Future Earnings Growth Volatility and the Value Premium

The value premium is well established in empirical asset pricing, but to date there is little understanding as to its fundamental drivers. We use a stochastic earnings valuation model to establish a direct link between the volatility of future earnings growth and firm value. We illustrate that risky earnings growth affects growth and value firms differently. We provide empirical evidence that the volatility of future earnings growth is a significant determinant of the value premium. Using data on individual firms and characteristic-sorted test portfolios, we also find that earnings growth volatility is significant in explaining the cross-sectional variation of stock returns. Our findings imply that the value premium is the rational consequence of accounting for risky earnings growth in the firm valuation process.

Validation Française de l’Échelle de la Personnalité Temporelle (French Validation of the Time Personality Indicator)

RÉSUMÉ Certains auteurs ont développé un intérêt pour la compréhension des aptitudes associées à la gestion du temps. Ainsi, plusieurs définitions théoriques ont été proposées afin de mieux cerner ce concept et une multitude de questionnaires a été développée afin de le mesurer. La présente étude visait à valider la traduction française d’un de ces outils, soit le Time Personality Indicator (TPI). Des analyses exploratoires et confirmatoires ont été effectuées sur l’ensemble des données recueillies auprès de 1 267 étudiants et employés de l’Université Laval ayant complété la version française du TPI ainsi que d’autres mesures de la personnalité. Les résultats ont révélé qu’une solution à huit facteurs permet de mieux décrire les données de l’échantillon. La discussion présente les raisons pour lesquelles la version française du TPI est valide, identifie certaines limites de la présente étude et souligne l’utilité de cet outil pour la recherche sur la gestion du temps. (ABSTRACT: Numerous authors have developed an interest towards the understanding of the abilities related to time management. As a consequence, multiple theoretical definitions have been proposed to explain time management. Likewise, several questionnaires have been developed in order to measure this concept. The aim of this study was to validate a French version of one of these tools, namely the Time Personality Indicator (TPI). The French version of the TPI and other personality questionnaires were completed by 1267 students and employees of Université Laval. The statistical approach used included exploratory and confirmatory analyses. Results revealed that an eight factor model provided a better adjustment to the data. The discussion provides arguments supporting the validity of the French version of the TPI and underlines the importance of such a tool for the research on time management)

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

Critère de validation croisée pour le choix des modèles des petits domaines au niveau des unités

Ce mémoire s’intéresse à l’étude du critère de validation croisée pour le choix des modèles relatifs aux petits domaines. L’étude est limitée aux modèles de petits domaines au niveau des unités. Le modèle de base des petits domaines est introduit par Battese, Harter et Fuller en 1988. C’est un modèle de régression linéaire mixte avec une ordonnée à l’origine aléatoire. Il se compose d’un certain nombre de paramètres : le paramètre β de la partie fixe, la composante aléatoire et les variances relatives à l’erreur résiduelle. Le modèle de Battese et al. est utilisé pour prédire, lors d’une enquête, la moyenne d’une variable d’intérêt y dans chaque petit domaine en utilisant une variable auxiliaire administrative x connue sur toute la population. La méthode d’estimation consiste à utiliser une distribution normale, pour modéliser la composante résiduelle du modèle. La considération d’une dépendance résiduelle générale, c’est-à-dire autre que la loi normale donne une méthodologie plus flexible. Cette généralisation conduit à une nouvelle classe de modèles échangeables. En effet, la généralisation se situe au niveau de la modélisation de la dépendance résiduelle qui peut être soit normale (c’est le cas du modèle de Battese et al.) ou non-normale. L’objectif est de déterminer les paramètres propres aux petits domaines avec le plus de précision possible. Cet enjeu est lié au choix de la bonne dépendance résiduelle à utiliser dans le modèle. Le critère de validation croisée sera étudié à cet effet.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

Pricing derivatives with stochastic volatility

This Ph.D. thesis contains 4 essays in mathematical finance with a focus on pricing Asian option (Chapter 4), pricing futures and futures option (Chapter 5 and Chapter 6) and time dependent volatility in futures option (Chapter 7). In Chapter 4, the applicability of the Albrecher et al.(2005)'s comonotonicity approach was investigated in the context of various benchmark models for equities and com- modities. Instead of classical Levy models as in Albrecher et al.(2005), the focus is the Heston stochastic volatility model, the constant elasticity of variance (CEV) model and the Schwartz (1997) two-factor model. It is shown that the method delivers rather tight upper bounds for the prices of Asian Options in these models and as a by-product delivers super-hedging strategies which can be easily implemented. In Chapter 5, two types of three-factor models were studied to give the value of com- modities futures contracts, which allow volatility to be stochastic. Both these two models have closed-form solutions for futures contracts price. However, it is shown that Model 2 is better than Model 1 theoretically and also performs very well empiri- cally. Moreover, Model 2 can easily be implemented in practice. In comparison to the Schwartz (1997) two-factor model, it is shown that Model 2 has its unique advantages; hence, it is also a good choice to price the value of commodity futures contracts. Fur- thermore, if these two models are used at the same time, a more accurate price for commodity futures contracts can be obtained in most situations. In Chapter 6, the applicability of the asymptotic approach developed in Fouque et al.(2000b) was investigated for pricing commodity futures options in a Schwartz (1997) multi-factor model, featuring both stochastic convenience yield and stochastic volatility. It is shown that the zero-order term in the expansion coincides with the Schwartz (1997) two-factor term, with averaged volatility, and an explicit expression for the first-order correction term is provided. With empirical data from the natural gas futures market, it is also demonstrated that a significantly better calibration can be achieved by using the correction term as compared to the standard Schwartz (1997) two-factor expression, at virtually no extra effort. In Chapter 7, a new pricing formula is derived for futures options in the Schwartz (1997) two-factor model with time dependent spot volatility. The pricing formula can also be used to find the result of the time dependent spot volatility with futures options prices in the market. Furthermore, the limitations of the method that is used to find the time dependent spot volatility will be explained, and it is also shown how to make sure of its accuracy.

Empenhamento(s) organizacional(is): Dois laços afectivos, dois instrumentais, um normativo e um de ausência

Este artigo visa testar um hexa-modelo dimensional do empenhamento organizacional sugerido em pesquisas anteriores de Rego (2002b, 2003). O modelo difere do esquema tri-dimensional mais comum (afectivo, normativo e instrumental) no que concerne a três aspectos: a) a faceta afectiva é desmembrada em duas (empenhamento afectivo; futuro comum); b) a faceta instrumental é dividida nas facetas “escassez de alternativas” e “sacrifícios elevados”; c) é sugerida uma nova dimensão, designada “ausência psicológica” e que representa o “grau zero” do empenhamento. A amostra é constituída por 366 indivíduos, com actividades profissionais bastante distintas. Análises factoriais confirmatórias sugerem que o modelo de seis dimensões se ajusta satisfatoriamente aos dados, embora os modelos de quatro e cinco dimensões denotem igualmente boas qualidades psicométricas.

Influence des traits et troubles de personnalité sur l’intégration au travail de personnes aux prises avec un trouble mental grave

Bien que le travail soit bénéfique et souhaité par une majorité de personnes aux prises avec un trouble mental grave (TMG), les études réalisées auprès de cette clientèle montrent des taux d’emploi d’environ 10 à 20%. Parmi les services visant le retour au travail, les programmes de soutien à l’emploi (PSE) se sont montrés les plus efficaces avec des taux de placement en emploi standard oscillant entre 50 et 60%, sans toutefois garantir le maintien en emploi. Plusieurs études ont tenté de cerner les déterminants de l’obtention et du maintien en emploi chez cette population sans toutefois s’intéresser à la personnalité, et ce, bien qu’elle soit reconnue depuis toujours comme un déterminant important du fonctionnement des individus. De plus, peu de questionnaires d’évaluation de la personnalité selon le modèle de la personnalité en cinq facteurs (FFM) ont été utilisés auprès d’une clientèle avec un TMG et ceux-ci ont montré des propriétés psychométriques ne respectant pas des normes reconnues et acceptées. Cette thèse porte sur les liens entre la personnalité et l’intégration au travail chez les personnes avec un TMG. La première partie vise la validation d’un outil de mesure de la personnalité selon le FFM afin de répondre aux objectifs de la deuxième partie de la thèse. À cet effet, deux échantillons ont été recrutés, soit 259 étudiants universitaires et 141 personnes avec un TMG. Des analyses factorielles confirmatoires ont mené au développement d’un nouveau questionnaire à 15 items (NEO-15) dont les indices d’ajustement, de cohérence interne et de validité convergente respectent les normes établies, ce qui en fait un questionnaire bien adapté à la mesure de la personnalité normale dans des contextes où le temps d’évaluation est limité. La deuxième partie présente les résultats d’une étude réalisée auprès de 82 personnes aux prises avec un TMG inscrites dans un PSE et visant à identifier les facteurs d’obtention et de maintien en emploi chez cette clientèle, particulièrement en ce qui concerne la contribution des éléments normaux et pathologiques de la personnalité. Les résultats de régressions logistiques et de régressions de Cox (analyses de survie) ont démontré que l’historique d’emploi, les symptômes négatifs et le niveau de pathologie de la personnalité étaient prédictifs de l’obtention d’un emploi standard et du délai avant l’obtention d’un tel emploi. Une autre série de régressions de Cox a pour sa part démontré que l’esprit consciencieux était le seul prédicteur significatif du maintien en emploi. Malgré certaines limites, particulièrement des tailles d’échantillons restreintes, ces résultats démontrent la pertinence et l’importance de tenir compte des éléments normaux et pathologiques de la personnalité dans le cadre d’études portant sur l’intégration au travail de personnes avec un TMG. De plus, cette thèse a permis de démontrer l’adéquation d’un nouvel instrument de mesure de la personnalité auprès de cette clientèle. Des avenues futures concernant la réintégration professionnelle et le traitement des personnes avec un TMG sont discutées à la lumière de ces résultats.

Modelagem univariada e regressão múltipla na análise da oferta e do preço do boi gordo no mercado brasileiro

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, Programa de Pós-Graduação em Agronegócios, 2016.

Determinantes da estrutura de capitais do sector bancário português

Este estudo tem como objectivo analisar quais os factores que determinam a estrutura de capitais do sector bancário Português. Com o intuito de atingir o objectivo e assumindo a existência de uma estrutura óptima de capitais, recorrer-se-á ao modelo de regressão linear múltipla para verificar a aderência do processo de decisão às teorias acerca da estrutura de capitais, bem como quais dos factores analisados a afectarão significativamente. Os resultados obtidos sugerem que a rendibilidade, a dimensão, o risco e a tangibilidade são os principais determinantes da estrutura de capitais do sector bancário português. ABSTRACT: The main aim for this study is to verify which determinants influence the Portuguese bank's capital structure. ln order to achieve the above mentioned aim and assuming an optimal capital structure, we will apply a multiple linear regression model with the purpose of proving the capital structure theories existence and to observe which determinants influence it. The obtained results mention that profitability, size, risk and tangibility are the principal determinants of Portuguese bank's capital structure.

Children's Separation Anxiety Scale (CSAS): Psychometric Properties

This study describes the psychometric properties of the Children's Separation Anxiety Scale (CSAS), which assesses separation anxiety symptoms in childhood. Participants in Study 1 were 1,908 schoolchildren aged between 8 and 11. Exploratory factor analysis identified four factors: worry about separation, distress from separation, opposition to separation, and calm at separation, which explained 46.91% of the variance. In Study 2, 6,016 children aged 8–11 participated. The factor model in Study 1 was validated by confirmatory factor analysis. The internal consistency (α = 0.82) and temporal stability (r = 0.83) of the instrument were good. The convergent and discriminant validity were evaluated by means of correlations with other measures of separation anxiety, childhood anxiety, depression and anger. Sensitivity of the scale was 85% and its specificity, 95%. The results support the reliability and validity of the CSAS.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.