Tutkimuksen taustalla oli kryptovaluuttojen kasvava merkitys rahoitusmarkkinoilla sekä spot-Bitcoin ETF -rahastojen käyttöönotto Yhdysvalloissa vuonna 2024. Uudet sijoitustuotteet ovat lisänneet yksityissijoittajien mahdollisuuksia saada altistusta Bitcoiniin, mutta samalla ne ovat tuoneet mukanaan uusia riskejä. Tutkimuksen tavoitteena oli tunnistaa spot-Bitcoin ETF -rahastoihin liittyvät keskeiset riskit sekä tarkastella riskienhallinnan keinoja yksityissijoittajan näkökulmasta. Tutkimus toteutettiin integroivana kirjallisuuskatsauksena. Aineisto koottiin Google Scholar- ja ScienceDirect-tietokannoista, ja se rajattiin pääosin vuosien 2024–2025 julkaisuihin. Mukaan valittiin tutkimuksia, jotka käsittelivät spot-Bitcoin ETF- ja ETP-tuotteiden riskejä ja riskimekanismeja. Aineisto analysoitiin vertailemalla tutkimusten keskeisiä havaintoja ja ryhmittelemällä ne laajem-miksi riskiluokiksi. Tulosten perusteella spot-Bitcoin ETF -rahastoihin liittyvät riskit voidaan jäsentää useaan pääluokkaan. Keskeisimpiä olivat volatiliteettiriski, likviditeetti- ja hinnoitteluriski, seuranta- ja rakenneriski, säilytys- ja operatiiviset riskit, sääntely- ja markkinarakenteen riskit sekä käyttäytymisriskit. Tutkimustulokset osoittivat, että ETF-rakenne ei poista Bitcoin-markkinoihin liittyvää voimakasta hinnanvaihtelua, ja että tuotteisiin liittyy myös rakenteellisia ja markkinamekanismeihin liittyviä epävarmuustekijöitä. Tulosten pohjalta muodostettiin yksityissijoittajalle suunnattu riskikehikko, joka kokoaa keskeiset riskit ja auttaa niiden jäsentämisessä. Johtopäätöksenä todettiin, että spot-Bitcoin ETF -rahastot tarjoavat yksityissijoittajalle helpomman ja säännellymmän tavan sijoittaa Bitcoiniin, mutta ne eivät poista sijoittamiseen liittyviä keskeisiä riskejä. Riskienhallinta edellyttää sijoittajalta tuotteen rakenteen ymmärtämistä, kriittistä tiedon arviointia sekä oman riskinsietokyvyn huomioimista. Lisäksi havaittiin, että osa riskeistä liittyy markkinarakenteeseen ja sääntelyyn, eikä niitä voida täysin hallita yksittäisen sijoittajan toimesta.
This OSF project hosts the pre‑registered live forecast for Bitcoin, published as part of Chapter 14 of the book The Luxury Collapse Threshold: How to Predict When Status Symbols Lose Their Power. The forecast was registered before the outcome was known. It includes: a full Luxury Risk Index (LRI) assessment of Bitcoin; an Early Warning Dashboard signal analysis; a predicted trajectory for 2026–2031; explicit confirmation and falsification criteria. This registration is intended to be permanently archived and publicly citable. Readers of the book are invited to verify the forecast and track its accuracy over time.
Dynamiczny rozwój technologii rozproszonych rejestrów (DLT – Distributed Ledger Technology) oraz rosnące oczekiwania społeczne w zakresie transparentności finansów publicznych skłaniają do analizy możliwości wdrożenia technologii blockchain w systemie zarządzania wydatkami jednostek samorządu terytorialnego (JST). W artykule poddano ocenie potencjał blockchain jako narzędzia eliminującego asymetrię informacyjną i zwiększającego społeczną kontrolę nad finansami JST. Technologia ta, dzięki niezmienności rejestrów oraz kryptograficznemu potwierdzaniu transakcji, może przyczynić się do redukcji ryzyka korupcji i nadużyć budżetowych. Szczególną uwagę poświęcono aspektom prawnym implementacji blockchain w sektorze publicznym, w tym jego zgodności z ustawą o finansach publicznych, przepisami dotyczącymi zamówień publicznych oraz regulacjami RODO. W artykule przeprowadzono także analizę porównawczą międzynarodowych wdrożeń blockchain w administracji publicznej oraz zaproponowano model implementacji tej technologii w kontekście polskich JST.
This study conducts a scientometric analysis of global financial risk management research to map its intellectual structure, thematic trends, and collaboration networks over the period 2000–2025. Data were retrieved from the Scopus database using a comprehensive search strategy and analyzed with VOSviewer to visualize co-authorship patterns, country collaborations, keyword co-occurrences, thematic clusters, and temporal developments. The results indicate that risk management, risk assessment, and financial markets remain the most influential and frequently studied topics, while emerging themes such as sustainability, decentralized finance, cryptocurrency, and supply chain resilience reflect the field’s adaptation to evolving technological, economic, and environmental challenges. Collaboration analysis highlights the dominance of countries such as China, the United Kingdom, and India, alongside increasing participation from emerging economies. The study offers practical implications for policymakers and financial practitioners to align strategies with current research priorities, and theoretical contributions by identifying conceptual linkages and emerging research fronts. Limitations include reliance on a single database and the inherent biases of citation-based analysis.
The rapid digital transformation of the sports industry has opened up unprecedented opportunities for efficiency, transparency, and innovation. Traditional transaction models still suffer from significant challenges, including centralized control, lack of trust, and inefficiencies in revenue distribution. These issues often stem from reliance on intermediaries that introduce risks such as data manipulation, high operational costs, and delays in processing financial transactions. Blockchain technology presents a promising solution by enabling decentralized, secure, and transparent transactions, fostering greater trust among all stakeholders within the sports ecosystem. Existing approaches to digital transactions in the sports industry primarily depend on centralized financial institutions and third-party service providers, which not only limit transparency but also create barriers to financial inclusivity for athletes, clubs, sponsors, and fans. To address these critical limitations, we propose a blockchain-based digital transaction model that leverages smart contracts and distributed ledger technology (DLT) to enhance the efficiency, security, and fairness of transactions across the entire sports industry value chain. Our model integrates key economic principles with advanced network analysis to optimize revenue distribution, mitigate fraudulent activities, and enable real-time transaction verification. Through extensive simulations and empirical analysis, our results demonstrate a significant improvement in transaction speed, cost reduction, and overall transparency compared to conventional models. By decentralizing financial transactions, the proposed approach not only enhances financial inclusivity for all participants but also aligns with the broader vision of sustainable and equitable growth in the digital sports economy.
Cryptocurrency price prediction has become crucial for informed trading decisions due to the volatile nature of assets like Bitcoin, Ethereum, Ripple, and Litecoin. Traditional methods like ARIMA and GARCH struggle with this volatility, while modern approaches such as machine learning and deep learning provide better accuracy. This study evaluates advanced models, including LSTM, GRU, and Light GBM, to predict cryptocurrency prices and assess trading strategies before and after the COVID-19 pandemic. GRU and LSTM excel at identifying patterns in price data, with GRU performing best for Ripple. Ensemble methods like Light GBM proved highly accurate for Bitcoin and Ethereum across time periods. Simpler models like RNN were sufficient for Ripple and Litecoin. The COVID-19 pandemic significantly impacted market dynamics, emphasizing the importance of precise predictions. Trading strategies based on model predictions showed that ensemble methods like Light GBM yielded the highest profitability post-pandemic. The findings highlight the need to tailor models to specific cryptocurrencies and market conditions. Improved deep learning tools can enhance trading efficiency and provide actionable insights for investors and policymakers. Future research could focus on predicting multiple cryptocurrencies simultaneously and optimizing portfolio-based trading strategies. Key Words: LSTM, ARIMA, GARCH, RNN
This document provides a comprehensive analysis of sustainable data engineering practices, focusing on the ecological implications of contemporary methodologies. It examines power usage, carbon dioxide output, and electronic waste production in data centers, while exploring eco-friendly approaches such as energy-conserving hardware, streamlined data handling processes, and the adoption of sustainable power sources. The potential of AI enhanced optimization techniques, quantum computation, and distributed ledger systems to reduce environmental impact is also examined. The paper concludes with actionable strategies for corporations and regulators to enhance the sustainability of data engineering practices, ensuring that the expansion of our digital landscape does not occur at the cost of environmental health.
Umar Al Faruq, Dwi Fitrizal Salim, Farida Titik Kristanti
This study conducted a large-scale analysis to evaluate the performance of traditional and Markov-Switching GARCH (MS-GARCH) models to estimate the volatility of the top 10 cryptocurrencies by market capitalization. The study compared the performance of the models using goodness-of-fit measures, specifically the Deviance Information Criterion (DIC) and the Bayesian Predictive Information Criterion (BPC). Secondly, we assess the forecasting accuracy for one-day-ahead conditional volatility and Value-at-Risk (VaR). The results obtained show that, in a manner consistent with the findings for the broader cryptocurrency market, the time-varying regime-switching model exhibits superior performance in capturing the complex volatility patterns observed in cryptocurrencies when compared to the traditional GARCH model.
Recently, with the gradual development of machine learning technology, more and more people are trying to apply machine learning technology in various fields, and finance is one of the important fields. This work investigates the optimization of cryptocurrency portfolios by combining Long Short-Term Memory (LSTM) time series forecasting with traditional portfolio optimization methods. The focus of the paper is on using the historical price data from the past six years of Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) to train LSTM models, which are then used to predict the prices of these cryptocurrencies for the period from January to June 2024. These predictions are subsequently incorporated into an extended Markowitz framework to optimize the portfolio on a monthly basis. The results indicate that the LSTM-enhanced portfolio optimization method yields higher returns and better risk management compared to traditional methods. This finding could prove that it is feasible and effective to apply machine learning methods, especially time series forecasting methods, to cryptocurrency portfolios.
Luiz Koodi Hotta, Carlos Trucíos, Pedro L. Valls Pereira, Mauricio Zevallos
Recent studies have suggested that more complex models than GARCH are better suited for forecasting cryptocurrency risk measures, such as Value-at-Risk and Expected Shortfall. Among these studies, some highlight the advantages of MSGARCH models over traditional GARCH models. While improvements over single-regime GARCH models have been observed by using MSGARCH, the literature has only focused on the MSGARCH specification proposed by Haas, Mittnik and Paolella (Journal of Financial Econometrics, 2004) overlooking several other well-established MSGARCH specification alternatives. In this paper, we illustrate that exploring alternative MSGARCH specifications can lead to improvements in risk measure performance, emphasizing the potential benefits of using several specifications.
The cryptocurrency market is currently one of the most interesting areas for investment, attracting both experienced and casual investors. Although it can offer high returns, it also poses significant risks due to its high volatility. In this context, artificial intelligence, particularly through deep learning and machine learning algorithms, has played a key role in developing applications that provide investment advice, with the aim of maximizing returns and reducing investment risks. This study proposes a system for forecasting the closing prices of ten of the leading cryptocurrencies currently available in the market, presented in a web application capable of making predictions ranging from one to four hours. To achieve this, different models using various machine learning and deep learning algorithms were analyzed and tested, including Recurrent Neural Networks, time series analysis algorithms such as ARIMA, and even some more conventional regression algorithms. For algorithm comparison, minute step Bitcoin price data over a 30-day period was used to forecast prices 60 minutes ahead. Through extensive experimentation, the GRU neural network demonstrated superior predictive accuracy, achieving MAPE = 0.09\%, MSE = 5954.89, RMSE = 77.17, and MAE = 60.20. A web application was also developed, which integrates the best-performing model to provide real-time price predictions for multiple cryptocurrencies.
In an era where digitalization has dominated the financial world, cryptographic methods have become the foundation of secure transactions and data integrity. This report conducts an in-depth analysis of the cryptographic methods used in modern cryptocurrencies, namely Bitcoin and Ethereum, and traditional banking systems. The strengths, limitations and implications regarding security and scalability will be highlighted. Bitcoin, employing the usage of Elliptic Curve Cryptography (ECC) and the Secure Hash Algorithm (SHA-256) offers a robust and decentralized architecture heavily resistant to modern threats such as brute force attacks, as well as future threats that may arise with the rapid development of quantum computing. Ethereum takes the fundamental principles of Bitcoin, and enhances them with innovations like Keccak-256, and Recursive Length Prefix (RLP) encoding, optimizing the security and efficiency for complex operations such as smart contracts. Comparatively, traditional banking systems utilize a hybridized cryptographic system, incorporating the usage of methods like AES and ECC to balance security with performance within a centralized financial system, however often constrained by the vulnerabilities methods like AES brings, such as information leakage and overall human error. This comparative analysis highlights the trade-offs between these three systems, offering critical insights into the rapidly evolving role that cryptography is taking in shaping the future of the financial world. The findings presented in this report offer actionable recommendations for advancing cryptographic techniques and adopting decentralized systems to enhance the resilience of commonly used financial systems out in the world today.
Cryptocurrency markets are highly volatile and sentiment-driven, posing challenges to traditional forecasting methods. This paper presents Hard and Soft Information Fusion (HSIF), a novel Transformer-based dual-stream model that combines market data and social sentiment using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a financial sentiment analysis tool, and a bidirectional cross-attention mechanism. Evaluations on multi-year Bitcoin data show that HSIF achieves 97.48% accuracy and a 26.64% return, outperforming Long Short-Term Memory (LSTM)-based and other multimodal models. The results highlight the effectiveness of domain-specific sentiment embeddings and cross-modal attention in enhancing trend prediction accuracy for volatile cryptocurrency markets.
Since the introduction of Bitcoin in 2008, the size of the cryptocurrency market is becoming increasingly important for investors. Thus, the forecast of cryptocurrency price volatility is of particular interest to portfolio investors, as they are interested in accurately estimating the standard deviation of their portfolios to calculate the Value-at- Risk (VaR) as a risk measure for more optimal portfolio management. The HAR-RV model introduced by F. Corsi (in 2009) became more effective than the traditional GARCH type models in forecasting in the volatility of fi nancial assets. In the last decade, cryptocurrencies started to dominate both the social media and the fi nancial press. At the same time, some academic papers use social media data to enhance the cryptocurrency volatility forecasting models. In our paper, we study how the use of Google Trends data could improve the precision of one-day-ahead of Bitcoin price volatility models forecasts. We use three different measures of the forecast precision. All models are estimated in rolling windows in order to control for possible structural breaks. Also, we estimate the optimal length of rolling windows to provide the best forecast precision on the historical Bitcoin price data from January 1, 2018 to December 31, 2022. We verify that the predictive power of the chosen model statistically differs from other models via MCS-test. С появлением в 2008 г. биткоина размер рынка криптовалют вырос и стал важным для инвесторов. Таким образом, сегодня прогноз волатильности цены криптовалюты является предметом особого интереса для портфельных инвесторов, поскольку они заинтересованы в точной оценке стандартного отклонения их инвестиционных портфелей для вычисления сумм под риском (VaR) как меры риска для оптимального управления портфелем. HAR-RV-модель, предложенная F. Corsi (в 2009 г.), показала бóльшую эффективность при прогнозировании волатильности финансовых активов по сравнению с более традиционными моделями из семейства GARCH. В последнее десятилетие сообщения и статьи о криптовалютах стали все чаще появляться как в социальных медиа, так и в финансовой прессе. В то же время в некоторых академических исследованиях авторы используют данные социальных медиа для улучшения моделей прогноза волатильности криптовалют. В данной работе мы проводим анализ того, как использование данных Google Trends могут повысить точность моделей для однодневного прогноза волатильности цены биткоина. В исследовании мы используем три различные меры точности прогнозов. Все модели оцениваются в скользящих окнах для контроля на наличие структурных сдвигов в данных. Также мы подбираем оптимальный размер скользящего окна для получения наилучшего прогноза на исторических данных цены биткоина с 01.01.2018 по 31.12.2022. С помощью MCS-теста мы показываем, что прогнозная сила выбираемой модели статистически отличается от других.
Cryptocurrencies fluctuate in markets with high price volatility, which becomes a great challenge for investors. To aid investors in making informed decisions, systems predicting cryptocurrency market movements have been developed, commonly framed as feature-driven regression problems that focus solely on historical patterns favored by domain experts. However, these methods overlook three critical factors that significantly influence the cryptocurrency market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations, which can affect investors’ collaborative behaviors, 2) overall market sentiment, heavily influenced by news, which impacts investors’ strategies, and 3) technical indicators, which offer insights into overbought or oversold conditions, momentum, and market trends are often ignored despite their relevance in shaping short-term price movements. In this paper, we propose a dual prediction mechanism that enables the model to forecast the next day’s closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Furthermore, we introduce a novel refinement mechanism that enhances the prediction through market sentiment-based rescaling and fusion. In experiments, the proposed model achieves state-of-the-art performance (SOTA), consistently outperforming ten comparison methods in most cases. Our code and data can be found at https://github.com/aamitssharma07/SAL-Cryptopulse
Muhammad Yousaf, Muhammad Imran Tariq, Abdul Jabbar, Syed Qaiser Jalil
This comprehensive review paper explores the diverse landscape of cryptocurrency forecasting, tracing its evolution from an alternative to traditional monetary systems to its significant growth in the global financial arena. It consolidates existing research by categorizing and analyzing 234 scholarly articles, organizing them into machine learning, deep learning, deep reinforcement learning, and statistical methodologies, and evaluating the related metrics. The case study titled “Examining the performance differences between backtesting and forward testing” highlights the challenges investors face, as strategies that appear effective in backtesting often fail in practical use. Another case study, “Social Data Exploration in Cryptocurrency Trends,” examines how social media data can provide insights into market movements and investor sentiment, revealing the impact of social trends on cryptocurrency prices. The findings section provides a detailed view, illuminating trends such as yearly publication rates, methodological distributions, input features, training/testing splits, the total number of data samples considered, and forecasting time horizons. This survey paper serves as a valuable resource, providing researchers and investors with a solid foundation for understanding and navigating the dynamic field of cryptocurrency forecasting.
This paper argues that cryptocurrency, including cryptocurrencies such as Bitcoin, should be understood as qualified property. We build up support for this claim in three stages: first, we outline the diversity of cryptocurrencies, a diversity which is underappreciated in the current literature. We then outline the importance of theoretical presuppositions which operate in the ‘cryptocurrencies as property’ debate. This is followed by a detailed critique of established positions. We explore the shortcomings of three commonly-held views in the debate: that cryptocurrencies are property, belonging to a third category of personal property beyond the choses in possession/choses in action distinction; that cryptocurrencies are property and belong to the category of choses in action; and, lastly, that cryptocurrencies are not property at all. We then develop a new analysis of property in cryptocurrencies, taking into account both the variety of cryptocurrencies which exist and capturing the doctrine operating currently in property law, including its theoretical underpinnings. Through understanding rights in property as relative, and property itself as a scale, we avoid the shortcomings of other popular accounts whose shortcomings we identify, and show that cryptocurrencies are a form of choses in possession – namely, qualified property.
Pohsun Feng, Ziqian Bi, Yan, Lawrence K. Q., Yizhu Wen · 17 authors
A detailed exploration of blockchain technology and its applications across various fields is provided, beginning with an introduction to cryptography fundamentals, including symmetric and asymmetric encryption, and their roles in ensuring security and trust within blockchain systems. The structure and mechanics of Bitcoin and Ethereum are then examined, covering topics such as proof-of-work, proof-of-stake, and smart contracts. Practical applications of blockchain in industries like decentralized finance (DeFi), supply chain management, and identity authentication are highlighted. The discussion also extends to consensus mechanisms and scalability challenges in blockchain, offering insights into emerging technologies like Layer 2 solutions and cross-chain interoperability. The current state of academic research on blockchain and its potential future developments are also addressed.
With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.
The trinity of global warming, climate change, and air pollution casts an ominous shadow over society and the environment. At the heart of these threats lie carbon emissions, whose reduction has become paramount. Blockchain technology and the internet of things (IoT) emerge as innovative tools for establishing an efficient carbon credit exchange. This paper presents a blockchain and IoT-centric platform for carbon credit exchange, paving the way for transparent, secure, and effective trading. IoT devices play a pivotal role in monitoring and verifying carbon emissions, safeguarding the integrity and accountability of the trading process. Blockchain technology, with its decentralized and immutable nature, empowers the platform with transparency, reduced fraud, and enhanced accountability. This platform aims to arm organizations and individuals with the ability to actively curb carbon emissions, fostering collective efforts towards global pollution reduction goals.
Abstract The adoption of digital assets and distributed ledger technology in finance is rapidly increasing. This adoption introduces new types of risks, currently not adequately covered in conventional risk management frameworks. This paper identifies, reviews, and categorises these risks. It draws on a systematic review of literature and classifies the vulnerabilities by layer—network layer, consensus layer, protocol layer, and enablement layer.
This article attempts to challenge the argument that bitcoin cannot be owned within a libertarian legal order. According to the contested view, bitcoin, as a digital asset, does not meet the criteria for traditional ownership due to its nonphysical nature as an intangible asset. However, the counterargument presented in the article asserts that people should have property rights over bitcoin due to the facts that the technology behind bitcoin makes it a scarce rivalrous resource and conflicts over the use of bitcoin goods can arise. It is the general function of property rights to avoid possible clashes over the use of scarce, rivalrous resources by assigning rights of exclusive ownership; hence, property rights should extend to bitcoin. The article also discusses the implications of recognizing bitcoin as an ownable scarce resource within a private law society and addresses the challenges associated with penalizing bitcoin theft.