Blockchain Papers

Lingbing Feng, Jiajun Qi, Brian M. Lucey

No abstract is available for this record.

Ata Assaf, Ender Demir, Khaled Mokni

No abstract is available for this record.

Klaus Grobys

This paper explores whether the overall evolution of Bitcoin log-prices would manifest a log-period power-law singularity (LPPLS) signature, eventually resulting in the arrival of a finite-time singularity. Calibrating the LPPLS model using daily data on Bitcoin covering the 2011—2023 period, this study indeed finds evidence for a strong LPPLS signature suggesting the arrival of a spontaneous singularity in the year 2129. Further striking evidence suggests that Bitcoin will experience what we term a close-to-singularity-condition near to the year 2050—a remarkable coincidence with the recently documented arrival of a finite-time singularity in U.S. equities.

Walid Mensi, Mobeen Ur Rehman, Xuan Vinh Vo, Sang Hoon Kang

No abstract is available for this record.

Ling Mei-jun, Guangxi Cao

No abstract is available for this record.

Danilo Petti, Ivan Sergio

Bitcoin (BTC) represents an emerging asset class, offering investors an alternative avenue for diversification across various units of exchange. The recent global banking crisis of 9 March 2023 has provided an opportunity to reflect on how Bitcoin’s perception as a speculative asset may be evolving. This paper analyzes the volatility behavior of BTC in comparison to gold and the traditional financial market using GARCH models. Additionally, we have developed and incorporated a bank index within our volatility analysis framework, aiming to isolate the impact of financial crises while minimizing idiosyncratic risk. The aim of this work is to understand Bitcoin’s perception among investors and, more importantly, to determine whether BTC can be considered a new asset class. Our findings show that in terms of volatility and price, BTC and gold have responded in very similar ways. Counterintuitively, the financial market seems not to have experienced high volatility and significant price swings in response to the March 9th crisis. This suggests a consumer tendency to seek refuge in both Bitcoin and gold.

Jyoti Verma, Anjali Sharma, Gagan Deep

Non-fungible tokens (NFTs) are unique, irreplaceable, distinguishable, and tradeable physical as well as digital assets. These gained real hype on the sale of digital artworks like ‘The Merge' and ‘Beeple.' In June 2022, Bill Gates expressed skepticism regarding NFTs, likening them to the 'Greater Fool Theory' and indirectly disregarding the unnecessary hype for an overvalued asset that is being sold at a price that is more than its intrinsic value. This chapter aims to analyze if NFTs are just a matter of unnecessary hype or a real opportunity for businesses. It is based on exploratory research that justifies the real aim of NFTs, like whether it is just hype, a valuable opportunity for business houses, or a combination of both. It can be concluded that NFT being an extraordinary asset is likely to gather hype, which is capable of being turned into a business opportunity. The scope of the study is wider in the sense that it is wholly based on secondary data, which can ultimately be scope for academicians, and researchers to further implement it with primary research.

Lokman Salih Erdem, Hayriye Atik

Bitcoin'in 2009 yılında ortaya çıkmasıyla birlikte, birçok sektör üzerindeki etkileri gözlemlenmiştir. Ancak, kripto para piyasalarındaki yüksek volatilite ve merkezi bir kontrol olmaması, kripto paraların geleceği konusunda belirsizlik yaratmaktadır. Bu anlamda, finansal sektörlerin dinamik yapısı gereği diğer sektörlerden daha hızlı etkilendikleri doğal olarak kabul edilmektedir. Bu araştırmanın temel amacı, Bitcoin, Ethereum, Litecoin ve Ripple gibi dört kripto para biriminin yatırım aracı olarak potansiyelini değerlendirmektir. Bu amaç doğrultusunda, 1 Ocak 2018 - 1 Ocak 2023 tarihleri arasında, seçili kripto para birimlerinin getiri oranlarının volatilite özellikleri modellenmeye çalışılmıştır. Otoregresif koşullu değişen varyans modelleri (Autoregressive conditional heteroskedasticity - ARCH) analizi kullanılarak yapılan çalışmada, modelin volatilite tahmininin anlamlı sonuçlar vermesi üzerine VAR analizi ve Granger nedensellik ilişkileri eklenerek desteklenmiştir. Bu testlerin sonucunda kripto para birimlerinin risk profili incelenmiş ve gelecekteki fiyat hareketlerine ilişkin bir tahmin sağlanması amaçlanmıştır. Bu şekilde, kripto para birimlerinin potansiyel bir yatırım aracı olarak değerlendirilmesi konusunda tespitler yapılarak literatüre katkıda bulunulmuştur. Bu bağlamda, serilerde ARCH etkisi gözlemlenmiştir. Yapılan VAR ve Granger Nedensellik testleri sonucunda, Bitcoin'deki bir değişikliğin diğer altcoin'leri önemli ölçüde etkilediği ancak Ripple'da anlamlı bir etkinin olmadığı sonucuna varılmıştır.

Qigang Xiang

Cryptocurrency, a digital currency managed by decentralized networks, has gained immense popularity since the inception of Bitcoin. These digital assets, often characterized by extreme price volatility, have generated substantial interest from investors. Traditional financial models struggle to account for the unique dynamics and complexities of cryptocurrencies, prompting the adoption of deep learning techniques. This study investigates the use of Long Short-Term Memory (LSTM), Neural Networks, and Deep Learning (CNN) in predicting cryptocurrency prices. These deep learning models leverage various data sources, such as technical indicators and sentiment analysis, to gain a comprehensive understanding of cryptocurrency markets. The research evaluates the performance of these models using Root Mean Squared Error (RMSE) as the primary metric. The results demonstrate that the hybrid model, combining LSTM, Neural Networks, and Deep Learning, exhibits the highest predictive accuracy across multiple cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). However, challenges persist, such as model adaptability to unforeseen market events and data noise. Future developments may involve incorporating external factors and interdisciplinary collaboration to create more holistic valuation models. Despite these challenges, the study underscores the potential of hybrid deep learning models in enhancing cryptocurrency valuation accuracy and their relevance in risk management strategies for investors and traders.

Guoxuan Sun

The valuation and prediction of cryptocurrency prices have become increasingly important in the financial market. Therefore, this study aims to focus on the selection and evaluation of machine learning models for cryptocurrency valuation. Thus, two types of machine learning models, gradient boosting trees (Xgboost and LightGBM) and neural networks, are compared to determine their effectiveness in generating features for cryptocurrency valuation. Additionally, correlation tests are conducted to identify the most suitable input variables for the models. The results demonstrate that the generated features have a significant impact on the accuracy of machine learning predictions for cryptocurrency prices. It highlights the potential of machine learning models in accurately predicting and evaluating the value of cryptocurrencies. Overall, the findings of this study contribute to the understanding of the role of machine learning in cryptocurrency valuation and provide valuable insights for investors and researchers. By leveraging machine learning techniques, investors can make informed decisions and develop effective investment strategies in the cryptocurrency market. This study contributes to cryptocurrency valuation research. Leveraging machine learning enables informed decisions and effective investment strategies. Furthermore, the findings inform the development of advanced machine learning models and algorithms for cryptocurrency valuation.

Yuxin Zhang, Rajiv Garg, Linda L. Golden, Patrick L. Brockett · 5 authors

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.

Sonal Sahu, José Hugo Ochoa Vázquez, Alejandro Fonseca Ramírez, Jong‐Min Kim

This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to construct and assess portfolios across various rebalancing frequencies. Our empirical analysis reveals significant volatility, skewness, and kurtosis in cryptocurrencies, highlighting the need for sophisticated portfolio management techniques. We discover that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, especially in shorter-term investment horizons, delivering optimal returns to investors. Additionally, our findings emphasize the importance of dynamic portfolio management, stressing the necessity of regular rebalancing in the volatile cryptocurrency market. Overall, this study offers valuable insights into optimizing cryptocurrency portfolios, providing practical guidance for investors and portfolio managers navigating this rapidly evolving market landscape.

Saurabh Singh, Anil Audumbar Pise, Byungun Yoon

In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes crucial for making informed investment decisions. Notably, previous research has utilized machine learning methods to enhance the accuracy of Bitcoin price predictions. However, few studies have explored the potential of employing diverse modeling methods for sampling with varying data formats and dimensional characteristics. This study aims to identify the internal feature subset that yields the highest returns in forecasting Bitcoin's price. Specifically, Bitcoin's internal features were categorized into four groups: currency data, block details, mining information, and network difficulty. Subsequently, a long short-term memory (LSTM) artificial neural network was employed to predict the next day's Bitcoin closing price, utilizing various categorizations of feature subsets. The model underwent training using two and a half years of historical data for each feature. The findings revealed a mean absolute error rate of 6.38% when modeling with the block details category features. This enhanced performance primarily stemmed from the positive relationship between Bitcoin price and this data subset's low ambiguity. Experimental results underscored that, compared to other investigated feature subsets, the categorization of block detail features provided the most accurate Bitcoin price predictions, laying the foundation for future research in this domain.

Milind Tiwari, Cayle Lupton, Ausma Bernot, Khaled Halteh

Purpose This paper aims to investigate technological innovations within the crypto space that have engendered novel financial crime risks and their potential utilization amidst geopolitical conflicts. Design/methodology/approach The theoretical paper uses an analysis of recent geopolitical events, with a key focus on using cryptocurrencies to undertake illicit activities. Findings The study found that cryptocurrencies and the innovations made within the crypto domain are used for both legitimate and illicit purposes, including money laundering, terrorism financing and sanction evasion. Originality/value This research contributes to understanding the critical role cryptocurrencies play amidst geopolitical conflicts and emphasizes the need for regulatory considerations to prevent their misuse. To the best of the authors’ knowledge, this paper is the first scholarly contribution that considers the evolving mechanisms afforded by cryptocurrencies amidst geopolitical conflicts in undertaking illicit activities.

Yun Chen, Cong Yu

This paper analyses the connectedness between three traditional financial assets and cryptocurrencies from June 2019 to December 2022. We find that cryptocurrencies have the highest within-market connectedness, suggesting they are less influenced by other asset categories. The dynamic overall market connectedness undergoes four structural changes during the sample period, with two significant increases aligning with the early stages of the COVID-19 pandemic and the Russia–Ukraine conflict. When comparing the two stages before and after these increases, we observed an increase in spillover effects from cryptocurrencies to the other three asset categories.

Dong Guo, Hanlin Zhang

This paper introduces cryptocurrency into a two-country open-economy model. Based on the theoretical model, we employ the TVP-VAR model to study the dynamic interdependence among interest rate spread (a proxy in the monetary market), exchange rate (a proxy in the forex market), and Bitcoin transactions (a proxy in the cryptocurrency market). The key finding is that Bitcoin has an effect of de-fiatization in the global financial market. When there is a higher divergence in monetary policy between the US and China, Bitcoin attracts greater attention with a higher price, posing a competing force against USD. When there are greater fluctuations in the exchange rate of USD/CNY, Bitcoin diverts investors from CNY. The fiat currencies of the two largest economies are both losers while Bitcoin gains. Therefore, cryptocurrency not only decentralizes the role of commercial banks as a medium of payment, but also decentralizes the role of central banks as a monetary policymaker. In face of this challenge, it is suggested that central banks should embrace blockchain technology and develop their own digital currency to restore the trust lost in the global financial crisis. International collaborations in terms of regulation are necessary given its borderless and authority-less feature.

Young C. Joo, Sung Y. Park

There is increased interest in the dynamic relationships between cryptocurrency and commodity futures. This study examines the hedging performance of four well-known commodity futures against fluctuations in Bitcoin prices. Furthermore, this study used the DCC- and ADCC-MGARCH models to estimate conditional correlations and time-varying optimal hedge ratios between the returns of copper, gas, gold, and crude oil futures, and Bitcoin. We use a rolling window method to calculate one-step-ahead time-varying optimal hedge ratios and evaluate hedging performance. The empirical results show that gas and gold have hedge benefits to Bitcoin. However, crude oil shows poor hedge performance. From the results of one-step-ahead hedge ratios, for copper and oil, we find that hedge ratios increased and hedge effectiveness improved since the COVID-19 outbreak.

Arash Aloosh, Jiasun Li

We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect and characterize wash trading—a type of market manipulation in which a single trader clears the trader’s own limit orders to “cook” transaction records. Our finding provides direct evidence for the widely suspected “fake volume” allegation against cryptocurrency exchanges, which has so far only been backed by indirect estimation. We then use our direct evidence to evaluate various indirect techniques for detecting the presence of wash trades and find measures based on Benford’s law, trade size clustering, lognormal distributions, and structural breaks to be useful, whereas ones based on power law tail distributions to give opposite conclusions. We also provide suggestions to effectively apply various indirect estimation techniques. This paper was accepted by Professor Bruno Biais, finance. Funding: J. Li acknowledges support by the U.S. Department of Homeland Security [Grant 205187] through the Criminal Investigations and Network Analysis Center. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.01448 .

Astitav Mittal, S. Hariharasitaraman, R. Raja Subramanian

Decentralized markets like Bitcoin and Ethereum, which utilize blockchain technology, offer advantages such as increased transparency, lower transaction costs, and quicker settlement times when compared to regulated markets. However, these markets are also known for their higher volatility. On the other hand, traditional regulated markets such as stock and commodity exchanges have also been subject to volatility due to macroeconomic factors such as inflation, geopolitical tensions, and policy changes. Therefore, it is crucial to compare and analyze the volatility of both decentralized and regulated markets to understand their behaviour and potential risks. This paper aims to conduct comparative market research and trend analysis of volatility in decentralized and regulated markets, investigating the contributing factors such as market size, liquidity, regulations, and the impact of market events like economic downturns or regulatory changes. The study will also examine historical trends, explore the correlation between the volatility of these markets, assess the potential impact of market volatility on investors and traders, and analyze how different trading strategies and investor behaviours can affect market volatility. The research could provide valuable insights into the behaviour of decentralized and regulated markets, which could be useful for investors, traders, policymakers, and other stakeholders. The paper will conclude with a discussion of the findings and their implications for traders and investors in both types of markets.

Nishant Sapra, Imlak Shaikh, David Roubaud, Mehrad Asadi · 5 authors

No abstract is available for this record.

Foued Hamouda, Imran Yousaf, Muhammad Abubakr Naeem

No abstract is available for this record.

Ritesh Patel, Mariya Gubareva, Muhammad Zubair Chishti, Тамара Теплова

This paper studies dynamic connectedness between four prominent healthcare cryptocurrencies, namely MediBloc, MediShares, Medicalchain, and Dentacoin, and bond, equity, and commodity markets along with USD and Bitcoin. The daily data span from February 2018 to April 2023. We applied the quantile VAR method and the wavelet quantile correlation approach to measure the connectedness. The quantile VAR method reveals a strengthening interrelation among the assets during COVID-19 and Russia-Ukraine military conflict, while the wavelet quantile correlation highlights the existing diversification opportunities. To test portfolio performance, we resort to minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio techniques. The minimum variance portfolio is best performing portfolio with highest Sharpe ratio. In addition, considering the minimum connectedness and minimum correlation portfolios, the exposure to healthcare cryptocurrencies also improves portfolio diversification. Based on the hedging effectiveness, we show that the investment in the healthcare cryptocurrencies reduces the volatility for all the selected portfolios though currently in a limited degree. However, the investors are advised to regularly monitor their asset allocation as, with the passage of time, the strength of hedging attributes may change. Our study provides important implications for policymakers and the portfolio managers.

Sangita Choudhary, Anshul Jain, Pratap Chandra Biswal

No abstract is available for this record.

Ewa Feder‐Sempach, Piotr Szczepocki, Joanna Bogołębska

Abstract This article investigates five safe-haven asset responses from 2014 to 2022, including the unprecedented COVID-19 crisis, Russian invasion of Ukraine, and sharp US interest rate increases of 2015 and 2022. We apply the unique approach of the multivariate factor stochastic volatility (MSV) model, which is extremely efficient for financial market analysis and allows us to conduct dynamic factor analysis of safe-haven relationships that cannot be observed directly. The research sample consists of five prospective safe-haven assets—gold, bitcoin, the euro, the Japanese yen, and the Swiss franc—and five primary world stock market indices—the S&P 500, Financial Times Stock Exchange (FTSE) 100, DAX, STOXX Europe 600, and Nikkei 225. Our findings are useful for investors searching for the best safe-haven assets among gold, bitcoin, and currencies to hedge against financial turmoil in global stock markets. Our unique findings suggest that safe-haven effects work differently for gold and the yen; that is, the Japanese yen acts as the strongest safe haven across all stock indices. Bitcoin is not a strong safe-haven currency since it has zero days of negative correlations with the considered stock indices, but it is a weak safe-haven during times of financial distress. Consequently, we state that strong and weak safe-haven properties vary across time and place. The novelty of our study lies in the methodological complexity of the MSV model (used for the first time to find the best safe-haven asset properties), dynamic factor analysis, a long-term research sample covering the Russian invasion of Ukraine in 2022, and an international investor perspective focusing on the world’s leading stock markets. We extend earlier studies by analyzing the interrelations of the world’s leading stock market indices with five potential safe-haven assets during the long period of 2014–2022 and using a unique dynamic factor analysis to show the differentiated behaviors of the Japanese yen and gold. Additionally, the main innovative contribution is a new framework of weak and strong safe-haven asset classifications not previously applied in the literature.