Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

Modelling world investment markets using threshold conditional correlation models

In this paper we propose a parsimonious regime-switching approach to model the correlations between assets, the threshold conditional correlation (TCC) model. This method allows the dynamics of the correlations to change from one state (or regime) to another as a function of observable transition variables. Our model is similar in spirit to Silvennoinen and Teräsvirta (2009) and Pelletier (2006) but with the appealing feature that it does not suffer from the course of dimensionality. In particular, estimation of the parameters of the TCC involves a simple grid search procedure. In addition, it is easy to guarantee a positive definite correlation matrix because the TCC estimator is given by the sample correlation matrix, which is positive definite by construction. The methodology is illustrated by evaluating the behaviour of international equities, govenrment bonds and major exchange rates, first separately and then jointly. We also test and allow for different parts in the correlation matrix to be governed by different transition variables. For this, we estimate a multi-threshold TCC specification. Further, we evaluate the economic performance of the TCC model against a constant conditional correlation (CCC) estimator using a Diebold-Mariano type test. We conclude that threshold correlation modelling gives rise to a significant reduction in portfolio´s variance.

Large-scale analysis of orthologs and paralogs under covarion-like and constant-but-different models of amino acid evolution.

Functional divergence between homologous proteins is expected to affect amino acid sequences in two main ways, which can be considered as proxies of biochemical divergence: a "covarion-like" pattern of correlated changes in evolutionary rates, and switches in conserved residues ("conserved but different"). Although these patterns have been used in case studies, a large-scale analysis is needed to estimate their frequency and distribution. We use a phylogenomic framework of animal genes to answer three questions: 1) What is the prevalence of such patterns? 2) Can we link such patterns at the amino acid level with selection inferred at the codon level? 3) Are patterns different between paralogs and orthologs? We find that covarion-like patterns are more frequently detected than "constant but different," but that only the latter are correlated with signal for positive selection. Finally, there is no obvious difference in patterns between orthologs and paralogs.

Reaching for the Holy Grail: insights from infection/cure models on the prospects for vaccines for Trypanosoma cruziinfection

Prevention of Trypanosoma cruzi infection in mammals likely depends on either prevention of the invading trypomastigotes from infecting host cells or the rapid recognition and killing of the newly infected cells byT. cruzi-specific T cells. We show here that multiple rounds of infection and cure (by drug therapy) fails to protect mice from reinfection, despite the generation of potent T cell responses. This disappointing result is similar to that obtained with many other vaccine protocols used in attempts to protect animals from T. cruziinfection. We have previously shown that immune recognition ofT. cruziinfection is significantly delayed both at the systemic level and at the level of the infected host cell. The systemic delay appears to be the result of a stealth infection process that fails to trigger substantial innate recognition mechanisms while the delay at the cellular level is related to the immunodominance of highly variable gene family proteins, in particular those of the trans-sialidase family. Here we discuss how these previous studies and the new findings herein impact our thoughts on the potential of prophylactic vaccination to serve a productive role in the prevention of T. cruziinfection and Chagas disease.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Neuronal in vitro models for the estimation of acute systemic toxicity.

The objective of the EU funded integrated project "ACuteTox" is to develop a strategy in which general cytotoxicity, together with organ-specific endpoints and biokinetic features, are taken into consideration in the in vitro prediction of oral acute systemic toxicity. With regard to the nervous system, the effects of 23 reference chemicals were tested with approximately 50 endpoints, using a neuronal cell line, primary neuronal cell cultures, brain slices and aggregated brain cell cultures. Comparison of the in vitro neurotoxicity data with general cytotoxicity data generated in a non-neuronal cell line and with in vivo data such as acute human lethal blood concentration, revealed that GABA(A) receptor function, acetylcholine esterase activity, cell membrane potential, glucose uptake, total RNA expression and altered gene expression of NF-H, GFAP, MBP, HSP32 and caspase-3 were the best endpoints to use for further testing with 36 additional chemicals. The results of the second analysis showed that no single neuronal endpoint could give a perfect improvement in the in vitro-in vivo correlation, indicating that several specific endpoints need to be analysed and combined with biokinetic data to obtain the best correlation with in vivo acute toxicity.

Two-sided learning in new keynesian models: dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified

Probabilistic models to assist maintenance of multiple instruments

The paper discusses maintenance challenges of organisations with a huge number of devices and proposes the use of probabilistic models to assist monitoring and maintenance planning. The proposal assumes connectivity of instruments to report relevant features for monitoring. Also, the existence of enough historical registers with diagnosed breakdowns is required to make probabilistic models reliable and useful for predictive maintenance strategies based on them. Regular Markov models based on estimated failure and repair rates are proposed to calculate the availability of the instruments and Dynamic Bayesian Networks are proposed to model cause-effect relationships to trigger predictive maintenance services based on the influence between observed features and previously documented diagnostics

New frontiers in therapeutic resistance in cancer.

Despite advances in personalized medicine and targeted therapies, therapeutic resistance remains a persistent dilemma encountered by clinicians, scientists and patients. In this article we summarize the highlights of the third Quebec Conference on Therapeutic Resistance in Cancer. This unique meeting provided researchers and clinicians with insights into: intrinsic and acquired resistance; tumor heterogeneity; complexities of biomarker-driven trials; challenges of 'omics data analysis; and models of clinical applications of personalized medicine. Emphasized throughout the conference was the importance of collaborations - between industry and academia, and between basic researchers and clinicians - so that therapeutic resistance can be studied where it matters most, in patients.

Towards smart user models in open environments

Our work is concerned with user modelling in open environments. Our proposal then is the line of contributions to the advances on user modelling in open environments thanks so the Agent Technology, in what has been called Smart User Model. Our research contains a holistic study of User Modelling in several research areas related to users. We have developed a conceptualization of User Modelling by means of examples from a broad range of research areas with the aim of improving our understanding of user modelling and its role in the next generation of open and distributed service environments. This report is organized as follow: In chapter 1 we introduce our motivation and objectives. Then in chapters 2, 3, 4 and 5 we provide the state-of-the-art on user modelling. In chapter 2, we give the main definitions of elements described in the report. In chapter 3, we present an historical perspective on user models. In chapter 4 we provide a review of user models from the perspective of different research areas, with special emphasis on the give-and-take relationship between Agent Technology and user modelling. In chapter 5, we describe the main challenges that, from our point of view, need to be tackled by researchers wanting to contribute to advances in user modelling. From the study of the state-of-the-art follows an exploratory work in chapter 6. We define a SUM and a methodology to deal with it. We also present some cases study in order to illustrate the methodology. Finally, we present the thesis proposal to continue the work, together with its corresponding work scheduling and temporalisation

Application of the european customer satisfaction index to postal services. Structural equation models versus partial least squars

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services

From deep seated slope deformation to rock avalanche: Destabilization and transportation models of the Sierre landslide (Switzerland)

Sackung is a widespread post-glacial morphological feature affecting Alpine mountains and creating characteristic geomorphological expression that can be detected from topography. Over long time evolution, internal deformation can lead to the formation of rapidly moving phenomena such as a rock-slide or rock avalanche. In this study, a detailed description of the Sierre rock-avalanche (SW Switzerland) is presented. This convex-shaped postglacial instability is one of the larger rock-avalanche in the Alps, involving more than 1.5 billion m3 with a run-out distance of about 14 km and extremely low Fahrböschung angle. This study presents comprehensive analyses of the structural and geological characteristics leading to the development of the Sierre rock-avalanche. In particular, by combining field observations, digital elevation model analyses and numerical modelling, the strong influence of both ductile and brittle tectonic structures on the failure mechanism and on the failure surface geometry is highlighted. The detection of pre-failure deformation indicates that the development of the rock avalanche corresponds to the last evolutionary stage of a pre-existing deep seated gravitational slope instability. These analyses accompanied by the dating and the characterization of rock avalanche deposits, allow the proposal of a destabilization model that clarifies the different phases leading to the development of the Sierre rock avalanche.

Bayesian nash equilibrium in ''linear'' cournot models with private information about costs

Calculating explicit closed form solutions of Cournot models where firms have private information about their costs is, in general, very cumbersome. Most authors consider therefore linear demands and constant marginal costs. However, within this framework, the nonnegativity constraint on prices (and quantities) has been ignored or not properly dealt with and the correct calculation of all Bayesian Nash equilibria is more complicated than expected. Moreover, multiple symmetric and interior Bayesianf equilibria may exist for an open set of parameters. The reason for this is that linear demand is not really linear, since there is a kink at zero price: the general ''linear'' inverse demand function is P (Q) = max{a - bQ, 0} rather than P (Q) = a - bQ.