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1,505 results · page 27 of 63
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.
Jing Liu, Yuncheol Kang
No abstract is available for this record.
Weihao Han, David Newton, Emmanouil Platanakis, Haoran Wu · 5 authors
Abstract We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.
Apostolos Ampountolas
This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).
Dong Huang, William N. Goetzmann
Using transaction data from a large non-fungible token (NFT) trading platform, this paper examines how the behavioral bias of selection-neglect interacts with extrapolative beliefs, accelerating the boom and delaying the crash in the recent NFT bubble.We show that the pricevolume relationship is consistent with extrapolative beliefs about increasing prices which were plausibly triggered by a macroeconomic shock.We test the hypothesis that agents prone to selection-neglect formed even more optimistic beliefs and traded more aggressively than their counterparts during the boom.When liquidity for NFTs declined, observed NFT prices were subject to severe selection bias due in part to seller loss aversion delaying the onset of the crash.Finally, we show that market participants with sophisticated bidding behavior were less subject to selection bias and performed better.
Fabian E. Eska, Yanghua Shi, Erik Theissen, Marliese Uhrig‐Homburg
This paper examines the impact of cryptocurrency design features on their return volatility. We compile a sample of 58 cryptocurrencies, adopt the taxonomy of design features proposed by Eska et al. (2022), and estimate LASSO regressions. We document that older cryptocurrencies tend to be less volatile. Networks with mandatory transaction fees, cryptocurrencies based on (delegated) Proof-of-Stake, and those developed by private for-profit entities tend to be more volatile. Furthermore, we provide evidence that networks passing transaction fees and/or tips on to verifiers are associated with higher volatility levels.
Thomas Conlon, Shaen Corbet, Yang Hou
This paper investigates the price discovery relationships between FTT Token, issued by the cryptocurrency exchange FTX, and a set of assets and liabilities held by FTX amid a period of catastrophic financial decline by applying novel information flow measurement techniques. Results indicate that during key phases associated with the collapse of FTX, FTT Token had an informational lead over multiple assets, including cryptocurrencies such as Ethereum. Furthermore, we identify significant interactions between the FTT Token and both Robinhood shares and the token Serum, raising concerns about the direct influence of permissionless, technically valueless tokens on other assets and the potential challenges to market stability and investor protection. Our findings underscore the need for stronger policy-making, regulatory, and ethical considerations in cryptocurrency markets.
Hasib Shamshad, Fasee Ullah, Asad Ullah, Victor R. Kebande · 6 authors
The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.
Kyoung Jin Choi, Junkee Jeon, Byung Hwa Lim
No abstract is available for this record.
Mohamad Hassan Abou Daya, Carole Bernard
When entering into a tontine, the value of the tontine for the participant highly depends on its composition (e.g. the age of the participants, the amount invested by each of them already invested in the tontine). However, participants subscribe to the scheme without any knowledge of either the composition of the tontine, or, in some cases, its exact payout scheme. Herein, we quantify the value of this information using certainty equivalents in the expected utility setting and a measure for model risk that allows us to obtain bounds on the tontine value subject to uncertainty in certain characteristics. We then propose a smart contract that offers full disclosure of information in a tontine. We discuss the practical implementation of such a tontine and present some new risks that could arise.
Christian Fieberg, Gerrit Liedtke, Thorsten Poddig, Thomas Walker · 5 authors
Abstract We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.
Kin-Hon Ho, Monica Law, Yun Hou, Tse-Tin Chan
No abstract is available for this record.
Darren Aiello, Tetyana Balyuk, Marco Di Maggio, Mark J. Johnson · 6 authors
This paper uses transaction-level data across millions of accounts to identify cryptocurrency investors and evaluate how fluctuations in individual crypto wealth affect household consumption, equity investment, and local real estate markets.We estimate an MPC out of unrealized crypto gains that is more than double the MPC out of unrealized equity gains but smaller than the MPC from exogenous cash flow shocks.This MPC is mostly driven by increases in cash/check spending and mortgages.Moreover, households sell crypto to increase both discretionary as well as housing spending.As a result, crypto wealth causes house price appreciation-counties with higher crypto wealth see higher growth in home values following high crypto returns.Our results indicate that cryptocurrencies have substantial spillover effects on the real economy through consumption and investment into other asset classes.
Malvika Chhatwani, Arpit Kumar Parija
No abstract is available for this record.
Jian Liu, Jiansuer Julaiti, Shangde Gou
No abstract is available for this record.
Thomas Conlon, Shaen Corbet, Richard McGee
We examine the volume-volatility relationship across Bitcoin futures and spot markets, using daily realised volatility measures estimated from high frequency intraday data. We estimate realised spot volatility across five major exchanges using both the standard volume weighted price and using a new approach, inspired by the CME Bitcoin Reference Rate methodology. We find that unexpected trading volume is the most important explanatory variable for BRR spot volatility, explaining 20% of variation in price volatility at exchange level. Conversely, we find that both expected and unexpected CME Bitcoin futures volumes play a very limited or even calming role in systemic volatility. Our findings suggest that CME Bitcoin futures are not independently contributing to systemic risk in Bitcoin over the period studied.
Abba Suganda Girsang, Stanley
Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social media. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocurrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.