Blockchain Papers

Toan Luu Duc Huynh, Erik Hille, Muhammad Ali Nasir

No abstract is available for this record.

Jens Mattke, Christian Maier, Lea Reis, Tim Weitzel

Bitcoin is a well-established blockchain-based cryptocurrency that has attracted a great deal of attention from media and regulators alike. While millions of individuals invest in bitcoin, their motivations for doing so are less clear than with traditional investment decisions. We argue that the technical nature of bitcoin investments gives it unique characteristics and, consequently, that we lack a thorough understanding of how this affects the motivations behind bitcoin investment. We use a mixed method approach consisting of qualitative (n = 73) and quantitative (n = 150) studies and fuzzy-set qualitative comparative analysis (fsQCA) to identify seven bitcoin-specific motivations (profit expectancy, ease of bitcoin acquisition, support of bitcoin ideology, investment skills, risk affinity, anticipated and experienced inaction regret) and how configurations of them explain bitcoin investment. The findings reveal, among others, that some individuals invest in bitcoin because they support the bitcoin ideology. Contrary to the traditional investment literature, profit expectancy is not a necessary condition to the extent that there is one empirical configuration of motivations that explains that individuals also invest in bitcoin even if they do not expect profits. The results disclose non-trivial investment motivation configurations and lay the groundwork for future studies of the role of cryptocurrencies in society.

Şahin Telli, Hongzhuan Chen

No abstract is available for this record.

David Iheke Okorie, Boqiang Lin

No abstract is available for this record.

Andrés García-Medina, José B. Hernández C.

We investigate the effects of the recent financial turbulence of 2020 on the market of cryptocurrencies taking into account the hourly price and volume of transactions from December 2019 to April 2020. The data were subdivided into time frames and analyzed the directed network generated by the estimation of the multivariate transfer entropy. The approach followed here is based on a greedy algorithm and multiple hypothesis testing. Then, we explored the clustering coefficient and the degree distributions of nodes for each subperiod. It is found the clustering coefficient increases dramatically in March and coincides with the most severe fall of the recent worldwide stock markets crash. Further, the log-likelihood in all cases bent over a power law distribution, with a higher estimated power during the period of major financial contraction. Our results suggest the financial turbulence induce a higher flow of information on the cryptocurrency market in the sense of a higher clustering coefficient and complexity of the network. Hence, the complex properties of the multivariate transfer entropy network may provide early warning signals of increasing systematic risk in turbulence times of the cryptocurrency markets.

Monghwan Seo, Geonwoo Kim

In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.

Salim Lahmiri, Stelios Bekiros

No abstract is available for this record.

Dimitrios Koutmos, James E. Payne

No abstract is available for this record.

P. Dekker, Vasilios Andrikopoulos

The following topics are dealt with: cryptocurrencies; cryptography; distributed databases; data privacy; financial data processing; Internet; contracts; meta data; peer-to-peer computing; cryptographic protocols.

Chi‐Wei Su, Meng Qin, Ran Tao, Xuefeng Shao · 6 authors

No abstract is available for this record.

A. Hachicha, Fatma Hachicha

No abstract is available for this record.

Ismail O. Fasanya, Oluwatomisin J. Oyewole, Temitope F. Odudu

Purpose This paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018. Design/methodology/approach The authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market. Findings The authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively. Originality/value This study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

Nicholas Apergis, Dimitrios Koutmos, James E. Payne

No abstract is available for this record.

Mohammed Mudassir, Shada Bennbaia, Devrim Ünal, Mohammad Hammoudeh

No abstract is available for this record.

Olfa Kaabia, Ilyes Abid, Khaled Guesmi, Jean‐Michel Sahut

Cette étude explore comment un effondrement du cours du Bitcoin se transmet aux marchés pétroliers. Le Bitcoin est considéré comme une marchandise qui a une valeur intrinsèque qui augmente et diminue en fonction de l’offre et de la demande, tout comme le prix du pétrole brut. Nous définissons le choc simulé sur le cours du Bitcoin comme une mesure d’un crash soudain dans les cours du Bitcoin et évaluons ses impacts sur les différents prix du pétrole brut. En utilisant les prix de clôture quotidiens du Bitcoin et quatre prix de référence du pétrole brut du 26 septembre 2013 au 20 septembre 2019, nous appliquons un modèle VAR avec rétrécissement Bayesian et calculons les fonctions de réponses généralisées. Les résultats empiriques suggèrent qu’un effondrement du cours du Bitcoin a des effets significatifs sur le marché du pétrole, et par conséquent sur les pays exportateurs de pétrole. Les réponses généralisées confirment qu’il existe actuellement une forte corrélation et une relation positive entre les cours du Bitcoin et ceux du marché pétrolier.

Linh Nguyen, Thanaset Chevapatrakul, Kai Yao

No abstract is available for this record.

Anuphak Saosaovaphak Chukiat Chaiboonsri, Satawat Wannapan

Abstract Cryptocurrencies are unique and extra-ordinary currencies which to be econometrically forced into the linear model due to their systematic complexity and extreme movements. This paper was conducted to provide an alternative analysis as a solution for escaping the restrictions of traditional linear assumptions. Five predominant digital currencies such as Bitcoin (BTC), Stellar network (XLM), Litecoin (LTC), Ethereum Classic (ETC), and IOTA were chosen to be employed in the multiple processes based on Bayesian approaches. Market dominance and data regime classifications are the essential components that lead to successfully investigate the dependent structures and co-movements in the digital financial market. The empirical findings could assume that the modern time-series data was meticulously estimated by the flexible modern tool. Bayesian statistics and simulations have the sufficient potency as the suitable solution.

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.

Yosra Ghabri, Khaled Guesmi, Ahlem Zantour

No abstract is available for this record.

Sirikwan Jaroenwiriyakul, Wichyada Tanomchat

ABSTRACT This research examines the dynamic linkage between four major cryptocurrencies—Bitcoin, Ethereum, Ripple, and Litecoin—and stock markets in ASEAN-5. The findings revealed that first, linkage testing, in the long run using Engle and Granger co-integration, provided evidence of a relationship among all cryptocurrencies with the stock markets in ASEAN-5, with the exception of Malaysia. Secondly, using the dynamic conditional correlation model, the results showed that time-varying patterns of short-run correlations were found in all relationships. Moreover, the Litecoin linkage with ASEAN-5 markets fluctuated significantly. Further, Bitcoin’s dynamic linkage with the stock markets showed a very high correlation from 2013 to 2015, and then became close to stable until January 2020. Finally, this paper tested the determinants of the linkage cryptocurrencies with financial market factors, consisting of GOLD, CRUDE, FX, and INT. The empirical results showed that GOLD and INT did not affect the degree of linkage with the stock market or cryptocurrency, although both CRUDE and FX impacted it. As for recommendations and policy implications, the cryptocurrencies demonstrated a dynamic linkage with stock markets and exhibited extreme volatility, and therefore the five countries should prepare a policy or regular information regarding cryptocurrencies for investors or policymakers. On the other hand, investors should focus on indicators such as foreign exchange rates and crude oil prices prior to trading.

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.

Muhammad Yasir, Muhammad Attique, Khalid Latif, Ghulam Mujtaba Chaudhary · 7 authors

Purpose Business Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe. Design/methodology/approach This research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models. Findings The results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated. Originality/value The use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.

Wei Zhang, Pengfei Wang

No abstract is available for this record.

Chi‐Wei Su, Meng Qin, Ran Tao, Muhammad Umar

No abstract is available for this record.