Explosive behavior in the prices of Bitcoin and altcoins

Çağlı, Efe Çağlar

doi:10.1016/j.frl.2018.09.007

Cited by 71 publications

(40 citation statements)

References 24 publications

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“…Given the sample of the studied projects, we clustered the projects into two overarching domains: tokens and others (covering other decentralised applications such as decentralised insurance, gaming and escrows). This is because majority of the SC projects deployed on the Ethereum blockchain network are oriented towards the creation of a new crypto currency or alt coin . Four projects from the sample belonged to the token domain, while the other seven were put in the others group.…”

Section: Discussionmentioning

confidence: 99%

An empirical analysis of source code metrics and smart contract resource consumption

Ajienka

Vangorp

Capiluppi

2020

J Software Evolu Process

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A smart contract (SC) is a programme stored in the Ethereum blockchain by a contract-creation transaction. SC developers deploy an instance of the SC and attempt to execute it in exchange for a fee, paid in Ethereum coins (Ether). If the computation needed for their execution turns out to be larger than the effort proposed by the developer (i.e., the gasLimit), their client instantiation will not be completed successfully. In this paper, we examine SCs from 11 Ethereum blockchain-oriented software projects hosted on GitHub.com, and we evaluate the resources needed for their deployment (i.e., the gasUsed). For each of these contracts, we also extract a suite of object-oriented metrics, to evaluate their structural characteristics. Our results show a statistically significant correlation between some of the object-oriented (OO) metrics and the resources consumed on the Ethereum blockchain network when deploying SCs. This result has a direct impact on how Ethereum developers engage with a SC: evaluating its structural characteristics, they will be able to produce a better estimate of the resources needed to deploy it. Other results show specific source code metrics to be prioritised based on application domains when the projects are clustered based on common themes. KEYWORDS abstract syntax-tree (AST), blockchain-oriented software (BOS), Chidamber and Kemerer (C&K), object-oriented (OO), object-oriented programming (OOP), smart contract (SC) 1 INTRODUCTION A blockchain is a shared ledger that stores transactions in a decentralised 1 peer-to-peer network of computers also known as nodes. Blockchain transactions can be composed of contract creation transactions and contract function invoking transactions. The former deploys and records a smart contract (SC) on the blockchain, and the latter causes the execution of a contract functionality. 2,3 The third transaction type is the token or cryptocurrency transfer transaction such as Bitcoin transfers on the Bitcoin Blockchain or Ether transfers on the Ethereum Blockchain. As a whole, the blockchain technology provides a decentralised, trustless platform that combines transparency, immutability and consensus properties to enable secure, pseudo-anonymous transactions. SCs are the programmes stored in a blockchain by a contract-creation transaction. In the last few years, SCs have been used in different scenarios: in voting platforms to secure votes; to automatically process insurance claims according to agreed terms and postal companies for payments on delivery. 5 Porru et al 6 defined the term blockchain-oriented software (BOS) as a software that contributes to the realization of a blockchain project. This definition includes both blockchain platforms (or networks), such as Bitcoin and Ethereum, and general blockchain software commonly referred to as decentralised apps (DApps). 7 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work...

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Section: Discussionmentioning

confidence: 99%

An empirical analysis of source code metrics and smart contract resource consumption

Ajienka

Vangorp

Capiluppi

2020

J Software Evolu Process

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“…Furthermore, Bitcoin and other cryptocurrencies are strongly interlinked reflected by volatility spill-over, volatility co-movement, lead-lag effect, market co-movement [47][48][49][50][51]. This shows co-integration relationships and dynamic inter-dependent between bitcoin and other cryptocurrencies exist, suggesting that co-crash in the cryptocurrency assets − crash in one cryptocurrency leads to crash in other cryptocurrencies − is possible to happen.…”

Section: Literature Reviewmentioning

confidence: 92%

Detecting Early Warning Signals of Major Financial Crashes in Bitcoin Using Persistent Homology

2020

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This study explores persistent homology to detect early warning signals of the 2017 and 2019 major financial crashes in Bitcoin. Sliding window is used to obtain point cloud datasets from a multidimensional time series (Bitcoin, Ethereum, Litecoin and Ripple). We apply persistent homology to quantify transient loops that appear in multiscale topological spaces, which associated on each point cloud dataset and encode the quantified information in a persistence landscape. Temporal changes in persistence landscapes are measured via their 1-norms. Consequently, a new representative is attained, called 1-norms time series. The 1-norms is associated with indicators: autocorrelation function at lag 1, variance and mean power spectrum at low frequencies to detect the signals. By using Kendall's tau correlation and significance test, significant rising trend events that occur before major financial crashes in Bitcoin are defined as the signals. A threshold is determined to scan entire data and record all the significant rising trend events. Lastly, we compare 1-norms with residuals time series, which is another representative obtained from de-trending approach. Our result portrays that autocorrelation function at lag 1 and variance of the 1-norms successfully detect early warning signals before the 2017 and 2019 major financial crashes. However, variance of the 1norms is better since it able to signal another 2018 major financial crash. For the residuals, no early warning signals are detected. Hence, persistent homology provides a better representative than de-trending approach. Overall, persistent homology is a promising method to detect early warning signals of major financial crashes in Bitcoin. INDEX TERMS Cryptocurrencies, bitcoin, persistent homology, critical transition, early warning signals.

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“…Search data was obtained from Google trends for the keyword "bitcoin". Price of cryptocurrency Ripple (XRP), which is the third biggest cryptocurrency in terms of market capitalization, was also considered as an exogenous factor for Bitcoin price prediction (Cagli 2019). Bitcoin data for all the exogenous and endogenous factors for the period 01/01/2010 to 30/06/2019 were collected and a total of 3469 time series observations were obtained.…”

Section: Data Collection and Feature Engineeringmentioning

confidence: 99%

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Dutta¹,

Kumar

Basu³

2019

SSRN Journal

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In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

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Explosive behavior in the prices of Bitcoin and altcoins

Cited by 71 publications

References 24 publications

An empirical analysis of source code metrics and smart contract resource consumption

An empirical analysis of source code metrics and smart contract resource consumption

Detecting Early Warning Signals of Major Financial Crashes in Bitcoin Using Persistent Homology

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

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