A multiple factor model for European stocks

We present an empirical study focusing on the estimation of a fundamental multi-factor model for a universe of European stocks. Following the approach of the BARRA model, we have adopted a cross-sectional methodology. The proportion of explained variance ranges from 7.3% to 66.3% in the weekly regressions with a mean of 32.9%. For the individual factors we give the percentage of the weeks when they yielded statistically significant influence on stock returns. The best explanatory power – apart from the dominant country factors – was found among the statistical constructs „success“ and „variability in markets“.Vorgestellt wird eine empirische Studie, welche die Schätzung eines fundamentalen Multi-Faktor-Modells für ein Universum europäischer Aktien beinhaltet. Als Methode wurde in Anlehnung an die Vorgehensweise im BARRA-Modell der Querschnittsanalyse der Vorzug gegeben. Der Anteil der erklärten Varianz beläuft sich in den wöchentlichen Regressionen auf 7,3% bis 66,3% bei einem Durchschnitt von 32,9%. Für die einzelnen Faktoren wird die Häufigkeit angegeben, mit der sie sich in den Regressionen signifikant erwiesen haben. Den höchsten Erklärungsgehalt im Untersuchungszeitraum hatten Länderfaktoren, aber auch Konstrukte wie „Success“ oder „Variability in Markets“

Conic Multi-Task Classification

Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.Comment: Accepted by European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)-201

Investigation of statistical techniques to select optimal test levels for spacecraft vibration tests Final report, 1 Nov. 1969 - 31 Oct. 1970

Statistical techniques for selecting optimal test levels for vibration tests of spacecraft hardwar

ESTIMATING THE COST OF FOOD SAFETY REGULATION TO THE NEW ZEALAND SEAFOOD INDUSTRY

In New Zealand, the Animal Products Act 1999 requires all animal product processing businesses to have a HACCP-based risk management program by the end of 2002. This paper attempts to measure the effects of such regulation on the variable cost of production of the New Zealand seafood industry. Using the framework developed by Antle (2000), a model of quality-adjusted translog cost function is estimated using census of production data from 1929 to 1998. Our results show that variable costs could increase from 2% to 22% or from 2 cents to 19 cents per kilogram.HACCP, compliance costs, seafood, Production Economics,

Many-body dispersion effects in the binding of adsorbates on metal surfaces

A correct description of electronic exchange and correlation effects for molecules in contact with extended (metal) surfaces is a challenging task for first-principles modeling. In this work we demonstrate the importance of collective van der Waals dispersion effects beyond the pairwise approximation for organic--inorganic systems on the example of atoms, molecules, and nanostructures adsorbed on metals. We use the recently developed many-body dispersion (MBD) approach in the context of density-functional theory [Phys. Rev. Lett. 108, 236402 (2012); J. Chem. Phys. 140, 18A508 (2014)] and assess its ability to correctly describe the binding of adsorbates on metal surfaces. We briefly review the MBD method and highlight its similarities to quantum-chemical approaches to electron correlation in a quasiparticle picture. In particular, we study the binding properties of xenon, 3,4,9,10-perylene-tetracarboxylic acid (PTCDA), and a graphene sheet adsorbed on the Ag(111) surface. Accounting for MBD effects we are able to describe changes in the anisotropic polarizability tensor, improve the description of adsorbate vibrations, and correctly capture the adsorbate--surface interaction screening. Comparison to other methods and experiment reveals that inclusion of MBD effects improves adsorption energies and geometries, by reducing the overbinding typically found in pairwise additive dispersion-correction approaches

Managing the Medical Matrix: A DAIS for Artificial Intelligence in Health Care (and Beyond)

AI offers “huge and wide-reaching potential” in health; the futures of health care and AI are deeply interconnected. Use of AI provides the field with never-before imagined opportunities to streamline and delve more deeply into medical care, including disease identification, diagnosing conditions, and a simpler way to crowdsource and develop treatment plans. Its broad inclusion in the field has created a pressing need for more, and better, regulation. Improved regulation is especially critical because of the possibility that mismanaged AI will allow for incorrect diagnosis of patients or biased predictions and outcomes. In fact, numerous examples of such bias – and attempts to manage bias – already exist, which raises major ethical questions surrounding the use of AI and presents the issue of how to avoid health disparities in AI. In this Note, I argue that AI is not being adequately managed at the federal level. I further argue that the lack of management is largely due to a general failure to mandate standards for data sourcing, cleaning, and testing. The health care field is rife with examples of the effects of poor management, some of which have immediate and devastating impacts on patients; however, mismanagement of AI is not limited to health care alone. The potential problems that arise from lack of oversight span across industry lines. Thus, no single industry or existing federal agency can claim full ownership of, or expertise in, AI as a tool. I therefore propose that the best possible solution would be to form an entirely new top-level federal agency. This new agency would be tasked with creating federally mandated standards for ethical AI data sourcing, cleaning, and testing across industries. It would provide comprehensive management of AI datasets that do not fall under the umbrella of an existing agency such as the Food & Drug Administration (FDA). I further propose that the new regulatory body be named the “Department of Artificial Intelligence Standardization,” or DAIS

Effects of tin on microstructure and mechanical behavior of Inconel 718

Columbium, for which the United States is 100 percent import reliant, is of strategic importance to the U.S. aerospace industry. A major amount of the Cb is used in Inconel 718. Should Cb sources be disrupted, it may be desired to use a grade of Cb melting stock having greater Sn content then the preferred vacuum rade. Additions of Sn to Inconel 718 were varied from none added to 1 wt %. The Sn additions below 800 ppm had no detrimental effects on 650 C stress rupture behavior; however, 1-wt % Sn severely degraded both life and ductility. Additions of Sn in excess of 200 ppm were slightly detrimental to the 425 C tensile yield strength and ductility. The Sn additions had no effect on the microstructure of Inconel 718 even after stress rupture testing for over 6000 hr at 650 C

Magnetic nanowires as permanent magnet materials

We present the fabrication of metallic magnetic nanowires using a low temperature chemical process. We show that pressed powders and magnetically oriented samples exhibit a very high coercivity (6.5 kOe at 140 K and 4.8 kOe at 300 K). We discuss the magnetic properties of these metamaterials and show that they have the suitable properties to realize "high temperature magnets" competitive with AlNiCo or SmCo permanent magnets. They could also be used as recording media for high density magnetic recording.Comment: 5 pages, 5 figure