261 research outputs found
Sensing Social Media Signals for Cryptocurrency News
The ability to track and monitor relevant and important news in real-time is
of crucial interest in multiple industrial sectors. In this work, we focus on
the set of cryptocurrency news, which recently became of emerging interest to
the general and financial audience. In order to track relevant news in
real-time, we (i) match news from the web with tweets from social media, (ii)
track their intraday tweet activity and (iii) explore different machine
learning models for predicting the number of the article mentions on Twitter
within the first 24 hours after its publication. We compare several machine
learning models, such as linear extrapolation, linear and random forest
autoregressive models, and a sequence-to-sequence neural network. We find that
the random forest autoregressive model behaves comparably to more complex
models in the majority of tasks.Comment: full version of the paper, that is accepted at ACM WWW '19
Conference, MSM'19 Worksho
Time-varying volatility in Bitcoin market and information flow at minute-level frequency
In this paper, we analyze the time-series of minute price returns on the
Bitcoin market through the statistical models of generalized autoregressive
conditional heteroskedasticity (GARCH) family. Several mathematical models have
been proposed in finance, to model the dynamics of price returns, each of them
introducing a different perspective on the problem, but none without
shortcomings. We combine an approach that uses historical values of returns and
their volatilities - GARCH family of models, with a so-called "Mixture of
Distribution Hypothesis", which states that the dynamics of price returns are
governed by the information flow about the market. Using time-series of
Bitcoin-related tweets and volume of transactions as external information, we
test for improvement in volatility prediction of several GARCH model variants
on a minute level Bitcoin price time series. Statistical tests show that the
simplest GARCH(1,1) reacts the best to the addition of external signal to model
volatility process on out-of-sample data.Comment: 17 pages,11 figure
Forecasting mid-price movement of Bitcoin futures using machine learning
In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil
Close to the metal: Towards a material political economy of the epistemology of computation
This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of both cryptoasset mining and machine learning models. The political economy associated with video games and Bitcoin and Ethereum mining provided a staggering growth in performance and energy efficiency and this, in turn, fostered a change in the epistemological understanding of AI: from rules-based or symbolic AI towards the matrix multiplications underpinning connectionism, machine learning and neural nets. Combining a material political economy of markets with a material epistemology of science, the article shows that there is no clear-cut division between software and hardware, between instructions and tools, and between frameworks of thought and the material and economic conditions of possibility of thought itself. As the microchip shortage and the growing geopolitical relevance of the hardware and semiconductor supply chain come to the fore, the paper invites social scientists to engage more closely with the materialities and hardware architectures of ‘virtual’ algorithms and software
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
Cryptoart: Ethical Challenges of the NFT Revolution
The digital transformation of the art world has become a revolution for the
sector. Cryptoart, based on non-fungible tokens (NFT), is attracting the
attention of artists, collectors and enthusiasts for its ability to tokenise
any element that can be sold as art in the digital market. That means it is
able to become a scarce resource and an economic asset by encapsulating the
market value of a piece of digital art, which may or may not have a reference
in the real world. This study will delve into the ethical aspects underlying
what is known as the NFT Revolution, particularly impacts related to the abuse
or destruction of cultural heritage, speculation and the generation of economic
bubbles and environmental unsustainability
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