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2,329 results ¡ page 19 of 98
Sami Ben Jabeur, Giray GĂśzgĂśr, Hichem Rezgui, Kamel Si Mohammed
No abstract is available for this record.
Goodwell Okechukwu
Purpose: This study sought to explore cryptocurrency and its role in portfolio diversification. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executiveâs time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cryptocurrency and its role in portfolio diversification. Preliminary empirical review revealed that incorporating cryptocurrencies into investment portfolios offered promising diversification benefits due to their low correlation with traditional assets, despite their high volatility and regulatory uncertainties. It highlighted the significant risk management challenges posed by cryptocurrencies' extreme price fluctuations and the evolving regulatory landscape. The study emphasized the importance of careful, limited allocation to cryptocurrencies, robust risk management practices, and continuous market monitoring. Ultimately, it suggested that cryptocurrencies could enhance portfolio performance when strategically used alongside traditional diversification methods. Unique Contribution to Theory, Practice and Policy: The Modern Portfolio Theory, Efficient Market Hypothesis and Behavioural Finance Theory may be used to anchor future studies on portfolio diversification. The study recommended a cautious yet strategic inclusion of cryptocurrencies in investment portfolios to enhance diversification, emphasizing the importance of ongoing research, robust risk management, and proactive monitoring due to their high volatility and regulatory uncertainties. It called for clear and consistent regulatory frameworks to protect investors while fostering market growth, and highlighted the need for collaboration between academia, industry, and regulatory bodies to improve financial literacy and market stability. These recommendations aimed to contribute to theoretical, practical, and policy aspects of cryptocurrency investments.
Dawei Shang, Ziyu Guo, Hui Wang
No abstract is available for this record.
Guangxi Cao, Meijun Ling, Jing-Wen Wei, Chen Chen
No abstract is available for this record.
G. Zhang, Alexander J. Sannella, Gerard Brennan, Muhammad Talha Afzal
⢠Proposing a dynamic valuation framework for fair value estimates for illiquid cryptocurrency. ⢠Discussing compliance with fair value accounting standards and cryptocurrency reporting requirements. ⢠Factoring in comparable assetsâ market information and news big data analytics that measure market participantsâ attention and sentiment in the valuation framework. ⢠Empirically testing the valuation framework with historical market data. ⢠Developing a machine learning valuation model that achieved 87 % prediction accuracy. To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participantsâ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participantsâ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.
An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
Mohamed Shaker Ahmed, Ahmed ElâMasry, Aktham Maghyereh, Satish Kumar
No abstract is available for this record.
Guishu Yang
With particular attention to variables like volatility and the performance of the U.S. stock market, this study attempts to conduct a thorough quantitative examination of the relationship between the cryptocurrency market. By using sophisticated mathematical modeling approaches, such as regression analysis and correlation methodologies, it is hoped to identify the key characteristics of these markets as well as the degree to which cryptocurrency volatility and stock market success are causally related. The use of historical data, spanning a specific time (from July 1st, 2019, to July 1st, 2023) around 4 index price-day transaction data will be made, with a focus on high-frequency data for improved accuracy. The results of this study, which examine each option's characteristics or attributes, will add to the larger body of scholarly literature on the integration of cryptocurrencies into conventional financial markets. Moreover, drawing conclusions about some effects or prospective connections between cryptocurrencies and the financial industry based on their similarity to the American stock market. To pave the way for better-informed financial decision-making, this research aims to deepen our understanding of the interactions and spillover effects between cryptocurrency volatility and the American stock market.
George Vlahavas, Athena Vakali
This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.
Vladimir Lucic, Artur Sepp
Currently, the most liquidly traded options on the crypto underlying are the so-called inverse options. An inverse option contract is quoted and traded in the units of the underlying cryptocurrency. The main economic reason for the popularity of inverse contracts in the crypto exchanges (such as Deribit) is that inverse contracts enable traders to operate without maintaining fiat cash accounts. For the theoretical part, we show that inverse options are just regular vanilla options considered under the martingale measure using the forward of the underlying as the numĂŠraire. This measure requires an adjustment to option delta. For the empirical part, we use Deribit options data of past five years to backtest delta-hedged option strategies. We introduce USD and Coin accounting of trading Profit&Loss (P&L) which is important for designing strategies in crypto options. We show empirically that USD and Coin accounting rules are equivalent when performance is measured is Coin and USD units, respectively. We establish that the risk-premia observed in options on Deribit is negative and significant so that strategies selling volatility are expected to generate positive risk-adjusted performance in the long-term.
Rilwan Sakariyahu, Rodiat Lawal, Rasheed A. Adigun, Audrey Paterson ¡ 5 authors
Recent studies document that cryptocurrencies offer an alternative store of value, medium of exchange and can be used to hedge against currency and price fluctuations. However, the frequent collapse of the crypto-market undermines its safe-haven characteristics, as investorsâ fear and anxiety could intensify market volatility and trigger a financial crisis. Motivated by the current global vicissitudes, this study examines the impact of uncertainty and sentiment factors on price behaviour of cryptocurrencies. To estimate our model, we used daily, low, high and closing price data for major crypto projects, from January 2018 to January 2023. We show that economic and political uncertainty factors significantly drive crypto prices. Furthermore, the interaction between sentiment dynamics as expressed by investors on different social platforms has a significant adverse effect on the returns of the cryptocurrency market, and the impact is more pronounced for tokens within the same ecosystem. Using the asymmetric GARCH-MIDAS model and TVP-VAR, we also demonstrate the existence of a significant contagion among tokens within the same ecosystem when bad (or good) news occurs. Considering the massive unprotected losses incurred by crypto investors during crises, our results provide important insights into how portfolio managers can effectively design investment strategies.
Ashutosh Singla
The emergence of decentralized finance (DeFi) has transformed traditional financial systems by leveraging blockchain technology to offer decentralized solutions for trading and liquidity provision. Within the CORE Chain ecosystem, the COREDAO VIP token plays a pivotal role in facilitating liquidity pooling and automated trading across various COREDAO-based tokens. This research investigates the impact of COREDAO VIP token within decentralized exchanges (DEX) such as ICECREAMSWAP, LFGSWAP, SHADOWSWAP, and ARCHERSWAP. By analyzing its integration into these platforms, the study explores how COREDAOVIP enhances liquidity management, reduces slippage, and supports automated trading strategies. Key aspects examined include the token's utility, governance implications, and its influence on trading dynamics within the COREDAO ecosystem. Through comprehensive analysis and empirical insights, this research aims to provide a nuanced understanding of COREDAOVIP token's role in advancing decentralized finance practices and its implications for future blockchain-based financial ecosystems.
Yuanli Cai, Bingqiao Luo, Qian Wang, Nuo Chen ¡ 6 authors
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the unique opportunities presented by the cryptocurrency market, noted for its on-chain data's transparency and the critical influence of off-chain signals like news, remain largely untapped by LLMs. This work aims to bridge the gap by developing an LLM-based trading agent, CryptoTrade, which uniquely combines the analysis of on-chain and off-chain data. This approach leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market. CryptoTrade incorporates a reflective mechanism specifically engineered to refine its daily trading decisions by analyzing the outcomes of prior trading decisions. This research makes two significant contributions. Firstly, it broadens the applicability of LLMs to the domain of cryptocurrency trading. Secondly, it establishes a benchmark for cryptocurrency trading strategies. Through extensive experiments, CryptoTrade has demonstrated superior performance in maximizing returns compared to traditional trading strategies and time-series baselines across various cryptocurrencies and market conditions. Our code and data are available at \url{https://anonymous.4open.science/r/CryptoTrade-Public-92FC/}.
Luca Galati, Alexander Webb, Robert I. Webb
To what extent does the collapse of a digital token spread contagion across cryptocurrency markets? How do markets incorporate information in this turbulent setting? We examine contagion effects across major digital exchanges during the collapse of the FTX exchange and its token, FTT. We find evidence of contagion across crypto exchanges. We also examine the information cascade effects of other crypto assets on FTX when nearly all withdrawals were prohibited. We find abnormal returns for major assets, indicating a flight to safety from less to more authoritative digital assets. The implications for traders, exchanges, and policymakers are discussed.
NgĂ´ ThĂĄi HĆ°ng
Decentralized finance (DeFi) is a brand-new disruptive procedure that encourages the use of blockchain technology for developing and distributing a variety of financial goods and services. This study investigates the time-varying and asymmetric interplay between DeFi and CEE stock returns, concentrated around the COVID-19 outbreak and the Russo-Ukrainian conflict. While the associations between other cryptocurrencies and conventional assets have been studied, DeFi assets have not. For this purpose, we employ the multivariate DECO-GARCH model and cross-quantilogram framework. The results reveal a positive equicorrelation between DeFi and CEE stock market returns. Notably, the influence of DeFi on CEE stock markets is greater during the COVID-19 outbreak and the Russo-Ukrainian conflict than in the other periods. Furthermore, the cross-quantilogram estimations uncover that CEE stock markets depend less on the DeFi market at longer lag lengths. This means that the diversification benefits of DeFi against CEE stock market returns are more important for long-run investment horizons. In general, our research offers a new understanding of dependence structures, which might help investors make better investment decisions and direct their trading strategies.
Bhaskar Tripathi, Rakesh Kumar Sharma
No abstract is available for this record.
Tarek Ibrahim Eldomiaty, Nada Khaled
This paper examines the effect of the riskiness of the top four cryptocurrencies on the riskiness of stock market indexes in Egypt, being recognized as a developing country. The analysis uses daily data on cryptocurrencies and the three stock market indexes covering January 2020 to January 2023. The risk is measured using the holding period Value at Risk (VaR). The GMM results show that (a) cryptocurrency volatility is negatively associated with the volatility of stock market indexes. That is, the higher the investorsâ interest in trading cryptocurrencies, the lower the volatility of stock market indexes as investors trade stocks less frequently, (b) cryptocurrencies can provide hedge and diversification benefits, and (c) the relationship between volatilities of cryptocurrencies and stock market indexes varies across indexes, therefore, contingent.
Abdulrahman Alhaidari, Bhavani Kalal, Balaji Palanisamy, Shamik Sural
Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.
Pengcheng Zhang, Kunpeng Xu, Jian Huang, Jiayin Qi
Abstract This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market, using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1, 2017 to July 1, 2022. The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods. Additionally, we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns. However, when positive investor sentiment prevails in the cryptocurrency market, the holiday effect weakens, implying that positive investor sentiment attenuates the holiday effect. Robustness tests based on the Bitcoin market generate consistent results. Moreover, this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon. These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.
Ksenija DoroslovaÄki, Nikola GradojeviÄ, Albane Tarnaud
This article develops a method for extracting information related to the underlying stock and cryptocurrency market sentiment from European put and call option prices. We study the evolution of market sentiment and predictability of prices in the S&P 500 index and Bitcoin (BTC/USD) futures markets during the 2020â2022 period. Several innovative temporal entropic and nonentropic measures of market sentiment based on a pessimistic, a market consensus, and an optimistic view are proposed in our nonlinear forecasting models. We show that these measures have significant predictive power for future spot prices at longer forecast horizons, where they statistically and economically outperform alternative models. We also find that the BTC/USD market is more susceptible to extreme sentiments reflected in demand-based shocks, while the information regarding the degree of pessimism in relation to the market consensus is more useful in forecasting the spot S&P 500 index movements in the presence of systemic shocks.
Hongyin Chen, Amit Vaisman, Ittay Eyal
Automated Market Makers (AMMs) are a cornerstone of decentralized finance. They are smart contracts (stateful programs) running on blockchains. They enable virtual token exchange: traders swap tokens with the AMM for a fee, while liquidity providers supply liquidity and receive these fees. Demand for AMMs is growing rapidly, but our experiment-based estimates show that current architectures cannot meet the projected demand by 2029. This is because the execution of existing AMMs is non-parallelizable. We present SAMM, an AMM comprising multiple shards. All shards are AMMs running on the same chain, but their independence enables parallel execution. The security of SAMM, unlike in classical sharding solutions, relies on incentive compatibility. Therefore, SAMM introduces a novel fee design. Through analysis of Subgame-Perfect Nash Equilibria (SPNE), we show that SAMM incentivizes the desired behavior: liquidity providers balance liquidity among all shards, overcoming destabilization attacks, and trades are evenly distributed. We validate our game-theoretic analysis with a simulation using real-world data. We evaluate SAMM by implementing and deploying it on local testnets of the Sui and Solana blockchains. To our knowledge, this is the first quantification of high-demand-contract performance. SAMM improves throughput by 5x and 16x, respectively, potentially more with better parallelization of the underlying blockchains. It is directly deployable, mitigating the upcoming scaling bottleneck.
Juan Lin, Peng Wang, Zhonghe Yuan
This study investigates the impact of market liquidity on Bitcoinâs diversification performance against global equity risk, using gold as a comparative benchmark. By using hourly data to construct the liquidity measures, our findings suggest that an increase in Bitcoinâs market liquidity significantly enhances Bitcoinâs effectiveness as a diversifier relative to gold.
Tian Chen, Jun Deng, Jing Nie
No abstract is available for this record.
Andrei Shynkevich
Soccer is the most popular sport in the world, and bitcoin is a global asset. Penalty shootouts represent an important stage in a soccer match and generate an enormous amount of public interest and attention. Bitcoin trading experiences a significant drop in volume and volatility during penalty shootouts at major international soccer tournaments. After the conclusion of penalty shootouts, the intensity of bitcoin trading exhibits a swift and a strong rebound. Significant fluctuations in trading volume and volatility of bitcoin around penalty shootouts are primarily driven by the variation in number of trades rather than in trade size.