Search

Financial stock return correlations have been analyzed through the lens of random matrix theory to differentiate the underlying signal from spurious correlations. The continuous spectrum of the eigenvalue distribution derived from the stock return correlation matrix typically aligns with a rescaled Marchenko-Pastur distribution, indicating no detectable signal. In this study, we introduce a stochastic field theory model to establish a detection threshold for signals present in the limit where the eigenvalues are within the continuous spectrum, which itself closely resembles that of a random matrix where standard methods such as principal component analysis fail to infer a signal. We then apply our method to Standard & Poor's 500 financial stocks' return correlations, detecting the presence of a signal in the largest eigenvalues within the continuous spectrum., Comment: 12 pages, 7 fugures

Applying a network analysis to stock return correlations, we study the dynamical properties of the network and how they correlate with the market return, finding meaningful variables that partially capture the complex dynamical processes of stock interactions and the market structure. We then use the individual properties of stocks within the network along with the global ones, to find correlations with the future returns of individual S&P 500 stocks. Applying these properties as input variables for forecasting, we find a 50% improvement on the R2score in the prediction of stock returns on long time scales (per year), and 3% on short time scales (2 days), relative to baseline models without network variables., Comment: 25 pages, 10 figures

Financial stock returns correlations have been studied in the prism of random matrix theory, to distinguish the signal from the "noise". Eigenvalues of the matrix that are above the rescaled Marchenko Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis we use complex network analysis to simulate the "noise" and the "market" component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated "market" random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short time scale., Comment: 16 pages, 4 figures

The advent of online social networks has led to the development of an abundant literature on the study of online social groups and their relationship to individuals' personalities as revealed by their textual productions. Social structures are inferred from a wide range of social interactions. Those interactions form complex -- sometimes multi-layered -- networks, on which community detection algorithms are applied to extract higher order structures. The choice of the community detection algorithm is however hardily questioned in relation with the cultural production of the individual they classify. In this work, we assume the entangled nature of social networks and their cultural production to propose a definition of cultural based online social groups as sets of individuals whose online production can be categorized as social group-related. We take advantage of this apparently self-referential description of online social groups with a hybrid methodology that combines a community detection algorithm and a natural language processing classification algorithm. A key result of this analysis is the possibility to score community detection algorithms using their agreement with the natural language processing classification. A second result is that we can assign the opinion of a random user at >85% accuracy., Comment: 10 pages, 8 figures

The COVID-19 pandemic that emerged in 2020 has highlighted the complex interplay between vaccine hesitancy and societal polarization. In this study, we analyse the dynamical polarization within a social network as well as the network properties before and after a vaccine was made available. Our results show that as the network evolves from a less structured state to one with more clustered communities. Then using an agent-based modeling approach, we simulate the propagation of a virus in a polarized society by assigning vaccines to pro-vaccine individuals and none to the anti-vaccine individuals. We compare this propagation to the case where the same number of vaccines is distributed homogeneously across the population. In polarized networks, we observe a significantly more widespread diffusion of the virus, highlighting the importance of considering polarization for epidemic forecasting., Comment: 21 pages, 5 figures

This article provides an understanding of Natural Language Processing techniques in the framework of financial regulation, more specifically in order to perform semantic matching search between rules and policy when no dataset is available for supervised learning. We outline how to outperform simple pre-trained sentences-transformer models using freely available resources and explain the mathematical concepts behind the key building blocks of Natural Language Processing., Comment: 20 pages, 3 figures

Polarization of opinions about vaccination can have a negative impact on pandemic control. In this work we quantify this negative impact for the transmission of COVID-19, using an agent based simulation in an heterogeneous population with multi-type networks, representing different types of social interactions. We show that the clustering of unvaccinated individuals, associated with polarization of opinion, can lead to significant differences in the evolution of the pandemic compared to deterministic model predictions. Under our realistic baseline scenario these differences are a 33pc increase of the effective reproduction number, a 157pc increase of infections at the peak and a 30pc increase in the final cumulative attack rate., Comment: 5pages, 3 figures

We present a new analysis of the inferred growth rate of cosmic structure measured around voids, using the LOWZ and the CMASS samples in the twelfth data release (DR12) of SDSS. Using a simple multipole analysis we recover a value consistent with $\Lambda$CDM for the inferred linear growth rate normalized by the linear bias: the $\beta$ parameter. This is true in both the mock catalogues and the data, where we find $\beta=0.33\pm0.06$ for the LOWZ sample and $\beta=0.36\pm0.05$ for the CMASS sample. This work demonstrates that we can expect redshift-space distortions around voids to provide unbiased and accurate constraints on the growth rate, complementary to galaxy clustering, using simple linear modelling., Comment: 10 pages, 7 figures

The environmental dependence of galaxy clustering encodes information about the physical processes governing the growth of cosmic structure. We analyze the baryon acoustic peak as a function of environment in the galaxy correlation function of the Baryon Oscillation Spectroscopic Survey CMASS sample. Dividing the sample into three subsets by smoothed local overdensity, we detect acoustic peaks in the six separate auto-correlation and cross-correlation functions of the sub-samples. Fitting models to these correlation functions, calibrated by mock galaxy and dark matter catalogues, we find that the inferred distance scale is independent of environment, and consistent with the result of analyzing the combined sample. The shape of the baryon acoustic feature, and the accuracy of density-field reconstruction in the Zeldovich approximation, varies with environment. By up-weighting underdense regions and down-weighting overdense regions in their contribution to the full-sample correlation function, by up to 50%, we achieve a fractional improvement of a few per cent in the precision of baryon acoustic oscillation fits to the CMASS data and mock catalogues: the scatter in the preferred-scale fits to the ensemble of mocks improves from 1.45% to 1.34% (pre-reconstruction) and 1.03% to 1.00% (post-reconstruction). These results are consistent with the notion that the acoustic peak is sharper in underdense environments., Comment: 11 pages, 6 figures, accepted for publication in MNRAS

The growth rate of cosmic structure is a powerful cosmological probe for extracting information on the gravitational interactions and dark energy. In the late time Universe, the growth rate becomes non-linear and is usually probed by measuring the two point statistics of galaxy clustering in redshift space up to a limited scale, retaining the constraint on the linear growth rate f. In this letter, we present an alternative method to analyse the growth of structure in terms of local densities, i.e. f(Delta). Using N-body simulations, we measure the function of f(Delta) and show that structure grows faster in high density regions and slower in low density regions. We demonstrate that f(Delta) can be modelled using a log-normal Monte Carlo Random Walk approach, which provides a means to extract cosmological information from f(Delta). We discuss prospects for applying this approach to galaxy surveys., Comment: 5 pages, 2 figures

Using cosmic voids to probe the growth rate of cosmic structure, and hence the nature of dark energy, is particularly interesting in the context of modified gravity theories that rely on the screening mechanism. In this work we improve the modelling of redshift-space distortions around voids in the dark matter density field, and thus reduce systematic errors in the derivation of cosmological parameters. We also show how specific types of voids can be used to better probe the growth rate, using a flexible void finder. We apply our results to test for a quintessence type of dark energy vs. a LCDM model, and find a good agreement with the fiducial cosmology after implementing an analytical correction to the radial velocity profiles around voids. We additionally outline characteristic imprints of dark energy in the dark matter velocity distributions around voids., Comment: 12 pages, 11 figures

Taipan is a multi-object spectroscopic galaxy survey starting in 2017 that will cover 2pi steradians over the southern sky, and obtain optical spectra for about two million galaxies out to z<0.4. Taipan will use the newly-refurbished 1.2m UK Schmidt Telescope at Siding Spring Observatory with the new TAIPAN instrument, which includes an innovative 'Starbugs' positioning system capable of rapidly and simultaneously deploying up to 150 spectroscopic fibres (and up to 300 with a proposed upgrade) over the 6-deg diameter focal plane, and a purpose-built spectrograph operating from 370 to 870nm with resolving power R>2000. The main scientific goals of Taipan are: (i) to measure the distance scale of the Universe (primarily governed by the local expansion rate, H_0) to 1% precision, and the structure growth rate of structure to 5%; (ii) to make the most extensive map yet constructed of the mass distribution and motions in the local Universe, using peculiar velocities based on improved Fundamental Plane distances, which will enable sensitive tests of gravitational physics; and (iii) to deliver a legacy sample of low-redshift galaxies as a unique laboratory for studying galaxy evolution as a function of mass and environment. The final survey, which will be completed within 5 years, will consist of a complete magnitude-limited sample (i<17) of about 1.2x10^6 galaxies, supplemented by an extension to higher redshifts and fainter magnitudes (i<18.1) of a luminous red galaxy sample of about 0.8x10^6 galaxies. Observations and data processing will be carried out remotely and in a fully-automated way, using a purpose-built automated 'virtual observer' software and an automated data reduction pipeline. The Taipan survey is deliberately designed to maximise its legacy value, by complementing and enhancing current and planned surveys of the southern sky at wavelengths from the optical to the radio., Comment: Published in PASA; 29 pages, 17 figures, 2 tables

Using a Monte Carlo random walks of a log-normal distribution, we show how to qualitatively study void properties for non-standard cosmologies. We apply this method to an f(R) modified gravity model and recover the N-body simulation results of (Achitouv et al. 2016) for the void profiles and their deviation from GR. This method can potentially be extended to study other properties of the large scale structures such as the abundance of voids or overdense environments. We also introduce a new way to identify voids in the cosmic web, using only a few measurements of the density fluctuations around random positions. This algorithm allows to select voids with specific profiles and radii. As a consequence, we can target classes of voids with higher differences between f(R) and standard gravity void profiles. Finally we apply our void criteria to galaxy mock catalogues and discuss how the flexibility of our void finder can be used to reduce systematics errors when probing the growth rate in the galaxy-void correlation function., Comment: 12 pages,10 figures, match the PRD manuscript

We present a new test of gravitational physics by comparing the growth rate of cosmic structure measured around voids with that measured around galaxies in the same large-scale structure dataset, the low-redshift 6-degree Field Galaxy Survey. By fitting a self-consistent Redshift Space Distortion model to the 2D galaxy-galaxy and void-galaxy correlation functions, we recover growth rate values f\sigma_8 = 0.42 \pm 0.06 and 0.39 \pm 0.11, respectively. The environmental-dependence of cosmological statistics can potentially discriminate between modified-gravity scenarios which modulate the growth rate as a function of scale or environment and test the underlying assumptions of homogeneity and isotropy., Comment: 12 pages, 4 figures, accepted in PRD

We calculate the dark matter halo correlation function in redshift space using the Gaussian streaming model (GSM). To determine the scale dependent functions entering the streaming model we use local Lagrangian bias together with Convolution Lagrangian perturbation theory (CLPT) which constitutes an approximation to the Post-Zel'dovich approximation. On the basis of N-body simulations we demonstrate that a smoothing of the initial conditions with the Lagrangian radius improves the Zel'dovich approximation and its ability to predict the displacement field of proto-halos. Based on this observation we implement a "truncated" CLPT by smoothing the initial power spectrum and investigate the dependence of the streaming model ingredients on the smoothing scale. We find that the real space correlation functions of halos and their mean pairwise velocity are optimised if the coarse graining scale is chosen to be 1 Mpc/h at z=0, while the pairwise velocity dispersion is optimised if the smoothing scale is chosen to be the Lagrangian size of the halo. We compare theoretical results for the halo correlation function in redshift space to measurements within the Horizon Run 2 N-body simulation halo catalog. We find that this simple two-filter smoothing procedure in the spirit of the truncated Zel'dovich approximation significantly improves the GSM+CLPT prediction of the redshift space halo correlation function over the whole mass range from large galaxy to galaxy cluster-sized halos., Comment: 24 pages, 18 figures

We test the imprint of f(R) modified gravity on the halo mass function, using N-body simulations and a theoretical model developed in (Kopp et al. 2013). We find a very good agreement between theory and simulations. We extend the theoretical model to the conditional mass function and apply it to the prediction of the linear halo bias in f(R) gravity. Using the halo model we obtain a prediction for the non-linear matter power spectrum accurate to ~10% at z=0 and up to k=2h/Mpc. We also study halo profiles for the f(R) models and find a deviation from the standard general relativity result up to 40%, depending on the halo masses and redshift. This has not been pointed out in previous analysis. Finally we study the number density and profiles of voids identified in these f(R) N-body simulations. We underline the effect of the bias and the sampling to identify voids. We find significant deviation from GR when measuring the f(R) void profiles with fR0<-10^{-6}., Comment: 15 pages, 10 figures

Precise measurements of the baryon acoustic oscillation (BAO) scale as a standard ruler in the clustering pattern of large-scale structure is a central goal of current and future galaxy surveys. The BAO peak may be sharpened using the technique of density-field reconstruction, in which the bulk displacements of galaxies are estimated using a Zel'dovitch approximation. We use numerical simulations to demonstrate how the accuracy of this approximation depends strongly on local environment, and how this information may be used to construct an improved BAO measurement through environmental re-weighting and using higher-order perturbation theory. We outline further applications of the displacement field for testing cosmological models., Comment: 13 pages, 14 figures

We compare analytical predictions of void volume functions to those measured from N-body simulations, detecting voids with the zobov void finder. We push to very small, nonlinear voids, below few Mpc radius, by considering the unsampled DM density field. We also study the case where voids are identified using halos. We develop analytical formula for the void abundance of both the excursion set approach and the peaks formalism. These formula are valid for random walks smoothed with a top-hat filter in real space, with a large class of realistic barrier models. We test the extent to which the spherical evolution approximation, which forms the basis of the analytical predictions, models the highly aspherical voids that occur in the cosmic web, and are found by a watershed-based algorithm such as zobov. We show that the volume function returned by zobov is quite sensitive to the choice of treatment of sub-voids, a fact that has not been appreciated previously. For reasonable choices of sub-void exclusion, we find that the Lagrangian density delta_v of the zobov voids -- which is predicted to be a constant delta_v = -2.7 in the spherical evolution model -- is different from the predicted value, showing substantial scatter and scale dependence. This result applies to voids identified at z=0 with effective radius between 1 and 10 Mpc/h. Our analytical approximations are flexible enough to give a good description of the resulting volume function; however, this happens for choices of parameter values that are different from those suggested by the spherical evolution assumption. We conclude that analytical models for voids must move away from the spherical approximation in order to be applied successfully to observations, and we discuss some possible ways forward., Comment: 12 pages,5 figures Published in MNRAS

We compute the critical density of collapse for spherically symmetric overdensities in a class of f(R) modified gravity models. For the first time we evolve the Einstein, scalar field and non-linear fluid equations, making the minimal simplifying assumptions that the metric potentials and scalar field remain quasi-static throughout the collapse. Initially evolving a top hat profile, we find that the density threshold for collapse depends significantly on the initial conditions imposed, specifically the choice of size and shape. By imposing `natural' initial conditions, we obtain a fitting function for the spherical collapse delta_c as a function of collapse redshift, mass of the overdensity and f_{R0}, the background scalar field value at z=0. By extending delta_c into drifting and diffusing barrier within the context of excursion set theory, we obtain a realistic mass function that might be used to confront this class of scalar-tensor models with observations of dark matter halos. The proposed analytic formula for the halo mass function was tested against Monte Carlo random walks for a wide class of moving barriers and can therefore be applied to other modified gravity theories., Comment: 24 pages, 13 figures, corresponds to published version

The high-mass end of the halo mass function is a sensitive probe of primordial non-Gaussianity (NG). In a recent study [9] we have computed the NG halo mass function in the context of the Excursion Set theory and shown that the primordial NG imprint is coupled to that induced by the non-linear collapse of dark matter halos. We also found an excellent agreement with N-body simulation results. Here, we perform a more accurate computation which accounts for the interval validity of the bispectrum expansion to next-to-leading order and extend the calculation to the case of a non-vanishing primordial trispectrum., Comment: 9 pages 3 figures

The mass distribution of dark matter halos is a sensitive probe of primordial non-Gaussianity (NG). We derive an analytical formula of the halo mass function by perturbatively computing excursion set path-integrals for a non-Gaussian density field with non-vanishing skewness, fnl. We assume a stochastic barrier model which captures the main features of the ellipsoidal collapse of halos. Contrary to previous results based on extensions of the Press-Schechter formalism to NG initial conditions, we find that the non-spherical collapse of halos directly alter the signature of primordial NG. This points toward a potential degeneracy between the effect of primordial non-Gaussianity and that of non-linear halo collapse. The inferred mass function is found to be in remarkable agreement with N-body simulations of NG local type. Deviations are well within numerical uncertainties for all values of -80 < fNL^loc < 300 in the range of validity of the perturbative calculation. Moreover, the comparison with simulation results suggests that for fNL > 150 or fNL < -50 the non-linear collapse of halos, as described by our barrier model, strongly deviates from that of Gaussian initial conditions. This is not surprising since the effect of non-linear gravitational processes may be altered by initially large NG. Hence, in the lack of prior theoretical knowledge, halo collapse model parameters should be included in statistical halo mass function data analysis which aim to constrain the signature of primordial NG, Comment: We found a coding-bug in our numerical calculation of the function U3(S) which affected the comparison with non-Gaussian N-body simulations. Results change only quantitatively. 29 pages, 8 figures, typos corrected and added clarifications on the barrier model