A Monte-Carlo Method for Optimal Portfolios

This paper provides (i) new results on the structure of optimal portfolios, (ii) economic insights on the behavior of the hedging components and (iii) an analysis of simulation-based numerical methods. The core of our approach relies on closed form solutions for Melliavin derivatives of diffusion processes which simplify their numerical simulation and facilitate the computation and simulation of the hedging components of optimal portfolios. One of our procedures relies on a variance-stabilizing transformation of the underlying process which eliminates stochastic integrals from the representation of Malliavin derivatives and ensures the existence of an exact weak approximation scheme. This improves the performance of Monte-Carlo methods in the numerical implementation of portfolio rules derived on the basis of probabilistic arguments. Our approach is flexible and can be used even when the dimensionality of the set of underlying state variables is large. We implement the procedure for a class of bivariate and trivariate models in which the uncertainty is described by diffusion processes for the market price of risk (MPR), the interest rate (IR) and other relevant factors. After calibrating the models to the data we document the behavior of the portfolio demand and the hedging components relative to the parameters of the model such as risk aversion, investment horizon, speeds of mean-reversion, IR and MPR levels and volatilities. We show that the hedging terms are important and cannot be ignored for asset allocation purposes. Risk aversion and investment horizon emerge as the most relevant factors: they have a substantial impact on the size of the optimal portfolio and on its economic properties for realistic values of the models' parameters. Cet article établit des résultats nouveaux sur (i) la structure des portefeuilles optimaux, (ii) le comportement des termes de couverture et (iii) les méthodes numériques de simulation en la matière. Le fondement de notre approche repose sur l'obtention de formules explicites pour les dérivées de Malliavin de processus de diffusion, formules qui simplifient leur simulation numérique et facilitent le calcul des composantes de couverture des portefeuilles optimaux. Une de nos procédures utilise une transformation des processus sous-jacents qui élimine les intégrales stochastiques de la représentation des dérivées de Malliavin et assure l'existence d'une approximation faible exacte. Cette transformation améliore alors la performance des méthodes de Monte-Carlo lors de l'implémentation numérique des politiques de portefeuille dérivées par des méthodes probabilistes. Notre approche est flexible et peut être utilisée même lorsque la dimension de l'espace des variables d'états sous-jacentes est large. Cette méthode est appliquée dans le cadre de modèles bivariés et trivariés dans lesquels l'incertitude est décrite par des mouvements de diffusion pour le prix de marché du risque, le taux d'intérêt et les autres facteurs d'importance. Après avoir calibré le modèle aux données nous examinons le comportement du portefeuille optimal et des composantes de couverture par rapport aux paramètres tels que l'aversion au risque, l'horizon d'investissement, le taux d'intérêt et le prix de risque du marché. Nous démontrons l'importance des demandes de couverture. L'aversion au risque et l'horizon d'investissement émergent comme des facteurs déterminants qui ont un impact substantiel sur la taille du portefeuille optimal et sur ses propriétés économiques.Optimal portfolios, hedging demands, Malliavin derivatives, explicit solutions, multiple state variables, IR-hedge, MPR-hedge, Monte Carlo simulation, Doss transformation, portfolio behavior, Portefeuilles optimaux, demandes de couverture, dérivées de Malliavin, solutions explicites, variables d'état multiples, couverture de taux d'intérêt, couverture de prix du risque de marché, simulation de Monte Carlo, transformation de Doss, comportement des portefeuilles

Dynamic instability transitions in 1D driven diffusive flow with nonlocal hopping

One-dimensional directed driven stochastic flow with competing nonlocal and local hopping events has an instability threshold from a populated phase into an empty-road (ER) phase. We implement this in the context of the asymmetric exclusion process. The nonlocal skids promote strong clustering in the stationary populated phase. Such clusters drive the dynamic phase transition and determine its scaling properties. We numerically establish that the instability transition into the ER phase is second order in the regime where the entry point reservoir controls the current and first order in the regime where the bulk is in control. The first order transition originates from a turn-about of the cluster drift velocity. At the critical line, the current remains analytic, the road density vanishes linearly, and fluctuations scale as uncorrelated noise. A self-consistent cluster dynamics analysis explains why these scaling properties remain that simple.Comment: 11 pages, 14 figures (25 eps files); revised as the publised versio

Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images

Purpose: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in-vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback. Methods: We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue. Results: We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1 and in the colon, the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks. Conclusions: We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.Comment: Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery (IJCARS

The galaxy correlation function as a constraint on galaxy formation physics

We introduce methods which allow observed galaxy clustering to be used together with observed luminosity or stellar mass functions to constrain the physics of galaxy formation. We show how the projected two-point correlation function of galaxies in a large semi-analytic simulation can be estimated to better than ~10% using only a very small subsample of the subhalo merger trees. This allows measured correlations to be used as constraints in a Monte Carlo Markov Chain exploration of the astrophysical and cosmological parameter space. An important part of our scheme is an analytic profile which captures the simulated satellite distribution extremely well out to several halo virial radii. This is essential to reproduce the correlation properties of the full simulation at intermediate separations. As a first application, we use low-redshift clustering and abundance measurements to constrain a recent version of the Munich semi-analytic model. The preferred values of most parameters are consistent with those found previously, with significantly improved constraints and somewhat shifted "best" values for parameters that primarily affect spatial distributions. Our methods allow multi-epoch data on galaxy clustering and abundance to be used as joint constraints on galaxy formation. This may lead to significant constraints on cosmological parameters even after marginalising over galaxy formation physics.Comment: 17 pages, 11 figures. Replaced to match the version accepted by MNRA

Existence of Many Positive Nonradial Solutions for a Superlinear Dirichlet Problem on thin Annuli

We study the existence of many positive nonradial solutions of a superlinear Dirichlet problem in an annulus in RN. Our strategy consists of finding the minimizer of the energy functional restricted to the Nehrai manifold of a subspace of functions with symmetries. The minimizer is a global critical point and therefore is a desired solution. Then we show that the minimal energy solutions in different symmetric classes have mutually different energies. The same approach has been used to prove the existence of many sign-changing nonradial solutions (see [5])

Dipolar Filtered magic-sandwich-echoes as a tool for probing molecular motions using time domain NMR

We present a simple $^1$H NMR approach for characterizing intermediate to fast regime molecular motions using $^1$H time-domain NMR at low magnetic field. The method is based on a Goldmann Shen dipolar filter (DF) followed by a Mixed Magic Sandwich Echo (MSE). The dipolar filter suppresses the signals arising from molecular segments presenting sub kHz mobility, so only signals from mobile segments are detected. Thus, the temperature dependence of the signal intensities directly evidences the onset of molecular motions with rates higher than kHz. The DF-MSE signal intensity is described by an analytical function based on the Anderson Weiss theory, from where parameters related to the molecular motion (e.g. correlation times and activation energy) can be estimated when performing experiments as function of the temperature. Furthermore, we propose the use of the Tikhonov regularization for estimating the width of the distribution of correlation times.Comment: 9 pages, 6 figure

Experimental Evidence for Quantum Interference and Vibrationally Induced Decoherence in Single-Molecule Junctions

We analyze quantum interference and decoherence effects in single-molecule junctions both experimentally and theoretically by means of the mechanically controlled break junction technique and density-functional theory. We consider the case where interference is provided by overlapping quasi-degenerate states. Decoherence mechanisms arising from the electronic-vibrational coupling strongly affect the electrical current flowing through a single-molecule contact and can be controlled by temperature variation. Our findings underline the all-important relevance of vibrations for understanding charge transport through molecular junctions.Comment: 5 pages, 4 figure

The benefits of family ownership, control and management on financial performance of firms

The benefits of family ownership and control of firms are at the center of the family firm debate. Previous studies have used either family ownership or management as proxies for control. Both indicators are off the mark, as they do not measure decision control as intended by the theory of the firm. This is the first study investigating the direct influence of family ownership, control and management on financial firm performance, while controlling for goal heterogeneity of different stakeholders. Our results clearly show that family control is beneficial for all stakeholders, while neither family ownership nor management influences financial performance. Monitoring behavior of families is the central component and essence of family firms and can be used as a point of departure for the development of a unified theory of family firms

Hedge Funds in Corporate Governance and Corporate Control.

Hedge funds have become critical players in both corporate governance and corporate control. In this article, we document and examine the nature of hedge fund activism, how and why it differs from activism by traditional institutional investors, and its implications for corporate governance and regulatory reform. We argue that hedge fund activism differs from activism by traditional institutions in several ways: it is directed at significant changes in individual companies (rather than small, systemic changes), it entails higher costs, and it is strategic and ex ante (rather than intermittent and ex post). The reasons for these differences may lie in the incentive structures of hedge fund managers as well as in the fact that traditional institutions face regulatory barriers, political constraints, or conflicts of interest that make activism less profitable than it is for hedge funds. But the differences may also be due to the fact that traditional institutions pursue a diversification strategy that is difficult to combine with strategic activism. Although hedge funds hold great promise as active shareholders, their intense involvement in corporate governance and control also potentially raises two kinds of problems: The interests of hedge funds sometimes diverge from those of their fellow shareholders; and the intensity of hedge fund activism imposes substantial stress that the regulatory system may not be able to withstand. The resulting problems, however, are relatively isolated and narrow, do not broadly undermine the value of hedge fund activism as a whole, and do not warrant major additional regulatory interventions. The sharpest accusation leveled against activist funds is that activism is designed to achieve a short-term payoff at the expense of long-term profitability. Although we consider this a potentially serious problem that arguably pervades hedge fund activism, we conclude that a sufficient case for legal intervention has not been made. This conclusion results from the uncertainties about whether short-termism is in fact a real problem and how much hedge fund activism is driven by excessive short-termism. But, most importantly, it stems from our view that market forces and adaptive devices taken by companies individually are better designed than regulation to deal with the potential negative effects of hedge fund short-termism while preserving the positive effects of hedge-fund activism

The SICOPEX program: Version V.0

SICOPEX is a System for Interactive Computation of Optimum Extractions. The program computes the septa positions, the kicker, bumper and septa strengths for a set of several extractions through the same extraction channel in the SPS long straight sections LSS2 or LSS6. These quantities are computed such as to maximise the relative gain of required clearances at the entrance of the electrostatic and magnetic septa. These extractions can be of different type (resonant or fast, including fast shaving) and at different energies. The first part of this paper describes the formulation of the extraction problem and the algorithm that is used to solve it. The second part explains the structure of the SICOPEX package, and gives details of how to run SICOPEX on a Windows PC