Blockchain Papers

Zheng Cao

No abstract is available for this record.

Matthias Milchrahm

Diese Arbeit untersucht, wie sich MicroStrategys Rolle als Bitcoin-Fonds auf die Aktienbewertung unter unterschiedlichen Marktbedingungen auswirkt und in welchem Verhältnis die Bewertung zur Höhe der gehaltenen Bitcoin-Bestände steht. Seit der Einführung der Bitcoin-Strategie im Jahr 2020 haben sich Bilanzstruktur, Marktwahrnehmung und Bewertungsdynamik des Unternehmens deutlich verändert. Diese Entwicklung wird anhand von Finanzdaten aus dem Zeitraum Q3 2020 bis Q4 2024 analysiert, die aus professionellen Finanzsystemen wie LSEG Workspace stammen. Die Daten umfassen historische Preisinformationen, Marktkapitalisierung und Bilanzauszüge und wurden zur Visualisierung in Excel aufbereitet. Vergleichende Kursverläufe zwischen Bitcoin und MicroStrategy wurden direkt in LSEG und Bloomberg erstellt.Zur Bewertung werden Kennzahlen wie das market capitalization-to-net asset value multiple (mNAV) sowie das Kurs-Buchwert-Verhältnis (P/B) herangezogen, wobei sich mNAV als aussagekräftiger erweist, insbesondere angesichts der Einschränkungen der bilanziellen Behandlung digitaler Vermögenswerte. Die empirische Analyse auf Basis von Zeitreihen, Bewertungsmultiplikatoren, Preisbeziehungen und marktrelevanten Ereignissen zeigt, dass MicroStrategy kein statisches Bewertungsverhältnis zu seinen Bitcoin-Beständen aufrechterhält. Vielmehr handelt die Aktie in Bullenmärkten häufig mit deutlichen Bewertungsaufschlägen, während in Bärenmärkten eher Abschläge oder Bewertungen nahe dem inneren Wert beobachtet werden. Dies deutet darauf hin, dass MicroStrategy oft als High-Beta-Proxy für Bitcoin agiert und nicht durchgehend als klassischer Fonds, der strikt zum NAV notiert.Obwohl die Korrelation zum Bitcoin-Kurs hoch ist, zeigen bestimmte Marktzyklen, dass MicroStrategy Kursanstiege häufig anführt und sich dabei überproportional bewegt, insbesondere in Aufwärtsphasen, was sich in teils sehr hohen Bewertungen niederschlägt. Abschließend erfolgt ein Vergleich mit dem Grayscale Bitcoin Trust (GBTC). Obwohl es sich hierbei um einen regulierten Bitcoin-Fonds handelt und MicroStrategy ein börsennotiertes Unternehmen ist, zeigt der Vergleich deutliche Parallelen in der Bewertungslogik. Dies erlaubt zusätzliche Einblicke in strukturelle und verhaltensbezogene Gemeinsamkeiten beider Vehikel.

Jobaer Hossain

No abstract is available for this record.

Steven Msomi, Andile Nyandeni

The study analyses the spillover effects of cryptocurrencies to establish if cryptocurrencies possess any hedging abilities for South African markets. Different and ZAR/USD exchange rate, Gold and Johannesburg All Share Index (JSE-ALSI) were studies between the period 01/01/2016 to 31/12/2020. The study employed the Baba, Engle, Kraft and Kroner (BEKK) and multiplicative dynamic conditional correlation (MDCC) multivariate generalised autoregressive conditional heteroscedasticity (GARCH) models. The results of the study indicate the presence of volatility spillovers from the cryptocurrencies to the South African markets through the JSE market and the Rand. A bidirectional shock transmission between the JSE market and Bitcoin and a unidirectional spillovers from Dogecoin and Litecoin to JSE was found. The study also proved that cryptocurrencies are not yet at a stage where they can replace Gold as a hedge tool. The results show predominantly low correlations between the South African market (JSE and The Rand) and cryptocurrencies. Suggesting the presence of diversification and hedging abilities of cryptocurrencies.

Krishna Mula

This article examines the transformative evolution of transaction processing systems from traditional batch processing to real-time payment mechanisms. The historical progression and architectural distinctions between these paradigms while analyzing the critical transition factors that facilitated this evolution. The discussion encompasses the enabling technologies—including API-driven banking, distributed ledger solutions, and cloud computing infrastructure—that have revolutionized payment processing capabilities. Through the demonstration of current implementation cases across peer-to-peer transfers, business transactions, and international remittances, the article provides insights into practical applications and market adoption patterns. The exploration extends to emerging trends, including central bank digital currencies, artificial intelligence for fraud detection, and enhanced security frameworks. The article concludes with a forward-looking discussion of research imperatives addressing cross-border payment efficiency, monetary policy implications in real-time environments, and financial inclusion opportunities through modernized payment infrastructure. This comprehensive article provides valuable perspectives on the technological, operational, and policy dimensions of payment system evolution for financial professionals, technology implementers, and policy researchers.

Syed Arslan Abbas, Nor Shaipah Abdul Wahab, Firdous Mohd Farouk

No abstract is available for this record.

Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem · 5 authors

Cryptocurrencies have reshaped finance with secure, decentralized trading, attracting investor interest due to high volatility and potential returns. Accurate price forecasting is essential for optimizing returns and managing risks in digital markets. This study introduces OPTICALS, a novel framework for daily cryptocurrency price forecasting, focusing on transparency, robust performance assessment, and interpretability in machine and deep learning models. Unlike existing methods, OPTICALS provides detailed insights into model predictions by optimizing hyperparameters and identifying each model’s strengths and limitations. The framework evaluates five models-XGBoost, LightGBM, LSTM, Bi-LSTM, and GRU-on three major cryptocurrencies: Ethereum, Binance, and Solana, known for high trading volumes and distinct characteristics. OPTICALS incorporates a “Look-back window” hyperparameter, using recent historical prices to predict next-day trends through Moving Averages analysis. This parameter refines lagged feature engineering to enhance trend capture and predictive accuracy. Models underwent rigorous evaluation, including multiple simulations and hyperparameter tuning. Gradient Boosting models were tuned via GridSearchCV and regularization to improve performance through diverse ensembles. RNN models were optimized by adjusting neurons, stacks, epochs, batch sizes, and optimizers. Predictions were validated against one-week-ahead prices to ensure robust accuracy. Findings show that GRU and XGBoost excel at predicting real-time trends, with GRU supporting day trading and XGBoost benefiting swing trading. This study advances cryptocurrency analytics, providing practical forecasting tools for traders, investors, and institutions to navigate volatility and manage risks effectively.

Basma Almisshal, Halil İbrahim Bulut

No abstract is available for this record.

Qihong Ruan

No abstract is available for this record.

Shirui Wang, Tianyang Zhang

No abstract is available for this record.

Anthony Alexander

No abstract is available for this record.

Bahram Alidaee, Haibo Wang, Wendy Wang

Since the introduction of Modern Portfolio Theory (MPT) in 1952, its practical applications, associated challenges, and computational efficiency on large and high-frequency datasets, particularly datasets not used to develop and optimize the model, have drawn extensive research interest. This study has examined the performance of various portfolio models that have explored the concept of MPT on U.S. stock and cryptocurrency markets, i.e., discrete Markowitz portfolio selection (DMPS), the optimal dynamic portfolio (ODP), the binary unconstrained ODP (BUODP) with a quantum annealing solver, and the 1/N naive diversification (ND). Their performance is then compared to the indices that measure the performance of corresponding market exchange-traded funds (ETFs) for stock markets. Our findings show that the DMPS and ODP perform better than other models, delivering better returns in a shorter period. Both run significantly faster than the BUODP (with quantum annealing) with computation time of approximately 0.5 seconds for the S&P 400, 500, and 600 markets whereas BUODP takes 30 seconds; they also mitigate risk and outperform ETFs and ND model for the out-of-sample test with diversified portfolios combining NASDAQ stocks and cryptocurrencies. Furthermore, we have analyzed the impact of data with different frequency intervals, i.e., weekly, daily, hourly, and one-minute, on portfolio performance of the stock markets. The results suggest that data collection frequencies do not make differences in portfolio selections and weights. This study contributes to the advancement of portfolio theory, providing insights and practical values, especially in addressing computational efficiency for high-frequency and large-scale datasets and saving computational costs.

<p>Zhongyuan Xu</p>

The cryptocurrency market poses a huge challenge to portfolio optimization due to its high volatility and complex market dynamics. To address these issues, this paper uses reinforcement learning (RL) algorithms for dynamic portfolio optimization, aiming to improve the return and risk control capabilities of the portfolio through intelligent decision-making. This paper adopts a strategy based on deep reinforcement learning. By interacting with the cryptocurrency market, the agent can continuously optimize asset allocation, maximize investment returns while controlling volatility. The experimental results show that compared with traditional strategies, the reinforcement learning model has obvious advantages in key indicators such as cumulative return rate, annualized volatility, maximum drawdown and Sharpe ratio. Specifically, the cumulative return rate of the reinforcement learning model reaches 85.12%, the annualized volatility is 45.76%, and the maximum drawdown is controlled at -22.34%, showing strong income acquisition and risk management capabilities. In addition, the dynamic adjustment of asset allocation has optimized the weights of various cryptocurrencies, effectively dispersed risks, and improved the overall performance of the investment portfolio.

Jingrui Li, Divykumar Patel

No abstract is available for this record.

Seyedeh Fatemeh Mottaghi, Bertram I. Steininger

No abstract is available for this record.

Josué Thélissaint

No abstract is available for this record.

Matthias Franz Krekeler

No abstract is available for this record.

William C. Johnson

No abstract is available for this record.

Ismail Jirou, Ikram Jebabli, Amine Lahiani

No abstract is available for this record.

Min-Bin Lin, anon anon, Ruitong Wang, Daniel Traian Pele

No abstract is available for this record.

Murray A. Rudd, Dennis Porter

We refine a bottom-up, quantity-clearing framework of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. This approach relies on first-principles economic supply-and-demand dynamics rather than assumptions about anticipated Bitcoin price appreciation, its price history, or its potential effectiveness in demonetizing other asset classes. We considered five key high-level factors that may affect price determination: level of market demand; intertemporal investment preferences; fiat-denominated withdrawal sensitivity; initial liquid supply; and daily withdrawal levels from liquid supply. With a goal of both increasing understanding of the impacts of price drivers and developing probabilistic forecasts, we show two models: (1) a baseline to assess the impacts of parameter changes, alone and in combination, on Bitcoin price trajectories and liquid supply over time and (2) a Monte Carlo simulation that incorporates uncertainty across a range of uncertain parameterizations and presents probabilistic price and liquid supply forecasts to 2036. Our baseline model highlighted the importance of liquid supply and withdrawal sensitivity in price impacts. The Monte Carlo simulation results suggest a 50% likelihood that Bitcoin price will exceed USD 5.17 M by April 2036. Generally, prices from the low single millions to the low tens of millions per Bitcoin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below BTC 2 M and withdrawal sensitivity is low. Our results help locate where right-tail risk and disorderly market outcomes concentrate and suggest that policy tools are available to help guide trajectories.

Aadi Singhi

This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Language Models (LLMs) for alpha generation and portfolio management in the cryptocurrencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to model using static regression models or neural networks trained solely on historical data. The proposed framework overcomes this by structuring LLMs into specialised agents for technical analysis, sentiment evaluation, decision-making, and performance reflection. The agents improve over time via a novel verbal feedback mechanism where a Reflect agent provides daily and weekly natural-language critiques of trading decisions. These textual evaluations are then injected into future prompts of the agents, allowing them to adjust allocation logic without weight updates or finetuning. Back-testing on Bitcoin price data from July 2024 to April 2025 shows consistent outperformance across market regimes: the Quantitative agent delivered over 30\% higher returns in bullish phases and 15\% overall gains versus buy-and-hold, while the sentiment-driven agent turned sideways markets from a small loss into a gain of over 100\%. Adding weekly feedback further improved total performance by 31\% and reduced bearish losses by 10\%. The results demonstrate that verbal feedback represents a new, scalable, and low-cost approach of tuning LLMs for financial goals.

Xiaohang Ren, Wenting Jiang, Kun Duan, Tapas Mishra

As one of the most prominent cryptocurrencies, Bitcoin has been at the forefront of a major revolution in the financial and technological sectors. This study utilizes data from social media to extract the emotional tendencies of investors in the Bitcoin market and analyze differences in investor behavior under various emotional features. We find that when investors exhibit reluctance (such as Sadness and Fear) to buy Bitcoin, it is the opportune moment to invest and achieve returns higher than expected. Conversely, when the emotional tone of investors becomes positive (such as Joy and Love), indicating a tendency to invest, we choose to avoid investing. Our research has also revealed that such emotional cues can assist in better predicting returns in the Bitcoin market. Analyzing market emotions contributes to a deeper understanding of market fluctuations and investor behavior. Our findings help stakeholders recognize the role of subjective emotions in the market and provide them with prudent investment advice: avoid relying excessively on the feelings of others, as this may trigger investment losses.

Giovanni Arroyo, Lawrence Millen

No abstract is available for this record.