arXiv subjects

Tomaso Aste

Publications and source records attributed to Tomaso Aste.

At least 55 records · Page 3Linked to original sources

arXiv2020

Market structure dynamics during COVID-19 outbreak

In this note, we discuss the impact of the COVID-19 outbreak from the perspective of the market-structure. We observe that the US market-structure has dramatically changed during the past four weeks and that the level of change has followed the number of infected cases reported in the USA. Presently, market-structure resembles most closely the structure during the middle of the 2008 crisis but there are signs that it may be starting to evolve into a new structure altogether. This is the first article of a series where we will be analyzing and discussing market-structure as it evolves to a state of further instability or, more optimistically, stabilization and recovery.

q-fin.ST↗

arXiv2019

A Decentralised Digital Identity Architecture

Current architectures to validate, certify, and manage identity are based on centralised, top-down approaches that rely on trusted authorities and third-party operators. We approach the problem of digital identity starting from a human rights perspective, with a primary focus on identity systems in the developed world. We assert that individual persons must be allowed to manage their personal information in a multitude of different ways in different contexts and that to do so, each individual must be able to create multiple unrelated identities. Therefore, we first define a set of fundamental constraints that digital identity systems must satisfy to preserve and promote privacy as required for individual autonomy. With these constraints in mind, we then propose a decentralised, standards-based approach, using a combination of distributed ledger technology and thoughtful regulation, to facilitate many-to-many relationships among providers of key services. Our proposal for digital identity differs from others in its approach to trust in that we do not seek to bind credentials to each other or to a mutually trusted authority to achieve strong non-transferability. Because the system does not implicitly encourage its users to maintain a single aggregated identity that can potentially be constrained or reconstructed against their interests, individuals and organisations are free to embrace the system and share in its benefits.

cs.CY↗

arXiv2019

P2P Loan acceptance and default prediction with Artificial Intelligence

Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. Logistic Regression was found to be the best performer for the first phase, with test set recall macro score of $77.4 \%$. Deep Neural Networks were applied to the second phase only, were they achieved best performance, with validation set recall score of $72 \%$, for defaults. This shows that AI can improve current credit risk models reducing the default risk of issued loans by as much as $70 \%$. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.

q-fin.RM↗

arXiv2019

Achieving competitive advantage in academia through early career coauthorship with top scientists

We quantify the long term impact that the coauthorship with established top-cited scientists has on the career of junior researchers in four different scientific disciplines. Through matched pair analysis, we find that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers with respect to peers with similar early career profiles. Such a competitive advantage materialises as a higher probability of repeatedly coauthoring work with top-cited scientists, and, ultimately, as a higher probability of becoming one. Notably, we find that the coauthorship with a top scientist has the strongest impact on the careers of junior researchers affiliated with less prestigious institutions. As a consequence, we argue that such institutions may hold vast amounts of untapped potential, which may be realised by improving access to top scientists.

physics.soc-ph↗

arXiv2019

Forecasting market states

We propose a novel methodology to define, analyze and forecast market states. In our approach market states are identified by a reference sparse precision matrix and a vector of expectation values. In our procedure, each multivariate observation is associated with a given market state accordingly to a minimization of a penalized Mahalanobis distance. The procedure is made computationally very efficient and can be used with a large number of assets. We demonstrate that this procedure is successful at clustering different states of the markets in an unsupervised manner. In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre- and post- crisis periods with one state gathering periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets. In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. This methodology opens the way to a range of applications in risk management and trading strategies in the context where the correlation structure plays a central role.

q-fin.ST↗

arXiv2019

Can Cryptocurrencies Preserve Privacy and Comply with Regulations?

Cryptocurrencies offer an alternative to traditional methods of electronic value exchange, promising anonymous, cash-like electronic transfers, but in practice they fall short for several key reasons. We consider the false choice between total surveillance, as represented by banking as currently implemented by institutions, and impenetrable lawlessness, as represented by privacy-enhancing cryptocurrencies as currently deployed. We identify a range of alternatives between those two extremes, and we consider two potential compromise approaches that offer both the auditability required for regulators and the anonymity required for users.

cs.CY↗

arXiv2019

Cryptocurrency market structure: connecting emotions and economics

We study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introduces several rigorous innovative methodologies applicable to this and to several other complex systems where a large number of variables interact in a non-linear way, which is a distinctive feature of the digital economy. The analysis of the dependency structure reveals that prices are significantly correlated with sentiment. The major, most capitalised cryptocurrencies, such as bitcoin, have a central role in the price correlation network but only a marginal role in the sentiment network and in the network describing the interactions between the two. The study of the causality structure reveals a causality network that is consistently related with the correlation structures and shows that both prices cause sentiment and sentiment cause prices across currencies with the latter being stronger in size but smaller in number of significative interactions. Overall our study uncovers a complex and rich structure of interrelations where prices and sentiment influence each other both instantaneously and with lead-lag causal relations. A major finding is that minor currencies, with small capitalisation, play a crucial role in shaping the overall dependency and causality structure. Despite the high level of noise and the short time-series we verified that these networks are significant with all links statistically validated and with a structural organisation consistently reproduced across all networks.

q-fin.ST↗

arXiv2019

The Multilayer Structure of Corporate Networks

Various company interactions can be described by networks, for instance the ownership networks and the board membership networks. To understand the ecosystem of companies, these interactions cannot be seen in isolation. For this purpose we construct a new multilayer network of interactions between companies in Germany and in the United Kingdom, combining ownership links, social ties through joint board directors, R\&D collaborations and stock correlations in one linked multiplex dataset. We describe the features of this network and show there exists a non-trivial overlap between these different types of networks, where the different types of connections complement each other and make the overall structure more complex. This highlights that corporate control, boardroom influence and other connections have different structures and together make an even smaller corporate world than previously reported. We have a first look at the relation between company performance and location in the network structure.

physics.soc-ph↗

arXiv2018

Predicting future stock market structure by combining social and financial network information

We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is measured as the co-movement of social media opinion on those same assets. Predictions are obtained with a simple model that uses link persistence and link formation by triadic closure across both financial and social media layers. Results demonstrate that the proposed model can predict future market structure with up to a 40\% out-of-sample performance improvement compared to a benchmark model that assumes a time-invariant financial correlation structure. Social media information leads to improved models for all settings tested, particularly in the long-term prediction of financial market structure. Surprisingly, financial market structure exhibited higher predictability than social opinion structure.

q-fin.ST↗

arXiv2018

Risk-Neutral Pricing and Hedging of In-Play Football Bets

A risk-neutral valuation framework is developed for pricing and hedging in-play football bets based on modelling scores by independent Poisson processes with constant intensities. The Fundamental Theorems of Asset Pricing are applied to this set-up which enables us to derive novel arbitrage-free valuation formulæ for contracts currently traded in the market. We also describe how to calibrate the model to the market and how trades can be replicated and hedged.

q-fin.TR↗

arXiv2018

Reciprocity and success in academic careers

The growing importance of citation-based bibliometric indicators in shaping the prospects of academic careers incentivizes scientists to boost the numbers of citations they receive. Whereas the exploitation of self-citations has been extensively documented, the impact of reciprocated citations has not yet been studied. We study reciprocity in a citation network of authors, and compare it with the average reciprocity computed in an ensemble of null network models. We show that obtaining citations through reciprocity correlates negatively with a successful career in the long term. Nevertheless, at the aggregate level we show evidence of a steady increase in reciprocity over the years, largely fuelled by the exchange of citations between coauthors. Our results characterize the structure of author networks in a time of increasing emphasis on citation-based indicators, and we discuss their implications towards a fairer assessment of academic impact.

physics.soc-ph↗

arXiv2017

Universal behaviour of the glass and the jamming transitions in finite dimensions

We investigate the glass and the jamming transitions of hard spheres in finite dimensions $d$, through a revised cell theory, that combines the free volume and the Random First Order Theory (RFOT). Recent results show that in infinite dimension the ideal glass transition and jamming transitions are distinct, while based on our theory we argue that they indeed coincide for finite $d$. As a consequence, jamming results into a percolation transition described by RFOT, with a static length diverging with exponent $ν=2/d$, which we verify through finite size scaling, and standard critical exponents $α= 0$, $β= 0$ and $γ= 2$ independent on $d$.

cond-mat.soft↗

arXiv2017

Sparse causality network retrieval from short time series

We investigate how efficiently a known underlying sparse causality structure of a simulated multivariate linear process can be retrieved from the analysis of time-series of short lengths. Causality is quantified from conditional transfer entropy and the network is constructed by retaining only the statistically validated contributions. We compare results from three methodologies: two commonly used regularization methods, Glasso and ridge, and a newly introduced technique, LoGo, based on the combination of information filtering network and graphical modelling. For these three methodologies we explore the regions of time series lengths and model-parameters where a significant fraction of true causality links is retrieved. We conclude that, when time-series are short, with their lengths shorter than the number of variables, sparse models are better suited to uncover true causality links with LoGo retrieving the true causality network more accurately than Glasso and ridge.

stat.ME↗

arXiv2017

Dynamic correlations at different time-scales with Empirical Mode Decomposition

The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales, at different lags and over time. We uncover a rich heterogeneity of interactions which depends on the time-scale and has important led-lag relations which can have practical use for portfolio management, risk estimation and investments.

cs.CE↗

arXiv2017

Excess reciprocity distorts reputation in online social networks

The peer-to-peer (P2P) economy relies on establishing trust in distributed networked systems, where the reliability of a user is assessed through digital peer-review processes that aggregate ratings into reputation scores. Here we present evidence of a network effect which biases digital reputation, revealing that P2P networks display exceedingly high levels of reciprocity. In fact, these are much higher than those compatible with a null assumption that preserves the empirically observed level of agreement between all pairs of nodes, and rather close to the highest levels structurally compatible with the networks' reputation landscape. This indicates that the crowdsourcing process underpinning digital reputation can be significantly distorted by the attempt of users to mutually boost reputation, or to retaliate, through the exchange of ratings. We uncover that the least active users are predominantly responsible for such reciprocity-induced bias, and that this fact can be exploited to obtain more reliable reputation estimates. Our findings are robust across different P2P platforms, including both cases where ratings are used to vote on the content produced by users and to vote on user profiles.

physics.soc-ph↗

arXiv2017

Blockchain Inefficiency in the Bitcoin Peers Network

We investigate Bitcoin network monitoring the dynamics of blocks and transactions. We unveil that 43\% of the transactions are still not included in the Blockchain after 1h from the first time they were seen in the network and 20\% of the transactions are still not included in the Blockchain after 30 days, revealing therefore great inefficiency in the Bitcoin system. However, we observe that most of these `forgotten' transactions have low values and in terms of transferred value the system is less inefficient with 93\% of the transactions value being included into the Blockchain within 3h. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes and calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording.

cs.CY↗

arXiv2017

Relation between regional uncertainty spillovers in the global banking system

We report on time-varying network connectedness within three banking systems: North America, the EU, and ASEAN. The original method by Diebold and Yilmaz is improved by using exponentially weighted daily returns and ridge regularization on vector autoregression (VAR) and forecast error variance decomposition (FEVD). We compute the total network connectedness for each of the three banking systems, which quantifies regional uncertainty. Results over rolling windows of 300 days during the period between 2005 and 2015 reveal changing uncertainty patterns which are similar across regions, with common peaks associated with identifiable exogenous events. Lead-lag relationships among changes of total network connectedness of the three systems, quantified by transfer entropy, reveal that uncertainties in the three regional systems are significantly causally related, with the North American system having the largest influence on EU and ASEAN.

q-fin.ST↗

arXiv2016

Parsimonious modeling with Information Filtering Networks

We introduce a methodology to construct parsimonious probabilistic models. This method makes use of Information Filtering Networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small sub-parts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust even for the estimation of inverse covariance of high-dimensional, noisy and short time-series. Applied to financial data our method results computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big datasets with large numbers of variables. Examples of practical application for forecasting, stress testing and risk allocation in financial systems are also provided.

cs.IT↗