On the role of local blockchain network features in cryptocurrency price formation

Dey, Asim Kumer; Akcora, Cüneyt Gürcan; Gel, Yulia R.; Kantarcıoğlu, Murat

doi:10.1002/cjs.11547

Cited by 19 publications

(13 citation statements)

References 38 publications

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“…Transaction volume, mining difficulty, and market information, i.e., past prices, are commonly used as features in the prediction models. Network structural features, such as centralities and motifs [186], can also provide predictive power.…”

Section: Prices Predictionmentioning

confidence: 99%

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

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Section: Prices Predictionmentioning

confidence: 99%

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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“…One focuses on the recognition of cybercriminal entities using supervised learning (Yin & Vatrapu, 2017) as well as topological data analysis (TDA) (Akcora et al, 2020), while another focuses on the recognition of common categories of entities for most transactions (Jourdan et al, 2018). Section 3.2 reviews Bitcoin price prediction from different perspectives such as probabilistic graphical models (Jourdan et al, 2018), Bayesian regression (Shah & Zhang, 2014), and feature selection on blockchain topological structure using Granger causality and TDA (Akcora et al, 2019; Abay et al, 2019; Dey et al, 2020).…”

Section: Supervised/unsupervised Learning Without Deep Methodsmentioning

confidence: 99%

“…Beside considering the effects of features of the Bitcoin network's topological structure on Bitcoin price formation and dynamics, topological features of other types of cryptocurrencies may also affect Bitcoin price. Dey et al (2020) evaluate Bitcoin price formation and dynamics using the chainlet model and joint topological features of Bitcoin and Litecoin. Specifically, the occurrence of distinct chainlets in Bitcoin and Litecoin networks, denoted by

O_{x \to y}^{B}

and

O_{x \to y}^{L}

, respectively, are considered.…”

Section: Supervised/unsupervised Learning Without Deep Methodsmentioning

confidence: 99%

Machine learning in/for blockchain: Future and challenges

et al. 2021

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Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.

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“…The authors document a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 min. The impact of news is further documented by Dey et al [62] regarding the use of chainlets to evaluate the role of the local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics, or by Nicola et al [63] regarding information theory measures extracted from a Gaussian Graphical Model constructed from daily stock time series of listed US banks.…”

Section: Introductionmentioning

confidence: 99%

Is It Possible to Forecast the Price of Bitcoin?

2021

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This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

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On the role of local blockchain network features in cryptocurrency price formation

Cited by 19 publications

References 38 publications

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Machine learning in/for blockchain: Future and challenges

Is It Possible to Forecast the Price of Bitcoin?

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