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.
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).
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.
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.
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.
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.
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.
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.
Deep learning methods have achieved significant success in various applications, including trend signal prediction in financial markets. However, most existing approaches only utilize price action data. In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the trend signal of Ethereum cryptocurrency. We conduct experiments to investigate the relationship between price action, candlestick patterns, and Ethereum-Bitcoin correlation, aiming to achieve highly accurate trend signal predictions. We evaluate and compare two different training strategies for Convolutional Neural Networks (CNNs), one based on transfer learning and the other on training from scratch. Our proposed 1-Dimensional CNN (1DCNN) model can also identify inflection points in price trends during specific periods through the analysis of statistical indicators. We demonstrate that our model produces more reliable predictions when utilizing multiple data representations. Our experiments show that by combining different types of data, it is possible to accurately identify both inflection points and trend signals with an accuracy of 98%.
In recent years, with the rapid development of blockchain technology, the emergence of Non-Fungible Tokens (NFTs) has become a disruptive and innovative application that has attracted widespread attention and triggered frenzy. This study examines the momentous but may be easily neglected price factor in the NFT market. Using hand-collected daily data on the number of followers of 150 NFTs on Discord from April 18 to 15 October 2022, empirical results find that the fan economy on social media platforms has a positive impact on NFT pricing. Furthermore, this impact has a certain time-lagged effect. To ensure the robustness of the research, this paper also collects Twitter followers as an alternative indicator to measure the fan economy, and all the empirical results of the Twitter platform are significant. The findings of this paper are of great significance for studying the factors affecting the price of NFTs and provide certain assistance for the decision-making of NFT issuers and investors.
Ahmet Faruk Aysan, Massimiliano Caporin, Oğuzhan Çepni
This paper analyzes the relationship between price jumps and news sentiment in cryptocurrencies. We detect jumps at the intraday level and correlate their occurrence with sentiment-related events through logistic regressions. We show that the release of information increases the probability of price jumps. By examining the content of news stories, we find that sentiment dimensions limited to emotions or related to market fundamentals have more potential to result in price jumps than others, suggesting that “words are not all created equal”. Jump sensitivity to news sentiment varies across different coin characteristics.
The long-lasting intermediated structure of international bond markets has come under scrutiny in recent times because of the risks it exposes final investors to, mostly in relation to the difficulties these investors face in enforcing their rights. Distributed ledger technologies (DLTs) have emerged as a strong contender in efforts to improve the position of final investors by shifting the market to a direct holding structure. In this context, it is necessary to ask if organising international bond markets under a DLT-based direct holding structure will effectively address the risks surrounding intermediated securities. Furthermore, it is important to assess the impact such a change is likely to have on other players in the market (including intermediaries and issuers), as well as on the financial system as a whole. With these questions in mind, this article begins with an explanation of the primary legal implication of holding intermediated securities, i.e., that final investors do not hold legal title over the bonds they have invested in because they are not engaged in a direct relationship with the issuer. The paper then proceeds to dissect the risks such arrangements expose investors to and contrast those risks with the benefits that intermediation afford to investors, issuers and the financial system in general. It is then argued that DLTs are not only inadequate to the task of addressing those risks, but would also eliminate most of the advantages of intermediation. The paper goes on to examine how investors and issuers are not incentivised to promote the development of a DLT-based bond market organised under a direct holding structure. It concludes with the suggestion that a better way to improve the position of final investors in bond markets is to explore how DLTs may enhance the benefits already created by intermediation, rather than relying on these technologies to eliminate intermediation altogether. In particular, it is submitted that DLTs may introduce efficiencies in the management of the bonds, the performance of obligations by issuers, the settlement process, the performance of securities financing transactions, and the provision of services by intermediaries.