Blockchain Papers

Cong Zhang, Zhenzhi He

No abstract is available for this record.

Jeffrey R. Black, Jobaer Hossain, Shawn McFarland

No abstract is available for this record.

A. Abuzov, M.E. Konovalova

Целью исследования является определение особенностей функционирования, роли и места рынка криптовалют в системе финансового рынка в современных условиях, его структуры.Источниками информации послужили научные труды, опубликованные в международных базах данных и российской электронной библиотеке.Рынок криптовалют, возникший чуть более десятилетия назад, претерпел стремительное развитие и трансформацию, став значимой частью глобальной финансовой системы.На сегодняшний день криптовалютный рынок представляет собой сложную систему, включающую тысячи различных монет и токенов, блокчейн-платформы, криптовалютные биржи, кошельки и прочие инфраструктурные элементы.Объем капитализации этого рынка достигает нескольких сотен миллиардов долларов, а ежедневные объемы торгов сопоставимы с традиционными фондовыми биржами.В результате исследования определены особенности функционирования, атрибутивные признаки и функции рынка криптовалют в сравнении с рынком денег и рынком финансового капитала.Это позволило определить роль и обособленное место рынку криптовалют в системе финансов наряду с рынком денег и финансового капитала.Структура рынка криптовалют представлена его основными элементами.Выявлено, что попытки урегулирования и стабилизации цен на рынке криптовалют посредством внедрения деривативов не решили полностью поставленную задачу.Рынок попрежнему зависим в больше степени от спроса и предложений на рынке

Isik Akin, Meryem Akın, Zafer Ozturk, Affan Hameed · 6 authors

Abstract This research employs a vector autoregression (VAR) analysis to explore the volatility and dynamic interactions between stock, commodity, and cryptocurrency markets. It focuses on the returns of the S&P 500, gold, crude oil, and Bitcoin to analyse their interconnections. Our results indicate that Bitcoin returns positively affect S&P 500 and crude oil, but negatively impact gold. Conversely, crude oil returns have a positive influence on gold but lead to decreased returns for Bitcoin and the S&P 500. Similarly, higher gold returns correspond to increased returns in crude oil and S&P 500 but decreased returns in Bitcoin. The rise of the S&P 500 negatively influences Bitcoin and crude oil returns, while gold returns remain unaffected. However, these relationships exhibit weak and limited strength. Including these assets in a portfolio can help risk mitigation, as Bitcoin diversifies crude oil, gold, and S&P 500, and crude oil diversifies S&P 500. These findings contribute to our understanding of global financial dynamics and inform decision-making in risk assessment, portfolio management, risk mitigation, and diversification strategies.

Chavan Rajkumar Dhaku, Durgashree Shivarudrasetty, Senthil Kumar Arumugam, V. R. Uma

No abstract is available for this record.

Rebecca Abraham

This study presents the mathematical formulations of investor sentiment for investors in cryptocurrencies. We assume that bitcoin prices are driven by investor sentiment measured in terms of Google search volume and social media posts. The current generation of retail investors uses non-traditional methods such as social media posts and Google searches to obtain information so that an increase in posts and searches on ‘bitcoin,’ indicate positive or negative investor sentiment. Mathematical formulations describe investor sentiment separately for risk-averse, moderate risk, and risk-taking investors. Risk-averse investors are considered to be aberrant in their investment in cryptocurrencies as they are naturally resistant to high-risk investments such as cryptocurrencies. Only risk-taking investors capture the fullest extent of irrational exuberance that prevailed in the cryptocurrency markets. However, risk-takers with very high-risk tolerance, such as hedge funds, trade in investments with volatility to capitalize upon the highest market prices for cryptocurrencies. Their behavior is modeled in cryptocurrency futures and cryptocurrency call options, and cryptocurrency put options. The insight provided by this paper is that the history of cryptocurrency prices is stored in a Laplace transform so that investor sentiment is based on the trajectory of past prices for cryptocurrencies and cryptocurrency futures. For cryptocurrency options, the history of volatility of prices is embedded in the Laplace transform, with increasing volatility embedded in call option prices, and decreasing volatility embedded in put option prices.

Daniel Tut

No abstract is available for this record.

Xudong Wang, Xiaofeng Hui

Bitcoin futures exchange‐traded funds (ETFs) are recent innovations in cryptocurrency investment. This article studies the price‐volume relationship in this market from an information perspective. We first propose effective mutual information which has better estimation accuracy to analyze the contemporaneous relationship. Using half‐hourly trading data of the world’s largest Bitcoin futures ETF, we find that trading volume changes and returns contain information about each other and are contemporaneously dependent. Then, we employ effective transfer entropy to examine the intertemporal relationship. The results show that there exists information transfer from volume changes to returns in most of our sample period, suggesting the presence of return predictability and market inefficiency. However, information transfer in the opposite direction occurs much less frequently, and the amount is typically smaller.

Katrin Tinn

This paper considers introducing asymmetric privacy in the design of central bank digital currencies (CBDC) and digital currencies more generally to preserve the privacy of money spent while keeping the benefits of digital records for money received. It is shown that this feature would help minimize real distortions between consumers, firms, and financiers while enabling tax optimization and better access to external financing. Protecting the privacy of consumers is desirable from a welfare and efficiency standpoint as long as there exist noticeable privacy concerns. Implementing asymmetric privacy is technologically feasible, using, for instance, zero-knowledge proofs or other privacy tools. This paper has been accepted by Lin William Cong for the Virtual Special Issue on Digital Finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.06830 .

Maria-Cristina Zwak-Cantoriu, Lucian Claudiu Anghel

The main purpose of this paper is to investigate the volatility in the cryptocurrency market and the relationships between them, using the cryptocurrencies: Bitcoin, Cardano and Stellar. The results obtained suggest that volatility in cryptocurrency prices is influenced by previous events and the level of past volatility. The propagation of volatility and shocks between the three analyzed cryptocurrencies, and the observation of the interconnection between them was also followed in this paper. By determining the degree of correlation between the analyzed cryptocurrencies, we observed that in the selected period there are quite strong positive correlations. Also, we noticed that the volatility of cryptocurrencies was strongly influenced by certain economically uncertain periods, a fact that caused their prices to have a strong fluctuation, especially in the period 2021-2023. The results obtained show an interdependence between the three cryptocurrencies, which is significant in the decision-making of investors. News and events play a significant role in the cryptocurrency market, especially those in the financial, technological, and political worlds that can have a considerable impact on cryptocurrency prices and volatility. In the case of Bitcoin, there was an increase in interest in this cryptocurrency when there were investments from large companies and financial institutions.

Abe Alexander, Lars Fritz

No abstract is available for this record.

Dyamappa S Hadakar, Sanjay Hanji, C. Prashantha

No abstract is available for this record.

Zehra Atik, Güven Murat, Bülent Güloğlu, Gülşah Hançerlioğulları Köksalmış · 5 authors

No abstract is available for this record.

Heba Mohammed Fadhil, Noor Q. Makhool

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The ability of the tool in analyzing past data on historical prices combined with machine learning, orchestrate an appealing scene of predictions equipped with choices and information, users turn into the main characters in a financial discovery story conducted by the cryptocurrency system. The numerical results also support the effectiveness of the tool as highlighted by standout corresponding numbers such as lower RMSE value 150.96 for ETH and minimized normalized RMSE scaled down to under, which is. The quantitative successes underline the usefulness of this tool to give precise predictions and improve user interaction in an entertaining world of cryptocurrency investments.

Xingyi Li, Yujun Liu, Zhuang Liu, Shushang Zhu

No abstract is available for this record.

Krzysztof Koszewski, Somnath Mazumdar, Anoop Kumar

Abstract In recent years, cryptocurrencies have been considered as an asset by public investors and received much research attention. It is a volatile asset, thus predicting its prices is not easy due to the dependence on multiple external factors. Machine learning models are becoming popular for cryptocurrency price predictions, while also considering social media data. In this article, we analyze the rate of return of three cryptocurrencies (Bitcoin, Ether, Binance) from an investor point of view. We also consider three traditional external variables: S&P 500 stock market index, gold price, and volatility index. The rate of return prediction is based on three stages. First, we analyze the correlation between the cryptocurrency returns and the traditional external variables. Next, we focus on the influential social media variables (from Twitter, Reddit, and Wikipedia). Later, we use these variables to improve prediction accuracy. Third, we test how the standard time series models (such as ARIMA and SARIMA) and four machine learning models (such as RNN, LSTM, GRU and Bi-LSTM) predict one-day rate of return. Finally, we also analyze the risk of investing in each cryptocurrencies using value risk statistics. Overall, our result shows no correlation between cryptocurrency returns and three traditional external variables. Second, we found that overall LSTM model is the best, GRU is the second-best prediction model, while the impact of the social media variables varies depending on the cryptocurrencies. Finally, we also found that investment in gold offers better returns than cryptocurrency during Covid-19-like situations.

Panayiotis M. Pourpourides

We investigate the long-term impact of macroeconomic and financial factors on cryptocurrency metrics using both parametric and non-parametric methods. Our analysis examines how these factors influence cryptocurrency prices, market capitalizations, and Bitcoin’s hash rate. The results establish that two key factors, the US dollar and the price of gold, adversely affect Bitcoin and other cryptocurrency metrics, including the prices and market capitalizations of decentralized finance and layer-one protocols. Bitcoin’s hash rate demonstrates greater market sensitivity than its price, with the dollar having a stronger impact on Bitcoin than gold. The dollar primarily affects Bitcoin’s price, whereas gold mainly influences its hash rate. These findings, along with Bitcoin’s properties, support the view of Bitcoin as a digital asset analogous to physical gold, playing a role similar to a substitute for the latter.

Juliane Proelss, Denis Schweizer, Bastien Buchwalter

The cryptocurrency market operates continuously, leading to frequent price fluctuations and information dissemination. This can hinder investors from reacting promptly to market changes, a phenomenon attributed to investors' limited attention. Research in traditional markets shows that the limited attention bias allows successful implementation of momentum strategies. However, past research on cryptocurrency markets finds mixed results. To resolve the puzzle, we utilize a survivorship bias-free dataset while accounting for variations in market capitalization and trading volume. This differentiation is crucial given young and tech affine retail investors' inclination toward smaller-capitalized cryptocurrencies, due to their higher risk tolerance and limited attention. More risk averse investors such as institutional investors, in contrast, focus more on top cryptocurrencies. In line with expectations, we find effective momentum strategies among larger-capitalized cryptocurrencies.

José Daniel Cardoso Rodrigues, Petros Golitsis, Pavlos Gkasis

With the rise of cryptocurrencies and their appeal as alternative investment assets, this study, using daily and weekly data from early 2015 to late 2023, aims to analyze the influence of economic and geopolitical uncertainty factors on cryptocurrencies, particularly Bitcoin, and forecast their volatility using GARCH, EGARCH, and GJR-GARCH models. Our findings reveal that the Geopolitical Acts Index (GPAs), the U.S. Economic Policy Uncertainty Index (EPU), and the Volume of Bitcoin transactions exhibit a positive significant impact on its returns, whereas the Cryptocurrency Uncertainty Index (UCRY), S&P 500, and Volatility Index (VIX) demonstrate a negative one. Furthermore, by decomposing geopolitical turbulence into Geopolitical Risks (GPRs) and Threats (GPTs), these variables were found to be less significant compared to Geopolitical Acts. Finally, the asymmetry analysis (leverage effects) reflects on how negative shocks exhibit a greater influence than positive ones on Bitcoin returns, indicating that adverse news in the media tends to impact the cryptocurrency returns more profoundly. Our conclusions contribute to the existing literature by exploring the role that Bitcoin, and cryptocurrencies in general, play as investment assets, when taking into consideration the volatility they entail, especially following negative shocks in an economy.

Ismail Jirou, Ikram Jebabli, Amine Lahiani

No abstract is available for this record.

Anastasiya Lavrova, Vladimir Korkhov, Albina Lavrova

No abstract is available for this record.

Efstathios Polyzos, Ghulame Rubbaniy, Mieszko Mazur

Abstract This paper proposes the use of social media as a proxy for financial information. Using an extended sample of 53,580,759 tweets and employing text analysis tools (Latent Dirichlet Allocation and Term Frequency–Inverse Document Frequency), we determine the information being exchanged on any given day. We train machine‐learning classifiers and forecast crypto price movements for more than 8000 cryptocurrencies and gauge market efficiency through successful forecasts based on public information. We propose various metrics of market efficiency for cryptocurrency assets and demonstrate that market efficiency is higher during the first 6 months after the Initial Coin Offering. We also examine the efficiency behavior of individual currencies during crisis periods.

Jasmina Džafić, Emir Hečimović

The cryptocurrency market has attracted considerable attention from investors and researchers alike. This paper examines the volatility patterns of two major cryptocurrencies utilizing GARCH modeling: Bitcoin, based on a proof-of-work mechanism, and Cardano, operating on a proof-of-stake mechanism. Our findings reveal differences in the volatility structures of the two cryptocurrencies, with Cardano demonstrating a reduced long-term volatility compared to Bitcoin. This study suggests that transitioning from proof-of-work to proof-of-stake mechanisms might lead to a decrease in market volatility.

Ilyas Agakishiev, Wolfgang Karl Härdle, Denis Becker, Xiaorui Zuo

Abstract There are many ways to model complex time series. The simplest approach is to increase the complexity, and thus, the flexibility of the model, for the entire time series. As an example, one could use a neural network. Another solution would be to change the parameters of a model dependent on the “state” or “regime” of the time series. A typical example here would be the Hidden Markov model (HMM). This paper combines the two concepts to create a Reinforcement Learning (RL) model that adds variables that depend on the state of the time series. To test the concept, the RL model is used with cryptocurrency data to determine the share to invest into the cryptocurrency index CRIX in order to maximize wealth. The results have shown that cryptocurrency metadata is useful as supplementary data for analysis of the respective prices. The Reinforcement learning model with regimes shows potential for investment management, but comes with some caveats.