Tomaso Aste

A tool for filtering information in complex systems

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties.

Scaling behaviors in differently developed markets

Scaling properties of four different stock market indices are studied in terms of a generalized Hurst exponent approach. We find that the deviations from pure Brownian motion behavior are associated with the degrees of development of the markets and we observe strong differentiations in the scaling properties of markets at different development stage.

Geometrical structure of disordered sphere packings

The three-dimensional structure of large packings of monosized spheres with volume fractions ranging between 0.58 and 0.64 has been studied with x-ray computed tomography. We search for signatures of organization, classifying local arrangements and exploring the effects of local geometrical constrains on the global packing. This study is the largest and the most accurate empirical analysis of disordered packings at the grain-scale to date, mapping over 380,000 sphere coordinates with precision within 0.1% of the sphere diameters. We discuss topological and geometrical methods to characterize and classify these systems emphasizing the implications that local geometry can have on the mechanisms of formation of these amorphous structures. Some of the main results are (1) the observation that the average number of contacts increases with the volume fraction; (2) the discovery that these systems have a very compact contact network; (3) the finding that disordered packing can be locally more efficient than crystalline packings; (4) the observation that the peaks of the radial distribution function follow power law divergences; (5) the discovery that geometrical frustration plays no role in the formation of such amorphous packings.

Investigating the geometrical structure of disordered sphere packings

Bead packs of up to 150,000 mono-sized spheres with packing densities ranging from 0.58 to 0.64 have been studied by means of X-ray computed tomography. These studies represent the largest and the most accurate description of the structure of disordered packings at the grain-scale ever attempted. We investigate the geometrical structure of such packings looking for signatures of disorder. We discuss ways to characterize and classify these systems and the implications that local geometry can have on densification dynamics.

Emergence of Gamma distributions in granular materials and packing models

We introduce a deductive statistical mechanics approach for granular materials which is formally built from few realistic physical assumptions. The main finding is an universal behavior for the distribution of the density fluctuations. Such a distribution is the equivalent of the MaxwellBoltzmann’s distribution in the kinetic theory of gasses. The comparison with a very extensive set of experimental and simulation data for packings of monosized spherical grains, reveals a remarkably good quantitative agreement with the theoretical predictions for the density fluctuations both at the grain level and at the global system level. Such agreement is robust over a broad range of packing fractions and it is observed in several distinct systems prepared by using different methods. The equilibrium distributions are characterized by only one parameter (k) which is a quantity very sensitive to changes in the structural organization. The thermodynamical equivalent of k and its relation with the ‘granular temperature’ are also discussed.

Onset of mechanical stability in random packings of frictional spheres

Using sedimentation to obtain precisely controlled packings of noncohesive spheres, we find that the volume fraction phiRLP of the loosest mechanically stable packing is in an operational sense well defined by a limit process. This random loose packing volume fraction decreases with decreasing pressure p and increasing interparticle friction coefficient mu. Using x-ray tomography to correct for a container boundary effect that depends on particle size, we find for rough particles in the limit p-->0 a new lower bound, phiRLP=0.550+/-0.001.

Understanding the source of multifractality in financial markets

In this paper, we use the generalized Hurst exponent approach to study the multi-scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multi-scaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved. This effect is robust and it is reproduced in several real financial data including stock market indices, exchange rates and interest rates. In order to understand the origin of this effect we investigate different simulated time series by means of the Markov switching multifractal model, autoregressive fractionally integrated moving average processes with stable innovations, fractional Brownian motion and Levy flights. Overall we conclude that the multifractality observed in financial time series is mainly a consequence of the characteristic fat-tailed distribution of the returns and time-correlations have the effect to decrease the measured multifractality.

Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series

We investigate the use of the Hurst exponent, dynamically computed over a weighted moving time-window, to evaluate the level of stability/instability of financial firms. Financial firms bailed-out as a consequence of the 2007–2008 credit crisis show a neat increase with time of the generalized Hurst exponent in the period preceding the unfolding of the crisis. Conversely, firms belonging to other market sectors, which suffered the least throughout the crisis, show opposite behaviors. We find that the multifractality of the bailed-out firms increase at the crisis suggesting that the multi fractal properties of the time series are changing. These findings suggest the possibility of using the scaling behavior as a tool to track the level of stability of a firm. In this paper, we introduce a method to compute the generalized Hurst exponent which assigns larger weights to more recent events with respect to older ones. In this way large fluctuations in the remote past are less likely to influence the recent past. We also investigate the scaling associated with the tails of the log-returns distributions and compare this scaling with the scaling associated with the Hurst exponent, observing that the processes underlying the price dynamics of these firms are truly multi-scaling.

Complex networks on hyperbolic surfaces

We explore a novel method to generate and characterize complex networks by means of their embedding on hyperbolic surfaces. Evolution through local elementary moves allows the exploration of the ensemble of networks which share common embeddings and consequently share similar hierarchical properties. This method provides a new perspective to classify network-complexity both on local and global scale. We demonstrate by means of several examples that there is a strong relation between the network properties and the embedding surface.

When Can Social Media Lead Financial Markets

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.