Cryptocurrency from Shariah perspective

For the past few years there has been a significant increase of people’s interest in crypto-currencies. Seminars and conferences have been organized to discuss the nature and feasibility of cryptocurrencies. Some argue that it is good to have an alternative to the current fiat money system in which the predominant role is played by banks, while a cryptocurrency does not require any bank account, tax payment and auditing. Some others disagree with these arguments and claim that any mode of payment in other than traditionally known instruments such as cash payment, telegraphic-transfers, cheques and so, will open the door to avoid tax and audits, which in turn may seriously effect a government’s budget and may even decrease GDP. This research uses theoretical, descriptive and analytical methods of research and therefore focuses on the following important points: a) defining the place of cryptocurrency in the financial system by determining the extent of its influence; b) reviewing the literature on the topic, which will allow us to determine the current understanding of the problem by modern science; c) unveiling the key requirements of Shari’ah for money and money circulation to formulate a standard understanding of money in Shari’ah; d) comparing the characteristics of paper money and crypto-currencies (using the bitcoins as an example). The authors believe in permissibility of using the cryptocurrencies but with strict reservations

Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques

The International Token Classification (ITC) Framework by the Blockchain Center in Frankfurt classifies 795 cryptocurrency tokens based on their economic, technological, legal and industry categorization. This work analyzes cryptocurrency data to evaluate the categorization with real-world market data. The feature space includes price, volume and market capitalization data. Additional metrics such as the moving average and the relative strengh index are added to get a more in-depth understanding of market movements. The data set is used to build supervised and unsupervised machine learning models. The prediction accuracies varied amongst labels and all remained below 90%. The technological label had the highest prediction accuracy at 88.9% using Random Forests. The economic label could be predicted with an accuracy of 81.7% using K-Nearest Neighbors. The classification using machine learning techniques is not yet accurate enough to automate the classification process. But it can be improved by adding additional features. The unsupervised clustering shows that there are more layers to the data that can be added to the ITC. The additional categories are built upon a combination of token mining, maximal supply, volume and market capitalization data. As a result we suggest that a data-driven extension of the categorization in to a token profile would allow investors and regulators to gain a deeper understanding of token performance, maturity and usage

Cascading Machine Learning to Attack Bitcoin Anonymity

Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network. In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation. Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.Comment: 15 pages,7 figures, 4 tables, presented in 2019 IEEE International Conference on Blockchain (Blockchain

Quantitative cryptocurrency trading: exploring the use of machine learning techniques

Machine learning techniques have found application in the study and development of quantitative trading systems. These systems usually exploit supervised models trained on historical data in order to automatically generate buy/sell signals on the financial markets. Although in this context a deep exploration of the Stock, Forex, and Future exchange markets has already been made, a more limited effort has been devoted to the application of machine learning techniques to the emerging cryptocurrency exchange market. This paper explores the potential of the most established classification and time series forecasting models in cryptocurrency trading by backtesting model performance over a eight year period. The results show that, due to the heterogeneity and volatility of the underlying financial instruments, prediction models based on series forecasting perform better than classification techniques. Furthermore, trading multiple cryptocurrencies at the same time significantly increases the overall returns compared to baseline strategies exclusively based on Bitcoin trading