The chaotic, self-similar and hierarchical patterns in Bitcoin and Ethereum price series

Partida, Alberto; Gerassis, Saki; Criado, Regino; Romance, Miguel; Giráldez, Eduardo; Taboada, J.

doi:10.1016/j.chaos.2022.112806

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…Yu studied the characteristics of the visibility graph of the gold price time series [37]. Partida et al investigated the hierarchical structure of the visibility graphs and horizontal visibility graphs of the Bitcoin and Ethereum price time series [38]. Xie et al investigated the network motif of the horizontal visibility graphs of heartbeat rate time series [39].…”

Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…Despite the extensive analysis about EMH's applicability to the BTC market (see [33] and Table A1 for a survey), there is no agreement on whether the periods of efficiency alternate either with mean reverting dynamics (as claimed in [4,34]) or with long-term memory trends (stated by [5,35,36]).…”

Section: Efficient Market Hypothesis (Emh) In the "Cryptosphere"mentioning

confidence: 99%

Chance or Chaos? Fractal Geometry Aimed to Inspect the Nature of Bitcoin

Cabezas-Rivas,

Sánchez-Coll,

Tormo-Xaixo

2023

Fractal Fract

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The aim of this paper is to analyse Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and future contracts, treasury bonds, stock indices, and growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale.

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“…Even though it may seem an obviousness to check the hypotheses before using a procedure and confirm the statistical significance of the computations, as pointed out in [15], many papers in the financial literature ignore naively both steps. Likewise, regardless of the more sophisticated statistical tools in the recent literature (as dfa or mf-dfa), as highlighted in [40], most of them assume that the variables creating time series follow a normal distribution, even knowing that this is typically not the case.…”

Section: Introductionmentioning

confidence: 99%

Living Geometry: “Let No One Ignorant of Geometry Exit This Classroom”

Cabezas-Rivas¹

2021

EDULEARN Proceedings

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The aim of this paper is to analyse the Bitcoin in order to shed some light on its nature and behaviour. We select 9 cryptocurrencies that account for almost 75% of total market capitalisation and compare their evolution with that of a wide variety of traditional assets: commodities with spot and futures contracts, treasury bonds, stock indices, growth and value stocks. Fractal geometry will be applied to carry out a careful statistical analysis of the performance of the Bitcoin returns. As a main conclusion, we have detected a high degree of persistence in its prices, which decreases the efficiency but increases its predictability. Moreover, we observe that the underlying technology influences price dynamics, with fully decentralised cryptocurrencies being the only ones to exhibit self-similarity features at any time scale.

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The chaotic, self-similar and hierarchical patterns in Bitcoin and Ethereum price series

Cited by 11 publications

References 40 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Chance or Chaos? Fractal Geometry Aimed to Inspect the Nature of Bitcoin

Living Geometry: “Let No One Ignorant of Geometry Exit This Classroom”

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