Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models

This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.

Technology and the need for an alternative view of the firm in post keynesian theory

Post Keynesian economics; theory of the firm

Laser-light scattering approach to peptide–membrane interaction

© International University Line, 2010Membrane-active peptides are becoming widely used, mainly due to their high therapeutic potential. Although the therapeutic action is characterized, the mechanisms of interaction are often unclear or controversial. In biophysical studies, non-invasive techniques are overlooked when studying the effect of peptides on membranes. Light scattering techniques, such as dynamic light scattering and static light scattering, can be used as tools to determine whether promotion of membrane aggregation in the presence of peptides and of self-peptide aggregation in solution occurs. More recently, light scattering has been used for evaluating the alteration on membrane surface charge (ζ-potential) promoted by membrane–peptide interactions. The data obtained by these techniques (either by themselves or combined with complementary experimental approaches) therefore yield valuable elucidations of membrane-active peptides’ mechanisms of action at the molecular level.This work was partially supported by the Fundação para a Ciência e Tecnologia (FCT) of the Portuguese Ministry of Science, Technology and Higher Education. M.M.D. acknowledges the grant SFRH/BD/41750/2007 from FCT

ScannerS: Constraining the phase diagram of a complex scalar singlet at the LHC

We present the first version of a new tool to scan the parameter space of generic scalar potentials, ScannerS. The main goal of ScannerS is to help distinguish between different patterns of symmetry breaking for each scalar potential. In this work we use it to investigate the possibility of excluding regions of the phase diagram of several versions of a complex singlet extension of the Standard Model, with future LHC results. We find that if another scalar is found, one can exclude a phase with a dark matter candidate in definite regions of the parameter space, while predicting whether a third scalar to be found must be lighter or heavier. The first version of the code is publicly available and contains various generic core routines for tree level vacuum stability analysis, as well as implementations of collider bounds, dark matter constraints, electroweak precision constraints and tree level unitarity.Comment: 24 pages, 4 figures, 3 tables. Project development webpage - http://gravitation.web.ua.pt/Scanner

The spatial structure of the financial development in Brazil

This paper explores the financial development in Brazil. It focuses on the impacts of the development level of a municipality’s financial system over its neighborhood, under the light of the Central Place Theory. Using a GMM estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors we investigate the spatial structure of the financial system in Brazil. The results point to a negative spatial association between the Brazilian municipalities’ financial system, in the way that a municipality with more developed financial system tends to be surrounded by municipalities with less developed financial systems.financial development, spatial structure, bank strategy, Brazil

NLO electroweak corrections in general scalar singlet models

If no new physics signals are found, in the coming years, at the Large Hadron Collider Run-2, an increase in precision of the Higgs couplings measurements will shift the dicussion to the effects of higher order corrections. In Beyond the Standard Model (BSM) theories this may become the only tool to probe new physics. Extensions of the Standard Model (SM) with several scalar singlets may address several of its problems, namely to explain dark matter, the matter-antimatter asymmetry, or to improve the stability of the SM up to the Planck scale. In this work we propose a general framework to calculate one loop-corrections in BSM models with an arbitrary number of scalar singlets. We then apply our method to a real and to a complex scalar singlet models. We assess the importance of the one-loop radiative corrections first by computing them for a tree level mixing sum constraint, and then for the main Higgs production process $gg \to H$. We conclude that, for the currently allowed parameter space of these models, the corrections can be at most a few percent. Notably, a non-zero correction can survive when dark matter is present, in the SM-like limit of the Higgs couplings to other SM particles.Comment: 35 pages, 3 figure