Expert political risk opinions and banking system returns: A revised banking market model.

Human behaviour in banking and finance systems is in part made up of a complex mix of political, social and cultural factors. These factors are reflected in expert opinion based political risk scores. Market inefficiency is largely a result of anomalies in human behaviour causing information asymmetries. A basic systemic market model is re-specified into a model for international banking systems, which controls for pure political risk. Samples of developed and developing banking systems are examined. Political risk factors and world banking returns are exogenous in models of country-banking system returns. New political information assists in explaining banking system stock returns. The findins should be of interest to investors in banking stocks. Banking regulators may be assisted in decisions on appropriate levels of regulatory capital as a benchmark for banking systems. The model could help to anticipate financial crises

Network theory and its applications in economic systems

This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc\ = 0, coupled SF networks are significantly more vulnerable with pc\ significantly larger than zero. 2) We analytically demonstrate that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008 - 2011

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics

Network theory and its applications in economic systems

This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc\ = 0, coupled SF networks are significantly more vulnerable with pc\ significantly larger than zero. 2) We analytically demonstrate that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008 - 2011

Cybersecurity Scenario Modeling: Imagining the Black Swans for Digital Infrastructures Risk Management

The term “digital infrastructures” is used to refer to one or more of a combination of IoT and its artifacts, the cloud, cyber-physical systems, and digitized business architectures. As digital infrastructures become increasingly complex and interdependent, impacts from disruptive events have the potential to be more harmful than mere inconveniences and financial losses. The risk from these catastrophic events to digital infrastructures may leave many organizations unprepared. To predict so-called “Black Swan Events to increasingly complex digital infrastructures this research in progress postulates that risk management activities should be conducted outside of existing frameworks. In this paper, we argue that qualitative scenario risk modeling exercises utilizing diverse stakeholders may become even more important than other types of risk analysis in the prediction of threats to digital infrastructures. We discuss the importance of diverse stakeholders in developing structured, qualitative, scenario models to predict Black Swan Events to digital infrastructures. We discuss potential issues and solutions for the cataloging and quantification of the use cases developed from qualitative event scenario modeling and the next steps for this research

Motivations for servitization: the impact of product complexity

Purpose To identify the commonalities and differences in manufacturers’ motivations to servitize. Design/methodology/approach UK study based on interviews with 40 managers in 25 companies in 12 sectors. Using the concept of product complexity, sectors were grouped using the Complex Products and Systems (CoPS) typology: non-complex products, complex products, and systems. Findings Motivations to servitize were categorised as competitive, demand-based (i.e., derived from the customer) or economic. Motivations to servitize vary according to product complexity, although cost savings and improved service quality appear important demand-based motivations for all manufacturers. Non-complex product manufacturers also focus on services to help product differentiation. For CoPS manufacturers, both risk reduction and developing a new revenue stream were important motivations. For uniquely complex product manufacturers, stabilising revenue and increased profitability were strong motivations. For uniquely systems manufacturers, customers sought business transformation, whilst new service business models were also identified. Research limitations/implications Using the CoPS typology, this study delineates motivations to servitize by sector. The findings show varying motivations to servitize as product complexity increases, although some motivational commonality existed across all groups. Manufacturers may have products of differing complexity within their portfolio. To overcome this limitation the unit of analysis was the SBU. Practical implications Managers can reflect on and benchmark their motivation for, and opportunities from, servitization, by considering product complexity. Originality/value The first study to categorise servitization motivations by product complexity. Identifying that some customers of systems manufacturers seek business transformation through outsourcing

Application of Artificial Neural Network in Process Safety Assessment

Quantitative risk assessment is a crucial step in safety analysis of process systems. Advancement of modern technologies has resulted in availability of large volume of process data. This tendency urges the need of developing new risk assessment approaches. Fault tree (FT), a conventional risk analysis method, is found to be ineffective in dynamic risk analysis and data analytics due to its static nature and reliance on experts‟ judgment in developing stage. The use of artificial neural network (ANN) in risk assessment of process systems is not a new concept. ANN is a structured model that is built upon data samples and learning algorithms to process complex input/output data in the way that it is trained. The application of ANN can help to overcome some of the limitations of FT. The dynamic and data-driven nature, independency on prior information on events relationships, and less reliance on experts‟ judgement are the advantages of ANN over FT. However, there is limited work on the development of ANN-based risk assessment models using the conventional methods such as FT as an informative base. This study proposes a methodology of mapping FT into ANN to support convenient and effective application of ANN in risk assessment. The proposed method is demonstrated through its application to failure analysis of one of the causes of Tesoro Anacortes Refinery accident. The results of network‟s accident modelling performance have shown that the ANN model (mapped from the FT) is an effective risk assessment technique in terms of application for estimation of the TE failure probability

Motivations for servitization: The impact of product complexity

Purpose – The purpose of this paper is to identify the commonalities and differences in manufacturers’ motivations to servitise. Design/methodology/approach – UK study based on interviews with 40 managers in 25 companies in 12 sectors. Using the concept of product complexity, sectors were grouped using the Complex Products and systems (CoPS) typology: non-complex products, complex products and systems. Findings – Motivations to servitise were categorised as competitive, demand based (i.e. derived from the customer) or economic. Motivations to servitise vary according to product complexity, although cost savings and improved service quality appear important demand-based motivations for all manufacturers. Non-complex product manufacturers also focus on services to help product differentiation. For CoPS manufacturers, both risk reduction and developing a new revenue stream were important motivations. For uniquely complex product manufacturers, stabilising revenue and increased profitability were strong motivations. For uniquely systems manufacturers, customers sought business transformation, whilst new service business models were also identified. Research limitations/implications – Using the CoPS typology, this study delineates motivations to servitise by sector. The findings show varying motivations to servitise as product complexity increases, although some motivational commonality existed across all groups. Manufacturers may have products of differing complexity within their portfolio. To overcome this limitation the unit of analysis was the strategic business unit. Practical implications – Managers can reflect on and benchmark their motivation for, and opportunities from, servitisation, by considering product complexity. Originality/value – The first study to categorise servitisation motivations by product complexity. Identifying that some customers of systems manufacturers seek business transformation through outsourcing

A stochastic approach for product costing in manufacturing processes

Nowadays, manufacturing companies are characterized by complex systems with multiple products being manufactured in multiple assembly lines. In such situations, traditional costing systems based on deterministic cost models cannot be used. This paper focuses on developing a stochastic approach to costing systems that considers the variability in the process cycle time of the different workstations in the assembly line. This approach provides a range of values for the product costs, allowing for a better perception of the risk associated to these costs instead of providing a single value of the cost. The confidence interval for the mean and the use of quartiles one and three as lower and upper estimates are proposed to include variability and risk in costing systems. The analysis of outliers and some statistical tests are included in the proposed approach, which was applied in a tier 1 company in the automotive industry. The probability distribution of the possible range of values for the bottleneck’s cycle time showcase all the possible values of product cost considering the process variability and uncertainty. A stochastic cost model allows a better analysis of the margins and optimization opportunities as well as investment appraisal and quotation activities.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 39479; Funding Reference: POCI-01-0247-FEDER-39479]