Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant
With the emergence of decentralized finance, new trading mechanisms called Automated Market Makers have appeared. The most popular Automated Market Makers are Constant Function Market Makers. They have been studied both theoretically and empirically. In particular, the concept of impermanent loss has emerged and explains part of the profit and loss of liquidity providers in Constant Function Market Makers. In this paper, we propose another mechanism in which price discovery does not solely rely on liquidity takers but also on an external exchange rate or price oracle. We also propose to compare the different mechanisms from the point of view of liquidity providers by using a mean / variance analysis of their profit and loss compared to that of agents holding assets outside of Automated Market Makers. In particular, inspired by Markowitz' modern portfolio theory, we manage to obtain an efficient frontier for the performance of liquidity providers in the idealized case of a perfect oracle. Beyond that idealized case, we show that even when the oracle is lagged and in the presence of adverse selection by liquidity takers and systematic arbitrageurs, optimized oracle-based mechanisms perform better than popular Constant Function Market Makers.
Essais sur la dynamique du prix des crypto-monnaies Cet essai, qui comprend trois recherches empiriques originales, met en lumière les caractéristiques du comportement du marché des crypto-monnaies en fonction des effets macroéconomiques exogènes, du réseau interne et de leurs “matières premières" - les marchés de l'énergie. Le premier chapitre étudie les réponses des rendements et de la volatilité des crypto-monnaies aux annonces de nouvelles macroéconomiques américaines. En utilisant les données \textit{intraday} de 5 minutes sur les prix des crypto-monnaies, nous trouvons des preuves de la réaction des rendements et de la volatilité des crypto-monnaies aux nouvelles macroéconomiques et les possibilités d'utiliser les crypto-monnaies comme un outil de refuge en raison de la différence de réponse aux nouvelles macroéconomiques américaines entre les crypto-monnaies et les autres actifs financiers conventionnels. Dans le deuxième chapitre, nous étudions la causalité dynamique sur le marché des crypto-monnaies du point de vue du rendement et de la liquidité en utilisant une méthode de réseau de causalité stable. Les résultats mettent en évidence la spéculation du marché en montrant que les principales crypto-monnaies ne sont pas les acteurs les plus influents du réseau. Le chapitre 3 traite du sujet controversé de la relation entre les crypto-monnaies et les marchés de l'énergie. En outre, la consommation d'énergie des crypto-monnaies augmente considérablement suite à la difficulté croissante du minage et à une gamme plus complète d'application de la technologie blockchain telle que NFT et DeFi. En utilisant le modèle VAR à paramètres variables dans le temps, ce chapitre complète la littérature existante sur la liaison entre les crypto-monnaies et les marchés de l'énergie. Contrairement à la plupart des chercheurs existants, notre étude aborde non seulement les principales crypto-monnaies consommatrices d'énergie, le Bitcoin et l'Ethereum, mais aussi d'autres actifs basés sur la blockchain.
This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.
This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed by effects of size, momentum, liquidity and volume and is robust to various measures of idiosyncratic volatility.
Being archetypal complex systems, financial markets exhibit rich set of dynamics in their interactions. In this paper, we focus on the recently evolved cryptocurrency market as an example of a complex system and analyse the evolution of cross correlation structure of cryptocurrencies in the 5 year period from 2017 to 2022. We observe characteristic correlation structures in the observation time window duration and use these specific structures to cluster the cryptocurrency market in 4 market states.
The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.
The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.
We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest cryptocurrencies. Our results show that all employed models make statistically viable predictions, whereby the average accuracy values calculated on all cryptocurrencies range from 52.9% to 54.1%. These accuracy values increase to a range from 57.5% to 59.5% when calculated on the subset of predictions with the 10% highest model confidences per class and day. We find that a long-short portfolio strategy based on the predictions of the employed LSTM and GRU ensemble models yields an annualized out-of-sample Sharpe ratio after transaction costs of 3.23 and 3.12, respectively. In comparison, the buy-and-hold benchmark market portfolio strategy only yields a Sharpe ratio of 1.33. These results indicate a challenge to weak form cryptocurrency market efficiency, albeit the influence of certain limits to arbitrage cannot be entirely ruled out.
In this study, we apply an interactive agent‐based model to investigate fundamentalists and positive-feedback traders behaviours in cryptocurrency markets. Our results suggested that fundamentalists pushed up cryptocurrency prices in some past periods. In addition, the cryptocurrency markets are unstable and agitated.
The goal of cryptocurrencies is decentralization. In principle, all currencies have equal status. Unlike traditional stock markets, there is no default currency of denomination (fiat), thus the trading pairs can be set freely. However, it is impractical to set up a trading market between every two currencies. In order to control management costs and ensure sufficient liquidity, we must give priority to covering those large-volume trading pairs and ensure that all coins are reachable. We note that this is an optimization problem. Its particularity lies in: 1) the trading volume between most (>99.5%) possible trading pairs cannot be directly observed. 2) It satisfies the connectivity constraint, that is, all currencies are guaranteed to be tradable. To solve this problem, we use a two-stage process: 1) Fill in missing values based on a regularized, truncated eigenvalue decomposition, where the regularization term is used to control what extent missing values should be limited to zero. 2) Search for the optimal trading pairs, based on a branch and bound process, with heuristic search and pruning strategies. The experimental results show that: 1) If the number of denominated coins is not limited, we will get a more decentralized trading pair settings, which advocates the establishment of trading pairs directly between large currency pairs. 2) There is a certain room for optimization in all exchanges. The setting of inappropriate trading pairs is mainly caused by subjectively setting small coins to quote, or failing to track emerging big coins in time. 3) Too few trading pairs will lead to low coverage; too many trading pairs will need to be adjusted with markets frequently. Exchanges should consider striking an appropriate balance between them.
Rongxin Chen, Gabriele M. Lepori, Chung-Ching Tai, Ming‐Chien Sung
Research on human attention indicates that objects that stand out from their surroundings, i.e., salient objects, attract the attention of our sensory channels and receive undue weighting in the decision-making process. In the financial realm, salience theory predicts that individuals will find assets with salient upsides (downsides) appealing (unappealing). We investigate whether this theory can explain investor behaviour in the cryptocurrency market. Consistent with the theory's predictions, using a sample of 1738 cryptocurrencies, we find that cryptocurrencies that are more (less) attractive to “salient thinkers” earn lower (higher) future returns, which indicates that they tend to be overpriced (underpriced). On average, a one cross-sectional standard-deviation increase in the salience theory value of a cryptocurrency reduces its next-week return by 0.41%. However, the salience effect is confined to the micro-cap segment of the market, and its size is moderated by limits to arbitrage.
Abstract This paper explores the use of machine learning algorithms and narrative sentiments when applied to the task of forecasting and trading Bitcoin. The forecasting framework starts from the selection among 295 individual prediction models. Three machine learning approaches, namely, neural networks, support vector machines, and gradient boosting approach, are used to further improve the forecasting performance of individual models. By taking data‐snooping bias into account, three different metrics are applied to examine the forecasting ability of each model. Our results suggest that the machine learning techniques always outperform the best individual model whereas the gradient boosting framework has the best performance among all the models. Finally, a time‐varying leverage trading strategy combined with narrative sentiments and volatility is proposed to enhance trading performance. This suggests that the hybrid leverage strategy provides the highest Bitcoin profits consistently among all trading exercises.
This paper examines the forecasting power of daily infectious disease-related uncertainty in predicting the realized volatility of nine foreign exchange futures and the Bitcoin futures series using the heterogeneous autoregressive realized variance model. Our results indicate that the infectious diseases-related uncertainty index plays a crucial role in predicting the future path of foreign exchange and Bitcoin futures realized volatility in all the selected time intervals. These findings have important implications for portfolio managers and investors during periods of high levels of uncertainty associated with infectious diseases.
At the beginning of 2020, the panic of Covid-19 had an excessive impact on global economics and the financial market. Based on the unit root test, this paper exposes the newly global Covid-19 confirmed cases and the rate of return of Ethereum and Bitcoin are stationary time series. This paper further completes the VAR model and ARMA-GARCH model. The VAR model examines the effect of newly confirmed cases on to rate of return of Bitcoin and Ethereum, and the ARMA-GARCH model scrutinizes the newly confirmed cases to the fluctuation of Bitcoin and Ethereum. This study found that the impact of the COVID-19 on cryptocurrency earnings was short-term, and did not improve the market volatility.
Non-fungible tokens (NFT) have recently emerged as a novel blockchain-hosted financial asset class that has attracted major transaction volumes. However, preprocessing and analysis of NFT transaction data, which investors often rely on for their investment decisions, pose several challenges not commonly encountered in traditional financial data. These challenges arise mainly due to the non-fungible nature of NFTs as well as the intrinsic characteristics of the blockchain, the primary data source for NFT transactions. Using data consisting of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These include price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history, and finally, severe price volatility. This paper provides an overall summary of the challenges associated with data analytics on NFT transaction data and lay a foundation for future research on the topic.
The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrencies—bitcoin, ethereum, ripple, litecoin and dash—and two cryptocurrency indices—Crypto Index (CRIX) and CCI30 Crypto Currencies Index—to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTs’ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide on-line availability, increasing capitalization, and potential profits. In the econophysics tradition, we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions, instead following heavy-tailed distributions. Entropy measures are applied, showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.
The cryptocurrency market has received much interest in the media and academia because of its large price fluctuations since the beginning of 2013. By looking into the impact of behavioral finance elements on investing decisions in the cryptocurrency market, investors who invest in the cryptocurrency market will be able to make better decisions. Based on studies on its principal currency, the 18th of December 2017 has been designated as the peak date of the cryptocurrency market's greatest bubble. A bibliometric approach by means of quantitative analytical methods was applied to discover the relationship between the keywords associated with cryptocurrency and behavioral finance. Articles were extracted from the Scopus database that was published between 2018 and 2021. Publication Year, nation, area of research, journal, authors, and organizational affiliations were all examined in the extracted records. The VOSviewer application was used to visualise relation between both the research themes. Analysis of 102 review and original articles exposed that the total number of publications has incessantly increased over the last 4 years. This study examines the countries that contribute more publications in the selected field of research. The current study uses bibliometric approaches to evaluate cryptocurrency research and highlighted current trends in the interaction between cryptocurrencies and behavioural finance using several metrics, as well as prospective future research hot spots in this sector.
Christoph Schlegel, Mateusz Kwaśnicki, Akaki Mamageishvili
We study axiomatic foundations for different classes of constant-function automated market makers (CFMMs). We focus particularly on separability and on different invariance properties under scaling. Our main results are an axiomatic characterization of a natural generalization of constant product market makers (CPMMs), popular in decentralized finance, on the one hand, and a characterization of the Logarithmic Scoring Rule Market Makers (LMSR), popular in prediction markets, on the other hand. The first class is characterized by the combination of independence and scale invariance, whereas the second is characterized by the combination of independence and translation invariance. The two classes are therefore distinguished by a different invariance property that is motivated by different interpretations of the numéraire in the two applications. However, both are pinned down by the same separability property. Moreover, we characterize the CPMM as an extremal point within the class of scale invariant, independent, symmetric AMMs with non-concentrated liquidity provision. Our results add to a formal analysis of mechanisms that are currently used for decentralized exchanges and connect the most popular class of DeFi AMMs to the most popular class of prediction market AMMs.
This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.
The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
José Luís Miralles Quirós, María del Mar Miralles Quirós
Research background: A current strand of the financial literature is focusing on detecting inefficiencies, such as the day-of-the-week effect, in the cryptocurrency market. However, these studies are not considering that there are no daily closes in this market, and it is possible to trade cryptocurrencies on a continuous basis. This fact may have led to biases in previous empirical results. Purpose of the article: We propose to analyse the day-of-the-week effect on the Bitcoin from an alternative perspective where each hourly data in a day is considered an event. Focusing on that objective, we employ hourly closing prices for Bitcoin which are taken from the Kraken exchange, one of the world leading exchanges and trading platforms in the cryptocurrency markets, for the period spanning from January 2016 to December 2021. Methods: Contrary to the previous empirical evidence, we do not calculate daily returns, but rather the first stage of our proposed approach is devoted to analysing the hourly mean returns for each of the 24 hours of the day for each day of the week. We look for statistically significant hourly mean returns that could advance the importance of the hourly differentiation in the Bitcoin market. In a second stage, we calculate different post-event cumulative returns which are defined as the change in log prices over a time interval. Finally, we propose different investment strategies simply based on the significant hourly mean returns we obtain and we evaluate their performance in terms of the Sharpe ratio. Findings & value added: We contribute to the debate about the degree of Bitcoin?s market efficiency by providing an alternative methodology based on an event study hourly approach. Furthermore, we provide evidence that by investing in different post-event hourly windows it is possible to outperform the classic buy-and-hold strategy.
Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.