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.
Purpose This study examine the response of liquidity of Bitcoin and Ethereum to the Russia-Ukraine war in an event study context and investigate whether the war had a transitory or a permanent effect on cryptocurrency liquidity. Design/methodology/approach A event study was applied to hourly transactions on Bitcoin and Ethereum cryptocurrencies from 1/02/2022 to 31/03/2022. This is period is subdivided in two sample periods to capture transitory and permanent effects. The transitory effect is investigated over a window spanning -20 and +20 days. For a more extended post-event period, a linear regression model was applied to analyze the effects of other factors on the liquidity risk of BTC and ETH. Findings The findings reveal a significant but temporary impact of the Russia–Ukraine war on the liquidity of Bitcoin and Ethereum. Liquidity levels have increased within the first two days around the event day and then returned to the pre-event level after that. However, the response of BTC and ETH cryptocurrencies' liquidities to the Russian invasion of Ukraine is not uniform. Originality/value This is the first paper that assesses the liquidity level of two major cryptocurrencies (Bitcoin and Ethereum) in response to an extreme event: the Russia–Ukraine war. The hypothesis is that trading in the cryptocurrency market will increase due to market participants' goal of evading regulatory sanctions. Furthermore, market participants may also take advantage of cryptocurrencies' popularity as safe-haven assets.
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.
Cryptocurrency has gained its popularity in recent years. Due to enormous profitability potential, many investors and researchers alike have taken an interest in this domain. There is a lot of data in the cryptocurrency market that has to be analysed to make the right choices quickly when trading. Many have tried to automate the trading process by utilizing various prediction models and reinforcement learning to further streamline the trading process. It is therefore important to collect and summarize current state of the art technologies that investors and researchers use to predict and automate the cryptocurrency trading process. This paper provides a repository of knowledge to find out what other researchers have done by covering more than 13 different machine learning methods and several hybrid methods. This paper is the initial research step to try to come up with a new state-of-the-art approach to programmatic trading by determining a method that can be researched further.
As cryptocurrencies become the target of many investors, it is speculated that there may be a correlation between the trading prices of cryptocurrencies and other assets (e.g., TESLA and BITCOIN). On this basis, we try to build an arbitrage model among the TESLA, BITCOIN, and DOGECOIN to validate the feasibility by simulations using their trading data for 5 years. After conducting the Augmented Dickey-Fuller test, Co-integration test, etc., TESLA and BITCOIN are best correlated that co-integrated over a relatively long period. Within the range of co-integration, we construct the arbitrage model and design the transaction signals by setting a certain threshold. Subsequently, backtestings are carried out accordingly, where different spreads as trading thresholds lead to different results with large differences in returns. These results shed light on the decision on arbitrage investments for cryptocurrencies and other assets.
Flash Loan, a popular lending service in the decentralized finance (DeFi) ecosystem, allows users to borrow a large number of virtual assets without any collateral. It can be leveraged to support many financial activities (such as arbitrage, collateral swap, self-liquidation, etc.), but unfortunately, it is often abused for malicious intent. One example of abusing flash loan servicing is to simultaneously sell and buy the same crypto currency on the same exchange to mislead the market, aka wash trading. It can manipulate the crypto currency market at a very low cost (anecdotally average around 0.033 ETH gas fee for each transaction on Ethereum mainnet), thereby dramatically damaging the stability and fairness of the market. More seriously, attackers can amplify the market impact by borrowing more assets from Flash Loan platforms. Until now, there has been little attention paid to Flash-Loan-based wash trading, but meanwhile, we have started to witness significant wash trading activities using Flash Loan. In this research, we analyze the properties of Flash-Loan-based wash trading in detail and propose a heuristic-based detection method. The real-world Flash Loan transaction data from Ethereum is used to verify our proposed detection method and more than 6,000 wash transactions were found. Moreover, we analyze the relationship between wash transactions and fluctuations in the price and volume of targeted assets. Finally, we evaluate the cost difference between traditional wash trading and Flash-Loan-based wash trading to reveal the attackers' motivation.
An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane
We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.
The growth in information and communication technology has led to phenomenons in the financial sector as well. This primarily alludes to the introduction of cryptocurrencies, a decentralised medium of exchange, which provides an alternative to the centuries-old idea of physical money. There is a visible relationship between the principles of behavioural finance and the value/returns that these cryptocurrencies have. These currencies are not dependent on the behaviour of the financial markets and economy but instead on the supply and demand of the currency along with its popularity which is dependent purely on the individuals.
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits in backtesting, which may suffer from the false positive issue due to overfitting. In this paper, we propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning. First, we formulate the detection of backtest overfitting as a hypothesis test. Then, we train the DRL agents, estimate the probability of overfitting, and reject the overfitted agents, increasing the chance of good trading performance. Finally, on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022 (during which the crypto market crashed two times), we show that the less overfitted deep reinforcement learning agents have a higher return than that of more overfitted agents, an equal weight strategy, and the S&P DBM Index (market benchmark), offering confidence in possible deployment to a real market.
Nur Azmina Mohamad Zamani, Jasy Liew Suet Yan, Ahmad Muhyiddin Yusof
The effect of sentiment from news sources on cryptocurrency prices has garnered significant interest from the financial community including researchers. However, current findings reporting the usefulness of sentiment features in cryptocurrency price prediction is still mixed. This paper aims to explore various feature combinations encompassing cryptocurrency historical prices and sentiment scores of the current day aggregated from English and Malay news sources to predict the closing price of the next day. Each news headline related to Bitcoin and Ethereum within the duration of one year is first manually annotated and then aggregated on a daily basis to generate the sentiment features. A Bi-GRU deep learning model is implemented to predict the cryptocurrency closing price of the next day given a combination of historical price and sentiment features of the current day. Our findings evidently show sentiment features when combined with the closing price of the current day contribute significantly to improve the model's performance. Our study is also the first attempt to examine the effect of Malay news sentiment on cryptocurrency prices and we have demonstrated that cryptocurrency price prediction models leveraging Malay news sentiment features for Bitcoin and Ethereum are able to yield performance that is at par with models using English news sentiment features.
Although NFTs (non-fungible tokens) and cryptocurrencies are active on the same market, their prices are not so closely related over time. The objective of this paper is to identify the relationship between the two types of assets (NFTs and the cryptocurrencies Ethereum, Crypto Coin, and Bitcoin), using data for the period between September 2020 until February 2022. The conclusions of the study are useful for cryptocurrency and NFT issuers, but also for investors on the financial market who are reconfiguring their portfolios with increasing frequency, and use these new assets for speculative or hedging purposes based on blockchain technology. The results highlighted relationships between NFTs and Ethereum, between Ethereum and Crypto Coin, and between Bitcoin and Ethereum, Ethereum being a bridge between all four. Therefore, NFTs present a relationship with Ethereum, the NFTs price had a causal effect on the price of Ethereum.
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
We study bitcoin to US dollar (BTCUSD) liquidity and liquidity determinants using order book data from three large cryptocurrency exchanges. The BTCUSD market is more liquid than US equity markets with bid–ask spreads often below 1 basis point. We find that BTCUSD liquidity is largely explained by same-exchange past liquidity, past cryptocurrency market-wide liquidity and volatility, and fees charged on the blockchain for bitcoin transfers. Surprisingly, we find that BTCUSD liquidity is unrelated to broader financial markets and financial market liquidity.
Wash trade is a common form of volume manipulation used to attract investors into the market and mislead them into making wrong investment judgments. Wash trade transactions are even more prominent in ERC20 cryptocurrencies. In this paper, we proposed two kinds of algorithms to reserve direct evidence of wash trade based on the on-chain transaction data of ERC20 cryptocurrencies. After labeling the wash trade, we continued to obtain features of the wash trade and quantify the volume of the wash trade. Our experiments show that for most ERC20 cryptocurrencies, the rate of wash trade reached over 15%. Specifically, over 30% of UNI token transactions were labeled as wash trade. It is demonstrated that the activations of most ERC20 cryptocurrencies are unreal, and restoring real data is necessary for market regulation.