Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

Abstract

Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.

Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach

“…These include the mining difficulty, security of wallets and cryptocurrency exchanges, blockchain-related energy consumption, the lack of international acceptance and legislation, etc. (Sabry et al, 2020).…”

“…Besides, many factors can influence the price of a cryptocurrency, and investors need to analyse these factors to properly predict cryptocurrency prices. These factors could range from social media, trends in search engines, and declarations by political leaders to the interactions among cryptocurrencies, blockchain data, and classic financial asset prices (Sabry et al, 2020). Consequently, understanding these factors that influence cryptocurrency prices is the first step in developing an accurate price prediction algorithm.…”

Chatbot as a Trading Tool in the Cryptocurrency Market

“…Варто зосередитися також на роботах F. Sabry, W. Labda та ін. [5], які досліджували використання методів штучного інтелекту та машинного навчання в криптовалютах як цифрових валютах чи криптоактивах і навколишній екосистемі. В икористання тематичного моделювання на основі word2vec та інших методів нейролінгвістичного програмування (НЛП) було продемонстровано в дослідах A. Burnie, E. Yilmaz [6] для аналізу тем, поданих на Reddit, про зміни цін у 2017 та 2018 рр.…”

“…Обговорення в їхній роботі проливає більше світла на інвесторів ринку криптовалют і поведінку «стада», яку демонструє один кластер, що призводить до спекулятивних змін цін. Своєю чергою, автори F. Sabry, W. Labda, A. Erbad, Q. Malluhi [5] вважають, що виконання подібного аналізу кластеризації поведінки безпосередньо на даних блокчейну може дати більш точну кластеризацію. Окрім цього, це може надати більше доказів щодо того, як різні показники впливають на поведінку інвесторів і, відповідно, -на ціну.…”

Uso de redes neurais profundas para previsão de curto prazo do preço da criptomoeda ethereum

“…No meta-estudo de Sabry et al (2020), pesquisou-se a aplicação de técnicas de inteligência artificial para enfrentar os desafios de processamento de informações para criptomoedas com sua vasta quantidade de transações diárias, negócios e notícias que estão além da capacidade humana de analisar e aprender. O artigo discute os recentes trabalhos de pesquisa e os compara em termos de técnicas e conjuntos de dados utilizados.…”