Este artículo explora los desafíos de la gobernanza algorítmica utilizando el estudio de caso de The DAO, una efímera tentativa de crear una organización autónoma descentralizada en la plataforma de blockchain Ethereum. A pesar de su breve existencia y la significativa pérdida de inversión debido a una explotación de seguridad, The DAO ofrece ideas críticas sobre las formas emergentes de autoridad algorítmica, la gobernanza práctica de sistemas autónomos y descentralizados, y las posibles fallas en el diseño de incentivos y la modelización de acciones. El artículo también profundiza en el problema de agencia en economía y la gobernanza corporativa, ilustrando cómo estos conceptos se entrelazan con la gobernanza algorítmica.
The article analyzes the modern crypto industry market, examines its role, significance, dynamics and possibilities of further application in our country. The history of the cryptocurrency concept is presented. The development path of the domestic cryptoindustry from complete non-recognition to attempts to regulate this sphere by law is illustrated. The advantages and disadvantages of cryptocurrency as a payment were studied, the trends of its development in Ukraine and in the whole world were considered. The research identified three main groups of digital currencies and assets: cryptocurrencies, stablecoins, and digital currencies of central banks. The first group includes traditional cryptocurrencies, that is, those that are not backed by other assets and have a decentralized register of owners (Bitcoin, , Ripple, Ethereum). The second group of digital currencies and assets includes stablecoins — cryptocurrencies backed by one asset or a basket of some assets. The third group includes digital currencies of central banks, which are currently issued in a limited way as part of pilot projects, or government-issued cryptocurrencies. An analysis of the cryptocurrency market was conducted; a conclusion about the positive dynamics of the use of cryptocurrency in our country was made. The dynamics of the ratio of the cryptocurrency market capitalization to the indicators of GDP are estimated. The impact of military events on the cryptoindustry development was studied. The contribution of the global crypto-community to Ukrainian charitable foundations was studied. The advantages of the latest technologies in the war conditions are indicated, in particular, the speed and efficiency of transaction processing, the absence of restrictions inherent in classical finance. The domestic and global experience of cryptocurrency regulation is analyzed. It is noted that it is necessary to develop further measures to regulate the cryptocurrency market in a legislative manner. It is proposed to create associations consisting of IT lawyers, cyber police and blockchain developers. This stimulates the emergence of new methods of combating terrorism and the development of a new legislative model. Possible directions of cryptocurrency development and prospects for their development are proposed.
This study aims to analyze the effect of Bitcoin price spillover volatility on Altcoin prices (Ethereum, Tether, Binance Coin) and the price of the S&P 500 Index. The data used is weekly data with a research period from January 2018 to December 2022. The analysis used in this study is the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model. The results show a volatility spillover effect between Bitcoin and Binance Coin with more positive shocks than adverse shocks in Bitcoin price volatility on Binance Coin price. Meanwhile, the spillover volatility between Bitcoin and Ethereum, Tether, and the S&P 500 Index cannot be known because the price data is homoscedastic, so it cannot be continued with EGARCH modelling because the data needs to meet the modelling requirements.
This research investigates the relationship between the price of the Ethereum token and Non-Fungible Token (NFT) trading activities on the Ethereum blockchain in the 2020s. Through regression analysis and t-tests, the study explores how changes in Ethereum's price and price volatility influence NFT trading behaviors. The regression analysis reveals significant positive correlations between Ethereum's price and various NFT trading variables, indicating that an increase in Ethereum's price positively impacts sales volume, unique sellers, unique buyers, and average sales of NFTs. Conversely, Ethereum's price volatility demonstrates significant negative associations with NFT trading activities, suggesting that market uncertainty and risk aversion influence investor decisions. The findings provide valuable insights for practitioners, investors, and traders in the NFT market, offering guidance for decision-making strategies and market trends; however, the study also acknowledges limitations in establishing causal relationships and suggests future research directions. Understanding the dynamics between Ethereum's price and NFT trading activities is crucial in navigating the evolving landscape of blockchain-based assets and decentralized markets.
The rise of Web 3.0, which is based on independent technologies like blockchain and smart contracts, marks a big change in the financial technology field. This research looks at all the different ways that Web 3.0 can be used in FinTech by looking at real-life examples from Ethereum, Betterment, Wealthfront, DeversiFi, Synthetix, Kyber Network, and Curve Finance. By combining ideas from McKinsey's research, the study shows that Web3 lending sites are growing quickly. In 2021 alone, they gave out over $200 billion in loans. The study shows how Ethereum can be used for smart contracts, how Betterment and Wealthfront's robo-advisory services use AI and machine learning, how DeversiFi's decentralized exchange handles privacy issues, how Synthetix creates on-chain digital assets, how Kyber Network's blockchain-based liquidity protocol is put into use, and how Curve Finance's decentralized platform handles stablecoin transactions. Although there has been success, integrating these technologies is still very hard. The main problems are unclear regulations and technical issues with security, scalability, and interoperability. The final success of Web 3.0 in FinTech will depend on how well these problems are solved, which will help find a good balance between fast technological progress and strong risk management.
We present SmartFix, a new technique for repairing vulnerable smart contracts. There is an urgent need to develop automatic bug-repair techniques for smart contracts, as smart contracts are safety-critical software and manual debugging is burdensome and error-prone. While several repair approaches have been proposed recently, they are unsatisfactory since no existing techniques can achieve high repairability, full automation, and safety guarantee at the same time, posing significant problems for practical use. SmartFix aims to address these shortcomings by using a “generate-and-verify” approach that iteratively enumerates candidate patches while validating their correctness by invoking a safety verifier. However, in this approach, a technical challenge arises as the search space is huge and the verification-based patch validation is expensive. To address this challenge, we present a novel technique for accelerating the generate-and-verify repair procedure using statistical models derived from the verifier’s feedback. Experimental results on real-world Ethereum smart contracts show that SmartFix is able to achieve a fix success rate of 94.8% for critical classes of vulnerabilities, far outperforming sGuard, the existing state-of-the-art technique whose success rate is 65.4%.
Kunsong Zhao, Zihao Li, Jianfeng Li, He Ye · 6 authors
Smart contracts play an increasingly important role in Ethereum platform. It provides various functions implementing numerous services, whose bytecode runs on Ethereum Virtual Machine. To use services by invoking corresponding functions, the callers need to know the function signatures. Moreover, such signatures provide crucial information for many downstream applications, e.g., identifying smart contracts, fuzzing, detecting vulnerabilities, etc. However, it is challenging to infer function signatures from the bytecode due to a lack of type information. Existing work solving this problem depended heavily on limited databases or hard-coded heuristic patterns. However, these approaches are hard to be adapted to semantic differences in distinct languages and various compiler versions when developing smart contracts. In this paper, we propose a novel framework DeepInfer that first leverages deep learning techniques to automatically infer function signatures and returns. The novelties of DeepInfer are: 1) DeepInfer lifts the bytecode into the Intermediate Representation (IR) to preserve code semantics; 2) DeepInfer extracts the type-related knowledge (e.g., critical data flows, constant values, and control flow graphs) from the IR to recover function signatures and returns. We conduct experiments on Solidity and Vyper smart contracts and the results show that DeepInfer performs faster and more accurate than existing tools, while being immune to changes in different languages and various compiler versions.
Blockchain is a distributed ledger that records transactions among users on top of a peer-to-peer network. Among all, Ethereum is the most popular general-purpose platform and its support of smart contracts led to a new form of applications called decentralized applications (DApps). A typical DApp has an off-chain frontend and on-chain backend architecture, and the frontend often needs interactions with the backend network, e.g., to acquire chain data or make transactions. Therefore, Ethereum nodes implement the official RPC specification and expose a uniform set of RPC methods to the frontend. However, the specification is not sufficient in two points: (1) lack of clarification for non-deterministic event handling, and (2) lack of specification for invalid arguments. To effectively disclose any deviations caused by the insufficiency, this paper introduces EtherDiffer that automatically performs differential testing on four major node implementations in terms of their RPC services. EtherDiffer first generates a non-deterministic chain by multi-concurrent transactions and propagation delay. Then, it applies our key techniques called property-based generation and type-preserving mutation to generate both semantically-valid and semantically-invalid-yet-executable test cases. EtherDiffer executes the test cases on target nodes and reports any deviations in error handling or return values. The evaluation showed the effectiveness of our test case generation techniques with the success ratios of 98.8% and 95.4%, respectively. Also, EtherDiffer detected 48 different classes of deviations including 11 implementation bugs such as crash and denial-of-service bugs. We reported 44 of the detected classes to the specification and node developers and received acknowledgements as well as bug patches. Lastly, it significantly outperformed the official node testing tool in every technical aspect. We believe that our research findings can contribute to more stable DApp ecosystem by reducing the inconsistencies among nodes.
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics.
ROCYS is a knowledge resource for practitioners, scientists, and researchers working in various fields of cyber security, hacking, digital forensics, cyber warfare, viruses and worms domain or critical infrastructure protection area.
In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.
Fathin Faizah Said, Raja Solan Somasuntharam, Mohd Ridzwan Yaakub, Tamat Sarmidi
Abstract Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.
Xihan Xiong, Zhipeng Wang, Xi Chen, William J. Knottenbelt · 5 authors
In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH and accrues staking rewards. LSDs improve the liquidity of staked assets by facilitating their use in secondary markets, such as for collateralized borrowing on Aave or asset exchanges on Curve. The composability of Lido, Aave, and Curve enables an emerging strategy known as leverage staking, an iterative process that enhances financial returns while introducing potential risks. This paper establishes a formal framework for leverage staking with stETH and identifies 442 such positions on Ethereum over 963 days. These positions represent a total volume of 537,123 ETH (877m USD). Our data reveal that 81.7% of leverage staking positions achieved an Annual Percentage Rate (APR) higher than conventional staking on Lido. Despite the high returns, we also recognize the potential risks. For example, the Terra crash incident demonstrated that token devaluation can impact the market. Therefore, we conduct stress tests under extreme conditions of significant stETH devaluation to evaluate the associated risks. Our simulations reveal that leverage staking amplifies the risk of cascading liquidations by triggering intensified selling pressure through liquidation and deleveraging processes. Furthermore, this dynamic not only accelerates the decline of stETH prices but also propagates a contagion effect, endangering the stability of both leveraged and ordinary positions.
Blockchain and tokens are relatively new research areas insufficiently explored from both technical and economic perspectives. Even though tokens provide benefits such as easier market access, increased liquidity, lower transaction costs, and automated transactional process, their valuation and price determination are still challenging due to factors such as a lack of intrinsic value, volatility, and regulation making trading risky. In this paper, we address this knowledge gap by reviewing the existing literature on token creation and valuation to identify and document the factors affecting their valuation, investment, and founding, as well as the most promising domains of applicability. The study follows the PRISMA methodology and uses the Web of Science database, defining clear research questions and objective inclusion criteria for the articles. We discuss token technical development, including creating, issuing, and managing tokens on an Ethereum blockchain using smart contracts. The study revealed several key factors that significantly impact the field of tokenomics: demand and supply, social incentives, market conditions, macroeconomics, collective behavior, speculation, and inclusion in index funds. The most relevant use cases of blockchain and tokens are related to the digitization of virtual and physical assets, accountability, and traceability usual in smart grids or supply chains management, social governance, and art and gamification including metaverse.
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification brought by centralized FL paradigm. Moreover, it is easier to allocate incentives to nodes with the help of the blockchain. Various centralized federated learning frameworks like FedML, have emerged in the community to help boost the research on FL. However, decentralized blockchain-based federated learning framework is still missing, which cause inconvenience for researcher to reproduce or verify the algorithm performance based on blockchain. Inspired by the above issues, we have designed and developed a blockchain-based federated learning framework by embedding Ethereum network. This report will present the overall structure of this framework, which proposes a code practice paradigm for the combination of FL with blockchain and, at the same time, compatible with normal FL training task. In addition to implement some blockchain federated learning algorithms on smart contract to help execute a FL training, we also propose a model ownership authentication architecture based on blockchain and model watermarking to protect the intellectual property rights of models. These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain). The source code is avaliable on https://github.com/GTMLLab/VeryFL.
Natkamon Tovanich, Myriam Kassoul, Simon Weidenholzer, Julien Prat
We study financial contagion in Compound V2, a decentralized lending protocol deployed on the Ethereum blockchain. We explain how to construct the balance sheets of Compound's liquidity pools and use our methodology to characterize the financial network. Our analysis reveals that most users either borrow stablecoins or engage in liquidity mining. We then study the robustness of Compound through a series of stress tests, identifying the pools that are most likely to set off a cascade of defaults.
In this article, we present the first proposal for contract signing based on blockchain that meets the requirements of fairness, hard-timeliness, and bc-optimism. The proposal, thanks to the use of blockchain, does not require the use of trusted third parties (TTPs), thus avoiding a point of failure and the problem of signatories having to agree on a TTP that is trusted by both. The presented protocol is fair because it is designed such that no honest signatory can be placed at a disadvantage. It meets the hard-timeliness requirement because both signatories can end the execution of the protocol at any time they wish. Finally, the proposal is bc-optimistic because blockchain functions are only executed in case of exception (and not in each execution of the protocol), with consequent savings when working with public blockchains. No previous proposal simultaneously met these three requirements. In addition to the above, this article clarifies the concept of timeliness, which previously has been defined in a confusing way (starting with the authors who used the term for the first time). We conducted a security review that allowed us to verify that our proposal meets the desired requirements. Furthermore, we provide the specifications of a smart contract designed for the Ethereum blockchain family and verified the economic feasibility of the proposal, ensuring it can be aligned with the financial requirements of different scenarios.
Yuheng Zhang, Liu Pin, Guojun Wang, Peiqiang Li · 8 authors
With the evolution of blockchain technology, the issue of transaction security, particularly on platforms like Ethereum, has become increasingly critical. Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions. In these attack scenarios, malicious actors monitor other users' transaction activities, then strategically submit their own transactions with higher fees. This ensures their transactions are executed before the monitored transactions are included in the block. The primary objective of this paper is to delve into a comprehensive classification of transactions associated with front-running attacks, which aims to equip developers with specific strategies to counter each type of attack. To achieve this, we introduce a novel detection method named FRAD (Front-Running Attacks Detection on Ethereum using Ternary Classification Model). This method is specifically tailored for transactions within decentralized applications (DApps) on Ethereum, enabling accurate classification of front-running attacks involving transaction displacement, insertion, and suppression. Our experimental validation reveals that the Multilayer Perceptron (MLP) classifier offers the best performance in detecting front-running attacks, achieving an impressive accuracy rate of 84.59% and F1-score of 84.60%.
Carlos Beis-Penedo, Francisco Troncoso-Pastoriza, Rebeca P. Díaz-Redondo, Ana Fernández-Vilas · 6 authors
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showcasing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings.
Sangeeta Gupta, Premkumar Chithaluru, May El Barachi, Manoj Kumar
Abstract In the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.
Muhammad Usman Abbas Gondal, Muhammad Attique Khan, Abdul Haseeb, Hussain Mobarak Albarakati · 5 authors
The supply chain systems in the food industry are complex, including manufacturers, dealers, and customers located in different areas. Currently, there is a lack of transparency in the distribution and transaction processes of online food trade. The global food supply chain industry has enormous hurdles because of this problem, as well as a lack of trust among individuals in the sector and a reluctance to share information. This study aims to develop a blockchain-based strawberry supply chain (SSC) framework to create a transparent and secure system for tracking the movement of strawberries from the farm to the consumer. Using Ethereum smart contracts, the proposed solution monitors participant interactions, triggers events, and logs transactions to promote transparency and informed decision-making. The smart contracts also govern interactions between vendors and consumers, such as monitoring the status of Internet of Things (IoT) containers for food supply chains and notifying consumers. The proposed framework can be extended to other supply chain industries in the future to increase transparency and immutability.
Teknolojinin gelişmesiyle birlikte pek çok alanda yenilikler yapılmıştır. Bu alanlardan biri de sanattır. Genel anlayışla sanat, galeri gibi kapalı mekanlarda, sınırlı kişilere yakından bakma imkanı sunarken, teknolojinin sağladığı imkanlar dahilinde sanatın oluşum ve dolaşım alanı genişlemiş, dünyanın bir ucundan diğer ucuna sanat eserine erişim sağlamak mümkün hale gelmiştir. Günümüzde popüler bir konu haline gelen, hızla yükselen, sanatı tuvalden sanal ortama taşıyan sanat alanlarından biri de NFT (Non-Fungible Token)’dir. NFT'ler, benzersiz ve değişmez dijital varlıklardır. Bilgisayar destekli teknolojinin kullanılması yeni bir olgu olmamakla beraber, sanat piyasasındaki geleneksel dinamikleri yıkan bir yapıya sahip olan NFT, dijital sanatın önemli ortamlarından biri haline gelmiştir. Özellikle sanat, oyun ve diğer sektörlerde sıkça kullanılmakta olan NFT’ler, sanatçılara birçok avantaj sunmaktadır. Araştırmada, NFT ve içeriğindeki; blokzincir teknolojisi, ethereum, metaverse (sanal evren) gibi kavramlar tanıtılmaya çalışılmıştır. Blokzincir teknolojisiyle birbirine bağlanan zincirlerin, bilgileri nasıl koruduğu, aynı zamanda metaverse ile birlikte gelecekteki sanatçılar ve izleyiciler arasındaki etkileşimi geliştirebilecek sanal dünyaları keşfetmeyi nasıl başarabileceklerinden bahsedilmiştir. Bu bağlamda NFT’nin önemli sanatçılarından olan, eserlerinde yapay zeka ve dijital manipülasyon tekniklerini kullanan Ender Diril’in, dijital sanat ve NFT’ye yaklaşımı, eserleri üzerinden NFT dünyası ortaya koyulmaya çalışılmıştır.
Vincent Gurgul, Stefan Lessmann, Wolfgang Karl Härdle
We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public . • NLP data from social media improve the accuracy of cryptocurrency forecasting models. • As a target variable, local extrema are a valid alternative to daily price changes. • Deep learning language models substantially outperform dictionary-based methodologies. • Both pre-trained and fine-tuned language models effectively quantify market sentiment.