Eminda Ishan De Silva, Gayithri Niluka Kuruppu, Sandun Dassanayake
Purpose The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021â2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market. Design/methodology/approach This research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods. Findings As per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market. Originality/value This study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.
The cryptocurrency market has been growing frantically in number of cryptocurrencies, online exchanges, and market capitalization, which has amplified the need for comprehensive and robust pricing models. Using a database of all eligible cryptocurrencies listed on the CoinMarketCap website, we study the relationship between returns and several potential pricing factors, such as size (market capitalization), momentum, liquidity, and maturity. The analysis was conducted from December 27, 2013, to December 29, 2020, using weekly data for 3'667 cryptocurrencies. Results point out that portfolios of cryptocurrencies with smaller market capitalization, higher reversal, lower liquidity, and lower maturity tend to offer higher returns. The 5-factor model that additionally includes illiquidity and maturity performs better than the 3-factor model previously proposed in the literature, meaning that illiquidity and maturity significantly help capture the cross-sectional cryptocurrency risk premia. The 5-factor model presented seems robust to different procedures to construct portfolios and factors.
We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.
Abstract The main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.
This paper provides the first analysis of non-fungible token (NFT) collection liquidity by applying a suite of widely used proxies that capture different dimensions of liquidity. Using transaction-level data from the OpenSea marketplace, manipulative trades are flagged and two novel methodologies for calculating liquidity are applied before performing a family of regressions to investigate its dynamics. I find that collection-specific attributes directly account for both NFT-specific liquidity idiosyncrasies and the impacts of manipulative trading. Following robustness tests, I identify that this collection-level power only exists in bull markets, similarly to real estate ZIP-code groupings. Finally, the estimated models reveal a non-linear liquidity pattern across a collectionâs lifetime, with successful collections dipping in liquidity before recovering quickly. This paper deepens our understanding of how liquidity operates at the collection level in NFTs, offering findings for liquidity researchers in non-fungible asset markets.
We provide a novel perspective on the Bitcoin market, investigating determinants of investor positions and their response to public information proxied by sentiment indicators. We distinguish between investors by size and observe their respective behaviour concerning incoming information. We find that price dynamics and media coverage lead to different decisions depending on the Bitcoin portfolio size. Retail investors react strongly to incoming public information and media narratives, with their decisions strongly influenced by sentiment and media attention. Conversely, the response of large-scale investors to such information is much weaker because they arguably have different, non-public information and divergent investment objectives.
In this paper, we analyze traders' behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patterns of trader behavior in relation to trading volume, open interest, liquidation, and leverage. Our detailed examination of DEXs, especially those utilizing the Virtual Automated Market Making (VAMM) Model, uncovers a differential impact of open interest on long versus short positions. In exchanges which operate under the Oracle Pricing Model, we find that traders primarily act as price takers, with their trading actions reflecting direct responses to price movements of the underlying assets. Furthermore, our research highlights a significant propensity among less informed traders to overreact to positive news, as demonstrated by an increase in long positions. This study contributes to the understanding of market dynamics in digital asset exchanges, offering insights into the behavioral finance for future innovation of decentralized finance.
Cryptocurrency investment approaches continue to evolve rapidly. Traditionally, cryptocurrency holders tend to actively support up to several distinct projects, focusing their selection criteria on specific project characteristics, project team and community, existing markets and liquidity levels, as well as the perception of each unique projectâs broadly understood âmission and visionâ and âfuture potential.â In this chapter, we will explore an index-based investment strategy as an alternative to the more traditional single- or oligo-asset approaches. In the index-based paradigm, multi-asset strategy involves equalization and redistribution of risk exposure across multiple, pre-vetted portfolio positions. This strategy, novel to the cryptocurrency space, also involves risk reduction through cost averaging, dilution of cyber security-related risk(s), as well as mitigation of liquidity restrictions related to individual-position market liquidity characteristics. Additional discussion of software platforms, including both custodial and non-custodial wallets, and the associated risk-benefit considerations, will also be included in this manuscript.
This article investigates the Ethereum Merge, which occurred on 15 September 2022, and we employ the time-series difference in differences (DiD) model and vector autoregression (VAR) models and analyse how the protocol change from proof-of-work to proof-of-stake (PoS) affects the dynamic relationship between cryptocurrency returns and network factors. The results show that the Merge caused a structural change between Ethereum and Bitcoin networks. The network factors of Ethereum show a significant increase compared to Bitcoin, the cointegration has been strengthened and the lag length is shortened after the Merge. The spillover effect on the Bitcoin network can be seen from both DiD and VAR, indicating the increasing impact of the Ethereum network on Bitcoin. The concern of losing the number of participants due to the implantation of PoS on cryptocurrency is not apparent on Ethereum Merge, and it increases the investorsâ attention and involvement.
W D Li, Lingfeng Bao, Jiachi Chen, John Grundy · 6 authors
The cryptocurrency market cap has experienced a great increase in recent years. However, large price fluctuations demonstrate the need for governance structures and identify whether there are market manipulations. In this article, we conduct three analysesâsocial media data analysis, blockchain data analysis, and price bubble analysisâto investigate whether market manipulation exists on Bitcoin, Ethereum, and Dogecoin platforms. Social media data analysis aims to find the reasons for price fluctuations. Blockchain data analysis is used to find detailed behavior of the manipulators. Price bubble analysis is used to investigate the relation between price fluctuation and manipulatorsâ behavior. By using the three analyses, we show that market manipulation exists on Bitcoin, Ethereum, and Dogecoin. However, market manipulation of Bitcoin is limited, and for most of Bitcoinâs price fluctuations, we found other explanations. The price for Ethereum is the most sensitive to technical updates. Technical companies/teams usually hype some new concepts (e.g., ICO, DeFi), which causes a price spike. The price of Dogecoin has a high correlation with Elon Muskâs X (formerly known as Twitter) activity, showing that influential individuals have the ability to manipulate its prices. In addition, the poor monetary liquidity of Dogecoin allows some users to manipulate its price.
This article investigates the effect of major exchange-related events on cryptocurrency markets with respect to the inclusion of cryptocurrency derivatives in major regulated exchanges. The worldâs first Ether futures launched on February 8, 2021, providing an ideal setting to investigate the price effect following its launch in the cryptocurrency market. The article also is a first attempt to explore the price reactions of cryptocurrency in the spot market to the effective trading of cryptocurrency derivatives in major regulated exchanges. Using an event-study methodology, the article shows the launch of cryptocurrency derivatives in major regulated exchanges experienced a significant increase in price on the announcement day, followed by a significant decrease in price a few months later. Such market anomalies are consistent with the stock price behaviors predicted by the index inclusion effect commonly documented in stock market literatures.
Salman Bahoo, Marco Cucculelli, Xhoana Goga, Jasmine Mondolo
Abstract Over the past two decades, artificial intelligence (AI) has experienced rapid development and is being used in a wide range of sectors and activities, including finance. In the meantime, a growing and heterogeneous strand of literature has explored the use of AI in finance. The aim of this study is to provide a comprehensive overview of the existing research on this topic and to identify which research directions need further investigation. Accordingly, using the tools of bibliometric analysis and content analysis, we examined a large number of articles published between 1992 and March 2021. We find that the literature on this topic has expanded considerably since the beginning of the XXI century, covering a variety of countries and different AI applications in finance, amongst which Predictive/forecasting systems, Classification/detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Furthermore, we show that the selected articles fall into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk and default evaluation, cryptocurrencies, derivatives, credit risk in banks, investor sentiment analysis and foreign exchange management, respectively. Future research should seek to address the partially unanswered research questions and improve our understanding of the impact of recent disruptive technological developments on finance.
Purpose The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns. Design/methodology/approach The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models. Findings This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors. Originality/value The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.
Marisa R. Ferreira, Francisco J. Silva, Gualter Couto
Volatility in the cryptocurrency market is an extremely important indicator for investors, as it allows them to manage their investment risk and define strategies that will result in profit maximization. Thus, this study focuses on determine which of the GARCH, EGARCH, and TGARCH models is the optimal model that best describes the daily returnsâ volatility of MATIC, SOL, BTT, and VET â four cryptocurrencies with relatively limited presence in the market compared to Bitcoin. The optimal model is selected by using the AIC statistical quality criterion. For the chosen sample period, empirical evidence suggests that EGARCH(1,1) and GARCH(1,1) are the most suitable models for describing the returnsâ volatility of MATIC and VET, respectively. In the cases of SOL and BTT, the lack of success in validating all the assumptions needed to apply GARCH models reveals that these models are not the most adequate for describing the cryptocurrencies under study.
This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception.
We study the temporal evolution of the holding-time distribution of bitcoins and find that the average distribution of holding-time is a heavy-tailed power law extending from one day to over at least $200$ weeks with an exponent approximately equal to $0.9$, indicating very long memory effects. We also report significant sample-to-sample variations of the distribution of holding times, which can be best characterized as multiscaling, with power-law exponents varying between $0.3$ and $2.5$ depending on bitcoin price regimes. We document significant differences between the distributions of book-to-market and of realized returns, showing that traders obtain far from optimal performance. We also report strong direct qualitative and quantitative evidence of the disposition effect in the Bitcoin Blockchain data. Defining age-dependent transaction flows as the fraction of bitcoins that are traded at a given time and that were born (last traded) at some specific earlier time, we document that the time-averaged transaction flow fraction has a power law dependence as a function of age, with an exponent close to $-1.5$, a value compatible with priority queuing theory. We document the existence of multifractality on the measure defined as the normalized number of bitcoins exchanged at a given time.
Purpose The emergence of cryptocurrencies has tremendously changed the way of financial transactions around the world which has led to form distinct discussions in the field regarding its reliability. This paper aims to evaluate the published literatures on cryptocurrency identifying its growth, citation, prolific authors, journals, countries, active funding agencies, collaboration pattern and emerging research hotspots in the area. Design/methodology/approach Scientometrics and Altmetrics parameters have been incorporated in the study. Literatures covered from the Scopus database searching within âArticle Title, Abstract, Keywordsâ with keywords âcryptocurrencyâ OR âdigital currencyâ OR âbitcoinâ OR âEthereumâ by limiting the time range of 2013â2022, English language and journal articles only. Total 6,107 documents have been identified. The further analysis and visualisation is performed using MSExcel, VOSviewer, Biblioshiny and Tableau. Another tool, Dimension.ai is used to identify the Altmetric Attention Score. Findings The findings reveal that the growth of research and citation rate hiked from the year 2017 till now. Elie Bouri is the top contributor, IEEE Access is the most prolific journal, China being the prolific country. Topics like Blockchain, Bitcoin, Ethereum, smart contracts, financial markets are emerging researched hotspots. The reliability of crypto market is still not clear because of its high volatility. The findings of the study will be more useful in the academia, subject specialists, research institutions, funding agencies, publishing agencies in decision-making. Originality/value To the best of the authorsâ knowledge, there is no such study found considering both Scientometrics and Altmetrics approaches on cryptocurrency research with the selected time bound.
Abstract We explore the impact of investorsâ beliefs on cryptocurrency demand and prices using new individual-level survey data and a structural characteristics-based demand model with differentiated cryptocurrencies and heterogeneous investors. We show that younger individuals with lower incomes are more optimistic about the future value of cryptocurrencies, as are late investors. We identify the model combining observable beliefs with an instrumental variable strategy that exploits variation in the production of different cryptocurrencies. Counterfactual analyses quantify the impact on portfolio allocations and equilibrium prices of (i) (regulating) entry of late optimistic investors, and (ii) growing concerns among investors about the sustainability of energy-intensive proof-of-work cryptocurrencies. (JEL: D84, G11, G41)