared to only using bitcoin’s previous day closing pricing.

Abdollah Ah Mand, Hassanudin Mohd Thas Thaker

<title>Abstract</title> <bold>Background: </bold>Cryptocurrencies, especially Bitcoin, has become popular for investors in recent years. The volatility of bitcoin and time horizon are the center point for investment decisions. However, attention is not often drawn to the relationship between bitcoin and equity indices. This study investigates the volatility and time frequency domain of bitcoin among five Asean countries through a rich database which covers daily data from July 2010 until April 2019.<bold>Methods: </bold>Advanced econometrics and Wavelets Cross-Coherence Spectrograms, this study investigates the existence of long run association between bitcoin and the studied market indices. M-GARCH analysis is been employed to investigate the unconditional volatility of market indices and Bitcoin.<bold>Results: </bold>The findings present the long run association<bold> </bold>with positive (Philippines) and negative (Japan, Korea, Singapore, Hong Kong) relations. Moreover, only one market (KOREA) shows a short run association with bitcoin. The M-GARCH analysis reveals, most of the selected Asean countries have a low unconditional volatility with bitcoin. Except for Philippines in which the co-movement is average, Wavelet analysis reveals the presence of a strong and long co-movements for most of the selected Asean countries with bitcoin.<bold>Conclusions: </bold>Most of our results are consistent and illustrate different dimensions of long and short run relationship, volatilities, correlations, and time-frequency analysis. This study utilized Asean emerging economies which are rarely available in the literature as existing studies are more skewed towards the West. We believe the outcomes of this study will be a significant for industry practitioners (i.e., retail and institutional investors) on designing better strategies to diversify the stock portfolio with different holding period horizons and dimensions.

Ioannis E. Livieris, Emmanuel Pintelas, Stavros Stavroyiannis, Panagiotis Pintelas

Nowadays, cryptocurrency has infiltrated almost all financial transactions; thus, it is generally recognized as an alternative method for paying and exchanging currency. Cryptocurrency trade constitutes a constantly increasing financial market and a promising type of profitable investment; however, it is characterized by high volatility and strong fluctuations of prices over time. Therefore, the development of an intelligent forecasting model is considered essential for portfolio optimization and decision making. The main contribution of this research is the combination of three of the most widely employed ensemble learning strategies: ensemble-averaging, bagging and stacking with advanced deep learning models for forecasting major cryptocurrency hourly prices. The proposed ensemble models were evaluated utilizing state-of-the-art deep learning models as component learners, which were comprised by combinations of long short-term memory (LSTM), Bi-directional LSTM and convolutional layers. The ensemble models were evaluated on prediction of the cryptocurrency price on the following hour (regression) and also on the prediction if the price on the following hour will increase or decrease with respect to the current price (classification). Additionally, the reliability of each forecasting model and the efficiency of its predictions is evaluated by examining for autocorrelation of the errors. Our detailed experimental analysis indicates that ensemble learning and deep learning can be efficiently beneficial to each other, for developing strong, stable, and reliable forecasting models.

Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet

We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art learning algorithms are often referred to as "black boxes" because of the high number of operations involved in their prediction process. Despite their powerful predictivity, this lack of interpretability may be highly restrictive for applications with critical decisions at stake. On the other hand, algorithms with a simple structure-typically decision trees, rule algorithms, or sparse linear models-are well known for their instability. This undesirable feature makes the conclusions of the data analysis unreliable and turns out to be a strong operational limitation. This motivates the design of SIRUS, which combines a simple structure with a remarkable stable behavior when data is perturbed. The algorithm is based on random forests, the predictive accuracy of which is preserved. We demonstrate the efficiency of the method both empirically (through experiments) and theoretically (with the proof of its asymptotic stability). Our R/C++ software implementation sirus is available from CRAN.

Petrônio Cândido de Lima e Silva, Carlos Alberto Severiano, Marcos Antônio Alves, Rodrigo Silva · 6 authors

In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying parameters adapted from the distribution of the data. In this approach, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The proposed method is capable of dynamically adapting its fuzzy sets to reflect the changes in the stochastic process based on the residual errors, without the need to retraining the model. This method can handle non-stationary and heteroskedastic data as well as scenarios with concept-drift. The proposed approach allows the model to be trained only once and remain useful long after while keeping reasonable accuracy. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. Non-parametric tests are employed to check the significance of the results. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base.

Saad Alahmari

No abstract is available for this record.

Mohsen Noroozinejad Farsangi, Farshid Keynia, Ehsan Noroozinejad Farsangi

Nowadays, accurate prediction of cryptocurrency price variation based on their important role in the world economy is an important and challenging issue. In this study, various parameters that affect the cryptocurrency value have been considered. For the first phase, four major price features of digital currencies have been analysed to determine the effect of each feature on the volatility prediction of future days. This study aims to understand and identify daily trends in the cryptocurrency market while gaining insight into optimal features surrounding their price. For the second phase, the price variation has been predicted with the highest possible accuracy with a new intelligent method. The proposed method consists of a neural network‐based prediction algorithm and particle swarm optimisation. The obtained results show the capbility of the proposed method.

Roy Cerqueti, Massimiliano Giacalone, Raffaele Mattera

Recently, cryptocurrencies have attracted a growing interest from investors,\npractitioners and researchers. Nevertheless, few studies have focused on the\npredictability of them. In this paper we propose a new and comprehensive study\nabout cryptocurrency market, evaluating the forecasting performance for three\nof the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in\nterms of market capitalization. At this aim, we consider non-Gaussian GARCH\nvolatility models, which form a class of stochastic recursive systems commonly\nadopted for financial predictions. Results show that the best specification and\nforecasting accuracy are achieved under the Skewed Generalized Error\nDistribution when Bitcoin/USD and Litecoin/USD exchange rates are considered,\nwhile the best performances are obtained for skewed Distribution in the case of\nEthereum/USD exchange rate. The obtain findings state the effectiveness -- in\nterms of prediction performance -- of relaxing the normality assumption and\nconsidering skewed distributions.\n

Febri Liantoni, Arif Agusti

Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.

Eyyüp Ensari Şahin

Blockchain teknolojisinin aracısız veri/para transferi gerçekleştirmesi ile tüm çevrelerin gündemine gelen Bitcoin, birçok yatırımcının ilgi odağı olmuş ve Bitcoin ’in popüler olması ile birçok kripto para piyasaya sürülmüştür. Kripto paralarda fiyat volatilitesinin yüksekliği hızlı para kazanma arzusu içinde olan ve risk iştahı yüksek olan yatırımcıları fiyat tahminlemesi ve fiyatları etkileyen değişkenlerin belirlenmesi noktasında analiz yapmaya itmiştir. Bu çalışmanın amacı Aralık 2017 itibari ile değeri yaklaşık 20.000 ABD Dolara ulaşan ve yüksek volatilitesi ile yatırımcıların sürekli gündeminde olan Bitcoin fiyatına etki eden faktörlerin belirlenmesidir. Bu amaçla çalışmada literatürde kullanılan değişkenlere (Altın ve ABD Dolar) ek olarak küresel risklerin (Finansal Baskı Endeksi ve Jeopolitik Risk Endeksi) etkisi de ölçülmeye çalışılmıştır. Çalışama da Bitcoin fiyatı üzerine etki etmesi muhtemel değişkenler Çok Değişkenli Uyarlanabilir Regresyon Uzanımları-MARS yöntemi ile analiz edilmiştir. Çalışmada kullanılan veriler 2012/1-2019/11 yılları arasında aylık verilerden oluşmaktadır. Çalışmanın sonucunda kullanılan tüm bağımsız değişkenlerin belirli şartlar altında Bitcoin fiyatına etki edebileceği sonucuna ulaşılmıştır.

Jonathan Sadighian

There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.

Eng Chuen Loh, Shuhaida Ismail, Azme Khamis, Aida Mustapha

Bitcoin is the most popular cryptocurrency with the highest market value. It was said to have potential in changing the way of trading in future. However, Bitcoin price prediction is a hard task and difficult for investors to make decision. This is caused by nonlinearity property of the Bitcoin price. Hence, a better forecasting method are essential to minimize the risk from inaccuracy decision. The aim of this paper is to compare two different training algorithms which are Levenberg-Marquardt (LM) backpropagation algorithm and Scaled Conjugate Gradient (SCG) backpropagation algorithm using Feedforward Neural Network (FNN) to forecast the Bitcoin price. After obtaining the forecasting result, forecast accuracy measurement will be carried out to identify the best model to forecast Bitcoin price. The result showed that the performance of Bitcoin price forecasting increased after the application of FNN – LM model. It is proven that Levenberg-Marquardt backpropagation algorithm is better compared to Scaled Conjugate Gradient backpropagation when forecasting Bitcoin price using FNN. The resulting model provides new insights into Bitcoin forecasting using FNN – LM model which directly benefits the investors and economists in lowering the risk of making wrong decision when it comes to invest in Bitcoin. Keywords: Bitcoin Price; Artificial Neural Network; Forecasting