A Network Model of Financial Markets

This thesis introduces a network representation of equity markets.The model is based on the premise that assets share dependencies on abstract ‘factors’ resulting in exploitable patterns among asset price levels.The network model is a collection of long-run market trends estimated by a 3 layer machine learning framework.The network model’s comprehensive validity is established with 2 simulations in the fields of algorithmic trading, and systemic risk.The algorithmic trading validation applies expectations derived from the network model to estimating expected future returns. It further utilizes the network’s expectations to actively manage a theoretically market neutral portfolio.The validation demonstrates that the network model’s portfolio generates excess returns relative to 2 benchmarks. Over the time period of April, 2007 to January, 2014 the network model’s portfolio for assets drawn from the S&P/ASX 100 produced a Sharpe ratio of 0.674.This approximately doubles the nearest benchmark. The systemic risk validation utilized the network model to simulate shocks to select market sectors and evaluate the resulting financial contagion.The validation successfully differentiated sectors by systemic connectivity levels and suggested some interesting market features. Most notable was the identification of the ‘Financials’ sector as most systemically influential and ‘Basic Materials’ as the most systemically dependent. Additionally, there was evidence that ‘Financials’ may function as a hub of systemic risk which exacerbates losses from multiple market sectors

Développement d'un modèle logiciel de cellule sur processeurs multi-cœurs pour la simulation de morphogenèse de tissus

The main purpose of this thesis is to present tools built in order to numerically study the development of cellular tissues through an individual-based approach that includes biomechanical elements as well as an artificial chemistry. The objective of such proposals is to gain a better understanding of the mechanisms that rule the development of multicellular tissues using numerical simulation as a complement to in vitro and in vivo experiments. This objective is difcult to achieve. However technological means in the field of cellular and molecular biology allow the observation and the gathering of many data. Moreover, the development of multi-core devices allows the simulation of complex systems, such as multi-cellular systems. Multi-cellular systems exhibit mainly two levels of complexity : the first one concerns the potentially large amount of cells they contain, which requires a considerable computing power when this point is addressed through individual-based approach; the second level concerns the diversity of biological cells’ behaviors. This level requires specific algorithms, for example to deal with complex behavior such as mitosis. The conception of 1) models that integrate biological data and 2) dedicated algorithms adapted to heterogenous and multi-core devices make it possible to solve, at least to some extent, these two levels of complexity. In order to numerically study both healthy and pathological tissue developpement, we propose two elements. The first is a biomechanical cell model that includes the behaviors involved in tissue morphogenesis (mitosis, diferentiation, adhesion, migration, cell-cell signaling and apoptosis). The second element is a parallel simulator that relies on a non-specialized software architecture and on dedicated data structures and algorithms used to benefit from the power of multi-core hardware. In this document, we present several case studies that gives some validation elements of both our model and our simulator.L’objectif principal de cette thèse est de proposer des outils permettant l’étude numérique du développement de tissus cellulaires à travers une approche individus-centrée comprenant des aspects biomécaniques et chimiques. De telles propositions doivent permettre de mieux comprendre les mécanismes régissant le développement de tissus multi-cellulaires grâce à la simulation numérique, en complément d’expérimentations in vitro et in vivo. La réalisation de ces objectifs est difficile, mais les avancées dans les domaines de la biologie cellulaire et moléculaire permettent l’observation et la collecte d’un grand nombre de données. En outre, le développement de matériels parallèles permet la simulation de systèmes complexes tels que des systèmes multi-cellulaires. Les systèmes multi-cellulaires exhibent essentiellement deux niveaux de complexité lorsque l’on souhaite les simuler: le premier concerne le nombre potentiellement très important de cellules qu’ils contiennent, nécessitant alors une grande puissance de calcul; le second concerne la multiplicité des comportements des cellules vivantes, tant au niveau individuel que collectif, ce qui requiert des algorithmes bien spécifiques. La conception de modèles, intégrant à la fois des données biologiques pertinentes, des algorithmes adaptés et reposant sur des processeurs puissants permet de résoudre, au moins en partie, ces deux niveaux de complexité, mais doivent reposer sur une architecture logicielle dédiée. Dans l’idée d’étudier, en simulation numérique, le développement de tissus sains et pathologiques, nos travaux apportent deux éléments. Le premier est un modèle biomécanique de cellule virtuelle comportant des processus impliqués dans la morphogenèse de tissus tels que la division, la différentiation, l’adhésion, la migration, la signalisation et l’apoptose. Le second est un simulateur parallèle reposant sur une architecture logicielle généraliste ainsi que sur des structures de données et des algorithmes originaux permettant d’exploiter la puissance de calcul offerte par les matériels multi-cœurs. Nous présentons dans ce mémoire plusieurs cas d’études qui permettent d’apporter des éléments de validation sur la réalisation de notre modèle et de notre simulateur