Blockchain Papers

Emmanouil Platanakis, Charles Sutcliffe

No abstract is available for this record.

Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Dias · 12 authors

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.

Kofi Agyarko, Albert Buabeng, Joseph Acquah

This study assessed the volatility and the Value at Risk (VaR) of daily returns of Bitcoins by conducting a comparative study in the forecast performance of symmetric and asymmetric GARCH models based on three different error distributions. The models employed are the SGARCH and TGARCH which were validated based on AIC, MAE and MSE measures. The results indicated that the SGARCHGED (1,1) with generalised error distribution term was identified as the best fitted GARCH model. Though, this best fitted model based on information loss (AIC) did not provide the best out-of-sample forecast, the differences was insignificant. Thus, the study clearly demonstrates that it is reliable to use the best fitted model for volatility forecasting. Also, to further validate the performance of the best fitted model, it was subjected to a historical back-test using Value at Risk (VaR). Though, it was evident from the study that no model was superior, it was indicated that an average loss of 1.2% is expected to be exceeded only 1% of the time. Moreover, volatility forecast from the back testing was relatively high during the first quarter of 2018 but begun decreasing steadily with time.

Daniel Traian Pele, Niels Wesselhöfft, Wolfgang Karl Härdle, Michalis Kolossiatis · 5 authors

The aim of this paper is to prove the phenotypic convergence of cryptocurrencies, in the sense that individual cryptocurrencies respond to similar selection pressures by developing similar characteristics. In order to retrieve the cryptocurrencies phenotype, we treat cryptocurrencies as financial instruments (genus proximum) and find their specific difference (differentia specifica) by using the daily time series of log-returns. In this sense, a daily time series of asset returns (either cryptocurrencies or classical assets) can be characterized by a multidimensional vector with statistical components like volatility, skewness, kurtosis, tail probability, quantiles, conditional tail expectation or fractal dimension. By using dimension reduction techniques (Factor Analysis) and classification models (Binary Logistic Regression, Discriminant Analysis, Support Vector Machines, K-means clustering, Variance Components Split methods) for a representative sample of cryptocurrencies, stocks, exchange rates and commodities, we are able to classify cryptocurrencies as a new asset class with unique features in the tails of the log-returns distribution. The main result of our paper is the complete separation of the cryptocurrencies from the other type of assets, by using the Maximum Variance Components Split method. More, we observe a divergent evolution of the cryptocurrencies species, compared to the classical assets, mainly due to the tails behaviour of the log-returns distribution. The codes used here are available via www.quantlet.de.

Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen

No abstract is available for this record.

Andary Dadang Yuliyono, Abba Suganda Girsang

In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one model to another model that use LSTM will take a very long time, thus a method is needed for optimizing hyperparameter to get a model with a small RMSE. This research proposed Artificial Bee Colony (ABC) as a method in optimizing hyperparameter for models that use LSTM. ABC is a metaheuristic method that mimics the behavior of bee colonies in foraging. Optimized hyperparameter in this research consisted of sliding window size, number of LSTM units, dropout rate, regularizer, regularizer rate, optimizer and learning rate. In this research the proposed method called as ABC-LSTM. Bitcoin prices historical data was used as the dataset for evaluating the prediction of the models. The best ABC-LSTM model resulted best RMSE of 189.61 compared to model that use LSTM without optimization resulted best RMSE of 236.17. This result showed that ABC-LSTM model outperformed models that use LSTM without optimization.

Darko Blazevic, Fredrik Marcusson

This study examines and compares the volatility in sample fit and out of sample forecast of four different heteroscedasticity models, namely ARCH, GARCH, EGARCH and GJR-GARCH applied to Bitcoin, Ethereum and Ripple. The models are fitted over the period from 2016-01-01 to 2019-01-01 and then used to obtain one day rolling forecasts during the period from 2018-01-01 to 2019-01-01. The study investigates three different themes consisting of the modelling framework structure, complexity of models and the relation between a good in sample fit and good out of sample forecast. AIC and BIC are used to evaluate the in sample fit while MSE, MAE and R2LOG are used as loss functions when evaluating the out of sample forecast against the chosen Parkinson volatility proxy. The results show that a heavier tailed reference distribution than the normal distribution generally improves the in sample fit, while this generality is not found for the out of sample forecast. Furthermore, it is shown that GARCH type models clearly outperform ARCH models in both in sample fit and out of sample forecast. For Ethereum, it is shown that the best fitted models also result in the best out of sample forecast for all loss functions, while for Bitcoin non of the best fitted models result in the best out of sample forecast. Finally, for Ripple, no generality between in sample fit and out of sample forecast is found.

Argimiro Arratia, Albert X. López-Barrantes

In early 2018 prices peaked at USD 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between prices and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Trends for the behavior of price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.

D. Olvera-Juarez, Eric Leonardo Huerta‐Manzanilla

The electronic transition has been gaining a large groundin recent decades due to the use of crypto currencies. One of the most popular is Bitcoin. It is open source, the transactions and the issuance of bitcoins occur collectively through the network.The analysis of the behavior of Bitcoin becomes a relevance to the prediction Price and achieve successful investments in it.This review is conducted for the analysis and comparison of the of the different prediction methods focused on the bitcoin price. Anemphasis is placed on those who have a structure as the basis of the ARIMA model, then adding to the hybrid methods, which use neural networks to complete the method.

Ryotaro Miura, Lukáš Pichl, Taisei Kaizoji

Realized volatility (RV) is defined as the sum of the squares of logarithmic returns on high-frequency sampling grid and aggregated over a certain time interval, typically a trading day in finance. It is not a priori clear what the aggregation period should be in case of continuously traded cryptocurrencies at online exchanges. In this work, we aggregate RV values using minute-sampled Bitcoin returns over 3-h intervals. Next, using the RV time series, we predict the future values based on the past samples using a plethora of machine learning methods, ANN (MLP, GRU, LSTM), SVM, and Ridge Regression, which are compared to the Heterogeneous Auto-Regressive Realized Volatility (HARRV) model with optimized lag parameters. It is shown that Ridge Regression performs the best, which supports the auto-regressive dynamics postulated by HARRV model. Mean Squared Error values by the neural-network based methods closely follow, whereas the SVM shows the worst performance. The present benchmarks can be used for dynamic risk hedging in algorithmic trading at cryptocurrency markets.

Giorgio Lucarelli, Matteo Borrotti

No abstract is available for this record.

Luisanna Cocco, Roberto Tonelli, Michele Marchesi

The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader's trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.

Nor Azizah Hitam, Amelia Ritahani Ismail, Faisal Saeed

Forecasting accurate future price is very important in financial sector. An optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) is introduced in forecasting the cryptocurrency future price. It is part of Artificial Intelligence (AI) that uses previous experience to forecast future price. Analysts and investors generally combine fundamental and technical analysis prior to decide the best price to execute their trades. Some may use Machine Learning Algorithms to execute their trades. However, forecasting result using basic SVM algorithms does not really promising. On the other hands, Particle Swarm Optimization (PSO) is known as a better algorithm for a static and simple optimization problem. Therefore, PSO is introduced to optimize the algorithms of SVM in cryptocurrency forecasting. The experiment of selected cryptocurrencies is conducted for this classifier. The experimental result demonstrates that an optimized SVM-PSO algorithm can effectively forecast the future price of cryptocurrency thus outperforms the single SVM algorithms.

Cathy Yi‐Hsuan Chen, Christian Hafner

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.

Shaen Corbet, Veysel Eraslan, Brian M. Lucey, Ahmet Şensoy

No abstract is available for this record.

Aniruddha Dutta, S. Sai Kumar, Meheli Basu

In today’s era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

Eyyüp Ensari Şahin, Oktay Özkan

Bitcoin, merkezi bir otoriteye veya finansal bir kuruluşa bağlı olmayan ve kriptografik özellikler içeren dijital (kripto) paralardan biridir. Bitcoin’ in Merkezi otoriteye bağlı olmaması ve fiyatını etkileyen faktörlerin arz ve talep ile açıklanması yüksek volatite ile sonuçlanmıştır. Son dönemlerde yatırımcıların en büyük endişesi fiyatlardaki aşırı volatilite durumudur. Çalışmada Blockchain Teknolojisi, Madencilik ve Blockchain Teknolojisinin bir çıktısı olan Bitcoin kısaca anlatılmıştır. Çalışmanın uygulama bölümünde literatürde sıklıkla kullanılan yöntemlerden olan ve asimetrik volatilitenin belirlenmesi amacıyla ARCH, GARCH, ARCHM, EGARCH ve TARCH modelleri kullanılmıştır. Bu amaçla Bitcoin/USD kuru kapanış fiyatlarından Bitcoine ilişkin tarihsel getiriler hesaplanmıştır. Hesaplama dönemi 01.01.2015-11.02.2018 olarak belirlenmiştir. Yapılan analizler sonucunda volatilite tahmini için en iyi sonuç veren TARCH yöntemi bulunmuştur.

Mustafa Çıkrıkçı, Mustafa Özyeşil

The main purpose of this study in determine whether Bitcoin is becoming an alternative investment option compared to other financial instruments in Turkey. To perform analysis for this purpose, we created sample includes Bitcoin, Bist 100 National Index, Bond, Euro and Gold. We calculated daily returns in terms of % for each type of instrument for the period range from 02.02.2012 to 17.12.2018. Methodology -In the study, we tested the stationarity of the series and the causality relationships between the series through the nonlinear unit root and causality tests. Stationarity of the series and causality relations between them are determined with the help of charts. Both in unit root test and in causality test, firstly we obtained test statistics and normalized them by using critical values then transferred them to the charts. In order to decide about when test statistics is higher than critical values null hypothesis is rejected. Findings-Bitcoin, Usd, Gold and Bond are found non-stationary for the whole period. We find out that returns of Euro is more stable than Usd. Bitcoin has been fluctuating during all period except for first months of 2013 while Gold's returns are intensively volatile when politic and economic risks are increasing. This show investors in Turkey still consider Gold as safe port for their investments. Usd currency is volatile for all period because of its increasing demand all over the world. In causality test we observed that there is a causality relation from Bitcoin's returns to Bist 100 returns. This result shows that Bitcoin is becoming an alternative (substitute) investment tool for domestic and foreign investors compared to Borsa Istanbul. There is more causality relation between Bitcoin and Gold than Bitcoin and Bist 100. Conclusion-Based on the findings of this analysis, , it may be accepted that Bitcoin has been becoming an alternative investment/savings tool for the Turkey case. Regulatory institutions should create a required legal framework for the Bitcoin because Bitcoin has a great potential to prevent of tax evasion, the terminate the informal economy and eliminate intermediation costs.

İbrahim Çütçü, Yunus Kılıç

Dijital para birimleri son yıllarda etkinliğini artırarak uluslararası piyasalarda önemli oranda talep görmeye başlamıştır. Dijital para birimleri arasında işlem hacmi ve getiri oranları dikkate alındığında, Bitcoin ön plana çıkmaktadır. Çalışmada, Bitcoin fiyatları ile döviz kurları arasındaki ilişki incelenmektedir. Bu doğrultuda kurulan model kapsamında, 24 Kasım 2013 - 04 Mart 2018 dönemlerini kapsayan haftalık veriler ile yapısal kırılmalı testler kullanılarak döviz kurları ile Bitcoin fiyatları arasındaki ilişki irdelenmiştir. Analizlerde kullanılan verilerin I (1) düzeyinde durağan olduğu tespit edilmiş olup yapısal kırılmaya izin veren Maki Eşbütünleşme testi sonuçlarına göre değişkenler arasında yapısal kırılmalarla birlikte uzun dönemli bir ilişki olduğu sonucuna ulaşılmıştır. Hacker-Hatemi-J Bootstrap Nedensellik testi sonuçlarında ise dolar kurundan Bitcoin fiyatlarına doğru %1 anlamlılık düzeyinde nedensellik ilişkisi tespit edilmiştir.

Roberto B. Corcino, Karl Patrick Casas, Allan Roy Elnar

In this paper, a mathematical model is constructed that would capture the pattern of the MSD of fluctuations of Bitcoin unit prices over time in daily basis by applying the method of White Noise Analysis. The raw data of 2805 ordered points (t,p) are used in the study, which are taken from coindesk.com, where p is the Bitcoin unit price at given time t.

Yunus Kılıç, İbrahim Çütçü

Kripto para olarak da adlandırılan dijital para fiyatlarındaki değişimler son yıllarda yatırımcıların oldukça ilgisini çekmiştir. Hızlı fiyat değişimlerinden getiri elde etmek isteyen yatırımcılar yeni bir varlık olan dijital paralara yönelmişlerdir. Bu doğrultuda, dijital paraların geleneksel menkul kıymetlerine alternatif olma ihtimalleri tartışılmaya başlanmıştır. Çalışmada, Bitcoin fiyatları ile Borsa İstanbul arasındaki eşbütünleşme ve nedensellik ilişkisini tespit etmek amaçlanmıştır. Bu kapsamda, Engle-Granger ve Gregory-Hansen eşbütünleşme testleri ile Toda-Yamamoto ve Hacker-Hatemi-J nedensellik testlerinden faydalanılmıştır. Bulgular, her iki eşbütünleşme testine göre Bitcoin fiyatları ile Borsa İstanbul endeks değeri arasında orta ve uzun vadede bir eşbütünleşme ilişkisinin olmadığını; nedensellik testlerinden sadece Toda-Yamamoto nedensellik testine göre Borsa İstanbul’dan Bitcoin fiyatlarına doğru tek yönlü nedensellik ilişkisi olduğunu göstermiştir.

Natalia Jerdack, Akmaral Dauletbek, Meredith Divine, Michael Hult · 5 authors

Cryptocurrencies have gained tremendous popularity over the past few years. The purpose of this study is to try to understand the factors that are driving cryptocurrency-related trading activities. Focusing on the well-established cryptocurrency called Bitcoin, we find that online search popularity and the volume of trade in unrelated stock markets positively and negatively, respectively, influence Bitcoin trading volume. We also find no statistical evidence that the underlying sentiment behind relevant financial news influence Bitcoin trading volume. We believe these results might be of great value to investors interested in cryptocurrencies and might instigate further research on this topic.

Mustafa Özyeşil

Purpose - This study conducts an analysis to reveal the interaction between Bitcoin and Exchange Rates to find out whether Bitcoin is becoming a substitution for the exchange rates.Methodology - To investigate the mutually interaction between the exchange rates and the Bitcoin, the interaction (relationship) between daily closing price of both exchange rates and Bitcoin was analyzed through the Var model. Thus, it was tried to show the sensitivity of the values of Bitcoin to the changes occured in the exchange rates.Findings - Based on Variance Decomposition analysis, BITCOIN and Euro can be considered as largely external variables and their prices are not significantly affected by USD. An interesting result in this study is that the USD exchange rate was found to be significantly sensitive to the Euro.Conclusion - Findings obtained from analysis show that Bitcoin and Excange Rates have not become an alternative tools for each other yet.

Jing‐Zhi Huang, William C. Huang, Jun Ni

There has been much debate about whether returns on financial assets, such as stock returns or commodity returns, are predictable; however, few studies have investigated cryptocurrency return predictability. In this article we examine whether bitcoin returns are predictable by a large set of bitcoin price-based technical indicators. Specifically, we construct a classification tree-based model for return prediction using 124 technical indicators. We provide evidence that the proposed model has strong out-of-sample predictive power for narrow ranges of daily returns on bitcoin. This finding indicates that using big data and technical analysis can help predict bitcoin returns that are hardly driven by fundamentals.