Cost Functions and Model Combination for VaR-based Asset Allocation using Neural Networks

We introduce an asset-allocation framework based on the active control of the value-at- risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with a traditional mean-variance allocator for constructing the portfolio. The second paradigm uses the network to directly make the portfolio allocation decisions. We consider a method for performing soft input variable selection, and show its considerable utility. We use model combination (committee) methods to systematize the choice of hyperparemeters during training. We show that committees using both paradigms are significantly outperforming the benchmark market performance. Nous introduisons un cadre d'allocation d'actifs basé sur le contrôle actif de la valeur à risque d'un portefeuille. À l'intérieur de ce cadre, nous comparons deux paradigmes pour faire cette allocation à l'aide de réseaux de neurones. Le premier paradigme utilise le réseau de neurones pour faire une prédiction sur le comportement de l'actif, en conjonction avec un allocateur traditionnel de moyenne-variance pour la construction du portefeuille. Le deuxième paradigme utilise le réseau pour faire directement les décisions d'allocation du portefeuille. Nous considérons une méthode qui accomplit une sélection de variable douce sur les entrées, et nous montrons sa très grande utilité. Nous utilisons également des méthodes de combinaison de modèles (comité) pour choisir systématiquement les hyper-paramètres pendant l'entraînement. Finalement, nous montrons que les comités utilisant les deux paradigmes surpassent de façon significative les performances d'un banc d'essai du marché.Value-at-risk, asset allocation, financial performance criterion, model combination, recurrent multilayer neural networks, Valeur à risque, allocation d'actif, critère de performance financière, combinaison de modèles, réseau de neurones récurrents multi-couches

Top-quark mass measurements: review and perspectives

The top quark is the heaviest elementary particle known and its mass ($m_{\rm top}$) is a fundamental parameter of the Standard Model (SM). The $m_{\rm top}$ value affects theory predictions of particle production cross-sections required for exploring Higgs-boson properties and searching for New Physics (NP). Its precise determination is essential for testing the overall consistency of the SM, to constrain NP models, through precision electroweak fits, and has an extraordinary impact on the Higgs sector, and on the SM extrapolation to high-energies. The methodologies, the results, and the main theoretical and experimental challenges related to the $m_{\rm top}$ measurements and combinations at the Large Hadron Collider (LHC) and at the Tevatron are reviewed and discussed. Finally, the prospects for the improvement of the $m_{\rm top}$ precision during the upcoming LHC runs are briefly outlined.Comment: 18 pages, 2 figures, Preprint submitted to Reviews in Physics (REVIP

A Multivariate Training Technique with Event Reweighting

An event reweighting technique incorporated in multivariate training algorithm has been developed and tested using the Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT). The event reweighting training are compared to that of the conventional equal event weighting based on the ANN and the BDT performance. The comparison is performed in the context of the physics analysis of the ATLAS experiment at the Large Hadron Collider (LHC), which will explore the fundamental nature of matter and the basic forces that shape our universe. We demonstrate that the event reweighting technique provides an unbiased method of multivariate training for event pattern recognition.Comment: 20 pages, 8 figure

Search for Higgs Bosons in e+e- Collisions at 183 GeV

The data collected by the OPAL experiment at sqrts=183 GeV were used to search for Higgs bosons which are predicted by the Standard Model and various extensions, such as general models with two Higgs field doublets and the Minimal Supersymmetric Standard Model (MSSM). The data correspond to an integrated luminosity of approximately 54pb-1. None of the searches for neutral and charged Higgs bosons have revealed an excess of events beyond the expected background. This negative outcome, in combination with similar results from searches at lower energies, leads to new limits for the Higgs boson masses and other model parameters. In particular, the 95% confidence level lower limit for the mass of the Standard Model Higgs boson is 88.3 GeV. Charged Higgs bosons can be excluded for masses up to 59.5 GeV. In the MSSM, mh > 70.5 GeV and mA > 72.0 GeV are obtained for tan{beta}>1, no and maximal scalar top mixing and soft SUSY-breaking masses of 1 TeV. The range 0.8 < tanb < 1.9 is excluded for minimal scalar top mixing and m{top} < 175 GeV. More general scans of the MSSM parameter space are also considered.Comment: 49 pages. LaTeX, including 33 eps figures, submitted to European Physical Journal

Artificial neural networks for selection of pulsar candidates from the radio continuum surveys

Pulsar search with time-domain observation is very computationally expensive and data volume will be enormous with the next generation telescopes such as the Square Kilometre Array. We apply artificial neural networks (ANNs), a machine learning method, for efficient selection of pulsar candidates from radio continuum surveys, which are much cheaper than time-domain observation. With observed quantities such as radio fluxes, sky position and compactness as inputs, our ANNs output the "score" that indicates the degree of likeliness of an object to be a pulsar. We demonstrate ANNs based on existing survey data by the TIFR GMRT Sky Survey (TGSS) and the NRAO VLA Sky Survey (NVSS) and test their performance. Precision, which is the ratio of the number of pulsars classified correctly as pulsars to that of any objects classified as pulsars, is about 96$\%$. Finally, we apply the trained ANNs to unidentified radio sources and our fiducial ANN with five inputs (the galactic longitude and latitude, the TGSS and NVSS fluxes and compactness) generates 2,436 pulsar candidates from 456,866 unidentified radio sources. These candidates need to be confirmed if they are truly pulsars by time-domain observations. More information such as polarization will narrow the candidates down further.Comment: 11 pages, 13 figures, 3 tables, accepted for publication in MNRA

Validation of nonlinear PCA

Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied to nonlinear PCA because of its inherent unsupervised characteristics. This paper presents a new approach for validating the complexity of nonlinear PCA models by using the error in missing data estimation as a criterion for model selection. It is motivated by the idea that only the model of optimal complexity is able to predict missing values with the highest accuracy. While standard test set validation usually favours over-fitted nonlinear PCA models, the proposed model validation approach correctly selects the optimal model complexity.Comment: 12 pages, 5 figure

Weight function method for precise determination of top quark mass at Large Hadron Collider

We propose a new method to measure a theoretically well-defined top quark mass at the LHC. This method is based on the "weight function method," which we proposed in our preceding paper. It requires only lepton energy distribution and is basically independent of the production process of the top quark. We perform a simulation analysis of the top quark mass reconstruction with $t\bar{t}$ pair production and lepton+jets decay channel at the leading order. The estimated statistical error of the top quark mass is about $0.4$ GeV with an integrated luminosity of $100$ fb$^{-1}$ at $\sqrt{s}=14$ TeV. We also estimate some of the major systematic uncertainties and find that they are under good control.Comment: 8 pages, 7 figures, version to appear in PL

Measurement of the forward-backward asymmetry in the distribution of leptons in ttˉt\bar{t} events in the lepton+jets channel

We present measurements of the forward-backward asymmetry in the angular distribution of leptons from decays of top quarks and antiquarks produced in proton-antiproton collisions. We consider the final state containing a lepton and at least three jets. The entire sample of data collected by the D0 experiment during Run II of the Fermilab Tevatron Collider, corresponding to 9.7 inverse fb of integrated luminosity, is used. The asymmetry measured for reconstructed leptons is $A_{FB}^l = \big(2.9 \pm 2.1(stat.) ^{+1.5}_{-1.7}(syst.) \big)$%. When corrected for efficiency and resolution effects within the lepton rapidity coverage of $|y_l|<1.5$, the asymmetry is found to be $A_{FB}^l = \big(4.2 \pm 2.3(stat.) ^{+1.7}_{-2.0}(syst.) \big)$%. Combination with the asymmetry measured in the dilepton final state yields $A_{FB}^l = \big(4.2 \pm 2.0(stat.) \pm 1.4(syst.) \big)$%. We examine the dependence of $A_{FB}^l$ on the transverse momentum and rapidity of the lepton. The results are in agreement with predictions from the next-to-leading-order QCD generator \mcatnlo, which predicts an asymmetry of $A_{FB}^l = 2.0$% for $|y_l|<1.5$.Comment: submitted to Phys. Rev.