Quantifying and modelling online decentralised systems: a complex systems approach

Cryptocurrencies are unique examples of decentralised socioeconomic systems. All the transactions, trading, and development are traceable and publicly available. Bitcoin, the first cryptocurrency, was introduced in 2009 launching a market of more than 2500 cryptocurrencies and has a value of more than 200 billion dollars. In comparison to the rising importance of cryptocurrencies in the financial world, the research on cryptocurrencies is still limited. In this thesis, we analyse three novel datasets namely, cryptocurrencies’ market data, cryptocurrencies’ Wikipedia page views and edits, and illicit transactions on Bitcoin. We study the cryptocurrencies ecosystem, including the market dynamics, the social attention and the transaction network. We find that the ecological neutral model can capture the market dynamics, hinting at the extent to which technological differences between cryptocurrencies are considered in investment decisions. We also investigate the relationship between information production and consumption and cryptocurrency market dynamics. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Finally, we assess dark markets’ Bitcoin transactions showing the ability of the markets to adapt to multiple closures, including law enforcement raids. We expect that our contribution will be of interest to researchers working on either cryptocurrencies or complex systems. We anticipate that adopting a complex systems approach, will spark more research that interweaves both the technological and socioeconomic aspect of cryptocurrencies

Inferring short-term volatility indicators from Bitcoin blockchain

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting

Multivariate time series forecasting involves the learning of historical multivariate information in order to predict the future values of several quantities of interests, accounting for interdependencies among them. In finance, several of this quantities of interests (stock valuations, return, volatility) have been shown to be mutually influencing each other, making the prediction of such quantities a difficult task, especially while dealing with an high number of variables and multiple horizons in the future. Here we propose a machine learning based framework, the DFML, based on the Dynamic Factor Model, to first perform a dimensionality reduction and then perform a multiple step ahead forecasting of a reduced number of components. Finally, the components are transformed again into an high dimensional space, providing the desired forecast. Our results, comparing the DFML with several state of the art techniques from different domanins (PLS, RNN, LSTM, DFM), on both traditional stock markets and cryptocurrencies market and for different families of volatility proxies show that the DFML outperforms the concurrent methods, especially for longer horizons. We conclude by explaining how we wish to further improve the performances of the framework, both in terms of accuracy and computational efficiency.SCOPUS: cp.kinfo:eu-repo/semantics/publishe