Un modèle d'interaction réaliste pour la simulation de marchés financiers

Dans les modèles de marché multi-agents utilisés habituellement, la structure du marché est presque toujours réduite à une équation qui aggrège les décisions des agents de façon synchrone pour mettre à jour le prix de l'action à chaque pas de temps. Sur les marchés réels, ce processus est totalement différent : le prix de l'action émerge d'interactions survenant de manière asynchrone entre les acheteurs et les vendeurs. Dans cet article, nous introduisons un modèle de marché artificiel conçu pour être le plus proche possible de la structure des marchés réels. Ce modèle est basé sur un carnet d'ordres à travers lequel les agents échangent des actions de manière asynchrone. Nous montrons que, sans émettre d'hypothèses particulières sur le comportement des agents, ce modèle exhibe de nombreuses propriétés statistiques des marchés réels. Nous soutenons que la plupart de ces propriétés proviennent de la manière dont les agents interagissent plutôt que de leurs comportements. Ce résutat expérimental est validé et renforcé grâce à l'utilisation de nombreux tests statistiques utilisés par les économistes pour caractériser les propriétés des marchés réels. Nous finissons par quelques perspectives ouvertes par les avantages de l'utilisation de tels modèles pour le développement, le test et la validation d'automates d'investissement. In usual multi-agent stock market models, market structure is mostly reduced to an equation matching supply and demand, which synchronously aggregates agents decisions to update stock price at each time steps. On real markets, the process is however very different: stock price emerges from one-to one asynchronous interactions between buyers and sellers at various time step. In this article, we introduce an artificial stock market model designed to be close to real market structure. The model is based on a centralized orderbook through which agents exchange stocks asynchronously.We show that, without making any strong assumption on agents behaviors, this model exhibits many statistical properties of real stock markets. We argue that most of market features are implied by the exchange process more than by agents behaviors. This experimental result is validated and strengthen using several tests used by economists to characterize real market. We finally put in perspective the advantages of such a realistic model to develop, test and validate behavior of automated trading agents

Prevalence of post-traumatic stress disorder and validity of the Impact of Events Scale - Revised in primary care in Zimbabwe, a non-war-affected African country

BACKGROUND: A critical step in research on the epidemiology of post-traumatic stress disorder (PTSD) in low-resource settings is the validation of brief self-reported psychometric tools available in the public domain, such as the Impact Event Scale - Revised (IES-R). AIMS: We aimed to investigate the validity of the IES-R in a primary healthcare setting in Harare, Zimbabwe. METHOD: We analysed data from a survey of 264 consecutively sampled adults (mean age 38 years; 78% female). We estimated the area under the receiver operating characteristic curve and sensitivity, specificity and likelihood ratios for different cut-off points of the IES-R, against a diagnosis of PTSD made using the Structured Clinical Interview for DSM-IV. We performed factor analysis to evaluate construct validity of the IES-R. RESULTS: The prevalence of PTSD was 23.9% (95% CI 18.9-29.5). The area under the curve for the IES-R was 0.90. At a cut-off of ≥47, the sensitivity of the IES-R to detect PTSD was 84.1 (95% CI 72.7-92.1) and specificity was 81.1 (95% CI 75.0-86.3). Positive and negative likelihood ratios were 4.45 and 0.20, respectively. Factor analysis revealed a two-factor solution, with both factors showing good internal consistency (Cronbach's factor-1 α = 0.95, factor-2 α = 0.76). In a post hoc analysis, we found the brief six-item IES-6 also performed well, with an area under the curve of 0.87 and optimal cut-off of 15. CONCLUSIONS: The IES-R and IES-6 had good psychometric properties and performed well for indicating possible PTSD, but at higher cut-off points than those recommended in the Global North

Sleep Treatment Outcome Predictors (STOP) Pilot Study: a protocol for a randomised controlled trial examining predictors of change of insomnia symptoms and associated traits following cognitive-behavioural therapy for insomnia in an unselected sample

The study represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust and King’s College London

Sleep Treatment Outcome Predictors (STOP) Pilot Study: a protocol for a randomised controlled trial examining predictors of change of insomnia symptoms and associated traits following cognitive–behavioural therapy for insomnia in an unselected sample

Introduction Cognitive–behavioural therapy for insomnia (CBT-I) leads to insomnia symptom improvements in a substantial proportion of patients. However, not everyone responds well to this treatment, and it is unclear what determines individual differences in response. The broader aim of this work is to examine to what extent response to CBT-I is due to genetic and environmental factors. The purpose of this pilot study is to examine feasibility of a design to test hypotheses focusing on an unselected sample, that is, without selection on insomnia complaints, in order to plan a larger behavioural genetics study where most participants will likely not have an insomnia disorder. Methods and analysis A two parallel-group randomised controlled trial is being conducted across three London universities. Female students (minimum age 18 years) enrolled on a psychology programme at one of the three sites were invited to participate. The target number of participants to be recruited is 240. Following baseline assessments, participants were randomly allocated to either the treatment group, where they received weekly sessions of digital CBT-I for 6 weeks, or the control group, where they completed an online puzzle each week for 6 weeks. Follow-up assessments have taken place mid-intervention (3 weeks) and end of intervention (6 weeks). A 6-month follow-up assessment will also occur. Primary outcomes will be assessed using descriptive statistics and effect size estimates for intervention effects. Secondary outcomes will be analysed using multivariate generalised estimating equation models. Ethics and dissemination The study received ethical approval from the Research Ethics and Integrity subcommittee, Goldsmiths, University of London (application reference: EA 1305). DNA sample collection for the BioResource received ethical approval from the NRES Committee South Central—Oxford (reference number: 15/SC/0388). The results of this work shall be published in peer-reviewed journals