Esther Cabezas-Rivas, Felipe SĂĄnchez, Isaac Tormo-Xaixo
The aim of this paper is to analyse the Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75\% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and futures contracts, treasury bonds, stock indices, growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of the Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale.
With the rapid development of cryptocurrencies, the volatility characteristics of their yields have received more and more attention. At the same time, many empirical studies show that the GARCH family model is more effective in describing the volatility of financial time series. Firstly, this paper briefly introduces the research background of cryptocurrency and the research method using GARCH model. Next, the daily rate of return is calculated and descriptive statistical analysis is carried out on the collected closing price data of cryptocurrency, and on this basis, the GARCH model is constructed for empirical test to explore the volatility characteristics of its rate of return. Then the corresponding research conclusions and relevant policy recommendations are given.
Abna Ajeesh, Lekshmi Prakash, Mohammad Ali Moni, V. Sreeraj
This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or âleverage effectâ showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curveâs leftward skew shows the marketâs increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a âlong memoryâ in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible and trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on Bitcoin (BTC) exchange rates against the U.S. dollar (USD) in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTCâUSD relative to other exchange rate pairs as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent arbitrage frictions associated with short-sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTCâUSD futures was beneficial to the Bitcoin spot market by making the underlying prices more informative. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: The authors acknowledge financial support from the Global Risk Institute. P. Augustin acknowledges financial support from the Canadian Derivatives Institute and from the Canada Research Chair Program of the Social Sciences and Humanities Research Council Canada. The paper has benefited significantly from a fellow visit of P. Augustin at the Center for Advanced Studies Foundations of Law and Finance funded by the German Research Foundation, project FOR 2774, and from a visiting position of P. Augustin at the finance department of the University of Luxembourg facilitated through the Inter Mobility Programme of the Luxembourg National Research Fund. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4900 .
Relatively narcissistic people are attracted to cryptocurrencies, though it is unclear whether they are differentially attracted to cryptocurrencies over other investments. Furthermore, theoretically, only narcissistic admiration, rather than narcissistic rivalry, would be expected to associate with attitudes toward cryptocurrencies. Intelligence and financial literacy are also proposed individual difference predictors of attitudes toward investments. Consequently, we administered measures of narcissistic admiration and rivalry, a financial literacy test, and a battery of intelligence tests to a sample of young adults (N = 372). Based on a structural equation model, narcissistic admiration and narcissistic rivalry differentially predicted attitudes toward cryptocurrencies (admiration, positively; rivalry, negatively), but both failed to associate significantly with attitudes toward stocks. Furthermore, financial literacy was a unique, positive predictor of attitudes toward stocks, whereas intelligence was a unique, negative predictor of attitudes toward cryptocurrencies. Our findings support the notion that narcissism is differentially associated with attitudes toward cryptocurrencies, though only narcissistic admiration (positively), consistent with the hypersensitivities to reward theory. Finally, higher levels of intelligence, controlling for financial literacy and narcissism, associated negatively with attitudes toward cryptocurrencies, perhaps due to the influence of scepticism.
Muhammad Mahmudul Karim, Md Hakim Ali, Larisa Yarovaya, Md Hamid Uddin ¡ 5 authors
Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.
Abstract In recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurrencies based on different neural networks such as long shortâterm memory, convolutional neural network and attention mechanism. Using an ensemble of three preâtrained language models, we extracted sentiment of cryptocurrencyârelated tweets posted between 1 January 2021 and 31 December 2021. We constructed 20 different versions of our model and evaluated their performance on data of 27 most traded cryptocurrencies using a history of previous days' sentiment data along with close prices as input data. The flexible input layer of our model enables different ways of feeding data into the model to adjust it for different cryptocurrencies to obtain better predictions. Our analysis revealed several important findings. We showed that longer sequences of input data achieve most accurate predictions on average. More specifically, using a history of 14â and 21âdays' data results in lowest RMSE values on average compared to using a history of 7 days. However, there is no significant difference between the results related to the input sequences with lengths of 14 and 21. In addition, our findings suggest that sentiment data can be useful in predicting prices for more than 70% of the studied cryptocurrencies. Thus, peoples' emotions, opinions, and sentiment that are expressed through their posts on Twitter platform play a significant role in prediction of cryptocurrencies' prices.
The study aims to investigate the causality relationship between investor happiness and cryptocurrency returns. The study is focused on the five largest cryptocurrencies, specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP), and Cardano (ADA). Twitter-based Happiness Index is used to measure investor happiness. The sample period covers the period between January 1, 2019, and October 2, 2021. The Zivot-Andrews test is employed to detect stationary of covariates. After ensuring that all variables are stationary at levels, the Granger causality test is adopted to understand the relationship between the happiness index and cryptocurrency returns. The impulse-response functions are illustrated. The results indicate that there is a uni-directional relationship from BTC to Happiness Index, and Happiness Index to ETH. Considering that the causal relationship between cryptocurrency returns and investor happiness differs between cryptocurrencies, it is thought that investors should closely monitor the happiness index and make adjustments in their portfolios in response to changes in investor happiness.
The emergence of non-fungible tokens (NFTs) has created a new market with significant implications for stakeholders, particularly in industries such as art, fashion, gaming, and real-world assets, leading to challenges in finance, financial pricing, financial management, risk management, and cryptocurrency issues. This research paper adopts a quantitative approach to provide a comprehensive analysis of the challenges associated with NFTs, including their impact on the art market, risks related to ownership rights, and proper financial statement treatment. Additionally, the paper examines the challenges of accounting for NFTs under the International Financial Reporting Standards (IFRS), including valuation, tax treatment, and accounting considerations. The use of artificial intelligence (AI) in creating, verifying, and authenticating NFTs, as well as detecting potential fraud and valuing them in the market, is also discussed. Finally, the paper provides recommendations for companies and accounting professionals on addressing the challenges associated with NFTs under IFRS. The research contributes to the ongoing debate on the best practices for NFT accounting, the evolving nature of digital assets, and the role of AI in this emerging market.
OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.
The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading.
Abstract Security token offerings (STOs) are a new means for ventures to raise funding, where digital tokens are issued as regulated investment products on the blockchain. We study market outcomes in the primary and secondary markets for security tokens and examine the associated determinants in the context of signaling theory. We analyze success determinants of 138 STOs and find that a pre-sale and the announcement of token transferability are positively related to the funding success and serve as positive quality signals for investors to overcome information asymmetries. We examine 108 security tokens traded on centralized and decentralized exchanges related to the rapidly evolving area of decentralized finance. There is hardly any underpricing in the market, and it is positively associated with the crypto market sentiment as an external signal. When traded on the secondary market, security tokens generate both extremely positive and negative returns for various short-term time horizons. We disentangle the liquidity situation in the market between centralized and decentralized exchanges and find that decentralized marketplaces are less liquid and offer lower barriers to entry, indicating slow market completion.
Using the capital asset pricing model, this article critically assesses the relative importance of computing ârealizedâ betas from high-frequency returns for Bitcoin and Ethereumâthe two major cryptocurrenciesâagainst their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the âpureâ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ârealizedâ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.
Purpose This study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques. Design/methodology/approach This work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the DieboldâYilmaz (DY) method (Diebold and YÄąlmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios. Findings This study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs. Research limitations/implications This study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al ., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk. Originality/value The contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.
Cryptocurrencies are significant improvements in the digital age that have changed the way we think about money. The first cryptocurrency was Bitcoin, introduced in 2009 and was created by Nakamoto. Due to their potential ups and downs, many people now think that cryptocurrencies are appropriate for use as an investment instrument, especially millennials who are attracted to higher-risk investment alternatives. A number of different investing options such as cryptocurrencies, gold, and other conventional assets like equities and bonds have unique characteristics and advantages. Itâs essential for investors to understand the similarities and differences between cryptocurrencies and other assets in order to create diversified portfolios. In this study, the optimum portfolio will be constructed using Bitcoin, Gold, LQ45 Index, and ABF IBI as the representative of Indonesia Bond Index. Mean-Variance Optimization will be used as an asset allocation method, and will be compared to the other methods such as Risk Parity, 60/40 Portfolio, and Equally Weighted to find a better risk-adjusted return. The Sharpe ratio analysis is used to evaluate the portfolio performance resulting from every method. The investment strategy will be simulated to know which strategy will result the best total return in the end of simulation period. According to risk, return, and the Sharpe ratio, Bitcoin could perform better than gold, LQ45, and ABF IBI. Furthermore, the Mean-Variance Optimization resulted the highest Sharpe ratio compared to the other methods. The optimal weight from the portfolio construction using Mean-Variance Optimization allocated 53% to ABFI index, 40% to Bitcoin, and 7% to gold, which resulted 48.2% portfolio return, 40.44% portfolio risk, and 1077.8% Sharpe ratio. From the investment strategy simulation, the quarterly rebalancing strategy was found to be the best strategy with the total return 223.36%.
Abstract Bitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intraâday highâfrequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intraâday conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intraâday volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive backâtesting results show absolute returns of 1500%â8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.
Sharda Kumari, Vipin Kumar, A. Sharmila, C. Ravindra Murthy ¡ 6 authors
Cryptocurrencies have experienced rapid growth and gained popularity worldwide. As a result, social media platforms have become an important source of information for investors and traders seeking to make decisions regarding cryptocurrencies. However, due to the sheer volume of data and the lack of effective analysis techniques, it can be challenging to identify key patterns and trends within social media forums. In this research paper, a blockchain based methodology is proposed for analysis of social media forums for cryptocurrency phase shifts. Specifically, we use sentiment analysis and topic modeling techniques to identify key themes and trends within cryptocurrency social media forums, and then use blockchain technology to verify and authenticate the data. Our results demonstrate that our approach is effective in identifying phase shifts in cryptocurrency markets. In recent years, social media forums have become a hub for discussions on cryptocurrency. The vast amount of data generated in these forums can be analyzed to predict the price shifts of different cryptocurrencies. However, traditional analysis methods may not be efficient enough to extract relevant information from this vast data. Therefore, this research study proposes a blockchain-based analysis approach to social media forums for cryptocurrency phase shifts. The proposed approach uses blockchain to ensure data immutability, and smart contracts to execute the analysis. The results obtained from the analysis can be used for prediction of price shifts of different crypto-currencies.
Hilda Hadan, Leah Zhang-Kennedy, Lennart E. Nacke, Ville Mäkelä
With the increasing popularity of cryptocurrency, many people are interested in cryptocurrency investments, but have so far hesitated. Many others have made investments without adequate preparation. To help interested investors improve their understanding of cryptocurrency and make rational investment decisions, it is important to study their concerns and motivations and to draw upon experienced investorsâ experiences and practices. Therefore, we surveyed crypto investors and inexperienced potential investors interested in trading cryptocurrency (n = 395). Our results showed that extreme price volatility is the primary incentive and a substantial obstacle to market participation. Fraud risks, lack of personal funds, insufficient knowledge, and difficulty identifying credible information sources are also common barriers. Our findings highlight the need to build trustworthy exchange platforms and integrate educational features. Based on the reported concerns and experiences, we (1) identify learning components for new investors, and (2) formulate design recommendations for beginner-friendly exchange platforms.
Purpose This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail. Design/methodology/approach This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary. Findings The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin. Practical implications The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks. Originality/value The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.