Nonlinear properties of the Korea fund market

Kim, Hongseok; Yim, Kyubin; Kim, Seunghwan; Oh, Gabjin

doi:10.3938/jkps.67.2039

Cited by 7 publications

(2 citation statements)

References 20 publications

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“… 18 Additionally, other studies examining the accessibility, such as time‐to‐treatment in cancer care, and efforts to evaluate cancer treatment processes or patterns, such as cancer diagnosis and comparison between hospitals providing cancer treatment, are insufficient. 19 , 20 , 21 , 22 Therefore, very few studies have examined aspects of cancer care from the perspective of the patient and the effect of cancer care patterns on health outcomes in South Korea.…”

Section: Introductionmentioning

confidence: 99%

Cancer care patterns in South Korea: Types of hospital where patients receive care and outcomes using national health insurance claims data

Choi

Kim

et al. 2023

Cancer Medicine

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Background: Although strengthening coverage has improved cancer care, there are concerns related to medical distortion. Previous studies have only examined whether patients visit a specific hospital, and not the continuum of patients with cancer, resulting in a lack of evidence in South Korea. This study aimed to investigate the patterns in hospital type for cancer care and analyze their association with outcomes. Methods:The data for this study were obtained from the National Health Insurance Services Sampled Cohort database. This study included patients with four types of cancer (top four cancer incidence in 2020): gastric (3353), colorectal (2915), lung (1351), and thyroid (5158) cancer. The latent class mixed model was used to investigate cancer care patterns, and multiple regression or survival analysis was performed to examine medical cost, length of stay (LOS), and mortality. Results:The patterns in each cancer type were classified into two to four classes, namely, mainly visited clinics or hospitals, mainly visited general hospitals, mainly visited tertiary hospitals (MT), and tertiary to general hospitals through trajectory modeling based on the utilization of cancer care. Compared to the MT pattern, other patterns were generally associated with higher cost, LOS, and mortality. Conclusion:The patterns found in this study may be a more realistic way of defining patients with cancer in South Korea compared to previous studies, and its

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Section: Introductionmentioning

confidence: 99%

Cancer care patterns in South Korea: Types of hospital where patients receive care and outcomes using national health insurance claims data

Choi

Kim

et al. 2023

Cancer Medicine

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“…28 December 1993 to 18 September 2009, soy meal from 17 July 2000 to 18 September 2009, No. 1 soybean from 15 March 2002 to 18 September 2009, and corn from 22 September 2004 to18 September 2009) in China (it is more complicated for the USA commodity futures)[809], the 1-min realized volatility of SSEC from 4 January 2000 to 31 December 2007[928], the daily returns of CIB-CNY Composite Index (CCI) compiled by the China Industrial Bank Research from 21 July 2005 to 30 June 2008[781], the daily returns of 144 Korean equity and balanced funds from 1 January 2002 to 31 December 2010[754], the daily returns of the CSI energy sub-industry index from 4 January 2005 to 15 June 2015[756], and the daily volume changes of the Moroccan MASI index from 1 January 2000 to 20 January 2017[896].There are also examples showing that∆α > ∆α FT > ∆α shuf ,(403)such as the 5 min returns of the SSEC and SZCI from 4 January 2002 to 31 December 2008 [832], the daily returns of the world gold prices from 1968 to 2010 [787], the daily logarithmic variations of individual and institutional traders' trading volume in Tehran Stock Exchange from 1998 to 2009 [847], the daily exchange rates of CAD/USD, JPY/USD, USD/GBP, DKK/USD, NOK/USD, MXN/USD, BRL/USD, INR/USD and THB/USD [611], the daily returns of CIB-CNY Composite Index from 30 June 2008 to 18 June 2010 [781], the 10-minute returns of the CSI 300 futures IF1009 from 16 April 2010 to 17 September 2010 [766], the daily spot rates of ships in tanker markets from 27 January 1998 (or 1 July 2004 or 15 July 2005) to 5 August 2013 [695], the daily returns of WTI oil and Baltic Exchange Dirty Tanker Index from 4 January 2000 to 4 November 2015 [875], and the daily returns of the Moroccan MASI index from 1 January 2000 to 20 January 2017 [896]. The impacts of different sources of apparent multifractality could change for different sample periods.…”

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confidence: 99%

Multifractal analysis of financial markets

Jiang,

Xie,

Zhou

et al. 2018

Preprint

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Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.

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Asymmetric joint multifractal analysis in Chinese stock markets

Chen

Zheng

2017

Physica A: Statistical Mechanics and its Applications

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Nonlinear properties of the Korea fund market

Cited by 7 publications

References 20 publications

Cancer care patterns in South Korea: Types of hospital where patients receive care and outcomes using national health insurance claims data

Cancer care patterns in South Korea: Types of hospital where patients receive care and outcomes using national health insurance claims data

Multifractal analysis of financial markets

Asymmetric joint multifractal analysis in Chinese stock markets

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