This thesis investigates key issues concerning how active equity fund managers add value: measuring alpha (Chapter 3), generating alpha (Chapters 4, 5 and 6) and transaction cost minimisation (Chapter 7). Chapter 3 proposes important methodological adjustments to the widely adopted benchmarking methodology of Daniel, Grinblatt, Titman and Wermers (1997). Applying this modified benchmark to a sample of active funds and simulated passive portfolios that mimic fund manager style characteristics, statistically lower tracking error is documented, compared with using the standard methodology. These findings suggest that improved specifications of characteristic benchmarks represent better methods in accurately quantifying fund manager skill. Chapter 4 examines a portfolio strategy which selects stocks using the undisclosed monthly holdings of Australian active funds. When considering a large range of strategies incorporating portfolio holdings information, the top performing strategies are robust to data-snooping and are economically and statistically significant when incorporating transaction costs. Accounting for look-ahead bias in the formation of a strategy, statistically significant alpha of at least 6.88 percent per year is found when following the best performing strategy holding 20 stocks or more in the previous month. Chapter 5 examines the relation of active equity fund managers location proximity to a stock??s corporate headquarter using portfolio holdings data. Contrary to much international research, this study reveals evidence inconsistent with a location advantage for Melbourne and Sydney-based funds. Chapter 6 examines retail investor trading on the Australian Stock Exchange. The performance of retail investors is highly heterogeneous: discount (non-discount) retail brokerage investors lose -0.59 (-0.05) percent intraday and experience negative (positive) returns over the subsequent year. These findings are inconsistent with retail investors exerting price pressure or providing liquidity to institutions. Chapter 7 examines whether equity fund managers use multiple brokers in a trade package in order to lower their price impact and brokerage costs. Using the daily trades of funds, multiple broker trades are not found to have lower costs compared to a single broker, even when controlling for the informativeness of the trade package and potential endogeneity. These findings suggest that fund managers do not lower their costs when using multiple brokers.

This thesis investigates key issues concerning how active equity fund managers add value: measuring alpha (Chapter 3), generating alpha (Chapters 4, 5 and 6) and transaction cost minimisation (Chapter 7). Chapter 3 proposes important methodological adjustments to the widely adopted benchmarking methodology of Daniel, Grinblatt, Titman and Wermers (1997). Applying this modified benchmark to a sample of active funds and simulated passive portfolios that mimic fund manager style characteristics, statistically lower tracking error is documented, compared with using the standard methodology. These findings suggest that improved specifications of characteristic benchmarks represent better methods in accurately quantifying fund manager skill. Chapter 4 examines a portfolio strategy which selects stocks using the undisclosed monthly holdings of Australian active funds. When considering a large range of strategies incorporating portfolio holdings information, the top performing strategies are robust to data-snooping and are economically and statistically significant when incorporating transaction costs. Accounting for look-ahead bias in the formation of a strategy, statistically significant alpha of at least 6.88 percent per year is found when following the best performing strategy holding 20 stocks or more in the previous month. Chapter 5 examines the relation of active equity fund managers location proximity to a stock??s corporate headquarter using portfolio holdings data. Contrary to much international research, this study reveals evidence inconsistent with a location advantage for Melbourne and Sydney-based funds. Chapter 6 examines retail investor trading on the Australian Stock Exchange. The performance of retail investors is highly heterogeneous: discount (non-discount) retail brokerage investors lose -0.59 (-0.05) percent intraday and experience negative (positive) returns over the subsequent year. These findings are inconsistent with retail investors exerting price pressure or providing liquidity to institutions. Chapter 7 examines whether equity fund managers use multiple brokers in a trade package in order to lower their price impact and brokerage costs. Using the daily trades of funds, multiple broker trades are not found to have lower costs compared to a single broker, even when controlling for the informativeness of the trade package and potential endogeneity. These findings suggest that fund managers do not lower their costs when using multiple brokers.

Foreign aid is the subject in development economics that created controversies about its influences on the economy of the recipient countries. This study is an attempt to explain the effects that aid may have on trade, with a focus on the trade costs associated with the creation of business ties. Tied aid creates incentives for the developing countries to keep positive trading relationships with their donors, mainly because of the diminishing trad-ing costs associated with long term contacts. Subsequently, programs related to infra-structure and trade enforcement have been launched, that work towards the integration of the Least Developing Countries into the world economy.This study includes the analysis of the trade flows and foreign aid disbursement be-tween the “Group of Seven” countries (G7) and the Least Developing Countries, for a time span of 22 years (1988-2009). The results show that aid does have a significant ef-fect on the trade flows between the developed and developing countries. The explana-tion to this is related to the trading costs and the infrastructure development that tends to diminish the costs linked to distance- and border-related issues, and the sunk costs of market research and entry. In accordance, the distance coefficient is smaller after 1997, as result of decreased trade costs and increased export flows from recipients to donors.

Le choix de portefeuille optimal d'actifs a été depuis longtemps et continue d'être un sujet d'intérêt majeur dans le domaine de la finance. L'objectif principal étant de trouver la meilleure façon d'allouer les ressources financières dans un ensemble d'actifs disponibles sur le marché financier afin de réduire les risques de fluctuation du portefeuille et d'atteindre des rendements élevés. Néanmoins, la littérature de choix de portefeuille a connu une avancée considérable à partir du 20ieme siècle avec l'apparition de nombreuses stratégies motivées essentiellement par le travail pionnier de Markowitz (1952) qui offre une base solide à l'analyse de portefeuille sur le marché financier. Cette thèse, divisée en trois chapitres, contribue à cette vaste littérature en proposant divers outils économétriques pour améliorer le processus de sélection de portefeuilles sur le marché financier afin d'aider les intervenants de ce marché. Le premier chapitre, qui est un papier joint avec Marine Carrasco, aborde un problème de sélection de portefeuille avec coûts de transaction sur le marché financier. Plus précisément, nous développons une procédure de test simple basée sur une estimation de type GMM pour évaluer l'effet des coûts de transaction dans l'économie, quelle que soit la forme présumée des coûts de transaction dans le modèle. En fait, la plupart des études dans la littérature sur l'effet des coûts de transaction dépendent largement de la forme supposée pour ces frictions dans le modèle comme cela a été montré à travers de nombreuses études (Dumas and Luciano (1991), Lynch and Balduzzi (1999), Lynch and Balduzzi (2000), Liu and Loewenstein (2002), Liu (2004), Lesmond et al. (2004), Buss et al. (2011), Gârleanu and Pedersen (2013), Heaton and Lucas (1996)). Ainsi, pour résoudre ce problème, nous développons une procédure statistique, dont le résultat est indépendant de la forme des coûts de transaction, pour tester la significativité de ces coûts dans le processus d'investissement sur le marché financier. Cette procédure de test repose sur l'hypothèse que le modèle estimé par la méthode des moments généralisés (GMM) est correctement spécifié. Un test commun utilisé pour évaluer cette hypothèse est le J-test proposé par Hansen (1982). Cependant, lorsque le paramètre d'intérêt se trouve au bord de l'espace paramétrique, le J-test standard souffre d'un rejet excessif. De ce fait, nous proposons une procédure en deux étapes pour tester la sur-identification lorsque le paramètre d'intérêt est au bord de l'espace paramétrique. Empiriquement, nous appliquons nos procédures de test à la classe des anomalies utilisées par Novy-Marx and Velikov (2016). Nous montrons que les coûts de transaction ont un effet significatif sur le comportement des investisseurs pour la plupart de ces anomalies. Par conséquent, les investisseurs améliorent considérablement les performances hors échantillon en tenant compte des coûts de transaction dans le processus d'investissement. Le deuxième chapitre aborde un problème dynamique de sélection de portefeuille de grande taille. Avec une fonction d'utilité exponentielle, la solution optimale se révèle être une fonction de l'inverse de la matrice de covariance des rendements des actifs. Cependant, lorsque le nombre d'actifs augmente, cet inverse devient peu fiable, générant ainsi une solution qui s'éloigne du portefeuille optimal avec de mauvaises performances. Nous proposons deux solutions à ce problème. Premièrement, nous pénalisons la norme des poids du portefeuille optimal dans le problème dynamique et montrons que la stratégie sélectionnée est asymptotiquement efficace. Cependant, cette méthode contrôle seulement en partie l'erreur d'estimation dans la solution optimale car elle ignore l'erreur d'estimation du rendement moyen des actifs, qui peut également être importante lorsque le nombre d'actifs sur le marché financier augmente considérablement. Nous proposons une méthode alternative qui consiste à pénaliser la norme de la différence de pondérations successives du portefeuille dans le problème dynamique pour garantir que la composition optimale du portefeuille ne fluctue pas énormément entre les périodes. Nous montrons que, sous des conditions de régularité appropriées, nous maîtrisons mieux l'erreur d'estimation dans le portefeuille optimal avec cette nouvelle procédure. Cette deuxième méthode aide les investisseurs à éviter des coûts de transaction élevés sur le marché financier en sélectionnant des stratégies stables dans le temps. Des simulations ainsi qu'une analyse empirique confirment que nos procédures améliorent considérablement la performance du portefeuille dynamique. Dans le troisième chapitre, nous utilisons différentes techniques de régularisation (ou stabilisation) empruntées à la littérature sur les problèmes inverses pour estimer le portefeuille diversifié tel que définie par Choueifaty (2011). En effet, le portefeuille diversifié dépend du vecteur de volatilité des actifs et de l'inverse de la matrice de covariance du rendement des actifs. En pratique, ces deux quantités doivent être remplacées par leurs contrepartie empirique. Cela génère une erreur d'estimation amplifiée par le fait que la matrice de covariance empirique est proche d'une matrice singulière pour un portefeuille de grande taille, dégradant ainsi les performances du portefeuille sélectionné. Pour résoudre ce problème, nous étudions trois techniques de régularisation, qui sont les plus utilisées : le rigde qui consiste à ajouter une matrice diagonale à la matrice de covariance, la coupure spectrale qui consiste à exclure les vecteurs propres associés aux plus petites valeurs propres, et Landweber Fridman qui est une méthode itérative, pour stabiliser l'inverse de matrice de covariance dans le processus d'estimation du portefeuille diversifié. Ces méthodes de régularisation impliquent un paramètre de régularisation qui doit être choisi. Nous proposons donc une méthode basée sur les données pour sélectionner le paramètre de stabilisation de manière optimale. Les solutions obtenues sont comparées à plusieurs stratégies telles que le portefeuille le plus diversifié, le portefeuille cible, le portefeuille de variance minimale et la stratégie naïve 1 / N à l'aide du ratio de Sharpe dans l'échantillon et hors échantillon. / The optimal portfolio selection problem has been and continues to be a subject of interest in finance. The main objective is to find the best way to allocate the financial resources in a set of assets available on the financial market in order to reduce the portfolio fluctuation risks and achieve high returns. Nonetheless, there has been a strong advance in the literature of the optimal allocation of financial resources since the 20th century with the proposal of several strategies for portfolio selection essentially motivated by the pioneering work of Markowitz (1952)which provides a solid basis for portfolio analysis on the financial market. This thesis, divided into three chapters, contributes to this vast literature by proposing various economic tools to improve the process of selecting portfolios on the financial market in order to help stakeholders in this market. The first chapter, a joint paper with Marine Carrasco, addresses a portfolio selection problem with trading costs on stock market. More precisely, we develop a simple GMM-based test procedure to test the significance of trading costs effect in the economy regardless of the form of the transaction cost. In fact, most of the studies in the literature about trading costs effect depend largely on the form of the frictions assumed in the model (Dumas and Luciano (1991), Lynch and Balduzzi (1999), Lynch and Balduzzi (2000), Liu and Loewenstein (2002), Liu (2004), Lesmond et al. (2004), Buss et al. (2011), Gârleanu and Pedersen (2013), Heaton and Lucas (1996)). To overcome this problem, we develop a simple test procedure which allows us to test the significance of trading costs effect on a given asset in the economy without any assumption about the form of these frictions. Our test procedure relies on the assumption that the model estimated by GMM is correctly specified. A common test used to evaluate this assumption is the standard J-test proposed by Hansen (1982). However, when the true parameter is close to the boundary of the parameter space, the standard J-test based on the chi2 critical value suffers from overrejection. To overcome this problem, we propose a two-step procedure to test overidentifying restrictions when the parameter of interest approaches the boundary of the parameter space. In an empirical analysis, we apply our test procedures to the class of anomalies used in Novy-Marx and Velikov (2016). We show that transaction costs have a significant effect on investors' behavior for most anomalies. In that case, investors significantly improve out-of-sample performance by accounting for trading costs. The second chapter addresses a multi-period portfolio selection problem when the number of assets in the financial market is large. Using an exponential utility function, the optimal solution is shown to be a function of the inverse of the covariance matrix of asset returns. Nonetheless, when the number of assets grows, this inverse becomes unreliable, yielding a selected portfolio that is far from the optimal one. We propose two solutions to this problem. First, we penalize the norm of the portfolio weights in the dynamic problem and show that the selected strategy is asymptotically efficient. However, this method partially controls the estimation error in the optimal solution because it ignores the estimation error in the expected return, which may also be important when the number of assets in the financial market increases considerably. We propose an alternative method that consists of penalizing the norm of the difference of successive portfolio weights in the dynamic problem to guarantee that the optimal portfolio composition does not fluctuate widely between periods. We show, under appropriate regularity conditions, that we better control the estimation error in the optimal portfolio with this new procedure. This second method helps investors to avoid high trading costs in the financial market by selecting stable strategies over time. Extensive simulations and empirical results confirm that our procedures considerably improve the performance of the dynamic portfolio. In the third chapter, we use various regularization (or stabilization) techniques borrowed from the literature on inverse problems to estimate the maximum diversification as defined by Choueifaty (2011). In fact, the maximum diversification portfolio depends on the vector of asset volatilities and the inverse of the covariance matrix of assets distribution. In practice, these two quantities need to be replaced by their sample counterparts. This results in estimation error which is amplified by the fact that the sample covariance matrix may be close to a singular matrix in a large financial market, yielding a selected portfolio far from the optimal one with very poor performance. To address this problem, we investigate three regularization techniques, such as the ridge, the spectral cut-off, and the Landweber-Fridman, to stabilize the inverse of the covariance matrix in the investment process. These regularization schemes involve a tuning parameter that needs to be chosen. So, we propose a data-driven method for selecting the tuning parameter in an optimal way. The resulting regularized rules are compared to several strategies such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio.