Alexey Mikhaylov, Hasan Dınçer, Serhat Yüksel, Gábor Pintér · 5 authors
Investors are looking for objects in which they invest their funds successfully, evaluating the effectiveness of alternative markets and their instruments. Historically, cash flow indicators most effectively reflected the mood of the masses in relation to any financial asset, both in the short-and long-term. This article examines in detail the queue of already completed, but not confirmed transactions in the bitcoin network. The mempool is able to timely display the growth in the number of transactions awaiting confirmation, which makes it a leading indicator of future cash flows that could affect the trading volumes and market prices of bitcoin. This study evaluates bitcoin mempool priorities and two different analyses have been conducted for this purpose. Firstly, the mempool periods are examined through a statistical analysis. Secondly, the performance determinants of mempool are assessed with q-ROF Multi-SWARA. In addition to q-ROF sets, weights are computed with IFS and PFS. Demonstrated here is that the results of all fuzzy sets are identical. This outcome explains the reliability of the findings and they indicate that a transaction is the most important determinant of the bitcoin mempool. It emerged that the adjusted mempool data (+16.7%) for 7-day and 30-day moving averages was able, with a time lag of 24–48 h, to indicate significant volatility of future bitcoin trading volumes (+1.6%) on average. The obtained values confirm the empirical conclusion reached here that the mempool growth leads to cash flow growth. An increase in future cash flows results in a substantial rise in future trading volumes. The key takeaway from the analysis is that mempool is able to effectively predict future increases in trading volumes based on the prior cash flow growth projected into mempool growth. However, as a price indicator, mempool does show mixed results with mostly uncertainty in the direction of price movement.
In this article, we discuss the central banks’ attitude to cryptocurrencies and focus more on European Central Bank. First, based on the analysis of scientific literature, we show that cryptocurrency is money and performs all of the functions of money, such as the exchange medium, value storage, and accounts unit. We found a positive correlation between the level of economic development of a country and the level of regulation and the integration of crypto cryptocurrencies into the economic system. We discuss not only the approach of the ECB to cryptocurrencies, but also how it developed. According to the study data, the ECB only began to respond to cryptocurrency as an equivalent monetary instrument in 2021, established regulatory mechanisms, and developed the digital euro project.
Abstract This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index, the study employs linear and nonlinear cointegration tests, a correlation coefficient measure, and fits different copula families, respectively. The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
The concept of digital cash has the potential to completely change how people think about money. Digital currency has emerged as a possible alternative for exchanging currency and traditional payment systems, in addition to a popular investment option due to its potential for high returns. One of the three main varieties of digital currency is cryptocurrency that is secured by blockchain technology. Bitcoin, Ethereum, and many other cryptocurrencies exist in crypto markets. Investing in cryptocurrencies still carries risks and uncertainties due to the price volatility. It is thus important to approach such investments with caution and thoroughly research the market and its risks before making investment decisions. This paper presents an application of AI technology for learning the price movement of Ethereum (ETH) which is second only to Bitcoin in market capitalization. Based on the Technical factor, the XGBoost model is constructed for classification of return on Ethereum close price. The technical indicators such as moving averages and relative strength index, together with the Bitcoin price trend are chosen to determine influence on Ethereum price further used for computing the short-term return separate into 3 classes: downtrend, sideway, and uptrend. The model performance is measured by multiclass ROC-AUC, achieving the micro-average ROC-AUC of 0.66 saying the model is reasonably good at predicting the overall trend of ETH price.
Radhakrishna Dodmane, K. R. Raghunandan, Krishnaraj Rao N S, Bhavya Kallapu · 7 authors
The advancements in communication speeds have enabled the centralized financial market to be faster and more complex than ever. The speed of the order execution has become exponentially faster when compared to the early days of electronic markets. Though the transaction speed has increased, the underlying architecture or models behind the markets have remained the same. These models come with their own disadvantages. The disadvantages are usually faced by non-institutional or small traders. The bigger players, such as financial institutions, have an advantage over smaller players because of factors such as information asymmetry and access to better infrastructure, which give them an advantage in terms of the speed of execution. This makes the centralized stock market an uneven playing field. This paper discusses the limitations of centralized financial markets, particularly the disadvantage faced by non-institutional or small traders due to information asymmetry and better infrastructure access by financial institutions. The authors propose the usage of blockchain technology and the data highway protocol to create a decentralized stock exchange that can potentially eliminate these disadvantages. The data highway protocol is used to generate new blocks with a flexible finality condition that allows for the consensus mechanism to configure security thresholds more freely. The proposed framework is compared with existing frameworks to confirm its effectiveness and identify areas that require improvement. The evaluation of the proposed approach showed that the improved highway protocol boosted the transaction rate compared to the other two mechanisms (PoS and PoW). Specifically, the transaction rate of the proposed model was found to be 2.2 times higher than that of PoS and 12 times higher than that of the PoW consensus model.
M. Karthik, Vethath Suryakumar. R, S Syedjaffar., Dharneesh. M.E
Abstract: Our project aim is to predict the future price of the bitcoin using machine learning algorithms. In the modern world cryptocurrency is become more trendy and reaches the youngsters to invest in the stock market and to generate some profitable trades. To invest their money in cryptocurrency we are just helping the investors like peoples and also involving the organization to invest in the bitcoin and to make good profitable trades. Initially it utilizes the historical data to predict the future price of the bitcoin. It involves considering factors such as market sentiment, news and events ,technical analysis, and global economic trends. Different machine learning algorithms are applied on the a data and the accuracy is compared to see which algorithm performed better. It includes the performance metrics like precision, recall scores are also taken into consideration for evaluating the model In cryptocurrency market it contains 'n' number of coins .Among those coins we can take any coin to predict the future price which will able to help the investors who are all investing their money in cryptocurrency market.
Stanisław Drożdż, Jarosław Kwapień, Marcin Wątorek
In relation to the traditional financial markets, the cryptocurrency market is a recent invention and the trading dynamics of all its components are readily recorded and stored. This fact opens up a unique opportunity to follow the multidimensional trajectory of its development since inception up to the present time. Several main characteristics commonly recognized as financial stylized facts of mature markets were quantitatively studied here. In particular, it is shown that the return distributions, volatility clustering effects, and even temporal multifractal correlations for a few highest-capitalization cryptocurrencies largely follow those of the well-established financial markets. The smaller cryptocurrencies are somewhat deficient in this regard, however. They are also not as highly cross-correlated among themselves and with other financial markets as the large cryptocurrencies. Quite generally, the volume V impact on price changes R appears to be much stronger on the cryptocurrency market than in the mature stock markets, and scales as $R(V) \sim V^α$ with $α\gtrsim 1$.
We present a wavelet analysis of retail investor attention and the daily returns of Bitcoin, Ethereum, and Litecoin at five selected crypto exchanges that identifies the fractal dynamics of the short- and long-term persistent processes. The investors’ attention is proxied by the Search Volume Index provided by Google at daily frequency. We detect significant temporal cyclical movements and coherence between cryptocurrency returns and retail investor attention at long investment horizons: from the beginning of 2017 to the middle of 2018 and, to a lesser degree, in 2019. Investment horizons that dominated in 2017 and 2018 were mainly driven by retail investor attention rather than by uncertainty, risk, or stock markets. Therefore, we do not confirm that cryptocurrencies can be considered a safe-haven asset in times of crisis because there is no significant negative comovement between the returns of cryptocurrencies and stock returns or economic uncertainty. Furthermore, the phase shift analysis indicates that attention can serve as a leading indicator for the cryptocurrency returns, particularly in 2017 and 2018. Therefore, retail investors are encouraged to use the Search Volume Index as an early warning indicator in case of sudden changes in the cryptocurrency returns to maximise profits or minimise losses.
Abstract This study examines the connectedness in high-order moments between cryptocurrency, major stock (U.S., U.K., Eurozone, and Japan), and commodity (gold and oil) markets. Using intraday data from 2020 to 2022 and the time and frequency connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018), we investigate spillovers among the markets in realized volatility, the jump component of realized volatility, realized skewness, and realized kurtosis. These higher-order moments allow us to identify the unique characteristics of financial returns, such as asymmetry and fat tails, thereby capturing various market risks such as downside risk and tail risk. Our results show that the cryptocurrency, stock, and commodity markets are highly connected in terms of volatility and in the jump component of volatility, while their connectedness in skewness and kurtosis is smaller. Moreover, jump and volatility connectedness are more persistent than that of skewness and kurtosis connectedness. Our rolling-window analysis of the connectedness models shows that connectedness varies over time across all moments, and tends to increase during periods of high uncertainty. Finally, we show the potential of gold and oil as hedging and safe-haven investments for other markets given that they are the least connected to other markets across all moments and investment horizons. Our findings provide useful information for designing effective portfolio management and cryptocurrency regulations.
Abstract Novel technologies allow cryptocurrency exchanges to offer innovative services that set them apart from other exchanges. In this paper we study the distinct features of cryptocurrency fee schedules and the implications for optimal trade execution. We formulate an optimal execution strategy that minimizes the trading fees charged by the exchange. We further provide a proof for the existence of an optimal execution strategy for this type of fee schedule. In fact, the optimal strategy involves both market and limit orders on various price levels. The optimal order distribution scheme depends on the market conditions expressed in terms of the distribution of limit order execution probabilities and the exchange's specific configuration of the fee schedule. Our results indicate that a strategy kernel with an exponentially decaying allocation of trade volume to price levels further away from the best price provides a superior performance and potential reduction of trade execution cost of more than 60%. The robustness of these results is confirmed in an empirical study. To our knowledge this is the first study of optimal trade execution that takes into consideration the full fee schedule of exchanges in general.
Walid Mensi, Mariya Gubareva, Hee-Un Ko, Xuan Vinh Vo · 5 authors
This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile interdependence between the analyzed variables. Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles, implying that diversification benefits for these assets may differ widely across normal and extreme market conditions. Under normal market conditions, the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions. Moreover, we show that under all market conditions, cryptocurrencies have a leadership influence over the volatility indices. Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges, as we show that cryptocurrency and volatility markets are insignificantly (weakly) connected under normal (extreme) market conditions.
Cryptocurrencies that are virtual and dematerialized are online and entirely digital currencies. Cryptocurrency has been the subject of many studies from different aspects; however, it is still a new area of investment for businesses as there are positive trends in the crypto space. Trending technologies, on the other hand, are making significant changes in all industries, and the cryptocurrency market is no exception. The employment of trending technologies can facilitate the crypto market with pattern recognition and secure transactions. This chapter aims to analyze the application of trending technologies in the crypto market and the benefits they can add to traders and brokers.
This paper examines the hedging effectiveness of Bitcoin futures by comparing one form of the constant model, the conventional OLS method, with the time-varying model in estimating the optimal hedge ratio. For the time-varying model, we employ a powerful technique, Kalman filter, a r ecursive a lgorithm w hich h as n umerous real-time, technological applications, but has not been employed in the context of Bitcoin optimal hedge ratio analysis. Through applying the spot and futures daily settlement prices from 18th December 2017 to 30th November 2022 to the two models, we confirm that t he B itcoin futures is an effective instrument for risk hedging. Additionally, we find the dynamic model based on the Kalman filter p erforms b etter - especially in 2019 and 2020 - than the conventional OLS method in terms of risk reduction, supporting previous findings in the context of other commodity futures. We also certify that the Kalman filter s uccessfully c aptures the trend of the optimal hedge ratio, thus enabling hedgers to decide when to change their hedging strategy. Furthermore, we verify the volatile evolution of the estimated time-varying Bitcoin optimal hedge ratio, suggesting the need to further search for a better hedging instrument which achieves a less volatile time path to avoid excessive trading costs.
Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.
Johan Hagelskjar Sjursen, Weizhi Meng, Wei-Yang Chiu
In the current literature, many solutions for solving blockchain scaling have been tried historically, whereas most of them usually may compromise the decentralisation. Ethereum has chosen to scale by switching to Proof of Stake consensus and adding data sharding to allow Layer 2 execution to be cheaper. However, in the light of cross-domain Maximal Extractable Value (MEV), even this strategy may have centralising forces built-in. In this work, we focus on cross-domain MEV and try to identify cross domain arbitrage. In particular, we extract Uniswap data from four different domains and provide an initial analysis of how to identify cross domain arbitrages.
We investigate the connectedness of automated market makers (AMM) that play a pivotal role in liquidity and ease of operations in the decentralized exchange (DEX). By applying the TVP-VAR model, our findings show higher level of connectivity during periods of turmoil (such as Delta, Omicron variants of SARS-Covid, and the Russia Ukraine conflict). Furthermore, risk transmission/reception is found to be independent of the platform on which they typically run (Ethereum based AMMs were both emitters as well as receivers). Pancake (a Binance based AMM) and Perpetual Protocol (Ethereum based AMM) emerged as moderate to high receivers of risk transmission, whereas all of the other AMMs, including Ethereum, were found to be risk emitters at varying degrees. We argue that AMMs typically depend on the underlying smart contracts. If the contract is flexible, AMMs can vary (either receiver or emitter), otherwise AMMs behave in tandem.
From gold standard currencies to fiat money secured by government credit, to today's cryptocurrencies, the basic form of money and mankind's perception of its value has shifted dramatically. This paper will demonstrate the value and risk assessment of the two cryptocurrencies with the highest market share, i.e., Bitcoin and Ethereum. Although the current technology of cryptocurrencies is not perfect, it will improve over time and their value will increase due to the high demand for them. This aim of the study to give first-time investors an understanding of the valuation and risks of cryptocurrencies, rather than treating them as simple financial assets for investment. According to the analysis, the value and risk of Bitcoin depend deeply on many characteristics that were initially built into it. It also has an impact on the value of other virtual currencies at the same time. On the other hand, Ether is a much more open platform, so its value and risk depend more on the various applications and contracts built into a blockchain than Bitcoin. These results shed the light on guiding the further exploration of solving the safety problem of cryptocurrencies from different perspectives.
Purpose This paper provides a thorough examination of Socios.com, a blockchain platform that integrates token sales with the fan experience in the sports industry. The study focuses on three key aspects: the performance, bubble phenomenon and dynamics of fan tokens. The author aims to address important questions that may concern potential supporters and investors. Might sports fans incur financial losses due to their team loyalty? Is the fan token market just a passing trend? Are fan tokens driven by the behaviour of the cryptocurrency market? Design/methodology/approach This analysis aims to involve several methodologies. The author evaluates the short- and long-term performance of fan tokens by computing first-day and buy-and-hold (abnormal) returns. The author also employs the Phillips, Shi, and Yu's (PSY) real-time bubble detection method to investigate the presence of bubble phenomenon in the fan token market segment. Finally, the author examines the potential dependences between fan tokens, Chiliz and the cryptocurrency market (represented by the CCi30 index) using both Pearson/Kendall correlations and the wavelet coherence approach. Findings The study presents three notable contributions to the existing literature. First, the author demonstrates that investing in fan tokens to support one's favourite sports teams can lead to financial losses, whereas traders can potentially outperform the market by investing in Chiliz. Second, the author states that fan tokens were a short-lived trend, as evidenced by their decline in value after the bubble burst in 2021. Third, the findings indicate that the fan token market was influenced by the cryptocurrency market and Chiliz during periods of market downturns. Originality/value To the best of author’s knowledge, this is the first paper to conduct a comprehensive analysis of the performance, bubble phenomenon and dynamics of the token market fan segment, along with the exclusive on-platform currency, Chiliz.
Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.