Blockchain Papers

Chang Wang, Qunhong Sun, Qibin Huang, Yifan Hu · 5 authors

No abstract is available for this record.

Barbara Čeryová, Peter Árendáš

No abstract is available for this record.

Andrew Hornback, Robert E. Whaley

Bitcoin has emerged as a promising addition to long-term investment portfolios due to its lack of correlation with traditional asset classes. Spot bitcoin exchange-traded funds (ETFs) provide a secure, familiar, and convenient way to invest in bitcoin. Since their launch on 11 January 2024, they have garnered more than $75 billion in new assets under management and their performance relative to bitcoin futures and futures-based bitcoin ETFs has been nothing short of extraordinary. This study examines the performance of spot bitcoin ETFs during their first year of trading. In doing so, it highlights the complexities and inconsistencies in US regulatory decision-making.

Mieszko Mazur, Efstathios Polyzos

Inflows to the newly established bitcoin exchange-traded funds (ETFs) surpassed $20 billion in the first several weeks of trading and are considered record-high by ETF standards. In this article, we provide an early examination of the bitcoin spot ETFs listed on US exchanges and their effect on bitcoin price formation. We establish several empirical facts: 1) daily capital flows to new spot bitcoin ETFs exceed $500 million or roughly 10,000 bitcoins, and surpass bitcoin mining production by the factor of 5; 2) net flows to ETFs are a strong positive predictor of bitcoin price levels with the R-squared of 95%; 3) most bitcoin price changes occur outside ETF trading hours; 4) an increase in bitcoin price leads to abnormal ETF trading volume; 5) inflows to bitcoin ETFs correlate with outflows from gold ETFs. Overall, during the period studied, capital flows to spot-bitcoin-ETFs emerge as a dominant single factor predicting bitcoin valuation effects. "IBIT is the fastest-growing ETF in the history of ETFs.

Lakshit Jain, Luis Velez-Figueroa, Surya Karlapati, Mary Forand · 6 authors

BACKGROUND: Cryptocurrency trading seemingly mirrors the high-risk, high-reward nature of gambling, and may cause significant psychological challenges to traders. As cryptocurrency trading becomes mainstream, this scoping review aims to synthesize evidence from empirical studies to understand the emotional, cognitive, and social influences on cryptocurrency traders, and identify associated mental health traits/attributes influencing their behaviors. METHODS: This review adhered to PRISMA-ScR guidelines, pooling in 13 studies involving 11,177 participants across multiple countries. A detailed literature search was conducted up to August 4, 2024, and was rerun on October 9, 2024 using databases including PubMed/Medline, Web of Science, Embase, and Scopus. Keywords used included psychiatry, psychology, mental health, cryptocurrency, trading behavior, mental health, substance use, gambling, investment, and/or emotional impact. These terms were refined through iterative searches to retrieve the most relevant studies. RESULTS: The scoping review found several key psychological factors affecting cryptocurrency trading behaviors. Many traders exhibited addiction-like behaviors, compulsively trading even when it leads to financial losses. Social media was found to have a strong influence, encouraging herd behavior and impulsive decision-making to follow trends. High levels of psychological distress, including anxiety and depression, were found to be linked to the market's volatility and risks. Overconfidence bias was observed to make traders underestimate risks and overestimate their ability to predict the market. Cognitive biases like confirmation bias and the disposition effect caused traders to hold onto losing investments and sell winning ones too early. CONCLUSION: Due to the shared psychological traits between cryptocurrency trading and gambling, it is imperative to implement targeted early interventions to mitigate the risk of its progression into a pathological condition. Tools like the Problematic Cryptocurrency Trading Scale may help identify and manage risky behaviors. Ongoing research is crucial to identify both positive and negative impact of cryptocurrency trading to develop effective support systems and regulatory policies to address traders' mental well-being.

Viraj Nadkarni, Jiachen Hu, Ranvir Rana, Chi Jin · 6 authors

No abstract is available for this record.

Bo Yang, Wenhui Tu, Sixuan Li, Fangzhou Lu

We examine how Web3-specific education and AI-generated investment guidance affect retail investor performance in crypto markets. In a twelve-month randomized controlled trial with 3,948 participants trading real tokens on a simulated CEX, investors were assigned to a control group, Web3 education, AI recommendations, or both. Measured by raw return, alpha, and portfolio diversification, both interventions improved performance, with the combined treatment producing the largest gains. Education effects accumulated over time, AI effects were immediate, and benefits were greatest for less experienced investors and on high-complexity news days. A portion of gains persisted after support was withdrawn, especially for education-based treatments, suggesting lasting benefits from knowledge acquisition alongside real-time decision support.

Zhang Xiao

Nowadays, financial markets are becoming more and more complex, and new portfolios need to be built to cope with them. This paper aims to build a Markowitz model for portfolio research based on new calibrations for nine different industries. Firstly, the weights and minimum variance combinations are calculated by using valid information such as mean, standard deviation, variance, and covariance. Second, this paper aims to maximize the return of the portfolio, diversify the investment risk of the selected portfolio, and finally determine the optimal portfolio. The portfolio can be adjusted to reduce risk or increase return by adjusting the percentage of Bitcoin. This paper further explores the portfolio using Bitcoin as a variable. This paper derives the volatility and return of the least risky portfolio to be 11.04% and -0.46%, respectively, when the portfolio is calibrated without Bitcoin, and the volatility and return of its Sharpe optimal portfolio are 14.61% and 7.11%, respectively. When the portfolio contains Bitcoin, the volatility and return of its risk-minimal portfolio are 9.45% and 0.6%, respectively, and the volatility and return of its Sharpe-optimal portfolio are 16.31% and 37.35%, respectively. Ultimately, it is concluded that Bitcoin has some risk-reducing and return-enhancing effects.

Sergen Akarsu, Neslihan Yılmaz

We examine whether disagreement in social media discussions related to financial markets affects subsequent volatility and abnormal trading volume. We also compare how traditional and digital asset markets differ by comparing stocks and Bitcoin. We show that social media disagreement is positively associated with future market volatility and abnormal trading volume in the stock market. The effect of disagreement is more pronounced at the individual stock level than at the index level. A higher level of social media disagreement also increases the probability of extremely negative stock market returns. In contrast, disagreement in Bitcoin-related social media weakly affects subsequent volatility but does not affect trading volume or extremely negative returns. Our findings also reveal that market activity impacts the disagreement in the stock market and Bitcoin communities differently.

Santiago Carbó-Valverde, Pedro J. Cuadros‐Solas, Francisco Rodríguez Fernández

Acknowledging the potential threats posed to financial stability by owning cryptoassets combined with a lack of financial literacy, this paper investigates the relationship between financial literacy and cryptocurrency ownership using machine learning methods. Analyzing 2121 survey responses, it shows that financial literacy emerges as a crucial factor in cryptocurrency ownership, even when accounting for other factors such as age, income, and digital activity. A neural network model reveals that a unit increase in financial literacy reduces the probability of cryptocurrency ownership by 0.2. Causal forest analysis indicates that financial literacy bias positively impacts ownership likelihood (a point estimate of 75.30 %). However, the bias-corrected financial literacy measure has a negative effect of −25.40 % on ownership likelihood. This reveals that cognitive biases, particularly overconfidence, as a significant influence on cryptocurrency ownership. These results show that individuals with more financial literacy and with less biased self-assessments are less likely to hold cryptocurrencies.

Alfred Lehar, Christine A. Parlour

ABSTRACT Uniswap is a system of smart contracts on the Ethereum blockchain and is the largest decentralized exchange with a liquidity balance worth up to 4 billion USD and daily trading volume of up to 7 billion USD. It is a new model of liquidity provision, so‐called automated market making. For this new market form, we characterize equilibrium in the liquidity pools. We collect all 95.8 million Uniswap interactions and compare this automated market maker (AMM) to a centralized limit order book. We document absence of long‐lived arbitrage opportunities, and show conditions under which the AMM dominates a limit order market.

Li Gao, Shi Yuan, Yi Zheng

No abstract is available for this record.

Lucas Mussoi Almeida, Marcelo Perlin, Fernanda Müller

No abstract is available for this record.

Suleiman Umar Suleiman, Kamal Tasiu Abdullahi

This study analyses the effect of cryptocurrency on the Nigerian economy. The development of crypto-currency as a means of exchange without legal backing and invisibility of the identity of operators has posed peculiar challenges, such as illicit financial flow and terrorism, amongst others, to the country. This study, therefore, sought to examine the effect of crypto-currency on the Nigerian economy. The study hinged on social exchange theory. Secondary data were obtained from the CBN statistical bulletin and Global Financial Integrity Report for a period of six years from 2015 to 2020. The data were analyzed using a simple regression model. The result shows that R is 7.9%, which means that there is a low positive relationship between crypto-currency and the level of economic development in Nigeria. It further shows an adjusted R square of -38.4 which depicts that crypto-currency has a low inverse effect on the level of economic development in Nigeria. In conclusion, the computed p-value of 0.945, which is higher than the set p-value of 0.05, shows that crypto-currency does not have a significant effect on the level of economic development in Nigeria. Hence, it is recommended that, in order to sustain economic development from the activities of crypto-currency in Nigeria, the CBN needs to ensure that laws and mechanisms are put in place to capture the activities of crypto-currency in the country adequately.

Matthias Mück, Thomas Schmidl, Julian Wolf

This study investigates the efficiency of cryptocurrency markets by examining the presence and exploitability of arbitrage opportunities. Using high-frequency data from the Binance Exchange, we implement a triangular arbitrage strategy, considering Bitcoin, Litecoin, and the U.S. Dollar. We find 4,879 possible arbitrage opportunities. Although these findings suggest potential inefficiencies, transaction costs and limited trading volumes in the order book eliminate their profitability. Consequently, centralized cryptocurrency markets exhibit a high degree of efficiency. Moreover, our results suggest that the mere number of triangular arbitrage opportunities is not a reliable indicator of market inefficiency. • Triangular arbitrage opportunities at cryptocurrency exchanges do exist. • Transaction costs, potential slippage and limited trading volumes in the order book eliminate their profitability. • Centralized cryptocurrency markets exhibit a high degree of efficiency. • The mere number of triangular arbitrage opportunities is not a reliable indicator for market efficiency.

Huy Pham, Trang Ngoc Doan Tran, Ngoc Thi Thanh Nguyen, Khoa Dang Duong

This study delves into the impact of reversals and investor attention on cryptocurrency returns before and during the COVID-19 pandemic. We employ the Two Stages Least Squares to analyze a sample of the top 20 cryptocurrencies from January 2016 to April 2021. Our results reveal that investor attention positively influences bitcoin returns in both periods, with a more pronounced effect during the pandemic. Conversely, reversals demonstrate a positive correlation with cryptocurrency returns before the outbreak but a negative relationship during the pandemic. Our robustness test further indicates that investor attention positively affects the returns of small and medium-cap cryptocurrencies, while reversals only exhibit positive consequences for small-cap cryptocurrencies. Additionally, our findings highlight stablecoins as a safe haven during the epidemic. The results suggest that investor attention has little influence on the returns of stablecoins, indicating that these coins are primarily resistant to market sentiment due to their inherent stability. The negative impact of the pandemic on the crypto market demonstrates a downward trend through each wave. Despite aligning with attention-induced price pressure and behavioral finance hypotheses, our results do not support efficient market theory or the notion of heterogeneity among investors. This research provides valuable insights for investors and policymakers in devising effective strategies for the cryptocurrency market.

Aleksander Mercik, Barbara Będowska-Sójka, Sitara Karim, Adam Zaremba

No abstract is available for this record.

Gang Chu, Xiao Li, Dehua Shen, Andrew Urquhart

No abstract is available for this record.

Stefan Scharnowski, Hossein Jahanshahloo

ABSTRACT This paper provides a first economic analysis of liquid staking tokens, which are derivatives representing a share of staked tokens in Proof‐of‐Stake blockchains. We document substantial time‐variation in the “liquid staking basis” as given by the price difference between a derivative staking token and its underlying cryptocurrency. We find evidence that staking rewards, concentration risks, limits to arbitrage, and behavioral factors influence this basis. The liquid staking basis is wider when the yields offered by the liquid staking protocol are low relative to the alternative of staking directly, when cryptocurrency returns are more volatile, and when secondary market liquidity is low. In contrast, it is smaller when investors pay more attention to liquid staking and when investor sentiment is positive. Furthermore, liquid staking tokens contribute a significant and overall growing amount to price discovery in the underlying cryptocurrencies.

Alexander Karaivanov, Shayan Zarifian

We analyse the economic determinants and dynamics of transaction fees in the Ethereum blockchain before and after two significant platform updates. The first is the August 2021 EIP-1559 ‘London’ upgrade, a switch from user-bid gas price (transaction fee per unit of complexity) to a fee model in which the gas price is the sum of an algorithmically determined base fee and an optional priority fee (tip) chosen by the user. The second update (‘the Merge’) is the switch from proof-of-work to proof-of-stake transactions validation in September 2022. We estimate the impact on Ethereum transaction fees of both demand factors (block utilization, transaction type, ETH price in USD) and algorithmic supply-side factors (the block gas limit and base fee). Using data from nearly 900 million blockchain transactions, we find that the gas price is statistically significantly positively associated with the block utilization rate. A larger share of contract call transactions or legacy (user-bid gas price) transactions is linked with higher gas prices on average. On the supply side, a higher block gas limit is statistically significantly associated with lower gas prices.

Tapas Das, Shikha Arora, Shiju Sebastian, Seshanwita Das · 6 authors

This research examines the influence of herd mentality on market volatility, with a particular emphasis on the behavioral finance principles that contribute to the volatility of cryptocurrency markets. The participation of a diverse and global group of participants in cryptocurrency markets, in contrast to traditional financial markets, frequently results in significant volatility and speculative trading. This research investigates the extent to which investors exhibit a flocking tendency, which is the propensity of individuals to follow the actions of the majority rather than relying on their own independent analysis. The research employs sentiment analysis on market data and social media activity, as well as econometric models, to quantify the extent of herding during periods of elevated market volatility. The results should enable the development of risk-mitigation strategies during these unpredictable periods and provide insight into the cognitive factors that influence decisions regarding bitcoin investments.

Kenneth Blay, Ankit Agarwal, Yuan Gao, Nicholas Savoulides

In <ext-link><bold><italic>Bitcoin and Portfolio Choice: An Assessment of Bitcoin in Multi-Asset Portfolios</italic></bold></ext-link>, from the Winter 2024 issue of <bold><italic>The Journal of Beta Investment Strategies</italic></bold>, <bold>Kenneth Blay</bold>, <bold>Ankit Agarwal</bold>, <bold>Yuan Gao</bold>, and <bold>Nicholas Savoulides</bold> of <bold>Invesco</bold> find that adding small bitcoin (BTC) allocations to stock/bond portfolios may benefit investors despite an associated risk increase. The finding is based on data from January 2014 through December 2023. The optimal BTC allocation depends on the starting portfolio’s relative allocations to stocks and bonds. The authors simulated the performance of stock/bond/BTC portfolios and found that bond-heavy allocations outperformed those with higher allocations to stocks. The authors caution that historical BTC data is limited and may not reflect the asset’s performance in the future.

Debesh Bhowmik

The chapter examines the nature of long-run nonlinear trends of the closing price of Ethereum in terms of USD, from 2015m 08 to 2023m 05 using the econometric model of the Box and Jenkins’ ( 1976 ) methodology of ARIMA (p, d, q) and Hamilton (2018) decomposition. Additionally, the forecast behaviour for 2025m 01 was computed with/without the Hamilton regression filter. The automatically selected ARIMA model of Ethereum price is convergent, and its forecast path for 2025m 01 showed insignificance with seasonal fluctuations. However, its decomposition model is cyclical, cyclically trending, and seasonally fluctuated, with forecast behaviour that is convergent, stable and significant without seasonal variation.

Mabsur Fatin Bin Hossain, Lubna Zahan Lamia, Md Mahmudur Rahman, Md. Mosaddek Khan

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.