Understanding the impact of Twitter on the financial market

Negli anni la funzione dei social network è cambiata molte volte. Alle origini i social network erano uno strumento di connessione tra amici, ora sono siti internet in cui le persone mettono informazioni e quando un social network ha milioni di utenti, diventa un’incredibile sorgente di dati. Twitter è uno dei siti internet più visitati, e viene descritto come “the SMS of internet”, perchè è un social network che permette ai suoi utenti di inviare e leggere messaggi corti, di 140 caratteri, chiamati “tweets”. Con il passare del tempo Twitter `e diventato una fonte fondamentale di notizie. Il suo grande numero di utenti permette alle notizie di espandersi nella rete in modo virale. Molte persone hanno cercato di analizzare il potere dei tweet, come il contenuto positivo o negativo, mentre altri hanno cercato di capire se avessero un potere predittivo. In particolare nel mondo finanziario, sono state avviate molte ricerche per verificare l’esistenza di una effettiva correlazione tra i tweets e la fluttuazione del mercato azionario. L’effettiva presenza di tale relazione unita a un modello predittivo, potrebbe portare allo sviluppo di un modello che analizzando i tweets presenti nella rete, relativi a un titolo azionario, dia informazioni sulle future variazioni del titolo stesso. La nostra attenzione si è rivolata alla ricerca e validazione statistica di tale correlazione. Sono stati effettuati test su singole azioni, sulla base dei dati disponibili, poi estesi a tutto il dataset per vedere la tendenza generale e attribuire maggior valore al risultato. Questa ricerca è caratterizzata dal suo dataset di tweet che analizza un periodo di oltre 2 anni, uno dei periodi più lunghi mai analizzati. Si è cercato di fornire maggior valore ai risultati trovati tramite l’utilizzo di validazioni statistiche, come il “permutation test”, per validare la relazione tra tweets di un titolo con i relativi valori azionari, la rimozione di una percentuale di eventi importanti, per mostrare la dipendenza o indipendenza dei dati dagli eventi più evidenti dell’anno e il “granger causality test”, per capire la direzione di una previsione tra serie. Sono stati effettuati anche test con risultati fallimentari, dai quali si sono ricavate le direzioni per i futuri sviluppi di questa ricerca.

Impact of the financial crisis on cross-border mergers and acquisitions and concentration in the global banking industry

The global banking industry has seen dramatic changes in the past 40 years. Most recently, the financial liberalization of emerging markets and the global financial crisis have significantly impacted the market share of banks worldwide. This article investigates the impact of the 2007–2008 financial crisis on cross-border mergers and acquisitions (M&As) in the banking sector and emphasizes the role of emerging-market banks in the postcrisis consolidation trend. Using M&A data and concentration data over the period 2000–2013, our analysis indicates that the financial crisis had a significant impact on worldwide M&As, especially on the direction of the transactions. Emerging-market banks appear to be major acquirers in the postcrisis period, targeting both neighboring countries and developed economies in Europe. We also observe an increase in bank concentration in developed markets most hit by the financial crisis, especially in the United States and the United Kingdom, whereas bank concentration decreased in emerging markets.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

The structure and resilience of financial market networks

Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience. (C) 2012 American Institute of Physics. [doi:10.1063/1.3683467]

Quantifying and modeling financial market fluctuations

Die Entstehung eines Marktpreises für einen Vermögenswert kann als Superposition der einzelnen Aktionen der Marktteilnehmer aufgefasst werden, die damit kumulativ Angebot und Nachfrage erzeugen. Dies ist in der statistischen Physik mit der Entstehung makroskopischer Eigenschaften vergleichbar, die von mikroskopischen Wechselwirkungen zwischen den beteiligten Systemkomponenten hervorgerufen werden. Die Verteilung der Preisänderungen an Finanzmärkten unterscheidet sich deutlich von einer Gaußverteilung. Dies führt zu empirischen Besonderheiten des Preisprozesses, zu denen neben dem Skalierungsverhalten nicht-triviale Korrelationsfunktionen und zeitlich gehäufte Volatilität zählen. In der vorliegenden Arbeit liegt der Fokus auf der Analyse von Finanzmarktzeitreihen und den darin enthaltenen Korrelationen. Es wird ein neues Verfahren zur Quantifizierung von Muster-basierten komplexen Korrelationen einer Zeitreihe entwickelt. Mit dieser Methodik werden signifikante Anzeichen dafür gefunden, dass sich typische Verhaltensmuster von Finanzmarktteilnehmern auf kurzen Zeitskalen manifestieren, dass also die Reaktion auf einen gegebenen Preisverlauf nicht rein zufällig ist, sondern vielmehr ähnliche Preisverläufe auch ähnliche Reaktionen hervorrufen. Ausgehend von der Untersuchung der komplexen Korrelationen in Finanzmarktzeitreihen wird die Frage behandelt, welche Eigenschaften sich beim Wechsel von einem positiven Trend zu einem negativen Trend verändern. Eine empirische Quantifizierung mittels Reskalierung liefert das Resultat, dass unabhängig von der betrachteten Zeitskala neue Preisextrema mit einem Anstieg des Transaktionsvolumens und einer Reduktion der Zeitintervalle zwischen Transaktionen einhergehen. Diese Abhängigkeiten weisen Charakteristika auf, die man auch in anderen komplexen Systemen in der Natur und speziell in physikalischen Systemen vorfindet. Über 9 Größenordnungen in der Zeit sind diese Eigenschaften auch unabhängig vom analysierten Markt - Trends, die nur für Sekunden bestehen, zeigen die gleiche Charakteristik wie Trends auf Zeitskalen von Monaten. Dies eröffnet die Möglichkeit, mehr über Finanzmarktblasen und deren Zusammenbrüche zu lernen, da Trends auf kleinen Zeitskalen viel häufiger auftreten. Zusätzlich wird eine Monte Carlo-basierte Simulation des Finanzmarktes analysiert und erweitert, um die empirischen Eigenschaften zu reproduzieren und Einblicke in deren Ursachen zu erhalten, die zum einen in der Finanzmarktmikrostruktur und andererseits in der Risikoaversion der Handelsteilnehmer zu suchen sind. Für die rechenzeitintensiven Verfahren kann mittels Parallelisierung auf einer Graphikkartenarchitektur eine deutliche Rechenzeitreduktion erreicht werden. Um das weite Spektrum an Einsatzbereichen von Graphikkarten zu aufzuzeigen, wird auch ein Standardmodell der statistischen Physik - das Ising-Modell - auf die Graphikkarte mit signifikanten Laufzeitvorteilen portiert. Teilresultate der Arbeit sind publiziert in [PGPS07, PPS08, Pre11, PVPS09b, PVPS09a, PS09, PS10a, SBF+10, BVP10, Pre10, PS10b, PSS10, SBF+11, PB10].

Dipolar cross-correlation effects in the nuclear overhauser experiments of weakly and strongly coupled spins

The effect of dipolar cross correlation in 1H---1H nuclear Overhauser effect experiments is investigated by detailed calculation in an ABX spin system. It is found that in weakly coupled spin systems, the cross-correlation effects are limited to single-quantum transition probabilities and decrease in magnitude as ωτc increases. Strong coupling, however, mixes the states and the cross correlations affect the zero-quantum and double-quantum transition probabilities as well. The effect of cross correlation in steady-state and transient NOE experiments is studied as a function of strong coupling and ωτc. The results for steady-state NOE experiments are calculated analytically and those for transient NOE experiments are calculated numerically. The NOE values for the A and B spins have been calculated by assuming nonselective perturbation of all the transitions of the X spin. A significant effect of cross correlation is found in transient NOE experiments of weakly as well as strongly coupled spins when the multiplets are resolved. Cross correlation manifests itself largely as a multiplet effect in the transient NOE of weakly coupled spins for nonselective perturbation of all X transitions. This effect disappears for a measuring pulse of 90° or when the multiplets are not resolved. For steady-state experiments, the effect of cross correlation is analytically zero for weakly coupled spins and small for strongly coupled spins.

Influence of cross-correlation between dipolar and chemical shift anisotropy relaxation mechanisms upon the transverse relaxation rates of 15N in macromolecules

Heteronuclear multiple-quantum coherence relaxation rate are calculated for the individual transitions of the S spin in an AIS nuclear spin system assuming that the heteronucleus (S spin) has relaxation contributions from both intramolecular dipole-dipole and chemical shift anisotropy relaxation. The individual multiplet components of the heteronuclear zero- and double-quantum coherences are shown to have different transverse relaxation rates. The cross-correlation between the two relaxation mechanisms is shown to be the dominant cause of the calculated differential line broadening. Experimental data are presented using as an example a uniformly 15N labelled sample of human epidermal growth factor.

Cross-correlation-aided transport in stochastically driven accretion flows

The origin of linear instability resulting in rotating sheared accretion flows has remained a controversial subject for a long time. While some explanations of such non-normal transient growth of disturbances in the Rayleigh stable limit were available for magnetized accretion flows, similar instabilities in the absence of magnetic perturbations remained unexplained. This dichotomy was resolved in two recent publications by Chattopadhyay and co-workers Mukhopadhyay and Chattopadhyay, J. Phys. A 46, 035501 (2013); Nath et al., Phys. Rev. E 88, 013010 (2013)] where it was shown that such instabilities, especially for nonmagnetized accretion flows, were introduced through interaction of the inherent stochastic noise in the system (even a ``cold'' accretion flow at 3000Kis too ``hot'' in the statistical parlance and is capable of inducing strong thermal modes) with the underlying Taylor-Couette flow profiles. Both studies, however, excluded the additional energy influx (or efflux) that could result from nonzero cross correlation of a noise perturbing the velocity flow, say, with the noise that is driving the vorticity flow (or equivalently the magnetic field and magnetic vorticity flow dynamics). Through the introduction of such a time symmetry violating effect, in this article we show that nonzero noise cross correlations essentially renormalize the strength of temporal correlations. Apart from an overall boost in the energy rate (both for spatial and temporal correlations, and hence in the ensemble averaged energy spectra), this results in mutual competition in growth rates of affected variables often resulting in suppression of oscillating Alfven waves at small times while leading to faster saturations at relatively longer time scales. The effects are seen to be more pronounced with magnetic field fluxes where the noise cross correlation magnifies the strength of the field concerned. Another remarkable feature noted specifically for the autocorrelation functions is the removal of energy degeneracy in the temporal profiles of fast growing non-normal modes leading to faster saturation with minimum oscillations. These results, including those presented in the previous two publications, now convincingly explain subcritical transition to turbulence in the linear limit for all possible situations that could now serve as the benchmark for nonlinear stability studies in Keplerian accretion disks.

Fluorescence cross-correlation spectroscopy to study hemodynamics

[EN] In the recent years a series of optical correlation techniques have been developed in order to be able to measure flow velocity with high spatial resolution while being non-invasive in order to be employed in-vivo on biological organisms. The technique employed in my thesis work, scanning laser image correlation (SLIC), is a powerful approach for the detection of flow motions because it overcomes some limitations of the classical spectroscopy techniques. SLIC method consists in repeated laser scans over a linear pattern and on the cross correlation of the signal emitted by the excited fluorophores in different positions along the scan line. Therefore, the resulting measurements for flow velocity are really accurate.

Cross-correlation of excitation functions for different fragments and different scattering angles in Al-27(F-19, x)y reactions

Excitation functions have been measured for different projectile-like fragments produced in Al-27(F-19,x)y reactions at incident energies from 110.25 to 118.75 MeV in 250 keV steps. Strong cross section fluctuations of the excitation functions are observed. The cross- correlation coefficients of the excitation functions for different atomic number Z and for different scattering angle theta(cm) have been deduced. These coefficients are much larger than the statistical theoretical calculated ones. This indicates that there are strong correlations between different exit channels in the dissipative heavy ion Collision of Al-27(F-19,x)y.

Cross-correlation between WMAP and 2MASS: non-Gaussianity induced by the SZ effect

We study the non-Gaussianity induced by the Sunyaev-Zel'dovich (SZ) effect in cosmic microwave background (CMB) fluctuation maps. If a CMB map is contaminated by the SZ effect of galaxies or galaxy clusters, the CMB maps should have similar non-Gaussian features to the galaxy and cluster fields. Using the WMAP data and 2MASS galaxy catalogue, we show that the non-Gaussianity of the 2MASS galaxies is imprinted on WMAP maps. The signature of non-Gaussianity can be seen with the fourth-order cross-correlation between the wavelet variables of the WMAP maps and 2MASS clusters. The intensity of the fourth-order non-Gaussian features is found to be consistent with the contamination of the SZ effect of 2MASS galaxies. We also show that this non-Gaussianity can not be seen by the high-order autocorrelation of the WMAP. This is because the SZ signals in the autocorrelations of the WMAP data generally are weaker than the WMAP-2MASS cross-correlations by a factor f(2), which is the ratio between the powers of the SZ-effect map and the CMB fluctuations on the scale considered. Therefore, the ratio of high-order autocorrelations of CMB maps to cross-correlations of the CMB maps and galaxy field would be effective to constrain the powers of the SZ effect on various scales.

Cross-correlation frequency resolved optical gating analysis of broadband continuum generation in photonic crystal fiber: Simulations and experiments

Numerical simulations are used to study the temporal and spectral characteristics of broadband supercontinua generated in photonic crystal fiber. In particular, the simulations are used to follow the evolution with propagation distance of the temporal intensity, the spectrum, and the cross-correlation frequency resolved optical gating (XFROG) trace. The simulations allow several important physical processes responsible for supercontinuum generation to be identified and, moreover, illustrate how the XFROG trace provides an intuitive means of interpreting correlated temporal and spectral features of the supercontinuum. Good qualitative agreement with preliminary XFROG measurements is observed. © 2002 Optical Society of America.