Blockchain Papers

Hemang Subramanian, Patricia Angle, Florent Rouxelin, Ziyang Zhang

No abstract is available for this record.

Boxiang Jia, Dehua Shen, Wei Zhang

No abstract is available for this record.

JunHyeong Jin, JiHoon Jung, Kyojik Song

The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.

Haolan Cheng

On 16th March 2022, U.S. Federal Reserve increased the interest rate the first time, and in the whole year, U.S. Federal Reserve made seven increments on interest rate. As this will affect the value of dollar, many American financial assets were also affected by it, including ETH, one of the most famous cryptocurrencies. This paper uses the history data of ETH price from January 2018 to July 2023 and constructs ARIMA model without Federal Reserve increasing the interest rate to compare with the reality in order to comprehend how the increasing interest rate affected the price of ETH, and use the model to predict the trend of Ethereum’s price. With the influence of increasing interest rate, the price of Ethereum should decrease. However, after the U.S. Federal Reserve increased interest rate, the price of Ethereum went up for a while then dropped dramatically. And the reasons why this delay appears are the delay of policy and the first increment of interest rate is not attractive enough for investors to change their strategies. That can bring some inspirations to policymakers. They should acknowledge that there will be a delay in the market after the policy is released and they could give some potential signs or preferences on the new policy to reduce the shock to market. For investors, they could pay more attention on relevant policy and make use of the delay to make more money.

Nuttapong Tungdajahirun, Woratat Makasiranondh, Papangkorn Pidchayathanakorn, Parkpoom Chaisiriprasert · 8 authors

The rapid growth and unique advantages of cryptocurrencies have made them an attractive asset for investment portfolios. However, investing in cryptocurrencies comes with risks, especially market volatility. This paper explores the benefits of Bitcoin’s market dominance, including liquidity, stability, and practical utility. It also reviews multiple previous articles about using Artificial Intelligence (AI) for cryptocurrency trading to enhance the feasibility and profitability of cryptocurrency investments, focusing specifically on Bitcoin. It further discusses the potential advantages of using AI in developing predictive models for market trends and managing grid trading strategies to stabilize returns and lower risks. While research on the application of grid trading strategy with AI in the cryptocurrency market is limited, existing studies in other asset classes demonstrate the effectiveness of AI-based methods. Finally, this paper outlines novel potential future research that aims to integrate AI with the Grid Trading Strategy. This integration seeks to enhance the sustainability of cryptocurrency investments while simultaneously amplifying their profitability.

Jasmine Sabeena, Pinki Sagar

Cryptocurrency markets' extreme volatility demands advanced predictive models. The proposed neural network approach utilizes extensive research and real-image exchange data, revealing the Digital Internet of Things' impact. Addressing consumer influence on prices is vital. Our frame working corporate “doesn't make sense. It should probably be “Our framework incorporates. At the core is an enhanced deep learning framework, integrating autoregressive integrated moving average (ARIMA) with convolutional neural networks (CNNs). This synergy captures intricate price patterns. We integrate sentiment analysis from various sources and block chain data for a holistic market view. Model robustness is bolstered with hyper parameter optimization and cross-validation. Real-time data integration ensures timely predictions. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics are used in performance evaluation. Empirical evidence high lights our model's superiority in predicting cryptocurrency market variations. Compared to traditional methods like ARIMA, it offers substantial improvements, benefiting traders, investors, and decision-makers. Future enhancements include ensemble models, hyper parameter tuning, advanced deep learning, realtime data integration, and model interpretability, empowering stakeholders with precise insights into evolving cryptocurrency markets.

Adithya Bhaskara, Rafael Frongillo, Lindgren, Elias, Maneesha Papireddygari

Liquidity provisioning in automated market makers is the practice of recruiting third-party liquidity providers (LPs) to contribute assets to the market in exchange for fees skimmed off of trades. This paper introduces a general framework for liquidity provisioning in cost function prediction markets. Our most general protocol allows LPs to submit or update an arbitrary cost function that specifies their liquidity over the entire price space. We show that our protocol encapsulates several notions of running market makers in parallel, which we prove to be equivalent. We also recover existing protocols from decentralized finance as special cases. In our protocol, liquidity can be expressed as a matrix-valued function, which we argue is necessary with three or more securities. Due to this inherent multidimensionality, the design of trading fees with three or more securities is nontrivial: we show that natural axioms on the design of these fees are incompatible.

Kushal Babel, Mojan Javaheripi, Yan Ji, Mahimna Kelkar · 6 authors

We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts. Lanturn discovers strategies comprising of concrete transactions for extracting economic value from smart contracts interacting with a particular transaction environment. We formulate the strategy discovery as a black-box optimization problem and leverage a novel adaptive learning-based algorithm to address it.

Barkha Dhingra, Shallu Batra, Vaibhav Aggarwal, Mahender Yadav · 5 authors

Purpose The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility. Design/methodology/approach This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area. Findings The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area. Originality/value This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

Tianyi Ma

This article examines whether cryptocurrency (crypto) hedge funds successfully time the bitcoin market. The author uses a joint market-timing model to assess the bitcoin market return– and volatility–timing skills of crypto hedge fund managers. For a one-month holding period, the difference in the out-of-sample alpha between the top timers and bottom timers is 11.832% per month. The analysis shows that younger funds with shorter lockup periods tend to have stronger bitcoin market return–timing skills. Moreover, funds with lower redemption periods tend to exhibit stronger bitcoin market volatility–timing skills. The author also observes that crypto hedge funds demonstrate stronger bitcoin market–timing skills during market downturns, likely because of bitcoin being a safe-haven asset in contrast to traditional stock market investments. The findings are important for private investors who are considering crypto hedge funds as an alternative investment.

Sherin M. Omran, Wessam H. El-Behaidy, Aliaa A. A. Youssif

A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.

Roman Kräussl, Alessandro Tugnetti

Abstract This paper provides a review of the development of the non‐fungible tokens (NFTs) market, with a particular focus on its pricing determinants, its current applications, and its future opportunities. We investigate the current state of the NFT markets and highlight the perception and expectations of investors toward these products. We summarize and compare the financial and econometric models that have been used in the literature for the pricing of non‐fungible tokens with a special focus on their predictive performance. We design a framework that can help to understand the price formation of NFTs. We further aim to shed light on the value‐creating determinants of NFTs in order to better understand investors’ behavior on the blockchain.

Annisa Permatasari, Wahyudi Widodo

Within the realm of investment, investors are presented with a multitude of options in terms of investment vehicles. Three options that are experiencing growing popularity include equities, collective investment schemes, and digital currencies. The literature search was conducted across multiple databases, including PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar, to explore the comparative aspects of stock investments, mutual funds, and cryptocurrencies. Stocks are a type of investment that symbolize ownership in a corporation, offering the possibility of long-term earnings through the firm's expansion and delivery of dividends. Stocks exhibit diverse levels of risk contingent upon the specific firm and industry and are typically more suitable for investors with long-term objectives. Mutual funds are financial instruments that aggregate capital from multiple investors and allocate it into a diversified collection of investments. This service offers automated diversification and is ideal for investors seeking to entrust the management of their portfolio to professionals. Mutual funds are appropriate for both short-term and long-term objectives. Cryptocurrencies are virtual assets that are bought and sold on cryptocurrency exchanges. Cryptocurrencies exhibit a high degree of speculation and volatility, characterized by swift and unpredictable price movements. Investing in cryptocurrencies necessitates a comprehensive comprehension of technical aspects and entails substantial risks, typically regarded as a short-term or speculative investment.

Christian Fieberg, Gerrit Liedtke, Daniel Metko, Adam Zaremba

Is there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the cross-section of cryptocurrency returns. We document a discernible pattern in factor premia: past winners consistently outperform losers. The effect persists across subperiods, withstands various methodological approaches, and its magnitude parallels that of its stock market counterpart. However, the autocorrelation in factor returns is not widespread and primarily stems from size and volatility anomalies. Additionally, unlike in stocks, cryptocurrency factor momentum originates from price momentum, which subsequently transfers to the factor level.

Ahmed W. Elroukh

Purpose This paper aims to investigate the impact of banning cryptocurrencies on stock markets. Design/methodology/approach The paper uses an event study approach and data from stock market indices in nine countries that imposed a ban. It uses the constant mean model and the market model, with two different benchmarks for global returns, to analyze if any of the stock indices show abnormal returns on or around the announcement of a cryptocurrency ban. Findings The analysis shows that banning cryptocurrencies did not affect the returns of stock markets in any of the countries studied, indicating that the cryptocurrency market and stock markets are decoupled from each other, or the ban was not effectively implemented. Originality/value To the best of the author’s knowledge, this paper is the first to explore the potential spillover effect of a cryptocurrency ban on stock markets. It also bridges two strands of literature: the relationship between cryptocurrencies and traditional assets, and the impact of cryptocurrency regulation on their returns.

Bin-xia Chen, Yan-Lin Sun

No abstract is available for this record.

Pengcheng Zhang, Deli Kong, Kunpeng Xu, Jiayin Qi

No abstract is available for this record.

Rui Dias, Mariana Chambino, Cristina Palma, Liliana Almeida · 5 authors

This chapter aims to analyze the price efficiency of Bitcoin (BTC), DASH, EOS, Ethereum (ETH), LISK, Litecoin (LTC), Monero, NEO, QUANTUM, RIPPLE, STELLAR, and ZCASH in their weak form between March 1, 2018 and March 1, 2023 and determine whether they experience overreactions. The results show that cryptocurrencies exhibit positive and negative autocorrelations, which can reduce volatility and moderate price fluctuations. The results also show persistence in cryptocurrency returns, suggesting long-term trends or market patterns that individual and institutional investors can exploit. It is essential to recognize that cryptocurrencies are characterized by a high degree of complexity and instability. Investors need to monitor market trends and make the necessary adjustments to their investment strategies to anticipate market changes.

Authors unavailable

No abstract is available for this record.

Kawal Gill, Harish Kumar, Amit Kumar Singh

Purpose : This study focused on evaluating the efficiency of the global cryptocurrency market under the efficient market hypothesis.Methodology : The study explored the presence of calendar effects in the form of the day-of-the-week and the weekend effect in the top 25 global cryptocurrencies and a constructed index using autocorrelation corrected ARCH family models on the log-returns of prices. Non-normality is tackled through the use of a non-parametric bootstrapped approach in addition to parametric estimation. Additionally, rolling window regression is employed as a dynamic framework.Findings : The findings of this study showed the presence of calendar effects in terms of higher returns for certain days over others, thereby concluding that the global cryptocurrency market as a whole was not efficient.Practical Implications : As a result of its informational inadequacies, it was implied that cryptocurrency markets were not even efficient in the weak form. This had practical implications for investors, whose aggressive search and trading tactics appear warranted in the context of market anomalies, as well as other market players, like regulators attempting to comprehend the structure of the cryptocurrency market from an efficiency standpoint and researchers attempting to investigate the efficiency-related behavior and psychology of crypto markets.Originality : This study is exceptional in that it uses a sample of cryptocurrencies that objectively covers the larger global cryptocurrency markets over the most extended amount of time possible, making it unique both for the participants in the crypto markets and for its contribution to the literature on market efficiency. The study employed an evolutionary methodology, taking into account the time-varying behavior of financial assets. As far as we can tell, this is one of the first studies to use this methodology and model formulation.

Terdthiti Chitkasame, Pichayakone Rakpho, Nachattapong Kaewsompong

No abstract is available for this record.

Xi Deng, Huiming Zhu, S X Li, Zishan Huang · 5 authors

This study measures time-frequency liquidity and investigates the quantile connectedness of cryptocurrencies, decentralized finance, and non-fungible tokens (NFTs). The empirical results reveal that high-liquidity cryptocurrencies and yield-farming tokens are the main net spillover transmitters of low-liquidity cryptocurrencies’ downside networks in the short term. In addition, the connectedness between high-liquidity cryptocurrencies and yield-farming tokens significantly increases in the long-term upside network. Finally, NFTs exhibit substantial risk-bearing abilities.

An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane

We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investors’ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrencies’ connectedness.

Mingjun Guo, Shouyang Wang, Yunjie Wei

This paper analyzes asset bubbles in the non-fungible token (NFT) and cryptocurrency markets, and assesses the impact of cryptocurrency prices and market sentiment on NFT bubble formation. We employ the Generalized Unit Root Test (GSADF test) to examine price bubbles across the NFT market, including sub-markets and related projects, as well as seven major cryptocurrencies. Our research identifies cryptocurrency bubbles in three distinct periods, with deflation observed in 2022. We establish a strong positive correlation between NFTs and most cryptocurrencies in terms of bubble dynamics. Notably, market sentiment indicators have varying effects on NFT bubbles; the VIX index has a positive impact, while the GEPU index and Google Trends data have negative effects. These findings provide valuable insights for regulators and investors into NFT bubble dynamics, cryptocurrency price behaviour, and market sentiment, facilitating informed decision-making.