Blockchain Papers

Ozan Nadirgil

No abstract is available for this record.

Michel Zaki Guirguis

No abstract is available for this record.

Le, Hau

This thesis aims to understand the nature of Bitcoin and the characteristics of Bitcoin-related equities using established asset pricing frameworks. It involves the empirical testing of two hypotheses. The first hypothesis posits that Bitcoin returns should be priced in the cross-section of expected stock returns, with a negative risk premium. Using a sample of 5,091 U.S.-listed stocks from March 2011 to April 2024, the cross-sectional analysis indicates that the risk premium associated with Bitcoin returns is not statistically significant. This finding challenges the “digital gold” narrative, which implies that Bitcoin functions as a safe-haven asset. Instead, the evidence suggests that portfolios with extreme Bitcoin betas consistently yield abnormal negative future returns, revealing a non-linear, inverted U-shaped relationship between Bitcoin beta and expected stock returns. While abnormal negative returns align more closely with speculative behavior, the interpretation regarding Bitcoin’s role remains theoretically challenging, as portfolios with the lowest Bitcoin betas also exhibit abnormal negative returns. The second hypothesis examines the risk determinants of Bitcoin-related stocks. This analysis is based on a sample of 20 Bitcoin-holding firms listed in the U.S. market, covering the three-year period from January 2020 to December 2022. The results indicate that the stock returns of these firms are significantly exposed to daily Bitcoin price fluctuations, exhibiting a positive beta. Additionally, the stock returns of Bitcoin-mining firms in the sample are significantly influenced by changes in Bitcoin mining difficulty, with a negative sensitivity—an effect not observed in other types of Bitcoin-holding firms. This suggests that Bitcoin-specific risk factors beyond price fluctuations may play a role in the risk-return dynamics of Bitcoin-related equities. Furthermore, a reverse size effect is observed within this sector: Bitcoin-related firms with larger market capitalizations tend to generate higher returns compared to smaller firms. This finding holds important implications for industry practice since it challenges the conventional belief that smaller stocks typically yield higher returns.

H. C. Li

We selected the daily trading data of BTC, SPY, DXY, GLD, and QQQ from Yahoo Finance, aiming to analyze the role of BTC in portfolios. This paper believes that BTC, as a high-risk asset, is speculative. Through correlation analysis, its returns were found to be independent of other traditional assets, proving that applying BTC to investment strategies could create arbitrage opportunities. Through various asset combinations in investment portfolio experiments, we found that the intervention of BTC could enhance the returns and optimal Sharpe ratio of the original investment portfolio, and the increase in the optimal Sharpe ratio decreased as the number of assets in the portfolio except for BTC increased. Therefore, for ordinary investors, we suggest adding 10% - 20% of BTC to a single asset. Through out-of-sample testing, we found that the investment strategy that includes BTC investment based on historical data, although it could not achieve the optimal Sharpe ratio, would have higher returns than the optimal Sharpe ratio investment portfolio without BTC intervention in the current period, considering that investors have certain risk tolerance, we believe that the effectiveness of historical investment strategies can be verified.

Jingrui Li, Ruming Liu, Steve Y. Yang

No abstract is available for this record.

Aaryan Gupta, Mayank Puri Goswami, Pratyush Tak, Varun Tiwari

No abstract is available for this record.

Lydia Deborah Isaac, Srisanjana Arunkumar, Vigneshwaran Sundaramurthi, Gowthamraj Bommannan

No abstract is available for this record.

Gading Aditya Perdana, Mochammad Irgi Aulia Kisdi, Irma Kartika Wairooy, Brilly Andro Makalew

We propose a novel machine learning framework to quantify the effects of regulatory policies on GDP normalized Bitcoin trading volume in the United States, Russia, and Indonesia. Our panel dataset integrates Bitcoin price series, country level adoption rates (2021–2024), macroeconomic indicators, and granular policy variables. An XGBoost regression model predicts future trading volume, and SHAP values to further elucidate feature importance and interactions. Using this calibrated model, we conduct policy ablation simulations by selectively removing regulatory instruments asset classification, licensing, taxation, AML/KYC stringency, and payment bans. Results indicate jurisdiction specific sensitivities: removing AML enforcement in the United States increases volume by +71.45%, while eliminating taxation in Indonesia reduces volume by -46.90%. Comprehensive removal of all regulations yields mixed outcomes: a +26.38% increase in the United States, versus -6.27% in Russia and -47.55% in Indonesia. These findings offer quantitative insights into the trade offs faced by policymakers when designing cryptocurrency regulation.

Abdullah Amberkhani, Harshitha Bolisetty, Ranjith Narasimhaiah, Ghulam Jilani · 9 authors

No abstract is available for this record.

Priyanka Ghosh, Ashwitha Shetty, K. A. Thanuja, K. Vinodha Devi

No abstract is available for this record.

Richard Beainy, Cesar Kamel

No abstract is available for this record.

Sumin Li, Rentao Wang, Yudong Wan, Jincheng Hu

The accurate prediction of cryptocurrency prices remains challenging due to their high volatility, which is driven by complex factors including market dynamics, macroeconomic conditions, and investor sentiment. Traditional econometric models, standalone machine learning methods, and deep learning architectures have shown limited effectiveness in capturing both short-term variations and long-range dependencies. To address these limitations, a hybrid deep learning model, L-FED, is proposed by integrating long-short term memory (LSTM) network with the FEDformer architecture, augmented by sentiment analysis. A parallel framework is adopted to enable bidirectional information interaction through local-global collaborative learning. A comprehensive feature engineering approach is also introduced, incorporating historical trading data, technical indicators, sentiment features, and LSTM-derived short-term guiding prices. The experimental results demonstrate that L-FED outperforms the existing baseline models in terms of prediction accuracy. On the Bitcoin and Ethereum datasets, L-FED achieves improvements of 16% and 12.8% in RMSE and MAPE, respectively, for Bitcoin, and 11.6% and 6.4% for Ethereum. Furthermore, sentiment analysis using the CryptoBERT model enhances price prediction accuracy by 19% and 2.9%, respectively, attributable to its pre-training on a large, domain-specific cryptocurrency corpus. Our code and datasets are publicly available at https://github.com/lsm-2024/L-FED.

N. Hafidi, Zakaria Khoudi, Mourad Nachaoui, Soufiane Lyaqini

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.

Weiqi Chen, Hang Zheng

The precise forecast of cryptocurrency prices is essential for portfolio investment because of their volatility and operability in virtual trading markets, which poses a huge trouble to investors' decision-making ability and investment planning. In this paper, we concentrated on the prediction of the recent trends of mainstream cryptocurrencies and selected them to optimize the portfolio to maximize profit and reduce risk. We used neural networks to solve this problem, which has three layers, including the LSTM layer, dropout layer, and dense layer. We focused on BCH, BTC, ETH, ETC, LTC, EOS, and XRP and collected their datasets for estimations. An LSTM model, a multi-task learning model, and a novel loss function, where the Negative Sharpe Ratio is provided, were implemented to predict the best portfolio (weights) for the cryptocurrencies mentioned above. The common evaluation indices, such as MSE, RMSE, MAE, and R-square ($ {R}^{2} $), can demonstrate the accuracy and reliability of the Neural Network models. Due to the significant price differences among currencies, the values of MSE, MSE, and MAE were large, making it difficult to evaluate their accuracy. Therefore, $ {R}^{2} $ was adopted. Finally, we simulated the portfolio investment and saw the revenue compared to the existing approaches. The new machine learning model abandoned the previous methods, which only predicted and analyzed a single cryptocurrency and ignored the correlations among them. Therefore, our innovation of this research was to use neural networks to consider investment plans combining multiple currencies while minimizing volatility and ensuring a large Sharpe Ratio, thereby obtaining the best portfolio investment of cryptocurrencies.

L. Yeswanth, M. Gunasekaran

No abstract is available for this record.

Anthony Alexander

No abstract is available for this record.

Xin Chen, Sicheng Wang, Xiaolan Yang, Xiaolan Yang · 7 authors

No abstract is available for this record.

Parijata Majumdar, Shankar Debnath, Sanjoy Mitra, Joyjit Dhar · 5 authors

No abstract is available for this record.

Reza Roshanpour, Aliakbar Khosravinejad, Gholamreza Abbasi, Amirreza Keyghobadi

We propose a GA-optimized self-attention LSTM (SAG-LSTM) for multi-asset price forecasting and evaluate it on daily series of crude oil, gold, and Bitcoin, augmented with trading volumes (01-Apr-2021 to 30-Dec-2024). The model marries LSTM sequence learning with a multi-head self-attention layer and a post-attention gating block; a genetic algorithm tunes key hyperparameters (learning rate, hidden size, epochs). Using a 30-day horizon and standard preprocessing with lagged features, we benchmark SAG-LSTM against SA-LSTM and vanilla LSTM on MSE, RMSE, MAE, andR2, supplemented by error-trend and residual diagnostics, a forecast coherence score, and inter-asset dynamic/cross-correlation analyses. SAG-LSTM consistently dominates the baselines across assets: out-of-sampleR2rises to 0.90 for oil, 0.94 for gold, and 0.88 for Bitcoin, with visibly flatter error profiles and tighter, near-zero residuals. Inter-asset analyses show time-varying contemporaneous correlations but weak lead–lag effects, clarifying when co-movement is episodic rather than persistent. The largest gains occur in oil, reflecting more structured fundamentals; improvements for gold and Bitcoin are material but tempered by regime shifts and sentiment-driven jumps. Training time is higher due to GA search (≈2,121 s), but inference is fast (≈0.40 s), making the approach suitable for infrequent retraining with near-real-time scoring. Our findings highlight the value of hybrid, optimization-aware deep architectures for medium-horizon forecasting while underscoring the limits of price-volume inputs in sentiment-sensitive markets. These results offer actionable guidance for practitioners and a roadmap for future research and policy.

Naresh Kumar Satish, Mathieu Mercadier, Cristina Hava Muntean, Anderson Augusto Simiscuka

The cryptocurrency market is widely regarded as one of the most volatile financial markets due to inconsistencies in its pricing factors. Despite this volatility, it continues to attract a large population of investors, many of whom incur significant losses. To address this challenge and support risk assessment for investors, users, and other stakeholders, this paper focuses on forecasting Ethereum prices by analyzing social media sentiment. The study gathers data from sources such as global news headlines and Reddit discussion forums, enhancing it with hybrid sentiment features derived from the VADER, BERT and TextBlob models. These sentiment insights are then correlated with Ethereums financial parameters to establish meaningful relationships within the data, which are used to train machine learning models. The study evaluates the predictive performance of Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Among these, Extreme Gradient Boosting demonstrated superior performance, effectively capturing complex relationships within the data and achieving an R-squared value of 0.982115. To further enhance the studys risk assessment capabilities, the concept of Explainable Artificial Intelligence (XAI) is employed to improve transparency and accountability in the model outcomes. Specifically, Shapley Additive Explanations (SHAP) are used to interpret the feature interactions within the Extreme Gradient Boosting model, thereby increasing its reliability and providing deeper insights into its decision-making process.

Ahmed El Youssefi, Mounia Alaoui, Abdelaaziz Hessane, Imad Zeroual · 5 authors

No abstract is available for this record.

P. H. V. Sesha Talpa Sai, Ganesh Acharya, S Pramod, Srinivasan Ganesan · 8 authors

No abstract is available for this record.