"Bitcoin is a decentralized payment system: a central institution to verify and settle transactions does not exist. What drives the transaction fees in this system? Can it remain tamper proof in the long run? We develop an empirical model to study how the demand for bitcoin influences transaction fees. To identify supply and demand effects, we analyze two types of eventsâtwo rapid price increases since late 2017 and the introduction of a new feature called Segwit. We find that Segwit reduced fee revenue by about 70%. Fee revenue is maximized at a block size of about 0.6 megabytes when Segwit adoption remains at current levels. In addition, large sustained price increases are required to keep mining rewards constant in the long run."
The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their relationship to Bitcoin's next-day volatility, with a focus on extreme volatility spikes. We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models when forecasting extreme volatility spikes for Bitcoin using CryptoQuant data as well as whale-alert tweets. We analysed our model with the Captum XAI library to investigate which features are most important. We also backtested our prediction results with different baseline trading strategies and the results show that we are able to minimize drawdown while keeping steady profits. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.
Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters. ⢠Development of a method to Detect Influencers in Network AutoRegressive models (DINAR). ⢠Investigation of Bitcoin users impact across regions on the digital money network. ⢠Influence is linked to economic need of using Bitcoin such as to circumvent sanctions and to avoid high inflation. ⢠The ban of cryptos in China caused a change in regions influence on the blockchain, moving from Asia to North America. ⢠Increase in BTC transaction fees shifted impact from less affluent regions and towards wealthier ones.
Ester FĂŠlezâViĂąas, Luke Johnson, TÄlis J. PutniĹĹĄ
We find evidence of systematic insider trading in cryptocurrency markets, where individuals use private information to buy coins prior to exchange listing announcements. Leveraging blockchain data, we identify the specific transactions and wallets (individuals) that consistently trade before announcements, ruling out alternative explanations. We estimate that insider trading occurs in 28-48% of cryptocurrency listings, yielding at least $30 million in trading profits. Unlike insider trading in stocks, scrutiny by authorities does not significantly reduce the level of insider trading, but pushes it underground via cryptocurrency concealment methods. These findings highlight the substantial challenges in policing cryptocurrency markets.
Non-Fungible Tokens (NFTs) are a relatively unexplored class of assets. Designing strategies to forecast NFT trends is an intricate task due to its extremely volatile nature. The market is largely driven by public sentiment and "hype", which in turn has a high correlation with conversations taking place on social media platforms like Twitter. Prior work done for modelling stock market data does not take into account the extent of impact certain highly influential tweets and their authors can have on the market. Building on these limitations and the nature of the NFT market, we propose a novel reach-aware temporal learning approach to make predictions for forecasting future trends in the NFT market. We perform experiments on a new dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions spanning over 15 NFT collections curated by us. Our model (TA-NFT) outperforms other state-of-the-art methods by an average of 36%. Through extensive quantitative and ablative analysis, we demonstrate the ability of our approach as a practical method for predicting NFT trends.
This paper examines pricing efficiency of cryptocurrencies and some traditional assets measuring the level of market efficiency with Adjusted Market Inefficiency Measure. The patterns of several cryptocurrenciesâ price dynamics over the last 4 years are compared with those of traditional assets. Correlation and mutual information matrices for AMIM are obtained using different estimation intervals. The results across different time scales are tested for noise using permutation entropy technique, empirical estimations are represented in statistical complexity plane to show the structure of efficiency links. Usage of AMIM in short window estimation is justified. Efficiency levels seem to be closely connected if judged from the standpoint of information theory at all time frames. Efficiency linkages become more linear at larger analysis periods. Cryptocurrencies seem to be more closely connected to equities, especially S&P500. Bursts of inefficiency on cryptocurrencies markets spread to equity markets and are possibly mediated in bank system. Commodities seem to be more independently priced.
Murat Tiniç, Ahmet Ĺensoy, Erdinç AkyÄąldÄąrÄąm, Shaen Corbet
Abstract In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverseâselection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverseâselection costs for major cryptocurrencies. Also, our results suggest that adverseâselection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverseâselection risk on transaction costs in cryptocurrency markets. Finally, we document that adverseâselection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.
This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be â24/7â, our findings suggest that both transaction and on-chain network activity are best described to be, at best, â12/5â, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.
We examine the turn of the month effect (TOM) in cryptocurrency markets. In contrast to most calendar effect studies, we do not take for granted that the TOM period is the last trading day of the month up to the first three trading days (-1, 3), as Lakonishok and Smidt (1988) proposed in their seminal paper, but we employ an optimisation algorithm which tests several four-day intramonth periods. Our findings confirm the existence of the TOM effect because the most profitable four-day periods are those between the last days of one month and the first trading days of the next one [the (-1, 3) definition is included in these combinations]. We reach the conclusion that the existence of a TOM effect may not always lead to higher profits in comparison with a buy-and-hold (BnH) strategy, but it presents better returns to risk reward and it could be beneficial for investment strategies.
Hossein Jahanshahloo, Felix Irresberger, Andrew Urquhart
This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for economically meaningful interpretations of user behavior. We identify and divide wallets into user categories - miners, exchanges, services, retail wallets and receiving-only addresses - and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. By matching historical transactions with minute-level price data, we estimate wallets' realized financial return and find that these user-types not only exhibit different transaction patterns and balances, but also different levels of financial performance. Our paper highlights opportunities for novel empirical research that exploits Bitcoin wallet-level data on individual user characteristics.
This paper aimed to assess the effect of the cryptocurrency market on firmsâ market value, especially on the sectoral level, in Africa. To reach the studyâs main goal, the authors adopted the Panel-Corrected Standard Errors (PCSEs) and Panel Double-Clustered Standard Errors (PDCSEs). Using firm-level data, the results of this study can be summarized as follows: (a) The cryptocurrency market hurts the firm market value in Africa. (b) The firms operating across different sectors respond disproportionally to the cryptocurrency market. For instance, the sectors that offer low returns in Africa (industrial, energy, financial) negatively respond to the cryptocurrency market, while the sectors that offer high returns (real estate and information technology) are not significantly affected. (c) The cryptocurrency market has a perverse effect on less experienced and highly indebted firms. (d) The consistent policies of governments to ban cryptocurrency do not work efficiently.
Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices across all different sets of crypto-assets, it remains unclear what and if there are tight relations between the trading prices of different cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation indicators to coin owners, suggesting possible acquisitions or sells. However, these correlations remain largely unvalidated. In this paper, we shed lights on the trend correlations across a large variety of cryptocoins, by investigating their coin-price correlation trends over a period of two years. Our experimental results suggest strong correlation patterns between main coins (Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting techniques for time-series modeling in the context of crypto-coins. We release our dataset and code to reproduce our analysis to the research community.
Cryptocurrencies are widely known for their limited publicly available information, making it challenging to predict market returns. Technical analysis has emerged as an essential tool in this context, but its effectiveness in the cryptocurrency market remains an open question. Using data from nearly 3,000 cryptocurrencies at daily, weekly, and monthly horizons from 2013 to 2022, we systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns and find that price-based signals are more effective in predicting short-term horizons, while volume-based signals are more powerful in predicting long-term horizons. Further analysis shows that machine learning techniques can significantly improve the performance of technical indicators, and technical indicators based on different information respond differently to the COVID-19 outbreak. These results provide direct evidence that volume imparts information to technical analysis independently of price.
Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.
Abstract In this study, an investigation is conducted into the phenomenon of price clustering in Bitcoin (BTC) denominated in the Japanese yen (JPY). It answers two questions using tick-by-tick data. The first is whether price clustering exists in BTC/JPY transactions, and the other is how the scale of price clustering varies throughout a trading day. With the assistance of statistical measures, the last two digits of BTC price were discovered to cluster at the numbers that end with â00â. In addition, the scales of BTC/JPY clustering at â00â tended to decline at the specific hour intervals. This study contributes to the emerging literature on price clustering and investor behavior.
Abstract Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, longâmemory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regressionâbased models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVIDâ19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.