Blockchain Papers

Emrah İsmail Çevik, Samet Günay, Sel Dibooğlu, Durmuş Çağrı Yıldırım

No abstract is available for this record.

Abu Hanifa Md. Noman, Muhammad Mahmudul Karim, M. Kabir Hassan, Muhammad Asif Khan · 5 authors

No abstract is available for this record.

Imran Yousaf, John W. Goodell

No abstract is available for this record.

Muhammad Shahzeb Khan, Sibghat Ullah Bazai, Muhammad Imran Ghafoor, Shah Marjan · 6 authors

This paper investigates the potential of using a gated recurrent unit (GRU) neural network (NN) for forecasting the prices of three popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). A dataset spanning from October 2021 to October 2022 was collected and used to train and evaluate the performance of the proposed model. The proposed GRU model was evaluated using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE) as evaluation metrics. The results of the study show that the GRU model achieved an RMSE of 366.0601 and a MAPE of 1.7268% for BTC, an RMSE of 37.6678 and a MAPE of 2.3342% for ETH, and an RMSE of 1.0902 and a MAPE of 1.7278% for LTC. The results indicate that the GRU model performed well in forecasting cryptocurrency prices and holds promise as an approach for further research in this field.

P. P. Jain, Akram Masoud Haddad, Ch. Paramaiah

Join the age-old debate over which asset is the superior store of value: Bitcoin or gold? These two heavyweight candidates have captured the interest of investors all across the world. Bitcoin's decentralized characteristics and limited supply make it an appealing alternative to existing fiat currencies, whereas gold has long been seen as a reliable hedge against inflation and currency depreciation. This paper goes into the benefits and cons of each asset, examining its ability to hold value over time. So, join us as we assess if Bitcoin or gold is the ultimate heavyweight champion of the global economy's store of value.

Facundo Carrillo, Elaine Hu

Maximum extractable value (MEV) has been extensively studied. In most papers, the researchers have worked with the Ethereum blockchain almost exclusively. Even though, Ethereum and other blockchains have dynamic gas prices this is not the case for all blockchains; many of them have fixed gas prices. Extending the research to other blockchains with fixed gas price could broaden the scope of the existing studies on MEV. To our knowledge, there is not a vast understanding of MEV in fixed gas price blockchains. Therefore, we propose to study Terra Classic as an example to understand how MEV activities affect blockchains with fixed gas price. We first analysed the data from Terra Classic before the UST de-peg event in May 2022 and described the nature of the exploited arbitrage opportunities. We found more than 188K successful arbitrages, and most of them used UST as the initial token. The capital to perform the arbitrage was less than 1K UST in 50% of the cases, and 80% of the arbitrages had less than four swaps. Then, we explored the characteristics that attribute to higher MEV. We found that searchers who use more complex mechanisms, i.e. different contracts and accounts, made higher profits. Finally, we concluded that the most profitable searchers used a strategy of running bots in a multi-instance environment, i.e. running bots with different virtual machines. We measured the importance of the geographic distribution of the virtual machines that run the bots. We found that having good geographic coverage makes the difference between winning or losing the arbitrage opportunities. That is because, unlike MEV extraction in Ethereum, bots in fixed gas price blockchains are not battling a gas war; they are fighting in a latency war.

Minghan Jiang, Yufei Xia

Non-fungible tokens (NFTs) have experienced wild market fluctuation during the past years, which leads to the high volatility of NFT’s daily price. This paper examines two potential volatility drivers of NFTs: macroeconomic fundamentals and investor attention. We employ the global and local economic policy uncertainty (EPU) indices as the economic fundamentals’ proxies. The investor attention is represented by the Google search volumes (GSV) or NFTs attention index. Based on the empirical results of a modified generalized autoregressive conditional heteroskedasticity –mixed-data sampling (G-M) model, we find that either economic fundamentals or investor attention can increase the volatility of NFTs significantly. The monthly global EPU index adjusted by the current GDP and weekly GSV contain complementary information. Macroeconomic fundamentals and investor attention can jointly model the volatility of NFTs better than considering only one explanatory variable, as suggested by the G-M model with two explanatory variables. The results remain robust to alternative Twitter-based EPU indices and the ongoing COVID-19 pandemic period.

Elie Bouri, Afees A. Salisu, Rangan Gupta

Abstract This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.

Chiang-Ching Tan, Pick-Soon Ling, Siew-Ling Sim, Kelvin Lee Yong Ming

This study examined the capabilities of six cryptocurrencies as a hedge and safe haven against the stock indices and foreign exchange rate in the East Asia-5 markets. According, MGARCH-DCC was adopted and implemented in data collection processes together with Rathner and Chiu regression method, which spanned from April 2013 to December 2019. The results revealed that these cryptocurrencies had dissimilar hedging and safe haven capabilities across various stock indices and exchange rates in the East Asia-5 markets. In particular, Bitcoin, Litecoin, and Ethereum offered strong hedge properties on most of the East Asia-5 equity indices. Moreover, Bitcoin and Litecoin only provided a safe haven for Japanese Yen currency, while Taiwanese equity indices and Chinese Yuan currency can be safely protected via an investment into Stellar.

Yuji Sakurai, Tetsuo Kurosaki

No abstract is available for this record.

İbrahim DAĞLI, Ceren PEHLİVAN, Ferhat Özbay

BACKGROUND: Like Bitcoin or any other cryptocurrencies, non-fungible tokens (NFTs) count on blockchain technology, and NFTs are the latest and the most popular in a series of blockchain solutions. Traders in this ecosystem need to pay a dynamic fee, called a gas fee, for making any transactions on the Ethereum blockchain. The gas fee is measured by gwei, and traders must consider this as an additional cost. So, the current price of this fee may affect the decision of NFT creators or traders. OBJECTIVE: This study investigates the interrelationships between NTFs, cryptocurrencies (Ethereum and BTC), and gas fees using daily market data from January 2019 to November 2021. METHOD: Fourier Shin’s (2016) cointegration test, Fully Modified Ordinary Least Squares, and Group Dynamic Least Squares tests were employed to analyze the data. Then, the variance Decomposition method was applied to determine what other variables explain the percentage of the total variance on NFTs— it also used Impulse-response functions for measuring the response of the NFTs variable for one standard deviation shock. RESULTS: Results show that an increase in gas fees, the daily volume of Bitcoin, and the daily volume of Ethereum decrease NFTs sales. There is a unidirectional relationship between lnSales and lnGasFee variables. Also, there is a determined unidirectional relationship between lnBTC and lnSales variables. Lastly, there is a one-way causality relationship between lnSales and lnETH variables. CONCLUSIONS: The primary causation of the relationship between NFTs, gas fees and Ethereum fees is most likely related to the use of Ethereum as the primary means of payment in the NFTs market and gas fees being a significant cost element in NFTs trading. Another point of view is that the dominance of Bitcoin in the market is very effective in pricing of other cryptocurrencies and in the sales and pricing of NFTs indirectly. It is supported by empirical findings that the main elements in the blockchain ecosystem are interrelated.

Ruhan Hou

In recent years, the digital world is fast speeding developed from decentralised concept to blockchain, then to cryptocurrency. Especially, cryptocurrency is a popular trending in recent decades that attracts different experts from various field. Its high volatility has been attracted plenty of investors while also brings the difficulty for realizing the price forecasting. On this basis, this study uses public cryptocurrency dataset and three analytical models to predict the direction of cryptocurrency’s price. To be specific, three underlying assets covering large proportion in cryptocurrency are selected, i.e., Bitcoin, Ethereum and Dogecoin. According to the analysis, the prediction results of different models and approaches will be presented. At the end of study, it gains that the optional model with appropriate hyperparameters based on the judgement of metrics values, which offers relevant suggestions for future works. These results shed light on guiding further exploration of cryptocurrency price prediction in terms the state-of-art machine learning scenarios.

Yuning Yang

Russia massively invaded Ukraine on February 24, 2022, unavoidably having an effect on the world economy and finance. This paper uses the event study to research the short-term response of the February 2022 top 5 variable-price cryptocurrencies (BTC, ETH, BNB, XRP, SOL) to the Russia-Ukrainian war under the constant mean model. The cryptocurrency volatility was dramatic during the event window, and cryptocurrencies did not show the characteristics of safe haven. Overall, the result of the effect of the Russia-Ukraine war on the cryptocurrency market was negative, with the least negative impact on SOL and the most negative impact on BNB, XRP. Finally, Using the different event window analysis, it shows the cryptocurrency market return volatility rebounded, but it does not sufficiently indicate there is a positive trend in the cryptocurrency market after the event. The analysis of this paper can provide some help for cryptocurrency investors in the event of unforeseen circumstances. And in the data selection, this paper doesn’t consider stablecoins.

Gaohao Zhu

Since Bitcoin was proposed in 2008, it has become a very valuable asset and an important part of many investors’ portfolios. It’s important to both understand Bitcoin mechanics and predict its valuation with the help of the state-of-art machine learning tools. The study develops four different models, including Ordinary Least Squares (OLS) regression model, Random Forest, Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM), to predict the return of Bitcoin and compare the performance of these models. According to the analysis, the daily changes in the high, low, close price of Bitcoin, and close price of Tesla stock, and gold price between yesterday and today are all strongly correlated to the Bitcoin return on tomorrow. The statistical approach, or OLS modeling, has the simplest algorithm whereas the highest accuracy rate. The LightGBM model and LSTM model have lower accuracy rates in order, but still exceed the 50% (random benchmark). The Random Forest model, as another type of decision tree algorithm, has similar prediction results with the LightGBM model but a lower accuracy rate that fails to reach the benchmark. Based on the analysis, multiple factors affect the Bitcoin return, and these results provide an insight for investors to the cryptocurrency market and the macroeconomic environment. It validates the effectiveness of several machine learning algorithms in Bitcoin return forecasting and supports future developments in related fields.

Kelan Gao

The global current situation continues to be turbulent. International crises like the Covid-19 epidemic and the continuing Russian-Ukrainian war have thrown the global economy for a loop. As a result, global economic policy uncertainty has spiked due to the resulting spike in energy prices and economic disruptions. During the outbreak of Covid-19, prices of bitcoin (BTC) have moved higher, but its hedging effect is weakening. Also, combined with rising global inflation expectations and the constant rate hikes by central banks against inflation, bitcoin's hedging effectiveness is waning due to its strong correlation with equities. Furthermore, with the outbreak of the Russian-Ukrainian war, the price of gold continued to rise, and the relationship between gold and the global financial market decreased, confirming gold's diversification ability in a crisis. Simultaneously, the link between gold and bitcoin has weakened marginally. Ultimately, preliminary evidence suggests that gold and bitcoin can be used as complements, rather than substitutes, for diversification purposes during a crisis. This article will construct a portfolio about bitcoin and gold, and examine how individual gold and bitcoin and this portfolio performed as hedging assets throughout the Covid-19 pandemic and the Russian-Ukrainian war.

Linxi Pan

Price prediction of cryptocurrencies is bound to get more opportunities for investors engaged in digital currency-related industries in order to earn more revenue. In the traditional forecasting methods, the problem of the high volatility of bitcoin price needs to be effectively solved, making the forecasting accuracy become low and ineffective. Due to the rapid development of artificial intelligence technology, more and more relevant algorithms were used for cryptocurrency price research. This study would compare and analyze the prediction effect of the ARIMA time-series model, the Random Forest algorithm of machine learning, and the LSTM algorithm of deep learning algorithm on cryptocurrencies price prediction to assist investors in making investment decisions. In this paper, five years of time-series data of Bitcoin, Ether, and Dogecoin is obtained from 2018 to 2022. Then, the training set and testing set are separated with 0.8:0.2 to test ARIMA, Random Forest, and LSTM algorithms. To evaluate the model, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and decidability coefficient (R2) are chosen as metrics to measure the prediction of each prediction model precision. Comparing the experimental results, the prediction accuracy of LSTM is better than that of Random Forest, and the prediction accuracy of Random Forest is better than ARIMA model. These results shed light on guiding further exploration of significant to cryptocurrency industry practitioners and visitors.

Qilan Lin

Contemporarily, cryptocurrency has a high market value, and the price of cryptocurrency fluctuates dramatically. This article analyzes the parameters effects of the LSTM model on Bitcoin price prediction accuracy based on Python and modules of Numpy, Pandas, Keras, Tensorflow, and Sklern. The analysis clarifies the relationship between the accuracy of Bitcoin price prediction and different parameters in the LSTM model. It is discovered that when larger batch sizes are supplied at minor epochs, the accuracy of Bitcoin price prediction declines. Meanwhile, the number of neurons affects the accuracy. In addition, compared to lengths of 14, 30, and 60, the prediction error grows greater when a single time sequence is 7 in length. Apart from that, at present, using closing prices from the past two years rather than the past 1 year, 3 years, or 5 years can make predictions more accurate. These findings shed light on recommendations for adjusting various parameters in the development of the LSTM model for Bitcoin price prediction.

Ahmed Ayadi, Yosra Ghabri, Khaled Guesmi

No abstract is available for this record.

Yang Shen, Haoyuan Wang

Currently, cryptocurrency has become a research and investment topic of great concern and attracted considerable attentions in a wide range of fields. Stocks are well known as a form of investment, and there are countless studies on how its price trends. Cryptocurrencies, however, are not easily predicted due to their extremely high volatility. This paper implements the forecasting results by taking three major cryptocurrencies (i.e., Bitcoin, Ethereum, Dogecoin) as examples for the period starting from January 1st, 2020 to May 31st 2022. Four popular prediction models (XGBoost, LightGBM, GARCH, ARIMA) are applied in Python by training models and testing the prediction results. According to the analysis, XGBoost and LightGBM can forecast the future prices for all three cryptocurrencies exactly apart from the turning points when prices rise and drop suddenly. Although the predicting trends of GARCH, ARIMA have differences from the real price, they can forecast well except the unexpected situations such as COVID-19. Overall, relatively reliable and accurate prediction models can be provided for investors to apply and make wise decisions in investments based on the results of this study.

Siyi Liao

Contemporarily, blockchains and cryptocurrencies have gained their popularity among investors and hedge funder, where both have bright prospects. On this basis, cryptocurrencies have been used in trading more and more with the development of website and computer. In this case, their prices fluctuations do have great significance to the public. This paper chooses three machine learning model (i.e., XGBoost, LightGBM and Linear Model) to predict the price of three cryptocurrencies (i.e., Bitcoin, Dogecoin and Ethereum). To be specific, this study uses the data from 2020-01-01 to 2022-12-07, including close price, open price, high price, low price, and the volume of trading coins. According to the analysis, Linear Model can predict the price best, with well-fitted trend prediction and accurate price prediction. In addition, other models can also have good predictions but they are not better than Linear model. These results can help others to predict the price of cryptocurrencies and have a deep understanding of cryptocurrency and machine learning.

Longguang Yang, Fengshuang Hou, Huihong Shi

This study provides a volatility estimation based on cross-market spreads by analyzing the behavior of Bitcoin cross-market arbitrageurs. This study crawls real-time price data from different exchanges for empirical analysis and verifies the accuracy and validity of the method employed by comparing it with the existing mainstream methods. The following conclusions are drawn: 1) The more exchanges that can be utilized, the smaller the Bitcoin price volatility, and the larger the cross-market spread, the better the estimation effect of the proposed method; and 2) Volume had no significant effect on the estimation using our method.

Ricardo Tovar-Silos

The following article explores the correlation between bitcoin and both stocks and gold.A Markov regimeswitching approach was used to identify and date two regimes in each of these financial assets.Stock returns are characterized by short-lived episodes of elevated volatility and negative returns whereas bitcoin returns are characterized by a persistent high volatility state with positive returns.Gold stayed in the low volatility period most of the time and only a few short-lived episodes of high volatility were identified during the first year of the pandemic.A concordance measure was computed to assess the synchronicity and correlation between the regimes.The regimes of bitcoin and gold are uncorrelated suggesting that bitcoin is not yet perceived as a safe haven like gold.The regimes of bitcoin and stocks were also uncorrelated suggesting that bitcoin may be used as a hedge against stocks.

Adrian Moroșan, Oana Oprişan, Eduard Alexandru Stoıca, Cosmin Tileagă

Through our study, we studied the perception of the students of an economic faculty speciality which are at the end of their studies and who will soon become economists, and their attitude towards the cryptocurrencies. Their contacts inside or outside the university led to their professional development because they brought to their attention the widening of the sphere of finance through the prism of a new concept that appeared fifteen years ago, that of cryptocurrency. The main scope of the paper is to understand how students currently relate to cryptocurrencies, after going through all the subjects in the curriculum of their economic specialization. The methodology will involve the use of a structured interview. Important results of our study will be related to the fact that the female students interviewed, who, unlike almost all of the female students, are or say that they will be involved in trading cryptocurrencies in the near future and to the fact that an important part of their information regarding the cryptocurrencies is obtained from outside the faculty. We will recommend, knowing the current situation of the interviewed students, to the teachers who teach various disciplines in the specialization of which the interviewed students are part of that they could try, in the situation where the taught subjects allow it, to offer to the students who will come in the following years additional information about the cryptocurrencies.

Reşat Ceylan, Cihat KARADEMİR, Şencan FELEK

Bu çalışmada, 2017M1-2022M1 dönemleri arasındaki veriler kullanılarak Bitcoin (BTC) ile Karbon Emisyonu (CO2) arasındaki ilişki incelenmiştir. Son zamanlarda yapılan çalışmalara istinaden kripto para ve enerji piyasalarının spekülatif ve kırılgan yapıya sahip olduğu ve bundan dolayı değişkenlerin doğrusal olmayan bir forma sahip olabileceği konusuna dikkat çekildiği gözlenmektedir. Dolayısıyla bu bilgiler çerçevesinde çalışmada öncelikle Luukkonen vd. (1988), Harvey vd. (2008) doğrusallık testi ve Kapetanios vd. (2003) doğrusal olmayan birim kök testi ile değişkenlerin doğrusallık sınaması yapılmaktadır. Akabinde değişkenlerin doğrusal olmayan forma sahip olduğu tespit edildiği için çalışmada Kapetanios vd. (2006) Doğrusal Olmayan Eşbütünleşme analizi kullanılmaktadır. Kapetanios vd. (2006) testi bulgularına göre BTC ile CO2 arasında uzun dönemde doğrusal olmayan bir eşbütünleşme ilişkisi olduğu tespit edilmektedir. Bu durum BTC ile CO2 arasındaki ilişkinin uzun dönemde dengeye doğrusal olmayan bir şekilde yakınsadığı sonucunu göstermektedir. Değişkenler arasında doğrusal olmayan eşbütünleşme ilişkisini tespit ettikten sonra bu ilişkinin yönünü belirlemek amacıyla yapılan Granger nedensellik testi sonucuna göre ise Bitcoin’den Karbon Emisyonuna doğru tek yönlü nedensellik olduğu tespit edilmektedir. Bu bulgu, BTC üretiminde kullanılan enerjinin çevre dostu kaynaklardan elde edilmesine yönelik politikaların benimsenmesi gerektiği biçiminde yorumlanabilir.