Запропоновано середовище імітаційного моделювання явища максимально екстрактованої вигоди MEV (англ. Maximal Extractable Value), реалізоване мовою програмування Python із використанням бібліотеки Gymnasium, яке відтворює взаємодію сховища-мемпулу, конструювальника блоків, агента MEV-екстрактора та AMM-пулу децентралізованої біржі. Формально середовище описано як розширений та частково спостережуваний процес прийняття рішень, у межах якого агент взаємодіє з дискретно-часовою моделлю епізодів, що відображає послідовність надходження транзакцій, побудови блоків і виконання swap-операцій обміну на децентралізованій крипто-біржі. Для моделювання адаптивної поведінки агента використано методи навчання з підкріпленням, а для кількісного аналізу втрат користувачів застосовано контрфактичний підхід до оцінювання, що дає змогу порівнювати результати виконання транзакцій у різних режимах впорядкування за однакових вхідних умов. У дослідженні використано раніше описаний авторами метод зменшення негативних ефектів MEV-екстракції на основі логічних часових міток Лампорта, який реалізує локальне причинно-наслідкове впорядкування транзакцій у межах окремого смарт-контракту без модифікації глобального механізму консенсусу мережі блокчейн Ethereum. Для оцінювання практичної ефективності цього підходу сформовано три сценарії моделювання: базовий сценарій без систематичної MEV-атаки для визначення накладних витрат застосування механізму захисту, сценарій систематичної sandwich-атаки для аналізу та здатності методу зменшувати втрати користувачів та обмежувати можливості MEV-екстрактора, а також сценарій параметричного аналізу, спрямований на дослідження компромісу між рівнем захисту та "вартістю" його застосування. Отримані результати показали, що запропонований метод MEV-захищеного впорядкування може зменшувати цінові втрати користувачів від sandwich-атак і, водночас, впливати на частоту відхилення транзакцій та пов'язані комісійні витрати, що вказує на наявність керованого компромісу між ефективністю захисту та накладними витратами його використання. Практична цінність роботи полягає у створенні відтворюваного середовища імітаційного моделювання для дослідження стратегічної поведінки MEV-агентів і перевірки механізмів зменшення негативних наслідків MEV у контрольованих умовах, що може бути використано для подальшого аналізу безпеки протоколів децентралізованих фінансів та проєктування нових методів впорядкування транзакцій.
The increase of decentralized social media systems provides them with liberty and openness, yet tends to interfere with privacy because transaction data is made publicly accessible in blockchains. In this paper, a Privacy-Preserving Consensus Mechanism (PPCM) has been proposed as a privacypreserving blockchain in anonymous decentralized social media systems. To preserve the confidentiality and integrity of transactions, the PPCM incorporates the advanced cryptography solutions, Zero-Knowledge Proofs (ZKPs), Homomorphic Encryption, and ring signatures. It utilizes a Decentralized Identity (DID) model of self-sovereign identity management and cross-platform nteroperability, and overlays a reputation-based layer of governance to encourage ethical behaviour without disclosing the identity of users. Scalability is ensured with sidechains, which remove high-frequency interaction points of the main blockchain to minimize latency. The model is a compromise between privacy and accountability and solves such issues as Sybil attacks, metadata leakage, and unethical use of anonymity. The PPCM offers a privacy-focused, scalable, and ethically regulated design of next-generation decentralized social media networks.
The paper presents two series representations of a L{\'e}vy process for the Generalized Tempered Stable (GTS) distribution: a series representation generated by the inverse tail integral and a short noise representation. Both series representations are used to simulate the daily returns of Bitcoin and Ethereum. The Q-Q plot analysis shows smooth linear patterns, indicating strong agreement between the empirical and theoretical GTS distributions.
Paolo Giudici, Alessandro Piergallini, Maria Cristina Recchioni, Emanuela Raffinetti
We consider the problem of developing explainable Artificial Intelligence methods to interpret the results of Artificial Intelligence models for time series data, taking time dependency into account. To this end, we extend the Shapley–Lorenz method, normalised by construction, to Artificial Intelligence for time series, such as neural networks and recurrent neural networks. We illustrate the application of our proposal to a time series of Bitcoin prices, which acts as the response variable, along with time series of classical financial prices, which act as explanatory variables. Three main findings emerge from the analysis. First, recurrent neural networks lead to a better performance, in terms of accuracy and robustness, with respect to classic neural networks. Second, the best performing models indicate that Bitcoin prices are affected mostly by their lagged values, and that their explainability, in terms of classical financial assets, is limited. Third, although limited, the contribution of classical assets to Bitcoin price prediction is well captured by recurrent neural networks.
The objective of this study is to analyse the correlation between Bitcoin and altcoins in the post-covid world and take advantage of this possible relationship to design investment strategies on Bitcoin based on the evolution of altcoins using Artificial Intelligence (AI) models. The sample of daily observations covers from January 2020 to February 2023, and the regressions performed between altcoins and Bitcoin are positive and 99 % significant, except for Dogecoin, which has a correlation with Bitcoin. If we add a lag, the estimated parameters are still 95 % significant, except for Dogecoin, so we can assume that the return of altcoins anticipates the evolution of Bitcoin. We train an artificial intelligence model in which the predictors are the observed daily return in altcoins and the target to predict is next day trend of Bitcoin (up or down). We use decision tree algorithms (J48), random forest and naive bayes, but in a retrospective cross-sectional validation with 10 sample partitions we obtain a poor predictive capacity of only a 51 % success rate in the best of cases. Therefore, despite the evident correlation between predictors and the objective variable, we should not implement this investment strategy.
Bitcoin, the largest cryptocurrency, is extremely volatile and hence needs a better model for its pricing. In the literature, many researchers have studied the effect of data normalization on regression analysis for stock price prediction. How has data normalization affected Bitcoin price prediction? To answer this question, this study analyzed the prediction accuracy of a Legendre polynomial-based neural network optimized by the mutated climb monkey algorithm using nine existing data normalization techniques. A new dual normalization technique was proposed to improve the efficiency of this model. The 10 normalization techniques were evaluated using 15 error metrics using a multi-criteria decision-making (MCDM) approach called technique for order performance by similarity to ideal solution (TOPSIS). The effect of the top three normalization techniques along with the min–max normalization was further studied for Chebyshev, Laguerre, and trigonometric polynomial-based neural networks in three different datasets. The prediction accuracy of the 16 models (each of the four polynomial-based neural networks with four different normalization techniques) was calculated using 15 error metrics. A 16 × 15 TOPSIS analysis was conducted to rank the models. The convergence plot and the ranking of the models indicated that data normalization plays a significant role in the prediction capability of a Bitcoin price predictor. This paper can significantly contribute to the research with a new normalization technique for utilization in varied fields of research. It can also contribute to international finance as a decision-making tool for different investors as well as stakeholders for Bitcoin pricing.
The article examines the possibility of increasing the attractiveness of international investments in the Ukrainian solar industry. The nature of alternative energy concepts is studied. A significant place of alternative energy sources in the general system of electricity production. The potential of using solar energy in Ukraine was assessed. The feasibility of using blockchain technology in energy. The advantages and disadvantages of using smart contracts in solar power plant projects to improve the innovation climate in the solar energy sector of Ukraine have been identified.
P. V. Nagamani, Gowri Anand, Srinivasa Prasanna, Basava Raju · 5 authors
The past several years have seen an increase in interest in trading that is supported by machine learning and artificial intelligence.Utilize automated trading with the aid of machine learning and artificial intelligence to reap the maximum rewards from the cryptocurrency market.For a specific time, we keep the daily data.We achieve excellent results by utilising tactics supported by cutting-edge algorithms.The results produced the expansion in the crypto currency industry with the aid of straight forward architecture and algorithms.The rise in market capitalization has led to a rise in popularity for the cryptocurrency in 2017.Today's market involves more than 1500 crypto currencies.For usage in online transactions, the crypto currency can be created.A crypto money technology is bitcoin.Bitcoin's value changes constantly, second by second.As a result, we apply machine learning architecture to forecast the value of the bitcoin price in this case.We are working to demonstrate that, in comparison to previous techniques and architectures, this ML architecture produces results that are more accurate.Our study use the Support Vector Machine(SVM) and K Nearest Neighbor(KNN)algorithms to successfully forecast bitcoin prices.The findings demonstrate that the Support Vector Machine(SVM) method outperforms the K Nearest Neighbor(KNN) method as it is currently being used.
We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey, Tibshirani & Wager, 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.
This paper establishes a brand-new perspective of analyzing the risk of crypto assets through a semi-nonparametric approach, discussing its theoretical advantages and testing its performance compared to parametric approaches and in terms of backtesting techniques and different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. Our comprehensive analysis for six cryptocurrencies shows that flexible semi-nonparametric approaches outperform risk measures of most crypto assets (particularly Bitcoin) and tend to provide the most conservative risk assessment. Furthermore, we propose the Median Shortfall as a robust-to-outliers and reliable risk measure for cryptocurrencies and discuss on the choice of the appropriate probability levels according to the assumed distribution. The evidence supports that Median Shortfall at 98.31 % and 98.51 % confidence levels as accurate alternatives to Value-at-Risk at 99 % and Expected Shortfall at 97.5 %.
In spite of optimistic predictions of experts in recent years, cryptocurrencies still hold an inferior position on financial market due to the inability to provide a satisfactory forecast of their dynamics. The latter essentially depends on various subjective factors: attitude of authorities, interest of business community and trust of population. Our hypothesis is that subjective factors are well reflected in Internet queries, and in this study we confirm it experimentally. In the paper, we describe formation of descriptors' vocabulary, creation of barometers, and building of predictive models. The source information covers the period 2019–2021 (2 years) with the step of 1 week. In the paper we consider 3 cryptocurrencies (XRP, Waves, ETH), 2 forecast horizons (week, month), 2 forecast periods (calm, crisis), 3 models (autoregression, regression, hybrid), 2 basic algorithms from the GMDH Shell software (combinatorial, neural network). We show that preliminary smoothing of cryptocurrencies dynamics essentially reduces forecast error. Baseline is presented by the popular Holt-Winters method. The results allow us to recommend the proposed approach for experts of cryptocurrency market.
Abstract Subject and purpose of work: The aim of this work is to present the application possibilities of ECONOMIC AND REGIONAL STUDIES STUDIA EKONOMICZNE I REGIONALNE ISSN 2083-3725 Volume 14, No. 2, 2021 ECREG STUDIES Vol. 14, No. 2, 2021 the weights of criteria is proposed, which maximizes the similarity of the final ranking to the other ones. Materials and methods: PROMETHEE II method and taxonomic measure were used to create rankings of exchanges. Hierarchical clustering combined with the k-means algorithm www.ers.edu.pl PDF OPEN ACCESS eISSN 2451-182X available data published on the Internet were analysed. Results: There was a high consistency in the ordering of exchanges when a multi-criteria and a multi-dimensional approach were used. Four groups of exchanges with a similar level of the values of net flows were identified. Exchanges in group one were characterized by the highest average net flows. Conclusions: The multi-criteria approach can be used as an alternative to the multi-dimensional assessment of cryptocurrency exchanges. The proposed simulation method for determining the weights of criteria can be helpful in case the researcher has no information about the importance of the criteria.
Kateryna Kolesnikova, Olga Mezentseva, Tleuzhan Mukatayev
The article is devoted to virtual currencies, which is a fast growing and popular market. It was found that for virtual currencies, in particular, for the cryptocurrency Bitcoin, there is a problem of uncontrolled money laundering. This is facilitated by pseudo-anonymization and the presence of illegal exchangers. In this paper, to solve this problem, the method of combining layers in convolutional neural networks is used, which is manifested in the stack layering.In CNN networks, convolutional and erecting layers are usually stacked in a stack, one above the other. The paper proposes a model of Bitcoin transaction analysis to identify anomalies related to money laundering. As such a model, it is proposed to take a combined method, which consists of the method of random forests, enhanced by information from the graph convolutional network, ie, embedded vertices. As a result of the model, we obtained indicators that indicate the presence of possible shadow transactions in the amount of 2-3% of the total market.
Pavel Mogilev, Anna Boldyreva, Mikhail Alexandrov, John Cardiff
Cryptocurrencies became one of the main trends in modern economy. However by the moment the forecast of cryptocurrencies values is an open problem, which is almost non-reflected in publications related to finance market. Reasons consist in its novelty, large volatility and its strong dependence on subjective factors. In this experimental research we show possibilities of GMDH-technology to give weekly and monthly forecast for values of cryptocurrency 'Waves' (waves/euro rate). The source information is week data covering the period 2017-2019. We tests 4 algorithms from the GMDH Shell platform on the whole period and on the crisis period 4-th quarter 2017 – 2nd quarter 2018. Baseline is provided by the popular statistical method of double exponential smoothing. The results of Pilot study can be considered as the very promising ones having in view the large variability of data.
The paper presents the use of states of explosionproof method for analyzing the behavior of systems that provide smart contract technology. The selected example system is ShadowEth, whose main task is to ensure sufficient confidentiality of information stored in the Ethereum blockchain currency. The Petri network model for the ShadowEth system has been presented. The system
Nikolay Miller, Yiming Yang, Bruce Sun, Guoyi Zhang
This research studies automatic price pattern search procedure for bitcoin cryptocurrency based on 1-min price data. To achieve this, search algorithm is proposed based on nonparametric regression method of smoothing splines. We investigate some well-known technical analysis patterns and construct algorithmic trading strategy to evaluate the effectiveness of the patterns. We found that method of smoothing splines for identifying the technical analysis patterns and that strategies based on certain technical analysis patterns yield returns that significantly exceed results of unconditional trading strategies.
Blockchain technology and smart contract development currently lacks clarity in its implementation. The complicated architecture of blockchain is an obstacle that developers face during design and implementation of blockchain-based systems. In this paper we propose a method based on Model Driven Architecture, which could be used for defining and specifying blockchain structure and behavior. Such approach could be used as one of the ways for describing blockchain-based systems in a more general language in order to facilitate blockchain development process.
Mariia Sigova, Igor Klioutchnikov, Anna Zatevakhina, Oleg I. Klioutchnikov
Is the distributed ledger technology able to predict the capital asset prices and digitizes any financial market participant? Prediction is a scary and tempting place to venture, profitable trading and the development of effective strategies. Will predictions fit into the everyday practice of financial markets? Is blockchain technology capable to revolutionize predictions? Is the blockchain able to compensate of the lack of individual intelligence in assessing market prospects? In the article, the authors do not so much raise these questions as they try to outline the directions for their solution. Financial inclusion is one of the problems that blockchain technology can solve, if it is applied properly. Transferring predictions to the online mode and providing each market participant with the opportunity to digitize their activities would help introduce new efficient mechanisms to improve performance of financial market practices. The article discusses two aspects of using blockchain technology in finance - forecasting financial markets using “collective knowledge” and digitizing assets of market participants based on blockchain. The article also raises the question of the possibility of using some fundamental physical laws for analyzing the impact of new technologies on the financial markets. An attempt is made to approach the assessment of the prospects of using a number of decentralized applications for financial market predictions and the creation of digital counterparts of market participants. A fundamental lesson from experiments in predicting financial markets and digitizing market participants is that decentralized solutions are still not sufficiently scaled and, as a result, are used by consumers.
Advanced Database Systems and Queries
Statistical and Computational Modeling
Advanced Research in Systems and Signal Processing