Follow blockchain research across journals, conferences, and preprint repositories.
2,329 results · page 42 of 98
Peter J. Phillips, Gabriela Pohl
No abstract is available for this record.
Sean Grover
Exchange-traded funds (ETFs) investing in bitcoin futures contracts first listed for trading in the fall of 2021. This research evaluates the extent to which the returns of bitcoin futures and bitcoin correspond to determine if bitcoin futures provide an effective proxy for a direct bitcoin investment. A no-arbitrage framework for bitcoin futures is established, which provides the basis for the empirical analyses that follow. The empirical analyses of returns correspondence between bitcoin futures and bitcoin use daily and monthly returns to estimate single-factor asset pricing regressions, finding coefficients of expected magnitude and that bitcoin returns explain over 97% of the variation in bitcoin futures returns. This research also estimates two-factor asset pricing regressions that include a novel excess carry term. The two-factor regressions find statistically significant excess carry term coefficients and over 99% explained variation. Finding strong evidence that the returns of bitcoin futures and bitcoin closely correspond, this research concludes that bitcoin futures provide an effective proxy for a direct bitcoin investment.
Olga Klein, Roman Kozhan, Ganesh Viswanath-Natraj, Junxuan Wang
No abstract is available for this record.
Sinda Hadhri
No abstract is available for this record.
David Ardia, Keven Bluteau
We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.
Basile Caparros, Amit Chaudhary, Olga Klein
Liquidity providers (LPs) on decentralized exchanges (DEXs) can protect themselves from adverse selection risk by updating their positions more frequently. However, repositioning is costly, because LPs have to pay gas fees for each update. We analyze the causal relation between repositioning and liquidity concentration around the market price, using the entry of blockchain scaling solutions, Arbitrum and Polygon, as our instruments. Lower gas fees on scaling solutions allow LPs to update more frequently than on Ethereum. Our results demonstrate that higher repositioning intensity and precision lead to greater liquidity concentration, which benefits small trades by reducing their slippage.
Mykola Pinchuk
This paper examines the response of major cryptocurrencies to macroeconomic news announcements (MNA). While other cryptocurrencies exhibit no reaction to major MNA, Bitcoin responds negatively to inflation surprise. Price of Bitcoin decreases by 24 bps in response to a 1 standard deviation inflationary surprise. This reaction is inconsistent with widely-held beliefs of practitioners that Bitcoin can hedge inflation. I do not find support for the hypothesis that the negative response of Bitcoin to inflation is due to its negative exposure to interest rates. Instead, I find support for the hypothesis that Bitcoin is strongly affected by the shift in consumption-savings decisions, driven by the rise in inflation. Consistent with this view, Bitcoin has negative exposure to a proxy for the consumption-savings ratio.
Antzelos Kyriazis, Iason Ofeidis, Georgios Palaiokrassas, Leandros Tassiulas
This paper studies the effects of unexpected changes in US monetary policy on digital asset returns. We use event study regressions and find that monetary policy surprises negatively affect BTC and ETH, the two largest digital assets, but do not significantly affect the rest of the market. Second, we use high-frequency price data to examine the effect of the FOMC statements release and Minutes release on the prices of the assets with the higher collateral usage on the Ethereum Blockchain Decentralized Finance (DeFi) ecosystem. The FOMC statement release strongly affects the volatility of digital asset returns, while the effect of the Minutes release is weaker. The volatility effect strengthened after December 2021, when the Federal Reserve changed its policy to fight inflation. We also show that some borrowing interest rates in the Ethereum DeFi ecosystem are affected positively by unexpected changes in monetary policy. In contrast, the debt outstanding and the total value locked are negatively affected. Finally, we utilize a local Ethereum Blockchain node to record the activity history of primary DeFi functions, such as depositing, borrowing, and liquidating, and study how these are influenced by the FOMC announcements over time.
Zhenzhen Fan, Feng Jiao, Lei Lü, Xin Tong
No abstract is available for this record.
Ahmed El Youssefi, Abdelaaziz Hessane, Ahmad El Allaoui, Imad Zeroual · 5 authors
No abstract is available for this record.
Paola Di Casola, Maurizio Michael Habib, David Tercero‐Lucas
No abstract is available for this record.
Ralitsa Petkova
No abstract is available for this record.
Chiara Lesa, Ronald Hochreiter
Pair trading is a strategy which relies on betting on the relative mispricing of the spread between two securities which share a long-term relationship. These strategies have shown to perform well with equities, however not much research has been conducted in the field of cryptocurrencies, even though this asset class has shown characteristics suggesting suitability for pair trading. The Distance Methods and Cointegration Method are applied to a set of cryptocurrencies at formation and trading periods of daily and hourly data. It is shown that the frequency of the selection period does not influence the pairs selected. Cointegration-selected pairs generally outperforms Distance selected pairs. When trading frequency is analysed, intraday trading is more profitable, but not when using a stop-loss. Cointegration over-performs Distance, as the cost of the latter selection are increased by the higher number of trades.
Federico P. Cortese, Petter N. Kolm, Erik Lindström
Abstract We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.
Jui‐Cheng Hung, Hung‐Chun Liu, J. Jimmy Yang
No abstract is available for this record.
Yuliya Guseva
Non-fungible tokens (NFTs) are used in numerous markets for collectibles, art, securities, and commodities. These are different markets, and there is no regulatory framework for all NFTs. To determine a proper legal regime, it is essential to locate the market to which an NFT belongs. This task requires a deep understanding of the economic realities of the associated rights, assets, and transactions. Economic-reality-based interpretations should provide a solid footing for better regulation of NFTs in the US and other jurisdictions grappling with NFT regulation. The new cryptoasset regime in the EU already incorporates a “substance over form” approach. In the US, courts have been successfully applying the Howey test to examine transactions and schemes and establish whether securities law should apply to cryptoassets. In 2023, the SEC and a US federal district court applied the Howey test to demonstrate why and how securities law built for legacy markets where mainstream assets are fungible could apply to transactions in non-fungible assets. The decisions are an example of establishing economic realities of transactions with novel assets regardless of the underlying technologies on which the assets are built. An economic reality approach should help courts and other policy-makers ascertain to which market an NFT belongs and which corresponding legal regime should govern.
Vaibhav Saha
Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.
Chuyi Sun
No abstract is available for this record.
Nir Chemaya, Dingyue Liu, Rob McLaughlin, Nicola Ruaro · 6 authors
No abstract is available for this record.
Kittiwin Kumlungmak, Peerapon Vateekul
Recently, reinforcement learning has been applied to cryptocurrencies to make profitable trades. However, cryptocurrency trading is a very challenging task due to the volatility of the market, especially during bearish periods. In addressing this problem, the existing literature employs single-agent techniques such as deep Q-network (DQN), advantage actor-critic (A2C), and proximal policy optimization (PPO), or their ensembles. Moreover, in the context of cryptocurrencies, the mechanisms for restricting losses during a bearish market are insufficiently robust. Consequently, the performance of reinforcement learning methods for cryptocurrency trading in the existing literature is constrained. To overcome this limitation, in this paper, we propose a novel cryptocurrency trading method based on multi-agent proximal policy optimization (MAPPO) with a collaborative multi-agent scheme and a local-global reward function to optimize both the individual and collective performance of the agents. Both a multi-objective optimization technique and a multi-scale continuous loss (MSCL) reward are used to train agents using a progressive penalty to avoid consecutive losses of portfolio value. As a result, better cumulative returns are achieved than when baseline methods are used. In addition, the superiority of our method is emphasized by the result of the bearish test set, where only our method can make a profit. Specifically, our method obtains a 2.36% cumulative return, whereas the baseline methods result in negative cumulative returns. In comparison to FinRL-Ensemble, a reinforcement learning-based method, our method achieves a 46.05% greater cumulative return in the bullish test set.
Jon A. Garfinkel, Lawrence Hsiao, Danqi Hu
No abstract is available for this record.
Lin William Cong, Pulak Ghosh, Jiasun Li, Qihong Ruan
Using proprietary data from the predominant cryptocurrency exchange in India together with the country's Household Inflation Expectations Survey, we document a significantly positive association between inflation expectations and individual cryptocurrency purchases.Higher inflation expectations are also associated with more new investors in cryptocurrencies.We investigate investment heterogeneity in multiple dimensions, and find the effect to be concentrated in Bitcoin (BTC) and Tether (USDT) trading.The results are robust after controlling for speculative demand captured by surveys of investors' expected cryptocurrency returns, and admit causal interpretations as confirmed using multiple instrumental variables.Our findings provide direct evidence that households already adopt cryptocurrencies for inflation hedging, which in turn rationalizes their high adoption in developing countries without a globally dominant currency.
Shubhangi Gautam, Pardeep Kumar
No abstract is available for this record.
Jing Liu, Yuncheol Kang
No abstract is available for this record.