Testing and comparing conditional CAPM with a new approach in the cross-sectional framework

This study examines the conditional relationship between beta and return for stocks traded on S&P 500 for the period from July 2001 to June 2011. The portfolios formed based on the Book value per share and betas using monthly data. A novel approach for capturing time variation in betas whose pattern is treated as a function of market returns is developed and presented. The estimated coefficients of a nonlinear regression constitute the basis of creating a two factor model. Our results indicate that the proposed specification outperforms alternative models in explaining the cross-section of returns

Cross-sectional conditional risk return analysis in the sorted beta framework: A novel Two Factor Model

This study examines the conditional relationship between beta and return for stocks traded on S&P 500 for the period from July 2001 to June 2011. The portfolios formed based on the Book value per share and betas using monthly data. A novel approach for capturing time variation in betas whose pattern is treated as a function of market returns is developed and presented. The estimated coefficients of a nonlinear regression constitute the basis of creating a two factor model. Our results indicate that the proposed specification surpasses alternative models in explaining the cross-section of returns

Does firing a CEO pay off?

We examine whether involuntary CEO replacements pay off by improving firm prospects. We find CEO successors’ acquisition investments to be associated with significantly higher shareholder gains relative to their predecessors and the average CEO. This improvement in post-turnover acquisition performance appears to be a function of board independence, hedge fund ownership, and the new CEO’s relative experience. CEO successors also create sizeable shareholder value by reversing prior investments through asset disposals and discontinuing operations and by employing more efficient investment strategies. Our evidence suggests that firing a CEO pays off

Quantitative description of temperature induced self-aggregation thermograms determined by differential scanning calorimetry

A novel thermodynamic approach for the description of differential scanning calorimetry (DSC) experiments on self-aggregating systems is derived and presented. The method is based on a mass action model where temperature dependence of aggregation numbers is considered. The validity of the model was confirmed by describing the aggregation behavior of poly(ethylene oxide)-poly(propylene oxide) block copolymers, which are well-known to exhibit a strong temperature dependence. The quantitative description of the thermograms could be performed without any discrepancy between calorimetric and van 't Hoff enthalpies, and moreover, the aggregation numbers obtained from the best fit of the DSC experiments are in good agreement with those obtained by light scattering experiments corroborating the assumptions done in the derivation of the new model

Structure of nanoparticles embedded in micellar polycrystals

We investigate by scattering techniques the structure of water-based soft composite materials comprising a crystal made of Pluronic block-copolymer micelles arranged in a face-centered cubic lattice and a small amount (at most 2% by volume) of silica nanoparticles, of size comparable to that of the micelles. The copolymer is thermosensitive: it is hydrophilic and fully dissolved in water at low temperature (T ~ 0{\deg}C), and self-assembles into micelles at room temperature, where the block-copolymer is amphiphilic. We use contrast matching small-angle neuron scattering experiments to probe independently the structure of the nanoparticles and that of the polymer. We find that the nanoparticles do not perturb the crystalline order. In addition, a structure peak is measured for the silica nanoparticles dispersed in the polycrystalline samples. This implies that the samples are spatially heterogeneous and comprise, without macroscopic phase separation, silica-poor and silica-rich regions. We show that the nanoparticle concentration in the silica-rich regions is about tenfold the average concentration. These regions are grain boundaries between crystallites, where nanoparticles concentrate, as shown by static light scattering and by light microscopy imaging of the samples. We show that the temperature rate at which the sample is prepared strongly influence the segregation of the nanoparticles in the grain-boundaries.Comment: accepted for publication in Langmui

Using a Rolling Vector Error Correction Model to Model Static and Dynamic Causal Relations between Electricity Spot Price and Related Fundamental Factors: The Case of Greek Electricity Market

The purpose of this study is to investigate short and long run relationships between electricity spot prices in Greece, Brent oil, natural gas, lignite fuel cost and carbon allowances using daily data from 2007 to 2014. Static and dynamic Johansen test are applied in order to identify long run relations and also to assess the evolution over time in the level of cointegration. Additionally we test for Granger Causality in a Vector error correction model and embrace impulse response and variance decomposition techniques to model the dynamic response of electricity prices in excitation of another variable. Overall our results suggest an important long run relation between spot electricity prices in Greece, natural gas price and carbon allowances, while in the short run electricity prices are not affected by any of the other variables, results that are of practical importance for the market regulator as well as the wholesale market participants. Keywords:  Vector Error Correction, Electricity Markets, Fuel Markets JEL Classifications: C4, C5 & C

Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition

We present dual-attention neural biasing, an architecture designed to boost Wake Words (WW) recognition and improve inference time latency on speech recognition tasks. This architecture enables a dynamic switch for its runtime compute paths by exploiting WW spotting to select which branch of its attention networks to execute for an input audio frame. With this approach, we effectively improve WW spotting accuracy while saving runtime compute cost as defined by floating point operations (FLOPs). Using an in-house de-identified dataset, we demonstrate that the proposed dual-attention network can reduce the compute cost by $90\%$ for WW audio frames, with only $1\%$ increase in the number of parameters. This architecture improves WW F1 score by $16\%$ relative and improves generic rare word error rate by $3\%$ relative compared to the baselines.Comment: Accepted to Proc. IEEE ICASSP 202

Ensemble Simulation From Multiple Data Sources In A Spatially Distributed Hydrological Model Of The Rijnland Water System In The Netherlands

Data for water management is increasingly easy to access, it has finer spatial and temporal resolution, and it is available from various sources. Precipitation data can be obtained from meteorological stations, radar, satellites and weather models. Land use data is also available from different satellite products and different providers. The various sources of data may confirm each other or give very different values in space and time. However, from these various data sources, it can often not be judged beforehand that one data is correct and others are wrong. Each source has its own value for a particular purpose. The Rijnland area in the Netherlands is one of the areas for which various data sources are available. Data sources that are researched in this paper are precipitation from rain gauges and radar, and three different land use maps. Various sources of data are used as input to the hydrological model (SIMGRO) of the water system to produce different discharge model output. Each run provides a member of the ensemble simulation which are combined to improve prediction of discharge from the catchment. It is shown that even simple averaging allows for increasing the model accuracy. Acknowledgement: This research is part of the EU FP7 MyWater research project. http://www.mywater-fp7.e

Re-structuring of marine communities exposed to environmental change

Species richness is the most commonly used but controversial biodiversity metric in studies on aspects of community stability such as structural composition or productivity. The apparent ambiguity of theoretical and experimental findings may in part be due to experimental shortcomings and/or heterogeneity of scales and methods in earlier studies. This has led to an urgent call for improved and more realistic experiments. In a series of experiments replicated at a global scale we translocated several hundred marine hard bottom communities to new environments simulating a rapid but moderate environmental change. Subsequently, we measured their rate of compositional change (re-structuring) which in the great majority of cases represented a compositional convergence towards local communities. Re-structuring is driven by mortality of community components (original species) and establishment of new species in the changed environmental context. The rate of this re-structuring was then related to various system properties. We show that availability of free substratum relates negatively while taxon richness relates positively to structural persistence (i.e., no or slow re-structuring). Thus, when faced with environmental change, taxon-rich communities retain their original composition longer than taxon-poor communities. The effect of taxon richness, however, interacts with another aspect of diversity, functional richness. Indeed, taxon richness relates positively to persistence in functionally depauperate communities, but not in functionally diverse communities. The interaction between taxonomic and functional diversity with regard to the behaviour of communities exposed to environmental stress may help understand some of the seemingly contrasting findings of past research