We obtain daily data of Bitcoin, Ethereum, Travala token, Kemacoin and Guider to investigate the implications of history's most famous five heists on travel and tourism. We find a statistically significant spillover effect in the cryptocurrency and tourism token markets with a limited impact on travel and tourism companies' stock prices. We also find evidence of herding behaviour and observe that overall market quality deteriorated because of the heists. To deal with these negative implications, we propose implementing tools based on artificial intelligence algorithms, emphasising the two leading cryptocurrencies – Bitcoin and Ethereum. Tracking major crypto wallets and ‘whales’ can help regulators identify potential hacks and mitigate systemic risk caused by spillovers in cryptocurrency markets.
Frequent price manipulation in the Bitcoin market will lead to market risk and seriously disrupt the financial order, but there is less research on its regulation. We address the Bitcoin price manipulation problem by building a regulatory game model. First, we study the price manipulation mechanism of the Bitcoin market based on behavioral finance and clarify the boundary conditions. Second, we introduce regulator constraints and establish a game model between the manipulator and the regulator. Further, through variable deconstruction, parameter verification, and simulation analysis, we explore how to achieve effective regulation of Bitcoin price manipulation. We find that the effective regulation of Bitcoin price manipulation can be achieved in three ways: (1) Adjust the penalty coefficient with a certain lower threshold so that the manipulator's expected return is negative; (2) Set the lowest possible price fluctuation standard while ensuring that it does not interfere with market-based transactions; (3) The simulation of price manipulation regulation is optimized and most efficiently controlled when the probability of investigation is dynamically adjusted by a concave function on the price fluctuation standard.
Modern Portfolio Theory (MPT) has long been a cornerstone in the realm of finance, aiding investors in navigating the complex terrain of risk and reward associated with diverse assets. This theory, formulated by Harry Markowitz in the 1950s, has traditionally guided investment decisions by optimizing the balance between different assets to achieve the desired level of risk and return. However, with the meteoric rise of cryptocurrencies as a new asset class, there is an increasing curiosity surrounding the applicability of MPT to this digital phenomenon. In response to this curiosity, this article undertakes the task of comprehensively assessing the compatibility of MPT with cryptocurrencies. To accomplish this, the research aggregates and analyzes the existing body of knowledge, thereby offering insights into the intersection of modern portfolio theory and the age of cryptocurrencies. A systematic literature review is conducted, encompassing 21 pertinent studies that explore various facets of this confluence. The findings of this article underscore an emerging trend in research, one that showcases the adaptability of MPT to innovative financial instruments like cryptocurrencies. These studies collectively illuminate the ways in which MPT can be employed to optimize portfolios that include digital assets, shedding light on strategies that account for the unique risk-return dynamics inherent in the crypto market. As the cryptocurrency landscape continues to evolve, it is evident that Modern Portfolio Theory is not only relevant but also adaptable, providing valuable tools to guide investors through the exciting yet volatile terrain of digital finance.
With the advent of the Web3.0 era, virtual assets have gained prominence in individuals’ asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the model’s predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.
The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction via statistical models and Machine Learning demonstrate the importance of variables such as the hash rate, the difficulty of mining or the cost per transaction when it comes to trade Bitcoin assets or predict the direction of price. Variables obtained from the blockchain network will be called here blockchain metrics. The corresponding indicators (inspired by the “Hash Ribbon”) perform well in locating buy signals. From our results, we conclude that such blockchain indicators allow obtaining information with a statistical advantage in the highly volatile cryptocurrency market.
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics.
In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.
Xihan Xiong, Zhipeng Wang, Xi Chen, William J. Knottenbelt · 5 authors
In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. LSDs improve the liquidity of staked assets by facilitating their use in secondary markets, such as for collateralized borrowing on Aave or asset exchanges on Curve. The composability of Lido, Aave, and Curve enables an emerging strategy known as leverage staking, an iterative process that enhances financial returns while introducing potential risks. This paper establishes a formal framework for leverage staking with stETH and identifies 442 such positions on Ethereum over 963 days. These positions represent a total volume of 537,123 ETH (877m USD). Our data reveal that 81.7% of leverage staking positions achieved an Annual Percentage Rate (APR) higher than conventional staking on Lido. Despite the high returns, we also recognize the potential risks. For example, the Terra crash incident demonstrated that token devaluation can impact the market. Therefore, we conduct stress tests under extreme conditions of significant stETH devaluation to evaluate the associated risks. Our simulations reveal that leverage staking amplifies the risk of cascading liquidations by triggering intensified selling pressure through liquidation and deleveraging processes. Furthermore, this dynamic not only accelerates the decline of stETH prices but also propagates a contagion effect, endangering the stability of both leveraged and ordinary positions.
The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.
On 16th March 2022, U.S. Federal Reserve increased the interest rate the first time, and in the whole year, U.S. Federal Reserve made seven increments on interest rate. As this will affect the value of dollar, many American financial assets were also affected by it, including ETH, one of the most famous cryptocurrencies. This paper uses the history data of ETH price from January 2018 to July 2023 and constructs ARIMA model without Federal Reserve increasing the interest rate to compare with the reality in order to comprehend how the increasing interest rate affected the price of ETH, and use the model to predict the trend of Ethereum’s price. With the influence of increasing interest rate, the price of Ethereum should decrease. However, after the U.S. Federal Reserve increased interest rate, the price of Ethereum went up for a while then dropped dramatically. And the reasons why this delay appears are the delay of policy and the first increment of interest rate is not attractive enough for investors to change their strategies. That can bring some inspirations to policymakers. They should acknowledge that there will be a delay in the market after the policy is released and they could give some potential signs or preferences on the new policy to reduce the shock to market. For investors, they could pay more attention on relevant policy and make use of the delay to make more money.
Adithya Bhaskara, Rafael Frongillo, Lindgren, Elias, Maneesha Papireddygari
Liquidity provisioning in automated market makers is the practice of recruiting third-party liquidity providers (LPs) to contribute assets to the market in exchange for fees skimmed off of trades. This paper introduces a general framework for liquidity provisioning in cost function prediction markets. Our most general protocol allows LPs to submit or update an arbitrary cost function that specifies their liquidity over the entire price space. We show that our protocol encapsulates several notions of running market makers in parallel, which we prove to be equivalent. We also recover existing protocols from decentralized finance as special cases. In our protocol, liquidity can be expressed as a matrix-valued function, which we argue is necessary with three or more securities. Due to this inherent multidimensionality, the design of trading fees with three or more securities is nontrivial: we show that natural axioms on the design of these fees are incompatible.
Kushal Babel, Mojan Javaheripi, Yan Ji, Mahimna Kelkar · 6 authors
We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts. Lanturn discovers strategies comprising of concrete transactions for extracting economic value from smart contracts interacting with a particular transaction environment. We formulate the strategy discovery as a black-box optimization problem and leverage a novel adaptive learning-based algorithm to address it.
Sherin M. Omran, Wessam H. El-Behaidy, Aliaa A. A. Youssif
A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.
Abstract This paper provides a review of the development of the non‐fungible tokens (NFTs) market, with a particular focus on its pricing determinants, its current applications, and its future opportunities. We investigate the current state of the NFT markets and highlight the perception and expectations of investors toward these products. We summarize and compare the financial and econometric models that have been used in the literature for the pricing of non‐fungible tokens with a special focus on their predictive performance. We design a framework that can help to understand the price formation of NFTs. We further aim to shed light on the value‐creating determinants of NFTs in order to better understand investors’ behavior on the blockchain.
Within the realm of investment, investors are presented with a multitude of options in terms of investment vehicles. Three options that are experiencing growing popularity include equities, collective investment schemes, and digital currencies. The literature search was conducted across multiple databases, including PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar, to explore the comparative aspects of stock investments, mutual funds, and cryptocurrencies. Stocks are a type of investment that symbolize ownership in a corporation, offering the possibility of long-term earnings through the firm's expansion and delivery of dividends. Stocks exhibit diverse levels of risk contingent upon the specific firm and industry and are typically more suitable for investors with long-term objectives. Mutual funds are financial instruments that aggregate capital from multiple investors and allocate it into a diversified collection of investments. This service offers automated diversification and is ideal for investors seeking to entrust the management of their portfolio to professionals. Mutual funds are appropriate for both short-term and long-term objectives. Cryptocurrencies are virtual assets that are bought and sold on cryptocurrency exchanges. Cryptocurrencies exhibit a high degree of speculation and volatility, characterized by swift and unpredictable price movements. Investing in cryptocurrencies necessitates a comprehensive comprehension of technical aspects and entails substantial risks, typically regarded as a short-term or speculative investment.
An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane
We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investors’ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrencies’ connectedness.
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.
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.
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.
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.