Spurious trend switching phenomena in financial markets

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

Coupling of intrinsic Josephson oscillations in layered superconductors by charge fluctuations

The coupling of Josephson oscillations in layered superconductors is studied with help of a tunneling Hamiltonian formalism. The general form of the current density across the barriers between the superconducting layers is derived. The induced charge fluctuations on the superconducting layers lead to a coupling of the Josephson oscillations in different junctions. A simplified set of equations is then used to study the non-linear dynamics of the system. In particular the influence of the coupling on the current-voltage characteristics is investigated and upper limits for the coupling strength are estimated from a comparison with experiments on cuprate superconductors.Comment: To be published in proceedings of SPIE conference San Diego 199

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

Quantifying the behavior of stock correlations under market stress

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

Sensor Networks for Monitoring and Control of Water Distribution Systems

Water distribution systems present a significant challenge for structural monitoring. They comprise a complex network of pipelines buried underground that are relatively inaccessible. Maintaining the integrity of these networks is vital for providing clean drinking water to the general public. There is a need for in-situ, on-line monitoring of water distribution systems in order to facilitate efficient management and operation. In particular, it is important to detect and localize pipe failures soon after they occur, and pre-emptively identify ‘hotspots’, or areas of the distribution network that are more likely to be susceptible to structural failure. These capabilities are vital for reducing the time taken to identify and repair failures and hence, mitigating impacts on water supply. WaterWiSe is a platform that manages and analyses data from a network of intelligent wireless sensor nodes, continuously monitoring hydraulic, acoustic and water quality parameters. WaterWiSe supports many applications including dynamic prediction of water demand and hydraulic state, online detection of events such as pipe bursts, and data mining for identification of longer-term trends. This paper describes the WaterWiSe@SG project in Singapore, focusing on the use of WaterWiSe as a tool for monitoring, detecting and predicting abnormal events that may be indicative of structural pipe failures, such as bursts or leaks.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

On-line Hydraulic State Estimation in Urban Water Networks Using Reduced Models

A Predictor-Corrector (PC) approach for on-line forecasting of water usage in an urban water system is presented and demonstrated. The M5 Model-Trees algorithm is used to predict water demands and Genetic Algorithms (GAs) are used to correct (i.e., calibrate according to on-line pressure and flow rate measurements) these predicted values in real-time. The PC loop repeats itself at each subsequent time-step with the forecasting model inputs being the corrected outputs of previous iterations, thus improving the model performances over time. To meet the computational efficiency requirements of real-time hydraulic state estimation, the urban network model which is comprised of over ten thousand pipelines and nodes is reduced using a water system aggregation technique. The reduced model, which resembles the original system's hydraulic performances with high accuracy, simplifies the computation of the PC loop and facilitates the implementation of the on-line model. The developed methodology is tested against the real input data of an urban water distribution system comprised of approximately 12500 nodes and 15000 pipes.Singapore-MIT Alliance for Research and TechnologySingapore. National Research Foundatio

Fractional Quantum Hall Effect in a Diluted Magnetic Semiconductor

We report the observation of the fractional quantum Hall effect in the lowest Landau level of a two-dimensional electron system (2DES), residing in the diluted magnetic semiconductor Cd(1-x)Mn(x)Te. The presence of magnetic impurities results in a giant Zeeman splitting leading to an unusual ordering of composite fermion Landau levels. In experiment, this results in an unconventional opening and closing of fractional gaps around filling factor v = 3/2 as a function of an in-plane magnetic field, i.e. of the Zeeman energy. By including the s-d exchange energy into the composite Landau level spectrum the opening and closing of the gap at filling factor 5/3 can be modeled quantitatively. The widely tunable spin-splitting in a diluted magnetic 2DES provides a novel means to manipulate fractional states

Multi-level automated sub-zoning of water distribution systems

Water distribution systems (WDS) are complex pipe networks with looped and branching topologies that often comprise of thousands of links and nodes. This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnections. This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Graph clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on network modularity property, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. The algorithms are adapted to WDS to provide a decision support tool for water utilities. The proposed methods are applied and results are demonstrated for a large-scale water distribution system serving heavily populated areas in Singapore