Dataset Information

Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity.

ABSTRACT: In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.

SUBMITTER: Xu R

PROVIDER: S-EPMC5286437 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity.

Xu Ronghua R Wong Wing-Keung WK Chen Guanrong G Huang Shuo S

Scientific reports 20170201

In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understand ...[more]

PMID: 28145494

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Project description:ImportanceWith rapid urbanization, understanding the role of residential environments in the development of depression has gained importance. However, the potential associations of residential space and density with depression have been understudied.ObjectiveTo investigate longitudinal associations of residential livable space and density with depression.Design, setting, and participantsThis longitudinal cohort study used data from 2 waves of the FAMILY Cohort, a population-based cohort in the Hong Kong Special Administrative Region, China, recruited between February 28, 2009, and March 28, 2011, at baseline and followed up between August 3, 2011, and June 19, 2013, at wave 2. Data were analyzed from September 1, 2020, through August 10, 2021. A total of 16 968 participants aged 16 years or older underwent assessment using the Patient Health Questionnaire-9 (PHQ-9) and had complete data across all variables.ExposuresResidential density was objectively assessed at 3 scales (within apartment, building block, and at neighborhood level) at baseline and follow-up.Main outcomes and measuresDepressive symptoms were defined in terms of a PHQ-9 threshold greater than or equal to 5 and probable major depression with a cutoff value greater than or equal to 10. A continuous PHQ-9 score was used as a secondary outcome. Multilevel logistic regression models were used to examine associations of the residential density with probable major depression and depressive symptoms adjusted for time-varying sociodemographic and lifestyle factors, comorbidities, and other environment variables.ResultsOf the 16 968 participants with complete data, the mean (SD) age was 45.5 (16.7) years, and 9328 participants (55.0%) were women at baseline. The mean follow-up was 2.2 years (range, 1.3-3.6 years). At the household level, after full adjustments, each IQR increment in residential livable area was associated with lower odds of probable major depression (adjusted odds ratio [aOR], 0.84; 95% CI, 0.71-0.98; P = .03) and depressive symptoms (aOR, 0.93; 95% CI, 0.86-1.00; P = .04). At the building-block level, each IQR increment in block density was associated with higher odds of depressive symptoms (aOR, 1.11; 95% CI, 1.01-1.22; P = .03), but only in single-housing environment models. The results remained consistent in models with continuous PHQ-9 evaluation.Conclusions and relevanceThis study's findings suggest that residential livable space and block density may play a role in the development of depression. Integrated mental health and housing policies promoting mental capital in cities, such as health-optimized density at the household and building-block levels, might serve as a population approach to reduce the burden of depression.

Project description:BACKGROUND:Chromosomal microarray (CMA) has been shown to be cost-effective over karyotyping in invasive prenatal diagnosis for pregnancies with fetal ultrasound anomalies. Yet, information regarding preceding and subsequent tests must be considered as a whole before the true cost-effectiveness can emerge. Currently in Hong Kong, karyotyping is offered free as the standard prenatal test while genome-wide array comparative genome hybridization (aCGH), a form of CMA, is self-financed. A new algorithm was proposed to use aCGH following quantitative fluorescent polymerase chain reaction (QF-PCR) as primary test instead of karyotyping. This study aims to evaluate the cost-effectiveness of the proposed algorithm versus the current algorithm for prenatal diagnosis in Hong Kong. METHODS:Between November 2014 and February 2016, 129 pregnant women who required invasive prenatal diagnosis at two public hospitals in Hong Kong were prospectively recruited. The proposed algorithm was performed for all participants in this demonstration study. For the cost-effectiveness analysis, cost and outcome (diagnostic rate) data were compared with that of a hypothetical scenario representing the current algorithm. Further analysis was performed to incorporate women's willingness-to-pay for the aCGH test. Impact of government subsidies on the aCGH test was explored as a sensitivity analysis. RESULTS:The proposed algorithm dominated the current algorithm for prenatal diagnosis. Both algorithms were equally effective but the proposed algorithm was significantly cheaper (p ? 0.05). Taking into account women's willingness-to-pay for an aCGH test, the proposed algorithm was more effective and less costly than the current algorithm. When the government subsidy reaches 100%, the maximum number of diagnoses could be made. CONCLUSION:By switching to the proposed algorithm, cost saving can be achieved whilst maximizing the diagnostic rate for invasive prenatal diagnosis. It is recommended to implement aCGH as a primary test following QF-PCR to replace the majority of karyotyping for prenatal diagnosis in Hong Kong.

Dataset Information

Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity.

Publications

Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity.

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