Ram Babu Roy

Identifying influential stock indices from global stock markets: A social network analysis approach

Abstract We have proposed a method to rank the stock indices from across the globe using social network analysis approach. The temporal evolution of correlation network and Minimum Spanning Tree (MST) of global stock indices have been analyzed using weekly returns of 93 stock indices for five-year period from the year 2006 through 2010 obtained from Bloomberg. We have chosen this period to study the behaviour of the stock market network before and after the collapse of Lehman Brothers in the USA. Our study attempts to answer the questions about identifying the most influential stock indices in the global stock market, regional influence on the comovement of stock indices, and the impact of the collapse of Lehman Brothers in the USA and the associated global financial crisis that followed on the dynamics of stock market network.

A Social Network Approach to Examine the Role of Influential Stocks in Shaping Interdependence Structure in Global Stock Markets

This paper investigates the role of influential stocks in shaping the emergent system-level interdependence in global stock markets using a large set of stocks selected from major stock market indices from across the globe. We have proposed a method to identify influential stocks using various centrality measures used in social network analysis literature. Our study shows how these influential stocks provide key linkages in integrating the global stock markets as an interconnected system. We have also shown that the regional influence dominates over the economic sector influence in shaping the topological structure of stock market network. The study also captures the change in the topology of this network following the collapse of Lehman Brothers.

A social network approach to change detection in the interdependence structure of global stock markets

A novel method is proposed to rank the stock indices from across the globe to capture changes in the dominance of an index with respect to other indices. A correlation-based network structure is formulated and centrality measures are used to track these changes. Temporal evolution of the minimum spanning tree derived from the network of 93 stock indices worldwide has been analyzed with data from Bloomberg for the 5-year period from year 2006 through 2010. Measures are suggested for identifying dominant stock indices in the global stock market. It is investigated how the stock market turbulence can be detected by measuring the relative change in the ranks of the stock indices and in the network centralization of the emergent network structure. The study reveals how inclusion of abstract non-living entities such as stock indices in the social network analysis framework can capture the latent interdependence as manifested in the stock market. The chosen period of study encompassed the behavioral change in the stock market network before and after the collapse of Lehman Brothers in the USA, revealing interesting counter-intuitive findings that the turbulence following the collapse of Lehman Brothers had a structure-loosening impact on the global stock market.

Equity-constrained dispatching models for emergency medical services

Purpose The purpose of this paper is to apply agent-based modeling and simulation concepts in evaluating different approaches to solve ambulance-dispatching decision problems under bounded rationality. The paper investigates the effect of over-responding, i.e. dispatching ambulances even for doubtful high-risk patients, on the performance of equity constrained emergency medical services. Design/methodology/approach Agent-based modeling and simulation was used to evaluate two different dispatching policies: first, a policy based on maximum reward, and second, a policy based on the Markov decision process formulation. Four equity constraints were used: two from the patients’ side and two from the providers’ side. Findings The Markov decision process formulation, solved using value iteration method, performed better than the maximum reward method in terms of number of patients served. As the equity constraints conflict with each other, at most three equity constraints could be enforced at a time. The study revealed that it is safe to over-respond if there is uncertainty in the risk level of the patients. Research limitations/implications Further research is required to understand the implications of under-responding, where doubtful high-risk patients are denied an ambulance service. Practical implications The need for good triage system is apparent as over-responding badly affects the operational budget. The model can be used for evaluating various dispatching policy decisions. Social implications Emergency medical services have to ensure efficient and equitable provision of services, from the perception of both patients and service providers. Originality/value The paper applies agent-based modeling to equity constrained emergency medical services and highlights findings that are not reported in the existing literature.

Integrating knowledge, attitude and practice in decision model for maternity care in emerging economy

Knowledge, attitude and practice (KAP) of stakeholders have high impact on the healthcare decisions of individuals. This paper investigates empirically the influence of KAP on rural pregnant womens health decisions. Maternal deaths might be reduced when we understand the associations of KAP on maternal health. It can be utilized by the organizations in policy making and to design services to reduce the risks during and after pregnancy. The study will contribute in designing solutions to achieve Millennium Development Goal 5, to reduce maternal deaths, which is currently lagging behind its target.

The Service Machine

We elaborate the metaphorical concept of Service Machine. An analogy between a Service Machine and a physical machine has been discussed with the help of a typical physical machine. A Service Machine is based on a fundamental demand-supply –constellation: what needs to be done and what can be done with the available means and circumstances. It must be deployed by users to get the job done. It can plan and execute service processes repeatedly upon demand. A template for designing a Service Machine is discussed along with some examples in service industry. The Service Machine template is a structured set of questions used to describe and analyze a service production system. This set of questions will guide a system designer to elaborate the description of individual building blocks of the service machine in a given context. The relationship between components of the Service Machine template is dynamic, i.e., the change in one of the component will affect other components. The Service Machines can be interconnected to form larger service production systems.

Smartphone-Based Ubiquitous Data Sensing and Analysis for Personalized Preventive Care: A Conceptual Framework

The burden of chronic diseases is rising and it is increasing the mortality rate, morbidity rate, and healthcare cost. To shift from reactive care to preventive care is inevitable. The concept of eHealth is buzzing around for a considerable time but it is not utilized in preventive care. It inspires us to do a literature survey of some of the recent seminal research papers on ubiquitous data sensing and behavioral interventions to promote personal wellness. As the outcome of this survey, the research challenges and opportunities in this domain are presented. The possible research objective and research questions are framed for further research in this field. Based on the knowledge gained from the survey analysis, a novel personalized behavior feedback-cum-intervention framework using smartphone-based data sensing is presented.

The Conceptual Tools

A set of generic thinking tools is required to understand and develop a concept. New concepts are always built upon existing knowledge bases. In this chapter, we discuss various generic thinking tools used for designing and developing the concept of Service Machine. As a service production system designer, one should understand various definitions of services, the concepts of systems and systems thinking to visualize different abstraction levels of a system from micro to macro, and relevant emerging technologies. Technology means a systematic and purposeful attempt to accomplish something based on knowledge about the underlying phenomena, be they natural, biological, or social. Knowledge has three aspects: ontology, epistemology, and dynamics. With knowledge, technologies can be developed. Service machine is proposed as a conceptual tool for understanding the way service production system is organized in a business. A service machine should be designed using socio-technical approach in order to achieve higher productivity and satisfaction of stakeholders. We also discuss how the service machines are related to the existing concept of Business Model Canvas (BMC). This would be helpful in improving existing services and creating new service businesses.

A mobile app based smoking cessation assistance using automated detection of smoking activity

The rising prevalence of non-communicable diseases like cardiovascular diseases, stroke, chronic obstructive pulmonary disease, cancer is a serious threat to the society. Tobacco smoking is one of the most prevailing risk factors. Due to its addictiveness, it is often very difficult to quit. Abstinent smokers often start with a sudden craving for smoking which results in lapse and then permanent relapse. In this paper, we propose a sensor-based approach for automated recognition of smoking activity. That may be used for providing interventions in near real-time via mobile app to promote smoking cessation. A 6-axis inertial sensor along with a heart rate sensor is to be used to develop a wearable band which could be worn on the wrist. A pilot study is conducted with four participants. Their hand movement pattern is recorded for around 5 minutes for smoking and non-smoking intervals each, using a sensor based unit worn on the wrist. This period includes smoking and non-smoking intervals. Preliminary analysis of the data shows that there exists a periodicity in the data during the smoking episode. During non-smoking interval the sensor signals are random and does not exhibit such periodicity. Further data collection with more number of participants in different environments, data preprocessing, analysis, training, model generation, and testing is under progress. Preliminary results of this pilot study have been discussed.

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.