Paweł Szydło, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
A non-fungible token (NFT) market is a new trading invention based on the blockchain technology, which parallels the cryptocurrency market. In the present work, we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, persistence, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market-even though young and governed by somewhat different mechanisms of trading-shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators.
In today's world, cryptocurrencies are no longer a technological miracle for a small group of programmers. They have become a very common investment instrument that attracts the attention of both traditional investors (funds and traders) and those who are not interested in classical markets and investing in general. This is especially true for bitcoin. The purpose of the study was to investigate the influence of behavioural psychology in making investment decisions in cryptocurrency markets. The research methods included analysing historical data on cryptocurrency prices, as well as observing investors' reactions to important events and news related to cryptocurrencies. In addition, behavioural analysis methods were used to understand and predict investors' reactions to various incentives and situations in the cryptocurrency markets. The results of the article describe the main provisions of behavioural finance, which are necessary for an overview of the cryptocurrency market. The impact of the main topics of behavioural finance research is also considered. It should be noted that in the absence of a large amount of data, the study of the cryptocurrency market and the behaviour of participants is mainly a hypothetical assessment, and the empirical aspects of the study are copied from the behavioural finance of the classical market. Considering the cryptocurrency market from the point of view of behavioural finance, the main points of view of different parties were considered: both supporters of cryptocurrency and those who consider this phenomenon to be an economic bubble in a technological wrapper. The information reflecting the main biases of behavioural finance, which relate to both classical markets and cryptocurrency markets, is systematised. The study of cryptocurrencies from the point of view of behavioural finance reflects the practical value in understanding the impact of behavioural factors on price dynamics and investment decisions in cryptocurrency markets
Abstract This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology—the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies.
Minority game theory has, traditionally, been used to simulate player behavior under various conditions in a number of stock markets. However, digital markets, such as cryptocurrencies, have been largely ignored by game theory models. Using a comparative approach, with data both from traditional equities markets and from Bitcoin this article presents a model of a dollar game and compares its outcome to real-life data. The paper aims to prove that game theory can be used to predict cryptocurrency markets similarly to how it is used to predict traditional stock markets. By using historical data from the London Stock Exchange and Bitcoin the paper demonstrates that a custom implementation of a dollar game can be used to predict general market trends and the overall impact of short-term investments in Bitcoin.
This study investigates the influence of monetary policy and monetary policy uncertainties on Bitcoin returns, utilizing monthly data of BTC, and MPU from July 2010 to August 2023, and employing the Markov Switching Means VAR (MSM-VAR) method. The findings reveal that Bitcoin returns can be categorized into two distinct regimes: 1) regime 1 with low volatility, and 2) regime 2 with high volatility. In both regimes, an increase in MPU leads to a decline in Bitcoin returns: -0.028 in regime 1 and -0.44 in regime 2. This indicates that monetary policy uncertainty exerts a negative influence on Bitcoin returns during both downturns and upswings. Furthermore, the study explores Bitcoin's sensitivity to Federal Open Market Committee (FOMC) decisions.
This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk–return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.
Decentralized Finance (DeFi) aims to use advancements in both computation and cryptography to tackle economic problems. Therefore, it must operate within the intersection of constraints from both the computer science and economic domains. We explore a foundational question at the junction of those fields: Can we synthesize variable market-clearing risk-free yield for native tokens via smart contracts? We use a stylized model representing a large class of decentralized consensus algorithms to show this is impossible. This undecidability result places bounds on what decentralized financial products can be built and constrains the shape of future developments in DeFi. Among other limitations, our results reveal that markets in DeFi are incomplete.
Given that technical trading charts are publicly available on popular financial websites such as Bloomberg and MarketWatch, it stands to reason that the same technical trading approaches may be applied to cryptocurrency markets. One of these trading strategies is the variable length moving average (VMA), whose flexibility benefit has not been fully explored in prior research. To fill this gap, we evaluate Bitcoin futures using VMA trading rules and provide the results in a heatmap diagram. This approach allows investors to choose the most effective VMA rules, potentially leading to profits. Furthermore, our approach may shed new light on previously unexplored investment thinking and practices that have the potential to improve investment outcomes.
This study examines the nexus between selected cryptocurrencies represented by Bitcoin and Ethereum and the expected market volatility denoted by the VIX index. We use daily data for the period of 06/01/2021 – 07/07/2023. Using Hong (2001) and Hong et al. (2009) Granger causality tests we find no causality in mean, however, we find strong bidirectional causality in variance for the variables at the 5% significance level across all lag lengths. At the time-varying causality analysis, we find BTC causes VIX in the 1st lag. The analysis findings have important implications for portfolio managers and potential investors. Although no direct causal relationship was found between Bitcoin and VIX, or Ethereum and VIX in terms of returns, the strong bidirectional causality in variance suggests that changes in volatility can affect related assets. This information can be utilized by portfolio managers to effectively assess mandates and manage portfolio risk.
Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This research focuses on developing a reinforcement learning (RL) framework to tackle the complexities of trading five prominent altcoins: Binance Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present the CausalReinforceNet~(CRN) framework, which integrates both Bayesian and dynamic Bayesian network techniques to empower the RL agent in trade decision-making. We develop two agents using the framework based on distinct RL algorithms to analyse performance compared to the Buy-and-Hold benchmark strategy and a baseline RL model. The results indicate that our framework surpasses both models in profitability, highlighting CRN's consistent superiority, although the level of effectiveness varies across different cryptocurrencies.
We provide a comprehensive investigation into the profitability of technical trading methods applied to the cryptocurrency pairs BTC/USDT and ETH/USDT. By employing rigorous evaluations and incremental examinations, we address the pervasive issue of data-snooping bias that often plagues the evaluation of trading strategies. Our empirical results indicate the lack of profitable technical trading strategies in both the analysis sample and prediction sample periods, even after rigorous adjustments for data snooping. These findings highlight the difficulties associated with selecting profitable technical trading strategies in the dynamic and volatile cryptocurrency market. Market participants, including individual traders, institutional investors, and regulatory bodies, should take note of our findings when making investment decisions based on technical analysis.
Life cycle investment models predict much higher participation and investment in cryptocurrencies than found empirically. We reconcile these differences by introducing information costs for cryptocurrency market participation to a standard consumption-investment life cycle model. Only with increasing per-period cryptocurrency market participation costs, are we able to simulate the observed cryptocurrency market participation patterns. The fact that neither fixed entry nor non-increasing per-period costs are able to replicate the survey findings suggests that other reasons, besides information costs, should be considered to explain lower-than-predicted investing in cryptocurrency.
Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest their assets into a liquidity pool. However, the prices at which a pooled asset is traded is often more stale than the prices on centralized and more liquid exchanges. This leads to the LPs suffering losses to arbitrage. This problem is addressed by adapting market prices to trader behavior, captured via the classical market microstructure model of Glosten and Milgrom. In this paper, we propose the first optimal Bayesian and the first model-free data-driven algorithm to optimally track the external price of the asset. The notion of optimality that we use enforces a zero-profit condition on the prices of the market maker, hence the name ZeroSwap. This ensures that the market maker balances losses to informed traders with profits from noise traders. The key property of our approach is the ability to estimate the external market price without the need for price oracles or loss oracles. Our theoretical guarantees on the performance of both these algorithms, ensuring the stability and convergence of their price recommendations, are of independent interest in the theory of reinforcement learning. We empirically demonstrate the robustness of our algorithms to changing market conditions.
This study aimed to examine the weak-form efficiency of some of the most capitalised cryptocurrencies. The sample consisted of 24 cryptocurrencies selected out of 30 cryptocurrencies with the highest market capitalisation as of October 19, 2022. Stablecoins were not considered. The study covered the period from January 1, 2018 to August 31, 2022. The results of robust martingale difference hypothesis tests suggest that the examined cryptocurrencies were efficient most of the time. However, their efficiency turned out to be time-varying, which validates the adaptive market hypothesis. No evidence was found for the impact of the coronavirus outbreak and the Russian invasion of Ukraine on the weak-form efficiency of the examined cryptocurrencies. The differences in efficiency between the most efficient cryptocurrencies and the least efficient ones were noticeable, but not large. The results also allowed to observe some slight differences in efficiency between the cryptocurrencies with the largest market cap and cryptocurrencies with the lowest market cap. However, the differences between the two groups were too small to draw any far-reaching conclusions about a positive relationship between the market cap and efficiency. The obtained results also did not allow us to detect any trends in efficiency.
Purpose This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect. Design/methodology/approach The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect. Findings The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect. Practical implications These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty. Originality/value Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.
John Kingsley Woode, Peterson Owusu, Anokye M. Adam, Emmanuel Assifuah-Nunoo · 5 authors
The study extends the literature on the nexus between cryptocurrency and uncertainty. This study proxied the cryptocurrencies and global uncertainty, respectively, with the seven most significant and variationally susceptible cryptos and the comprehensive world uncertainty in measuring the crypto-uncertainty nexus over the period (2015–2022) and further employing the quantile regression approach. The OLS model results point to a blend of both significant and insignificant relationship between global uncertainty and cryptocurrencies. These relationships were further examined in quantiles and further accounted for the impact of investor sentiments (VIX) and volatility (OVX), and the results were largely corroborated with the results from the conventional OLS, except for the Bitcoin, Litecoin, and Ripple markets. It was also discovered that the nexus changes across quantiles. The results revealed a blend of strong and weak hedges and safe havens among the selected cryptos against global uncertainty during normal and extreme market conditions. In the face of global turmoil, it was revealed that the average crypto market could serve as a safe haven. Also, the cryptos with an insignificant nexus with global uncertainty were found to be significantly affected by investor sentiment. These findings were further confirmed by the quantile-on-quantile and causality-in-quantile estimations. Given the intense precariousness and lack of hedge and haven capacities within the majority of the cryptocurrencies, it is pertinent for investors to consider the market in general as a means of diversifying their portfolios and reserve the hedge and haven option to the few markets that possess such luxury.
We study the stochastic structure of cryptocurrency rates of returns as compared to stock returns by focusing on the associated cross-sectional distributions. We build two datasets. The first comprises forty-six major cryptocurrencies, and the second includes all the companies listed in the S&P 500. We collect individual data from January 2017 until December 2022. We then apply the Quantal Response Statistical Equilibrium (QRSE) model to recover the cross-sectional frequency distribution of the daily returns of cryptocurrencies and S&P 500 companies. We study the stochastic structure of these two markets and the properties of investors' behavior over bear and bull trends. Finally, we compare the degree of informational efficiency of these two markets.
The stock market is a topic that is of interest to all sorts of people. It is a place where the prices change very drastically. So, something needs to be done to help the people risking their money on the stock market. The public's opinions are crucial for the stock market. Sentiment is a very powerful force that is constantly changing and having a significant impact. It is reflected on social media platforms, where almost the entire country is active, as well as in the daily news. Many projects have been done in the stock prediction genre, but since sentiments play a big part in the stock market, making predictions of prices without them would lead to inefficient predictions, and hence Sentiment analysis is very important for stock market price prediction. To predict stock market prices, we will combine sentiment analysis from various sources, including News and Twitter. Results are evaluated for two different cryptocurrencies: Ethereum and Solana. Random Forest achieved the best RMSE of 13.434 and MAE of 11.919 for Ethereum. Support Vector Machine achieved the best RMSE of 2.48 and MAE of 1.78 for Solana.
Quoc Minh Nguyen, Dat Tran, Juho Kanniainen, Alexandros Iosifidis · 5 authors
Many cryptocurrency brokers nowadays offer a va-riety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. The proposed algorithm constructs a portfolio that contains a pair of negatively correlated assets. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy. Extensive experiments were conducted using data collected from Binance spanning 19 months to evaluate the effectiveness of our approach. The backtest results show that the proposed algorithm can produce neural networks that are able to make profits in different market situations.
Wael Hemrit, Noureddine Benlagha, Racha Ben Arous, Mounira Ben Arab
Summary In this paper, we examine the connectedness between volatilities for various non‐fungible tokens (NFTs) and developed stock markets during the period from July 1, 2018, to June 15, 2022. With the use of the time‐varying connectedness methods to explore the volatility interdependences among these assets, we find that there is a significant volatility connectedness during Russia's invasion of Ukraine and COVID‐19 periods. Evidence emerging from this study advocates the inclusion of NFTs in developed stock markets for medium and long time periods only. The results also suggest that UK and Germany stock markets are the predominant market of spillover transmission, whereas the XTZ is the top net recipient/transmitter of volatility connectedness shocks. Moreover, Chinese stock market and ENJ offer more diversification gains than others, and the volatility connectedness from US stock market to NFTs is more pronounced in the long‐term than the short‐term. Our research provides some urgent and prominent insights to help investors and policymakers to be aware that NFTs are important hedge assets that should be added to stock portfolios during periods of geopolitical stability and in the post‐pandemic times.