Blockchain Papers

Sonia Arsi

No abstract is available for this record.

Athapol Ruangkanjanases, Taqwa Hariguna

This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoin’s daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = –0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volume–price–volatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.

George Rooney, Cäzilia Loibl

No abstract is available for this record.

Jinghao Yang

Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.

Tamminen, Tyko

This Master's thesis investigates the application of machine learning methods to cryptocurrency market prediction and the development of hybrid trading strategies that combine predictive signals with decentralized finance yield components. The study addresses how machine learning models can predict directional shifts in cryptocurrency markets and whether integrating DeFi yield elements can improve risk-adjusted portfolio returns compared to traditional buy-and-hold approaches. The empirical investigation examined multiple machine learning architectures for binary directional forecasting of Bitcoin price movements. Models were trained on data spanning January 2018 to August 2024 using walk-forward validation. LightGBMRegressor achieved 53 % directional accuracy, while Random Forest reached 52 % accuracy. Other tested models, including LSTM networks and MLP, performed within the 51-56 % accuracy range. These results indicate that while machine learning methods demonstrate potential for market direction prediction when combined with properly formatted datasets and appropriate technical indicators, achieving high prediction accuracy remains challenging. A composed trading strategy was developed that integrated LSTM predictions with real-world DeFi yield rates from liquidity pools. The strategy utilized actual yield data to provide realistic performance assessment. Despite modest directional prediction accuracy of 53 %, the hybrid approach reduced drawdown by 50 % compared to the benchmark buy-and-hold strategy. The DeFi yield component compensated for imperfect directional signals, demonstrating that yield-enhanced strategies can achieve adequate risk-adjusted returns even without superior prediction accuracy. The study also examined structural differences between decentralized and traditional financial systems. DeFi offers global accessibility, programmable infrastructure, and fast settlement, but faces challenges including security vulnerabilities and regulatory uncertainty. However, the primary contribution lies in demonstrating that hybrid strategies combining machine learning signals with DeFi yield mechanisms represent a viable approach to portfolio management, when effective risk management is implemented.

Ayben Koy, Semra Demir, Andaç Batur Çolak

No abstract is available for this record.

Gang Chu, Michael Dowling, Xiao Li

No abstract is available for this record.

Krekel, William Peter

This dissertation examines the evolving market microstructure of digital assets, focusing on transaction costs, liquidity provision returns, and the development of innovative exchange mechanisms. In three essays, the research provides empirical evidence on digital asset trading in both traditional and emerging decentralized market architectures. Each essay addresses previously unresolved questions, offering valuable insights for researchers, practitioners, and regulators to better understand and manage the benefits, costs, and risks of trading in digital asset markets.The first essay examines the cost of trading across digital assets in traditional centralized limit-order-book exchanges and a nascent, decentralized market architecture: the Automated Market Maker. By employing a novel methodology the study extends prior research that relies on less detailed, low-frequency information. The findings reveal transaction cost advantages for Automated Market Makers with remarkable stability across varying levels of market volatility, trading volume, and market capitalization. These results offer practical insights into execution venue selection and market design considerations.The second essay explores the evolution of Automated Market Makers, using the introduction of a new generation of these exchange architectures as a case study. In addition to documenting their technical advancements, the research shows that asset pairs migrate to the new Automated-Market-Maker models based on asset-specific fundamentals. The study makes key contributions through two experimental setups, demonstrating that reductions in inventory costs and the introduction of flexible fee tiers deliver welfare benefits for both liquidity demanders and providers. These findings enrich the broader discussion on market design and highlight the potential for innovative mechanisms to enhance efficiency in both decentralized and traditional financial systems.The third essay sheds light on liquidity provision in Automated Market Makers. Leveraging granular profitability data, the study finds that a small subset of liquidity providers dominate liquidity provision. These sophisticated agents achieve significantly higher absolute and relative profits compared to retail participants, while demonstrating a high level of skill. The emergence of these de-facto intermediaries challenges the decentralized finance ethos of disintermediation, highlighting that liquidity provision, even in decentralized markets, remains dominated by specialists. Understanding the composition of participants in these nascent markets is not only crucial for practitioners but also regulators, enabling them to develop targeted and effective policies that promote fair and competitive market environments.

Marcin Stawarz, Michał Dominik Stasiak

We investigate a multi-class machine learning (ML) framework to generate daily Bitcoin trading signals—Buy, Sell, or Hold. Three algorithms—XGBoost, LightGBM, and Random Forest—are compared with a naive buy-and-hold strategy. Using BTC/USD daily data (2015–2024), we apply a range of technical indicators across trend, momentum, volatility, and volume, later pruned by correlation analysis. A ±1% threshold defines the "Hold" zone to avoid minor fluctuations. Empirical tests show that LightGBM outperforms other models and even surpasses buy-and-hold in final portfolio value. Our findings support the design of tri-class ML strategies tailored for high-volatility markets like cryptocurrency.

Daisuke Yoshikawa

What are the key factors determining cryptocurrency prices? This study presents a novel perspective that considers the unique characteristics of the cryptocurrency market. While previous studies have used the value-weighted return of the entire cryptocurrency market as a proxy for the market return, this study demonstrates that Bitcoin (BTC) related features serve as the primary determinant of other cryptocurrencies’ prices. Furthermore, we find that BTC’s own price dynamics are primarily driven by trend-related factors. This finding highlights a fundamental difference in market structure compared to traditional equity markets, where market return as the value-weighted return of the entire stock market is dominant in shaping individual stock prices. To derive these conclusions, this study employs factor analysis using machine learning models such as Random Forest, LightGBM, and Transformer, in addition to a traditional linear predictor, to better capture the complexity of the cryptocurrency market. The findings of this study call for a reconsideration of analytical methods in cryptocurrency pricing and suggest practical implications for BTC-based market analysis and ETF design.

Rebeka Gulyás, Veronika Gál, Zoltán Sipiczki

This study explores how integrating cryptocurrencies into traditional financial portfolios can influence investment performance. Focusing on Bitcoin and Ethereum alongside key European stock indices (BUX, DAX, and FTSE), the analysis examines whether blockchain-based assets can enhance diversification and improve the balance between risk and return. Using weekly market data from 2019 to 2023, the research applies Markowitz mean–variance optimization to identify optimal asset allocations under different objectives such as maximizing the Sharpe ratio, minimizing risk, and maximizing returns. The findings reveal that cryptocurrencies show weak correlations with European stock indices, suggesting meaningful diversification potential. When included in portfolios, Bitcoin and Ethereum can significantly boost returns, though they also increase volatility. Portfolios optimized for risk reduction favored traditional indices, while those targeting higher returns relied predominantly on cryptocurrencies. Overall, combining digital and conventional assets produced a more balanced performance, with the Sharpe-ratio–maximized portfolio demonstrating the best trade‐off between stability and profitability. These results indicate that cryptocurrencies can play a valuable complementary role in modern portfolio construction. They are most suitable for investors willing to accept higher risk in exchange for potentially greater rewards, while more risk‐averse investors may benefit from maintaining a stronger focus on traditional equity indices. The study contributes to understanding how blockchain‐driven assets can expand financial opportunities and supports a broader view of diversification in contemporary investment strategies.

Jinyi Zhao, Haifeng Guo, Yuxi Zhang

No abstract is available for this record.

Jeremy Eng‐Tuck Cheah, Thong Dao, Hung Do, Tapas Mishra

ABSTRACT This paper investigates the stability and co‐movement of cryptocurrency assets in Decentralized Finance (DeFi), with a focus on the Speed of Adjustment (SA), the rate at which shocks dissipate, and prices revert to long‐run equilibrium. SA provides a critical measure of market efficiency and portfolio allocation in a highly volatile DeFi environment. We extend conventional cointegration analysis by applying a Fractionally Cointegrated Vector Autoregressive framework, which captures slow error corrections. Rolling estimations generate a time‐varying series of SA, allowing examination of its evolution and cross‐asset spillovers. The results reveal multiple cointegrating relationships, heterogeneous adjustment speeds, and strong contagion effects among DeFi assets. For instance, RPL exhibits rapid yet volatile adjustment, while LDO, BAL, and SNX revert more slowly, reflecting distinct risk‐return trade‐offs. Spillover analysis highlights high systemic interconnectedness, underscoring challenges for diversification and contagion management. Overall, dynamic SA emerges as a valuable forward‐looking indicator of stability in digital asset markets.

Deborah Adedigba, David Agbolade, Raza Hasan

Cryptocurrency markets are characterized by high volatility and complex patterns, creating both challenges and opportunities for traders and investors. This study introduces a machine learning framework for cryptocurrency trading optimization that leverages advanced analytical techniques to enhance trading decisions. We extracted historical data for 30 cryptocurrencies over a four-year period from Yahoo Finance. After preprocessing, we applied Principal Component Analysis (PCA) and K-means clustering to select representative coins. Four machine learning models (Gradient Boosting, XGBoost, Support Vector Regression, and Long Short-Term Memory networks) were trained to predict cryptocurrency price movements. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ). Gradient Boosting and XGBoost consistently outperformed SVR and LSTM models across all cryptocurrencies, with R 2 values of approximately 0.98 for most coins. The framework successfully identified trading signals through both moving average strategies and machine learning predictions, providing actionable insights for cryptocurrency traders. Our analysis demonstrates that ensemble-based models offer superior performance for cryptocurrency price prediction compared to neural network approaches. The integration of advanced visualization tools and trading signal generation creates a comprehensive system for data-driven cryptocurrency trading decisions.

Renuka Nuakarkar

This research paper presents the design and development of a Cryptocurrency Dashboard that applies data analytics techniques to the financial technology sector. The goal of this project is to visualize historical cryptocurrency data such as market capitalization, trading volume, and price fluctuations through an interactive and user-friendly interface. Using tools such as Python and Power BI, data was collected, cleaned, analyzed, and visualized to provide dynamic insights for investors and analysts. The dashboard enables efficient decision-making by simplifying complex financial data into clear and interpretable visuals. The study demonstrates how data analytics enhances understanding of cryptocurrency trends and contributes to evidence-based financial analysis in the digital economy.

Melanie Cao, Andy Hou

This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.

Ansh Goyanka, Shlok Khairnar, Harsh Jaisingpure, Anuradha Yenkikar · 6 authors

In the rapidly evolving decentralized finance landscape-where retail traders struggle to compete alongside institutional traders, this project provides a dedicated, Artificial Intelligence (AI) -powered trading assistant equipped with the ability to unlock professional(pro) trading strategies without any need to code. By increasingly integrating a trained machine learning model, where the database consists of over 1 million Solana memecoin data points, with a conversational AI interface and on-demand, blockchain-level analytics, the system enables users to trade on tokens such as Base Mainnet or Solana assets with one natural language command. The AI queries live Decentralised Exchange (DEX) data through The Graph protocol, determines matches, and generates$\mathbf{1 5}$-minute price predictions using the Long-Short Term Memory (LSTM) neural network. The AI performs all processes autonomously, allowing it to manage wallets and make trades using an independently validated Return of Interest (ROI) of 30.57 %. The platform simplifies candlestick patterns, liquidity-level data, and market indicators into chat-based task workflows over a 3-step process. The model is created with TensorFlow for predictive analytics, Collateralised Debt Position (CDP) Agents for engagement, and uses Coinbase's Software Development Kit (SDK) for trading. This research proves that AI can level the playing field for casual traders using complex algorithmic trading strategies and data through simple human engagement.

Kareem Kamal, Khaushbakht Kamal, Kainat Mustafa, Rashid Kamal · 9 authors

Cryptocurrency price prediction poses significant challenges due to the inherent volatility and nonlineardynamics of the market. This study introduces a hybrid stacked modeling framework that integrates machine learning (ML) and deep learning (DL) techniques, capitalizing on their complementary strengths-ML models are effective at capturing nonlinearfeature interactions in structured data, while DL architectures are adept at modeling temporal dependencies in sequential data. The proposed model leverages historical price data, technical indicators, macroeconomic variables, and sentiment metrics, with feature engineering applied to enhance predictive capability. Empirical evaluation was conducted through two experimental setups: (i) short-term, monthly segment analysis and (ii) long-term generalization via five-fold cross-validation. The hybrid model outperformed individual baseline models, achieving up to 18.3% lower RMSE and 6.7% higher directional accuracy. Additionally, it yielded superior risk-adjusted returns, with Sharpe Ratios reaching 0.094 on the Ethereum dataset. Beyond technical improvements, this research offers foresight into digital financial markets, providing a robust tool for investors, institutions, and policymakers navigating the evolving cryptocurrency landscape. The model supports more informed decision-making, enhances market oversight, and contributes to the development of adaptive regulatory frameworks for digital finance.

Xinxin Yu, Sin Huei Ng, Moau-Yong Toh

This paper analyzes the time-varying herding behavior in the non-fungible token (NFTs) and cryptocurrency markets and investigates their interrelationship. Using the daily market data from January 1st, 2020 to April 30th, 2023, our study covers the period characterized by Covid and post-Covid-19 induced global financial market volatility, capturing the dynamics in the global macroeconomic system and the Federal Reserve’s interest rate policy. Based on the rolling window method, our findings show the presence of herding behavior in both markets, where herding behavior in these markets may be influenced by the major events announcements particularly those related to the Federal Reserve's interest rate policy. Vector error correction model (VECM) indicates that the NFT market impacts the price of Ethereum, thereby influencing the broader cryptocurrency market. Such finding contributes to a deeper understanding of the market dynamics. By examining herding behavior, our findings indicate that the NFT market demonstrates relative independence from the volatile prices of the cryptocurrency market, suggesting the potential diversification benefits of incorporating NFTs for investors’ portfolio construction and risk management.

Oluseun Paseda

Purpose This paper reviews the application of game theory in finance, focusing on its role in modeling strategic interactions among market participants. It synthesizes classical models such as Nash equilibrium and signaling games while integrating emerging themes including behavioral finance, sustainability-linked decisions, decentralized finance (DeFi) and artificial intelligence (AI)-driven agents. The study aims to highlight how game-theoretic frameworks inform financial decision-making, market design and governance and to identify conceptual gaps and future research directions. Design/methodology/approach The study employs a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol, complemented by bibliometric mapping using VOSviewer. It analyzes 78 peer-reviewed articles published between 2000 and 2025 across five finance domains: asset pricing, corporate finance, investment strategies, financial markets and behavioral finance. Conceptual frameworks and taxonomies are developed to categorize game-theoretic models by strategic orientation and information structure, offering a structured synthesis of theoretical advancements and practical applications. Findings Game theory enhances understanding of strategic behavior in finance, particularly under conditions of asymmetric information and market complexity. Key findings include the relevance of signaling games in initial public offerings pricing, repeated games in environmental, social and governance commitments and mechanism design in DeFi governance. The review identifies gaps in behavioral integration, empirical validation and modeling of decentralized ecosystems. It proposes future research directions involving multi-agent learning, adaptive mechanism design and sustainability-linked financial strategies. Research limitations/implications The review is limited by its focus on published literature and may exclude emerging models in unpublished or proprietary research. Empirical validation of proposed frameworks remains a future research priority. Practical implications The paper offers actionable insights for regulators, investors and policymakers by applying game-theoretic tools to systemic risk management, portfolio allocation and financial regulation in digitized markets. Originality/value This study provides a novel synthesis of game theory’s evolution in finance, introducing conceptual frameworks that integrate behavioral, technological and sustainability-linked dimensions.

Dasril Aldo, Dimas Fanny Permadi Hebrasianto, Dedy Agung Prabowo, Miftahul Ilmi · 5 authors

Cryptocurrency markets are highly volatile, posing significant challenges for accurate price prediction. Solana (SOL), one of the largest cryptocurrencies by market capitalization, experiences sharp fluctuations that limit the effectiveness of traditional linear models such as Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH). To address this, the present study evaluates twelve Long ShortTerm Memory (LSTM) architecture variants, including vanilla LSTM, stacked LSTM, bidirectional LSTM, and hybrid models with attention and dropout mechanisms, for forecasting daily closing prices of the SOL/USD pair. Five years of OHLCV data from Coinbase were used, with an $80 / 10 / 10$ split for training, validation, and testing. Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and $\mathbf{R}^{\mathbf{2}}$. Results show that the LSTM-Base model (60-day window, 1-day horizon) achieved the best accuracy (MAE $=5.38$, RMSE $=6.73$, MAPE $=3.27 \%, \mathbf{R}^{\mathbf{2}}=\mathbf{0. 8 2}$), while LSTM-LB120-H7 (120-day window, 7-day horizon) performed poorly (MAE $=14.09$, RMSE $=17.44, \mathbf{R}^{2}=-0.34$). These findings highlight that simpler LSTM configurations are more effective for volatile crypto assets, offering both academic insight and practical benchmarks for traders, analysts, and policymakers.

Claudio Schapsis, Dorin Micu, Nikki Wingate

No abstract is available for this record.

Alex Brockbank, Charlene M. Kalenkoski, Christopher R. Browning, Michael Guillemette

Do financial advisors recommend cryptocurrency investment within a household portfolio? Cryptocurrencies have emerged in popularity as households seek to maximize returns. Financial advisors are expected to provide beneficial advice for a household in managing financial decisions including investments. The existing literature has examined this relatively new form of investing and found some determinants for cryptocurrency investment but has not sufficiently explored the association between this investment option and the investor’s use of a financial advisor. With data from the 2018 wave of the National Financial Capabilities Study (NFCS), this paper examines the relationship between cryptocurrency investment and the use of a financial advisor for American investors. The results suggest that investors who use a financial advisor are more likely to be invested in cryptocurrencies. Additional determinants seen in previous works are also confirmed in the current study; showing that men, younger investors, married investors, and investors with a higher tolerance for risk are more likely to have cryptocurrency investments.

Battilana, Matteo

No abstract is available for this record.