Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Predictive modeling of clinical outcomes for hospitalized COVID-19 patients utilizing CyTOF and clinical data.

In December 2019, an outbreak of a novel coronavirus initiated a global pandemic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes the disease coronavirus disease 2019 (COVID-19). Symptoms of infection with COVID-19 vary widely between individuals. While some infected individuals are asymptomatic, others need more extensive care and require hospitalization. Indeed, the COVID-19 pandemic was characterized by a shortage of hospital beds which presented additional complications in providing adequate care for patients. In this study, we used a combination of T cell population data collected from mass cytometry analysis and clinical markers to form a predictive model of clinical outcomes for hospitalized COVID19 patients. This thesis details the steps and analysis towards the design of the final model including data acquirement and preprocessing, missing data handling via multiple imputation, and repeated imputations inferences

2023 Summer Experience Program Abstracts

https://openworks.mdanderson.org/sumexp23/1130/thumbnail.jp

Identifying Immunological Signatures in Blood Predictive of Host Response to Plasmodium Falciparum Vaccines and Infections Using Computational Methods

Indiana University-Purdue University Indianapolis (IUPUI)Malaria infects more than 240 million people every year, causing more than 640,000 deaths in 2021 alone. The complex interactions between the Plasmodium parasites that cause malaria and host immune system have made it difficult to identify specific mechanisms of vaccine-induced and naturally acquired immunity. After more than half a century of research into potential immunization methods, reliable immune correlates of malaria protection still have yet to be identified, and questions underlying the reduced protective efficacy of malaria vaccines in field studies of endemic populations relative to non-endemic populations still remain. In this thesis, I use computational methods to identify biological determinants of whole-parasite vaccine-induced immunity and immune correlates of protection from clinical malaria. Our systems analysis of a PfSPZ Vaccine clinical trial revealed that innate signatures were predictive of increased antibody response but also a decrease in the cytotoxic response required for sterilizing immunity. Conversely, these myeloid signatures predicted protection against parasitemia for subjects receiving a saline placebo, suggesting a role for myeloid-lineage cells in clearing pre-erythrocytic parasite stages. Based on these findings, I created a structural equation model to examine the interactions between cellular, humoral, and transcriptomic responses and the effects these have on protection outcome. This revealed a direct positive effect of CD11+ monocyte-derived cells on parasitemia outcome post-vaccination that was mediated by the presence of P. falciparum-specific antibodies at pre-vaccination baseline. Additionally, this model illustrates an indirect role of CD14+ monocyte activation in restricting immune priming by the PfSPZ Vaccine. Together, this data supports our hypothesis that innate immune activation and antigen presentation are uncoupled from cytotoxic cell-dependent immunity from the PfSPZ Vaccine and that this effect may be antibody-dependent

Identifying immunological biomarkers of sepsis using cytometry bioinformatics and machine learning

Sepsis is a leading cause of mortality and significantly strains healthcare systems worldwide. Improving sepsis care and outcomes depends on appropriate risk stratification and timely identification of the causative pathogen to guide patient management and treatment. Enormous efforts have been made to identify diagnostic and prognostic biomarkers to aid decision making, but to date, they have failed to identify candidates with acceptable accuracy and precision to have an impact in the clinic. Past studies have often focused on individual biomarkers without considering the potential benefit of multi-marker panels incorporating deep immunological phenotyping. This work addressed this issue with a cross-disciplinary approach that integrated sepsis biomarker discovery, cytometry bioinformatics, and supervised machine learning. Firstly, a novel framework for cytometry data analysis was developed, along with a new ensemble clustering algorithm that reduced the risk of biasing exploratory analyses with the application of a single clustering technique. Secondly, the analysis framework was applied to a study cohort of severe sepsis patients, and their early immunological profile consisting of cellular and humoral parameters (within 36 hours of diagnosis) was determined. The captured immunological parameters were then combined with routine clinical data and lipid plasma concentrations to generate interpretable machine learning models for predicting mortality and the underlying cause of infection. The generated models discriminated between survivors and non-survivors, and between Gram-negative and Gram-positive infections, and identified potential combinations of biomarkers with predictive value

2011 IMSAloquium, Student Investigation Showcase

Inquiry Without Boundaries reflects our students’ infinite possibilities to explore their unique passions, develop new interests, and collaborate with experts around the globe.https://digitalcommons.imsa.edu/archives_sir/1003/thumbnail.jp