A Model of the Optimal Selection of Crypto Assets

Bartolucci, Silvia; Kirilenko, Andrei

doi:10.2139/ssrn.3578450

Cited by 5 publications

(2 citation statements)

References 29 publications

(29 reference statements)

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“…The crypto-currencies trading volume also has a granger-causality to energy 21st Century Approaches to Management and Accounting Research consumption [7]. A crypto asset is an intangible digital asset whose issuance, sale or transfer are secured by cryptographic technology and shared electronically via a distributed ledger [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Return and Risk of Cryptocurrency Bitcoin Asset as Investment Instrument

Dasman¹

2021

Accounting and Finance Innovations

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This study aims to explore the potential use of the cryptocurrency bitcoin as an investment instrument in Indonesia. The return obtained from bitcoin cryptocurrency is compared to other investment instruments, namely stock returns, gold and the rupiah exchange rate. The research period was carried out based on research data from 2011 to 2020. This study employee compares means test (t test) and analysis of variance (F test) on rate of return of bitcoin investment. The bitcoin return compare to the rate of return form the others investments instruments namely exchange rate, gold and stock. The study collected 120 data of each investments instruments: bitcoin, exchange rate, gold and stock from various of sources during 2011–2020. Then, we calculate the return and risk of individual investment instruments. The results showed that the bitcoin currency had the highest rate of return 18% with a standard deviation of 61% compared to exchange rate, gold and stock returns. While the rate of return for the others investment instruments showed less than 0.5% with standard deviation less than 5%. The rate of return bitcoin has significance difference compare to the rate of return of exchange rate, gold and stock. The study contribute for the investors who would like to invest on bitcoin. The investors should understand the characteristic of bitcoin in term of rate of returns and also the risk. This study also contributes to government of Indonesia on crypto currency development. The Indonesia government should adopt and regulate on crypto currency in the future to secure the investor and economic growth.

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Section: Introductionmentioning

confidence: 99%

Analysis of Return and Risk of Cryptocurrency Bitcoin Asset as Investment Instrument

Dasman¹

2021

Accounting and Finance Innovations

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“…Quantitative investigations aimed at extracting information from the time series of cryptocurrency prices and predicting prices drivers [1] or the next most likely jump, range from theoretical models of pricing and adoption of digital tokens [2][3][4] to machine learning [5,6] and neural network-driven [7] forecasts of prices and returns. Analyses of the cryptocurrency markets [8][9][10] yielded insights on their maturity, efficiency and structure.…”

Section: Introductionmentioning

confidence: 99%

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

et al. 2020

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The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how "healthy" and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it. In this paper, we investigate whether developers' emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developers' comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developers' comments over time, and test the corresponding time series (i.e. the affect time series) for correlations and causality with the Bitcoin/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developers' emotions and Bitcoin/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)-associated with the prediction of the prices of cryptocurrencies-significantly decreases when including the affect time series.

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A percolation model for the emergence of the Bitcoin Lightning Network

Bartolucci

Caccioli

Vivo

2020

Sci Rep

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The Lightning Network is a so-called second-layer technology built on top of the Bitcoin blockchain to provide "off-chain" fast payment channels between users, which means that not all transactions are settled and stored on the main blockchain. In this paper, we model the emergence of the Lightning Network as a (bond) percolation process and we explore how the distributional properties of the volume and size of transactions per user may impact its feasibility. The agents are all able to reciprocally transfer Bitcoins using the main blockchain and also -if economically convenient -to open a channel on the Lightning Network and transact "off chain". We base our approach on fitness-dependent network models: as in real life, a Lightning channel is opened with a probability that depends on the "fitness" of the concurring nodes, which in turn depends on wealth and volume of transactions. The emergence of a connected component is studied numerically and analytically as a function of the parameters, and the phase transition separating regions in the phase space where the Lightning Network is sustainable or not is elucidated. We characterize the phase diagram determining the minimal volume of transactions that would make the Lightning Network sustainable for a given level of fees or, alternatively, the maximal cost the Lightning ecosystem may impose for a given average volume of transactions. The model includes parameters that could be in principle estimated from publicly available data once the evolution of the Lighting Network will have reached a stationary operable state, and is fairly robust against different choices of the distributions of parameters and fitness kernels.Bitcoin, the pioneering cryptocurrency, has brought about an unprecedented revolution in the payment industry 1 . Despite its traction and success over the last ten years, the original blockchain -the technological infrastructure underlying Bitcoin -suffers from some limitations that may hinder the future growth and adoption of the cryptocurrency. One of the major issues is the scalability of the system: the current number of transactions validated via this platform is between 3 and 7 transactions per second, compared for instance to thousands of transactions handled by the Visa circuit 2 . The lack of scalability is mainly caused by constraints on throughput of transactions, with the block size fixed at 1MB, and by the high latency -with a new block created on average only every ten minutes. Those limitations are imposed to safeguard the security of the platform against malicious attacks and are difficult to relax without major changes in the protocol.The main solutions proposed to address the scalability issue include (i) changes to the main protocol (consensus algorithm, parameters) and (ii) sidechains and second-layer solutions (see 3 for a recent technical review). Notable examples of type-(i) solutions include new consensus protocols, which would allow a faster issuance of new blocks among other new features 4 . Sidechains are blockchains "conne...

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A Model of the Optimal Selection of Crypto Assets

Cited by 5 publications

References 29 publications

Analysis of Return and Risk of Cryptocurrency Bitcoin Asset as Investment Instrument

Analysis of Return and Risk of Cryptocurrency Bitcoin Asset as Investment Instrument

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

A percolation model for the emergence of the Bitcoin Lightning Network

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