3,476 research outputs found

    Spurious trend switching phenomena in financial markets

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    The observation of power laws in the time to extrema of volatility, volume and intertrade times, from milliseconds to years, are shown to result straightforwardly from the selection of biased statistical subsets of realizations in otherwise featureless processes such as random walks. The bias stems from the selection of price peaks that imposes a condition on the statistics of price change and of trade volumes that skew their distributions. For the intertrade times, the extrema and power laws results from the format of transaction data

    Semiholography for heavy ion collisions

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    The formation of QGP in heavy ion collisions gives us a great opportunity for learning about nonperturbative dynamics of QCD. Semiholography provides a new consistent framework to combine perturbative and non-perturbative effects in a coherent way and can be applied to obtain an effective description for heavy ion collisions. In particular, it allows us to include nonperturbative effects in existing glasma effective theory and QCD kinetic theory for the weakly coupled saturated degrees of freedom liberated by the collisions in the initial stages in a consistent manner. We argue why the full framework should be able to confront experiments with only a few phenomenological parameters and present feasibility tests for the necessary numerical computations. Furthermore, we discuss that semiholography leads to a new description of collective flow in the form of a generalised non-Newtonian fluid. We discuss some open questions which we hope to answer in the near future.Comment: 12 pages; 3 figures; Proceedings of Confinement XII @ Thessaloniki, Greece -- August 28 to September 4, 201

    Chiral transition in dense, magnetized matter

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    In the presence of a chemical potential, the effect of a magnetic field on chiral symmetry breaking goes beyond the well-known magnetic catalysis. Due to a subtle interplay with the chemical potential, the magnetic field may work not only in favor but also against the chirally broken phase. At sufficiently large coupling, the magnetic field favors the broken phase only for field strengths beyond any conceivable value in nature. Therefore, in the interior of magnetars, a possible transition from chirally broken hadronic matter to chirally symmetric quark matter might occur at smaller densities than previously thought.Comment: 5 pages, 2 figures, contribution to proceedings of "QCD@Work 2012", Lecce, Ital

    Using aircraft location data to estimate current economic activity

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    Aviation is a key sector of the economy, contributing at least 3% to gross domestic product (GDP) in the UK and the US. Currently, airline performance statistics are published with a three month delay. However, aircraft now broadcast their location in real-time using the Automated Dependent Surveillance Broadcast system (ADS-B). In this paper, we analyse a global dataset of flights since July 2016. We first show that it is possible to accurately estimate airline flight volumes using ADS-B data, which is available immediately. Next, we demonstrate that real-time knowledge of flight volumes can be a leading indicator for aviation’s direct contribution to GDP in both the UK and the US. Using ADS-B data could therefore help move us towards real-time estimates of GDP, which would equip policymakers with the information to respond to shocks more quickly

    Adaptive nowcasting of influenza outbreaks using Google searches

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    Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu levels can be forecast using historic flu measurements. Here, we build dynamic ‘nowcasting’ models; in other words, forecasting models that estimate current levels of influenza, before the release of official data one week later. We find that when using Google Flu Trends data in combination with historic flu levels, the mean absolute error (MAE) of in-sample ‘nowcasts’ can be significantly reduced by 14.4%, compared with a baseline model that uses historic data on flu levels only. We further demonstrate that the MAE of out-of-sample nowcasts can also be significantly reduced by between 16.0% and 52.7%, depending on the length of the sliding training interval. We conclude that, using adaptive models, Google Flu Trends data can indeed be used to improve real-time influenza monitoring, even when official reports of flu infections are available with only one week's delay

    Quantifying the relationship between financial news and the stock market

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    The complex behavior of financial markets emerges from decisions made by many traders. Here, we exploit a large corpus of daily print issues of the Financial Times from 2nd January 2007 until 31st December 2012 to quantify the relationship between decisions taken in financial markets and developments in financial news. We find a positive correlation between the daily number of mentions of a company in the Financial Times and the daily transaction volume of a company's stock both on the day before the news is released, and on the same day as the news is released. Our results provide quantitative support for the suggestion that movements in financial markets and movements in financial news are intrinsically interlinked

    Quantifying the behavior of stock correlations under market stress

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    Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios
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