Non-Parametric Analysis of Hedge Fund Returns: New Insights from High Frequency Data

This paper examines four different daily datasets of hedge fund return indexes: MSCI, FTSE, Dow Jones and HFRX, all based on investable hedge funds, and three different monthly datasets of hedge fund return indexes: CSFB, CISDM and HFR which comprise both investable and non-investable hedge funds. Our study, based on standard statistical analysis, non-parametric analysis of the distribution and non-parametric regressions with respect to the S&P500 index shows that key data biases and disparate index construction methodologies lead to different statistical properties of hedge fund databases. One key variable that highly affects the statistical properties of hedge fund index returns is the “investability” of hedge fundsHedge Fund, Risk Management, High frequency data

Celescope catalog of ultraviolet stellar observations. Magnetic tape version

Observational results obtained by the celescope experiment during the first 16 months of operation of NASA's Orbiting Astronomical Observatory are presented. Results of the stellar observations are listed along with selected ground-based information obtained from the available literature

Biomechanical evolution of solid bones in large animals: a microanatomical investigation

International audienc

Inconsistencies in Reported Employment Characteristics among Employed Stayers

The paper deals with measurement error, and its potentially distorting role, in information on industry and professional status collected by labour force surveys. The focus of our analyses is on inconsistent information on these employment characteristics resulting from yearly transition matrices for workers who were continuously employed over the year and who did not change job. As a case-study we use yearly panel data for the period from April 1993 to April 2003 collected by the Italian Quarterly Labour Force Survey. The analysis goes through four steps: (i) descriptive indicators of (dis)agreement; (ii) testing whether the consistency of repeated information significantly increases when the number of categories is collapsed; (iii) examination of the pattern of inconsistencies among response categories by means of Goodman's quasi-independence model; (iv) comparisons of alternative classifications. Results document sizable measurement error, which is only moderately reduced by more aggregated classifications. They suggest that even cross-section estimates of employment by industry and/or professional status are affected by non-random measurement error.industry, professional status, measurement errors, survey data

Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.Comment: For data and software related to this paper, see http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201

Identification, prediction and mitigation of sinkhole hazards in evaporite karst areas

Abstract Sinkholes usually have a higher probability of occurrence and a greater genetic diversity in evaporite terrains than in carbonate karst areas. This is because evaporites have a higher solubility, and commonly a lower mechanical strength. Subsidence damage resulting from evaporite dissolution generates substantial losses throughout the world, but the causes are only well-understood in a few areas. To deal with these hazards, a phased approach is needed for sinkhole identification, investigation, prediction, and mitigation. Identification techniques include field surveys, and geomorphological mapping combined with accounts from local people and historical sources. Detailed sinkhole maps can be constructed from sequential historical maps, recent topographical maps and digital elevation models (DEMs) complemented with building-damage surveying, remote sensing, and high-resolution geodetic surveys. On a more detailed level, information from exposed paleosubsidence features (paleokarst), speleological explorations, geophysical investigations, trenching, dating techniques, and boreholes, may help to recognize dissolution and subsidence features. Information on the hydrogeological pathways including caves, springs and swallow holes, are particularly important especially when corroborated by tracer tests. These diverse data sources make a valuable database - the karst inventory. From this dataset, sinkhole susceptibility zonations (relative probability) may be produced based on the spatial and temporal distribution of the features and good knowledge of the local geology. Sinkhole distribution can be investigated by spatial distribution analysis techniques including studies of preferential elongation, alignment and nearest neighbor analysis. More objective susceptibility models may be obtained by analyzing the statistical relationships between the known sinkholes and the conditioning factors, such as weather conditions. Chronological information on sinkhole formation is required to estimate the probability of occurrence of sinkholes (number of sinkholes/km² year). Such spatial and temporal predictions, derived from limited records and based on the assumption that past sinkhole activity may be extrapolated to the future, are non-corroborated hypotheses. Validation methods allow us to assess the predictive capability of the susceptibility maps and to transform them into probability maps. Avoiding the most hazardous areas by preventive planning is the safest strategy for development in sinkhole-prone areas. Corrective measures could be to reduce the dissolution activity and subsidence processes, but these are difficult. A more practical solution for safe development is to reduce the vulnerability of the structures by using subsidence-proof designs

Celescope catalog of ultraviolet stellar observations: 5068 objects measured by Orbiting Astronomical Observatory (OAO-2)

During the 16 months that the Celescope Experiment operated, it took 8000 frames of data. This report describes the experiment, the data it gathered, the format of the magnetic tape containing the data, and a number of programs that were written to read and manipulate the tape. The tape version of the catalog contains Data on 5068 stars and is available from the National Space Sciences Data Center

Internal Migration and Regional Population Dynamics in Europe: France Case Study

The paper examines the patterns of internal migration and population change in France over the recent decades at departément and commune scales. Regional population change is controlled by both natural increase and internal migration. There are two differing patterns of natural increase: north and east France has higher natural increase and south and east has lower. The geographic pattern of internal migration has changed substantially over the last 50 years, most dramatically in the Île-de-France, which showed the highest gains between 1954 and 1962 but the highest losses between 1975 and 1982. Urban growth, which was strong in the 1950s and 1960s, reversed in the 1970s favouring small towns but recovered slightly in the last 20 years. Migration gains and losses show a quite complicated pattern of depopulation of city centres combined with slow suburbanisation and advanced periurbanisation. Periurbanisation is evident in Paris region and in nearly all large urban agglomerations. Most other cities show suburbanisation or periurbanisation at various stages of development. Out-migration shows a clear division of the country into a northern part with higher rates, and a central and southern part of the country with lower out-migration. This simple pattern is modified by higher out-migration from some cities such as Lyon or Clermont-Ferrand and from isolated rural communes scattered all over the country. Out-migration also has a regional dimension: there are shifts towards more attractive areas, in particular Alpine region and Mediterranean and Atlantic coasts. Analysis of migration between size bands of rural and urban units shows a significant deconcentration process, and a similar pattern characterises migration between population density bands. The general movement is down the urban/density band hierarchy, from higher to lower urban/density bands. Deep rural areas are not attractive and excluded from the process of counterurbanisation. In addition, unemployment was found to have a strong and very efficient impact on migration behaviour. Analysis for 1990-1999 leads to slight modification of this picture: a slow recovery of central parts of the largest urban agglomerations and less differentiated patterns than in the 1980s. Deconcentration of the French population continues but is less powerful