A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management

On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data can enhance an RL-based system's return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively

Cryptocurrency price prediction using LSTM neural networks

The interest in cryptocurrencies is increasing among individuals and investors. Bitcoin is the leading existing cryptocurrency with the highest market capitalization. However, its high volatility aligns with political uncertainty making it very difficult to predict its value. Therefore, there is a need to create advanced models that use mathematical and statistical methods to reduce investment risk. This research aims to verify if long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM) neural networks, can be used with Savitzky–Golay filter to predict next-day bitcoin closing prices. We found evidence both networks can be used effectively to predict bitcoin prices. LSTM performed 4.49 mean absolute percentage error (MAPE) and BiLSTM 4.44 MAPE. We also found that using Savitzky– Golay filter and dropout regularization significantly improved the model’s prediction performance.O interesse em moedas digitais tem aumentado por parte de indivíduos e investidores. A bitcoin é a moeda digital com maior capitalização de mercado, no entanto, a sua alta volatilidade alinhada à incerteza política, torna muito difícil prever seu valor. Portanto, existe a necessidade de criar modelos avançados que utilizem métodos matemáticos e estatísticos para reduzir o risco de investimento. Este estudo tem como objetivo verificar se as redes neurais artificiais de memória longo curto prazo (LSTM) e redes bidirecionais de memória longo curto prazo (BiLSTM) podem ser usadas juntamente com o filtro Savitzky-Golay para prever os preços de fecho do dia seguinte da bitcoin. Os resultados mostraram que existe evidência que ambas as redes podem ser usadas de forma efetiva. LSTM obteve um erro percentual absoluto médio (MAPE) de 4.49 e BiLSTM um MAPE de 4,44. Também o uso do filtro Savitzky-Golay e regularização, melhora significativamente o desempenho de previsão dos modelos

Socio-Technical Phenomena Involving Blockchains: Review, Critique and Agenda

The paper reviews and assesses the state of blockchain research and conceptualises blockchain uses as socio-technical phenomena. The first blockchain use case emerged with the release of Bitcoin in 2008. Since then, blockchain use has proliferated in various other areas and encompassed an array of applications, such as tokens (cryptocurrencies and non-fungible tokens), decentralised autonomous organisations and smart contracts. The paper presents a structured literature review of 113 research papers in information systems and related fields (e.g., organization studies, management and human- computer interaction) that commonly study socio-technical phenomena and organisational uses of technology. Conceptualising blockchain uses as socio-technical phenomena highlights the necessity to account for business models, social implications and stakeholders’ values regarding blockchains beyond the blockchain technology ‘as such’. The review reveals that the existing literature has articulated substantial knowledge of some aspects of blockchain uses but lacks knowledge in other aspects. Therefore, we propose a research agenda

Forecasting mid-price movement of Bitcoin futures using machine learning

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference

Quantifying and modelling online decentralised systems: a complex systems approach

Cryptocurrencies are unique examples of decentralised socioeconomic systems. All the transactions, trading, and development are traceable and publicly available. Bitcoin, the first cryptocurrency, was introduced in 2009 launching a market of more than 2500 cryptocurrencies and has a value of more than 200 billion dollars. In comparison to the rising importance of cryptocurrencies in the financial world, the research on cryptocurrencies is still limited. In this thesis, we analyse three novel datasets namely, cryptocurrencies’ market data, cryptocurrencies’ Wikipedia page views and edits, and illicit transactions on Bitcoin. We study the cryptocurrencies ecosystem, including the market dynamics, the social attention and the transaction network. We find that the ecological neutral model can capture the market dynamics, hinting at the extent to which technological differences between cryptocurrencies are considered in investment decisions. We also investigate the relationship between information production and consumption and cryptocurrency market dynamics. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Finally, we assess dark markets’ Bitcoin transactions showing the ability of the markets to adapt to multiple closures, including law enforcement raids. We expect that our contribution will be of interest to researchers working on either cryptocurrencies or complex systems. We anticipate that adopting a complex systems approach, will spark more research that interweaves both the technological and socioeconomic aspect of cryptocurrencies

Financial Inclusion, Cryptocurrency, and Afrofuturism

As a community, Black people consistently face barriers to full participation in traditional financial markets. The decentralized nature of the cryptocurrency market is attractive to a community that has been historically and systematically excluded from the traditional financial markets by both private and public actors. As new entrants to any type of financial market, Black people have increasingly embraced blockchain technology and cryptocurrency as a path towards the wealth-building opportunities and financial freedom they have been denied in traditional markets. This Article analyzes whether the technology’s decentralized system will lead to financial inclusion or increased financial exclusion. Without reconciling the racially discriminatory history or effects of the current central financial system, the innovative decentralized appeal to Black people will do little to overcome economic inequity. It may be possible that some cryptocurrencies can be tools for financial inclusion by improving economic outcomes and building wealth outside of traditional financial institutions, but without an intervention, a decentralized system will not necessarily lead to decentralized wealth. The rise of cryptocurrency presents an opportunity to think about how to create a fairer, more inclusive financial system. Taken together with the financial exclusions of the past, cryptocurrency can be a vehicle through which we think about true financial inclusion. However, asking traditionally marginalized groups to participate in an extremely risky cryptocurrency market in pursuit of racial equity is an unrealistic solution given the legacy and reality of financial exclusion. A decentralized system cannot fix the systemic racial inequality that has been embedded in our financial systems. This Article proposes using an Afrofuturist framework in the shaping of policy toward cryptocurrency. An Afrofuturist paradigm pushes for systemic problems to be solved through wholesale systems change rather than tinkering at the margins. Moving forward using an Afrofuturist lens would facilitate a rethinking of our financial systems and the role of cryptocurrency as a portal for racial equity

Financial Inclusion, Cryptocurrency, and Afrofuturism

As a community, Black people consistently face barriers to full participation in traditional financial markets. The decentralized nature of the cryptocurrency market is attractive to a community that has been historically and systematically excluded from the traditional financial markets by both private and public actors. As new entrants to any type of financial market, Black people have increasingly embraced blockchain technology and cryptocurrency as a path towards the wealth-building opportunities and financial freedom they have been denied in traditional markets. This Article analyzes whether the technology’s decentralized system will lead to financial inclusion or increased financial exclusion. Without reconciling the racially discriminatory history or effects of the current central financial system, the innovative decentralized appeal to Black people will do little to overcome economic inequity. It may be possible that some cryptocurrencies can be tools for financial inclusion by improving economic outcomes and building wealth outside of traditional financial institutions, but without an intervention, a decentralized system will not necessarily lead to decentralized wealth. The rise of cryptocurrency presents an opportunity to think about how to create a fairer, more inclusive financial system. Taken together with the financial exclusions of the past, cryptocurrency can be a vehicle through which we think about true financial inclusion. However, asking traditionally marginalized groups to participate in an extremely risky cryptocurrency market in pursuit of racial equity is an unrealistic solution given the legacy and reality of financial exclusion. A decentralized system cannot fix the systemic racial inequality that has been embedded in our financial systems. This Article proposes using an Afrofuturist framework in the shaping of policy toward cryptocurrency. An Afrofuturist paradigm pushes for systemic problems to be solved through wholesale systems change rather than tinkering at the margins. Moving forward using an Afrofuturist lens would facilitate a rethinking of our financial systems and the role of cryptocurrency as a portal for racial equity