Blockchain Papers

Mohamed Malek Belhoula

No abstract is available for this record.

Oscar Adam Darmawan, Yaya Heryadi, Lukas Lukas, Lili Ayu Wulandhari · 5 authors

The volatile behavior of Bitcoin's price, especially during its halving periods, poses considerable obstacles for forecasting and decision-making in cryptocurrency trading. This paper presents a novel method that combines application fuzzy logic with Bollinger Bands to improve trading decision-making in times of high market volatility. This study conducted an experiment utilizing three fuzzy logic controllers and Bollinger Bands (BB) to determine the strength of buy, hold, and sell signals. This dataset includes the initial and final prices that are used to calculate the BB. The raw and computed values serve as the precise input parameters for the Fuzzy Inference System (FIS). The membership functions were categorized into four levels: very low, low, high, and very high, based on the input default settings utilized by traders. Rulesets were created using fuzzy logic to produce signals that indicate the level of strength of a trading advice. This study evaluate the effectiveness of this hybrid method in comparison to the traditional utilization of the Bollinger Band only indicator and Moving Average Convergence Divergence (MACD) indicator, which is widely favored by traders to identify possible market fluctuations. This methodology involves creating a trading simulation that is based on past Bitcoin halving events. The objective is to assess the efficacy of these strategies in managing heightened volatility. The application of fuzzy logic with the Bollinger Bands model yielded a success rate of 92.47% while analyzing 93 daily data points from the previous Bitcoin halving event on May 11, 2020.

Bhekisipho Twala, S. Aarif Ahamed, M. Mohamed Iqbal, Vaibhav Chaudhari · 10 authors

No abstract is available for this record.

N. Gowri Sree Lakshmi, V. Ajaykumar, Baldi Ashish, K. Hemalatha · 6 authors

The cryptocurrency market is known for its inherent volatility, making accurate predictions a challenging endeavor.In this research study, investigate the efficacy of logistic regression, support vector machines (SVM), decision trees and random forests for the task of Bitcoin price prediction.To address this, conduct a thorough analysis and comparison of these machine learning models using historical Bitcoin price data.By rigorously assessing their performance and predictive capabilities, this study aims to provide valuable insights for both cryptocurrency traders and researchers operating in the dynamic digital asset landscape.These results illuminate the strengths and weaknesses of each model, shedding light on their respective abilities to forecast Bitcoin price movements.Through this research, contribute to the growing body of knowledge surrounding cryptocurrency market analysis and prediction techniques.This analysis can inform traders' decision-making processes and assist researchers in developing more robust models in the exciting and rapidly evolving realm of cryptocurrency investment and analysis.

Ryeomyung Kang, Soosung Hwang, Jin-Ho Shin

No abstract is available for this record.

Rihab Qasim Abdulkadhim, Hasanen S. Abdullah, Mustafa Jasim Hadi

Abstract Decentralized cryptocurrencies have received much attention over the last few years. Bitcoin (BTC) has enabled straight online expenditures without the need for centralized financial institutions. Cryptocurrencies are used not only for online payments but are also increasingly used as financial assets. With the rise in the number of cryptocurrencies, including BTC, Ethereum (ETH), and Ripple (XRP), and the millions of daily trades through different exchange services, cryptocurrency trading is prone to challenges similar to those seen in the traditional financial industry, such as price and trend forecasting, volatility forecasting, portfolio building, and fraud detection. This study examines the use of Recurrent neural networks (RNNs) for predicting BTC, ETH, and XRP prices. Accurate price prediction is essential for investors and traders in this volatile market. Machine learning techniques, including RNNs, Long-Short-Term Memory (LSTM), and convolutional neural networks, have been employed to forecast cryptocurrency prices with varying degrees of success. The aim of this study is to evaluate the effectiveness of RNNs in predicting cryptocurrency prices and compare their performance with other established methods. The results indicate that RNNs, particularly LSTMs and Gated Recurrent Units, demonstrate excellent capabilities in accurately predicting currency prices and providing insights to investors and traders in the cryptocurrency market.

Heba Mohammed Fadhil, Noor Q. Makhool

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The ability of the tool in analyzing past data on historical prices combined with machine learning, orchestrate an appealing scene of predictions equipped with choices and information, users turn into the main characters in a financial discovery story conducted by the cryptocurrency system. The numerical results also support the effectiveness of the tool as highlighted by standout corresponding numbers such as lower RMSE value 150.96 for ETH and minimized normalized RMSE scaled down to under, which is. The quantitative successes underline the usefulness of this tool to give precise predictions and improve user interaction in an entertaining world of cryptocurrency investments.

David Alaminos, M. Belén Salas, Ángela Callejón Gil

<abstract> <p>The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.</p> </abstract>

Xingyi Li, Yujun Liu, Zhuang Liu, Shushang Zhu

No abstract is available for this record.

Yongrong Huang, Huiqing Wang, Zhide Chen, Chen Feng · 7 authors

Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies-Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)-over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.

M. Thamban Nair, Laila A. Abd-Elmegid, Mohamed I. Marie

Abstract Bitcoin (BTC) is one of the most important cryptocurrencies widely used in various financial and commercial transactions due to the fluctuations in the price of this currency. Recent research in large data analytics and natural language processing has resulted in the development of automated techniques for assessing the sentiment in online communities, which has emerged as a crucial platform for users to express their thoughts and comments. Twitter, one of the most well-known social media platforms, provides many tweets about the BTC cryptocurrency. With this knowledge, we can apply deep learning (DL) to use these data to predict BTC price variations. The researchers are interested in studying and analyzing the reasons contributing to the BTC price’s erratic movement by analyzing Twitter sentiment. The main problem in this article is that no standard model with high accuracy can be relied upon in analyzing textual emotions, as it represents one of the factors affecting the rise and fall in the price of cryptocurrencies. This article aims to classify the sentiments of an expression into positive, negative, or neutral emotions. The methods that have been used are word embedding FastText model in addition to different DL methods that deal with time series, one-dimensional convolutional neural networks (CONV1D), long-short-term memory networks (LSTMs), recurrent neural networks, gated recurrent units, and a Bi-LSTM + CONV1D The main results revealed that the LSTM method, based on the DL technique, achieved the best results. The performance accuracy of the methods was 95.01, 95.95, 80.59, 95.82, and 95.67%, respectively. Thus, we conclude that the LSTM method achieved better results than other methods in analyzing the textual sentiment of BTC.

Owen Chaffard, Pablo Mollá, Marc Cavazza, Helmut Prendinger

In the recent advancements in application of deep learning to time series forecasting, focus has shifted from training transformers end-to-end to efficiently leveraging the predictive capabilities of Large Language Models (LLMs). Models that encode the time series data to interact with a frozen LLM backbone have been shown to outperform transformers on all benchmark datasets. However, their efficiency on complex datasets, which do not show clear seasonality or trend, remains an open question. In this work, we seek to evaluate the performance of reprogrammed LLMs on the Bitcoin price chart, a financial time series known for its complexity and high volatility. We propose effective methods to improve the performance of Time-LLM, a State-of-the-art (SOTA) method, on such a time series. First, we propose structural improvements to Time-LLM. Second, we suggest an efficient way to handle the non-stationarity of the dataset. Finally, we propose an efficient method for passing additional financial information to the LLM. Our results demonstrate a 50% improvement on the average percentage loss and a 5% increase on accuracy of our adapted Time-LLM architecture on Bitcoin data when compared to SOTA models, including the original Time-LLM model. This highlights the impact on forecast accuracy of domain-specific decision making in data processing and feature selection.

Ismail Jirou, Ikram Jebabli, Amine Lahiani

No abstract is available for this record.

Wentian Kang, Xuan Yuan, Xiaohan Zhang, Yishan Chen · 5 authors

The risk prediction of financial markets is of paramount importance, with investor sentiment playing a critical role. However, current research appears to be lacking in-depth exploration of this particular aspect within the Bitcoin market. This study aims to explore the impact of market participants’ sentiment on risk prediction in the bitcoin market. We first applied ChatGPT to analyze the sentiment of crawled Bitcoin-related news headlines. Meanwhile, Monte Carlo simulation was employed to calculate value at risk (VaR). And we selected five conventional factors, including Bitcoin price, transaction volume, market share, hash rate, and average difficulty of mining. Finally, K-Nearest Neighbors (KNN) regression model was used to construct the model for predicting the risk of bitcoin market. We made a comparison between the accuracy outcomes when considering and not considering sentiment as factors. The results show that market participant’s sentiment is significantly associated with market risk, and the inclusion of sentiment can significantly improve the accuracy of the risk prediction model.

Yüksel Akay Ünvan

Cryptocurrencies, which have begun to become an important rival to cash due to the changing lifestyle and technological developments, are gradually increasing their coverage area. Whether Bitcoin prices, which have exhibited different behaviors over the years since the day they were developed, are on a rational basis has become an important topic of discussion. Within the scope of this study, bitcoin prices between 2010 and 2023 were analyzed and factors that could make price behavior meaningful were tried to be determined. In addition, a forecast was also made in which Bitcoin prices for the coming years were calculated on a daily basis together with various statistical parameters using the the triple exponential smoothing method based on same historical data, and the results were discussed from various perspectives. In Bitcoin prices, which change mainly within the framework of supply and demand balance, attention has been drawn to the importance of different factors such as rational or irrational herd behavior, decisions taken about Bitcoin or news that may affect this balance and fall within the scope of behavioral finance. Along with the behavioral finance parameters that will make Bitcoin price behavior meaningful, it may not always be possible to attribute some changes in the relevant data to a specific reason. The main view supporting this situation is based on the personal nature of cryptocurrency itself.

Adel Mahfooz, Joshua L. Phillips

Bitcoin is known for its high volatility, which makes it challenging to accurately predict future prices. In this study, we aim to forecast Bitcoin prices for a month by incorporating exogenous variables, specifically the interest rate and recession probability. Our primary objective is to explore whether these variables have a positive impact on the prediction of Bitcoin prices. We used two popular time series forecasting models: Long Short-Term Memory (LSTM) and Facebook Prophet. Our approach involves exploring the impact of these exogenous variables on the performance of the models and comparing their results through plots and cross-validation. We trained the models using historical Bitcoin price data along with exogenous variables and evaluated their performance on a test dataset. Our results indicate that LSTM outperforms Facebook Prophet in terms of Bitcoin price prediction accuracy. This is because, while Facebook Prophet is optimized for statistical forecasting modeling, LSTM has the capability to learn intricate patterns and relationships given the right architecture with sufficient neurons. Importantly, we demonstrate that incorporating interest rates and recession probabilities significantly enhances the predictive capability of our models. Our findings suggest that changes in interest rates and recession probabilities have an impact on Bitcoin prices, and our models perform better when equipped with this valuable information.

David Ardia, David Ardia, Keven Bluteau, Keven Bluteau

We study the relation between the promotion of a cryptocurrency on Twitter and its return dynamics around pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. We also find that, while tweets directly promoting pump schemes align with anticipated market phases, a noteworthy portion of indirect, non-pump-aware tweets significantly influence market movements pre-event.

Otabek Sattarov, Jaeyoung Choi

The rapid evolution of cryptocurrency markets and the increasing complexity of trading strategies necessitate a comprehensive understanding of price-prediction models and their direct impact on trading efficacy. While extensive research has been conducted separately on price prediction methods and trading strategies, there remains a significant gap in studies explicitly correlating precise price forecasts with successful trading outcomes. This review paper addresses this gap by critically examining the role of accurate cryptocurrency price predictions in enhancing trading strategies. We conducted a systematic review of sufficient scholarly articles and web resources, focusing on the methodologies and effectiveness of various predictive models and their integration into cryptocurrency trading strategies. Our selection criteria ensured the inclusion of papers that demonstrate methodological rigor, relevance, and recent contributions to the field, spanning from economic theories and statistical models to advanced machine learning techniques. The findings reveal that precise price predictions significantly contribute to the development of adaptive and risk-managed trading strategies, which are crucial in the highly volatile cryptocurrency market. The review also identifies current challenges and proposes directions for future research, emphasizing the need for interdisciplinary approaches and ethical considerations in predictive modeling. This synthesis aims to bridge the existing research gap and guide future studies, thereby fostering more sophisticated and profitable trading strategies in the cryptocurrency domain.

Muhammad Zubair, Jaffar Ali, Musaed Alhussein, Shoaib Hassan · 6 authors

Cryptocurrencies, recognized by their extreme volatility due to dependency on multiple direct and indirect factors, offer a significant challenge regarding precise price forecasting. This uncertainty has led to investment hesitation within the digital currency market. Previous research attempts have presented methodologies for price forecasting and trend prediction in cryptocurrencies. However, these forecasts have typically suffered from increased error rates, leaving the opportunity for improvement in this field. Furthermore, the influence of sentiment-based factors could compromise the reliability of price predictions. In this research, we have proposed a machine learning-driven framework that provides precise cryptocurrency price projections and adds an alert mechanism to guide investors. Our fundamental analyzer, Bi-LSTM and GRU hybrid model use historical data of digital currencies to train and reliably anticipate future values. Complementing this, a sentiment analyzer, utilizing a BERT and VADER hybrid model, analyzes sentiments to assess the forecast price as trustworthy or uncertain. Besides assisting investor decision-making, this technique also helps risk management in the dynamic realm of cryptocurrency. Our suggested approach delivers highly precise price predictions with dramatically decreased error rates compared to prior competitive studies. The proposed Bi-LSTM-GRU-BERT-VADER (BLGBV) model is tested for three cryptocurrencies, namely BTC, ETH, and Dogecoin and reports an average root mean square error (RMSE) of 0.0241%, 0.0645%, and 0.0978%, respectively.

Krzysztof Gogol, Robin Fritsch, Malte Schlosser, Johnnatan Messias · 6 authors

This paper studies liquid staking tokens (LSTs) on automated market makers (AMMs), both theoretically and empirically. LSTs are tokenized representations of staked assets on proof-of-stake blockchains. First, we model LST-liquidity on AMMs theoretically, categorizing suitable AMM types for LST liquidity and deriving formulas for the necessary returns from trading fees to adequately compensate liquidity providers under the particular price trajectories of LSTs. For the latter, two relevant metrics are considered: (1) losses compared to holding the liquidity outside the AMM (loss-versus-holding, or "impermanent loss"), and (2) the relative profitability compared to fully staking the capital (loss-versus-staking) which is specifically tailored to the case of LST-liquidity. Next, we empirically measure these metrics for Ethereum LSTs across the most relevant AMM pools. We find that, while trading fees often compensate for impermanent loss, fully staking is more profitable for many pools, raising questions about the sustainability of the current LST liquidity allocation to AMMs.

Azamjon Muminov, Otabek Sattarov, Daeyoung Na

In the Bitcoin trading landscape, predicting price movements is paramount. Our study focuses on identifying the key factors influencing these price fluctuations. Utilizing the Pearson correlation method, we extract essential data points from a comprehensive set of 14 data features. We consider historical Bitcoin prices, representing past market behavior; trading volumes, which highlight the level of trading activity; network metrics that provide insights into Bitcoin’s blockchain operations; and social indicators: analyzed sentiments from Twitter, tracked Bitcoin-related search trends on Google and on Twitter. These social indicators give us a more nuanced understanding of the digital community’s sentiment and interest levels. With this curated data, we forge ahead in developing a predictive model using Deep Q-Network (DQN). A defining aspect of our model is its innovative reward function, tailored for enhancing predicting Bitcoin price direction, distinguished by its multi-faceted reward function. This function is a blend of several critical factors: it rewards prediction accuracy, incorporates confidence scaling, applies an escalating penalty for consecutive incorrect predictions, and includes a time-based discounting to prioritize recent market trends. This composite approach ensures that the model’s performance is not only precise in its immediate predictions but also adaptable and responsive to the evolving patterns of the cryptocurrency market. Notably, in our tests, our model achieved an impressive F1-score of 95%, offering substantial promise for traders and investors.

Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang · 5 authors

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.

Aditya Dahatonde, Lajwanti Kute, Yash Shinde, Chetan Chavan · 6 authors

Cryptocurrencies are changing how we view and interact with traditional currencies, and they have become a disruptive force in the financial industry. Accurate price prediction is becoming more and more important as the bitcoin industry grows in size and complexity. This paper provides a thorough examination of deep learning models used in bitcoin price prediction. We explore the dynamic and unpredictable character of the cryptocurrency market, where price swings can happen quickly and without warning. To comprehend the present state of the art in this domain and pinpoint the shortcomings of the deep learning models in use today, we examine the body of existing literature. The data collecting and preprocessing methods used to get the bitcoin market data ready for modeling are described in the methodology section. Numerous deep learning models—Recurrent Neural Networks among them, Convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks are investigated. We go over hyperparameter tweaking, model training, and the assessment metrics that are used to gauge the performance of the model. We offer a thorough case study that focuses on forecasting the price of a particular cryptocurrency, like Bitcoin, in order to offer empirical insights. Our results provide light on the difficulties and possibilities involved in this project, emphasizing the need for creative solutions to address the market

Serkan NAS, Eyşe Ergin Ünal

Amaç–Bitcoin başta olmak üzere kripto varlık fiyatlarında meydana gelen hızlı değişimler gerek finansal yatırımcı gerekse medya tarafından ilgi görmektedir. Buna bağlı olarak kar elde etmek amacı başta olmak üzere pek çok farklı motivasyonla birçok araştırmacı ve finansal aktör, Bitcoin fiyatını etkileyen çeşitli faktörleri belirlemeye çalışmaktadır. Bitcoin fiyat hareketlerini etkilediği düşünülen Fed faiz oranı, altın ve Bitcoin’in farklı fiyat göstergeleri gibi öznitelikler üzerine detaylandırılan bir inceleme yürütülmektedir. Bu bağlamda fiyatları tahmin etmek için kullanılan çeşitli makine öğrenme algoritmaları üzerinde sistematik bir analiz yapılmaktadır.Yöntem –Farklı dört makine öğrenme modeli kullanılmış olup farklı tahmin hata oranları elde edilmiş ve her birinin çalışmada kullanılabileceği görülmüştür. Bulgular –Bitcoin veri seti için önerilen en iyi tahmin performansının sırasıyla Tesadüfi Ağaç (RF) %96,38, Karar Ağacı (DT) %96,28, Doğrusal Regresyon 95,06 ve Stokastik Gradient Descent(SGD) Doğrusal Regresyon %93,91 şeklinde olduğunu göstermektedir. Bitcoin fiyat değişimlerininFed faiz oranı ve altından ziyade kendi fiyat değişimlerinden daha yüksek oranda etkilendiği diğer sonuçlar arasında yer almaktadır. Tartışma –Tahmin modellemesinde en iyi sonuçları veren iki algoritmaya bakıldığında, gün içi en yüksek fiyatın son derece etkili olduğu söylenebilmektedir. En düşük fiyat ise ikinci derece en etkili özniteliktir. Söz konusu sonuç, Bitcoin’in en çok kendi fiyat dalgalanmalarından etkilendiğini göstermektedir.