Private equity : what happens next?

Previous academic research has focused on Private Equity funds’ superior performance, when compared to respective benchmarks. However, there is still a question to be answered: what happens after these funds’ targets leave Private Equity’s protective wing? From a sample of 426 observations, from which 180 had previously been funded by Private Equity, in both US stock exchanges, we built a first-differences regression model to investigate the subject. We found evidence showing that Private Equity’s superiority during funds’ lifetime is paid away in the years after. Our sample revealed a negative effect detected on long-term performance of firms whose Initial Public Offerings were Private Equity-backed. The data collected suggests a possible variation of the negative impact, depending on the industrial sector. There is also evidence that smaller firms are more affected than larger firms, by the identified Private Equity effect. The percentage of outstanding shares issued at IPO is positively correlated with the negative impact under scrutiny, when less than half of the total equity had been issued. Nevertheless, one must be cautious when interpreting the results, as our performance measures are scaled by the market capitalization, which is subject to investors’ overvaluation.A literatura académica tem vindo a concentrar-se na rentabilidade superior dos fundos de Private Equity, quando comparados com os respectivos benchmarks. No entanto, uma questão permanece por responder: o que acontece às empresas previamente detidas por estes fundos depois de deixarem de o ser? De uma amostra de 426 observações, das quais 180 foram previamente financiadas por Private Equity, em ambas as bolsas de valores dos EUA, construímos um modelo de regressão de primeiras diferenças para investigar o assunto. Encontrámos provas de que a rentabilidade superior verificada em Private Equity, durante a vida dos fundos, tem um reverso nos anos seguintes. A nossa amostra revelou um efeito negativo detectado no desempenho de longo prazo das empresas submetidas a Ofertas Públicas de Aquisição por fundos de Private Equity. Os dados recolhidos sugerem uma possível variação desse efeito, dependendo do sector industrial a que cada empresa pertence. Há também evidências de que empresas de menor dimensão de activos são mais as afectadas pelo efeito negativo de Private Equity. A percentagem de ações em circulação emitida em OPA está positivamente correlacionada com o efeito negativo identificado, o que, consequentemente, nos levou a resultados, estatisticamente verificados, de que este se encontra presente apenas perante percentagens inferiores a cinquenta porcento. Contudo, é preciso ter em conta que, na interpretação destes resultados, as medidas de desempenho utilizadas foram ajustadas pela capitalização de mercado, sujeita à sobrevalorização por parte dos investidores

Spatial and temporal distribution of cetaceans in the mid-Atlantic waters around the Azores

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Taylor & Francis for personal use, not for redistribution. The definitive version was published in Marine Biology Research 10 (2014): 123-137, doi:10.1080/17451000.2013.793814.Cetaceans living in offshore waters are under increasing pressure from anthropogenic activities. Yet, due to the lack of survey effort, relatively little is known about the demography or ecology of these populations. Spatial and temporal distribution of cetaceans in mid-Atlantic waters were investigated using a long term dataset collected from boat surveys and land-based observations around the Azores. From 1999 to 2009, 7307 cetacean schools were sighted during 271717 km of survey effort. In 4944 h of land-based observations, 2968 cetacean groups were detected. Twenty-four species were recorded: seven baleen whales, six beaked whales, eight dolphin species, Physeter macrocephalus, Kogia breviceps and K. sima. Overall, Delphinus delphis was the most frequently sighted species but its encounter rate decreased in June- November, coinciding with presence of Stenella frontalis in the region. Tursiops truncatus, P. macrocephalus and Grampus griseus were frequently encountered yearround, whereas large baleen whales showed a distinct peak in encounter rates in March-May. Mesoplodonts were fairly common and appear to be present throughout the year. These findings fill-in a significant gap in the knowledge of cetaceans occurring in a poorly studied region of the North Atlantic, providing much needed data to inform management initiatives.This work was supported by FEDER funds, through the Competitiveness Factors Operational Programme – COMPETE, by national funds, through FCT – Foundation for Science and Technology, under projects CETAMARH (POCTI/BSE/38991/01) and TRACE (PTDC/MAR/74071/2006), and by regional funds, through DRCT/SRCTE, under project MAPCET (M2.1.2/F/012/2011). We thank the Azorean Regional Government for funding POPA, the Shipowners Proprietors and the Association of the Tuna Canning Industries for their support to the programme. MAS was supported by an FCT postdoctoral grant (SFRH/BPD/29841/2006), and IC and RP were supported by doctoral grants SFRH/BD/41192/2007 and SFRH/BD/32520/2006. IMAR-DOP/UAç is the R&D Unit #531 and part of the Associated Laboratory #9 (ISR) funded through the pluri-annual and programmatic funding schemes of FCT-MCTES and DRCTAzores

Mamíferos Marinhos

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Dynamics of whale shark occurrence at their fringe oceanic habitat.

Studies have shown that the whale shark (Rhincodon typus), a vulnerable large filter feeder, seasonally aggregates at highly productive coastal sites and that individuals can perform large, trans-boundary migrations to reach these locations. Yet, the whereabouts of the whale shark when absent from these sites and the potential oceanographic and biological drivers involved in shaping their present and future habitat use, including that located at the fringes of their suitable oceanic habitat, are largely unknown. We analysed a 16-year (1998-2013) observer dataset from the pole-and-line tuna fishery across the Azores (mid-North Atlantic) and used GAM models to investigate the spatial and temporal patterns of whale shark occurrence in relation to oceanographic features. Across this period, the whale shark became a regular summer visitor to the archipelago after a sharp increase in sighting frequency seen in 2008. We found that SST helps predicting their occurrence in the region associated to the position of the seasonal 22°C isotherm, showing that the Azores are at a thermal boundary for this species and providing an explanation for the post 2007 increase. Within the region, whale shark detections were also higher in areas of increased bathymetric slope and closer to the seamounts, coinciding with higher chl-a biomass, a behaviour most probably associated to increased feeding opportunities. They also showed a tendency to be clustered around the southernmost island of Santa Maria. This study shows that the region integrates the oceanic habitat of adult whale shark and suggests that an increase in its relative importance for the Atlantic population might be expected in face of climate change

Mapped cetacean habitat suitability and richness in the Azores, links to ArcGIS files

Marine spatial planning and ecological research call for high-resolution species distribution data. However, those data are still not available for most marine large vertebrates. The dynamic nature of oceanographic processes and the wide-ranging behavior of many marine vertebrates create further difficulties, as distribution data must incorporate both the spatial and temporal dimensions. Cetaceans play an essential role in structuring and maintaining marine ecosystems and face increasing threats from human activities. The Azores holds a high diversity of cetaceans but the information about spatial and temporal patterns of distribution for this marine megafauna group in the region is still very limited. To tackle this issue, we created monthly predictive cetacean distribution maps for spring and summer months, using data collected by the Azores Fisheries Observer Programme between 2004 and 2009. We then combined the individual predictive maps to obtain species richness maps for the same period. Our results reflect a great heterogeneity in distribution among species and within species among different months. This heterogeneity reflects a contrasting influence of oceanographic processes on the distribution of cetacean species. However, some persistent areas of increased species richness could also be identified from our results. We argue that policies aimed at effectively protecting cetaceans and their habitats must include the principle of dynamic ocean management coupled with other area-based management such as marine spatial planning

Predictor variables used for GAM modelling of whale shark detections in the Azores EEZ.

<p>Predictor variables used for GAM modelling of whale shark detections in the Azores EEZ.</p

The Azores archipelago and the tuna fishing effort.

<p>(A) The location of the Azores archipelago in the North Atlantic. Also shown are documented locations in the North Atlantic where aggregations of whale sharks have been known to occur (black squares) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102060#pone.0102060-Sequeira3" target="_blank">[17]</a>. (A) Position of the nine islands that constitute the Azores archipelago together with the distribution of effort in the cumulative number of fishing events SU-1 within the Azores EEZ (black line). Large seamounts are represented by black triangles. Also shown are the names of the islands mentioned in the text.</p

16 years of whale shark sightings in the Azores EEZ.

<p>(A) The annual cumulative effort of the pole and line tuna fishery expressed as the number of recorded events year⁻¹. (B) The number of sightings across years for each of the month within whale shark season: April-June (dark grey), July (black), August (white) and September (light grey).</p

The influence of environmental variables on whale shark sightings.

<p>The partial residual plots for the five significant variables retained in the final GAM model, showing the probability of detecting a whale shark (black line  =  mean; shaded grey  = 95% CI) with changing environmental and spatial characteristics: (A) sea surface temperature, (B) primary productivity, (C) the slope of the underlying bathymetry, (D) distance to nearest seamount, (E) tuna fishing effort and (F) the North Atlantic Oscillation index.</p