Understanding the pattern of the BSE Sensex

Mukherjee, Indranil; Chatterjee, Soumya; Giri, Anju; Barat, P.

doi:10.1016/j.physa.2017.04.026

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…A complex adaptive system viewpoint makes it possible to apply modern scientific tools to study such vast and diverse system as a market, studying which otherwise is a very challenging task. The use of modern scientific methods to study finance is a relatively new approach [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] and several such studies have successfully characterized many different aspects of markets at different time scales [12] , [13] , [14] , [15] , [16] .…”

Section: Introductionmentioning

confidence: 99%

Inner composition alignment networks reveal financial impacts of COVID-19

Upadhyay

Mukherjee

Panigrahi

2023

Physica A: Statistical Mechanics and its Applications

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Section: Introductionmentioning

confidence: 99%

Inner composition alignment networks reveal financial impacts of COVID-19

Upadhyay

Mukherjee

Panigrahi

2023

Physica A: Statistical Mechanics and its Applications

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“…The ordinary PCA method only focuses on dominant PCs, and ignores the rest of the components, in order to reduce the large dimensionality of the data sets. In most cases, the proportion threshold is set as 80%-90% [ 3 , 4 ]. In other cases, the researchers cannot find exact implications of the high order PCs, respectively, so they consider them as noise components [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, the proportion threshold is set as 80%-90% [ 3 , 4 ]. In other cases, the researchers cannot find exact implications of the high order PCs, respectively, so they consider them as noise components [ 4 , 5 ]. PCA pays little attention to the economic meanings of the rest components and just removes the rest components without further analysis [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

General Component Analysis (GCA): A new approach to identify Chinese corporate bond market structures

Wang

Yan

et al. 2018

PLoS ONE

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PCA has been widely used in many fields to detect dominant principle components, but it ignores the information embedded in the remaining components. As a supplement to PCA, we propose the General Component Analysis (GCA). The inverse participation ratios (IPRs) are used to identify the global components (GCs) and localized components (LCs). The mean values of the IPRs derived from the shuffled data are taken as the natural threshold, which is exquisite and novel. In this paper, the Chinese corporate bond market is analyzed as an example. We propose a novel network method to divide time periods based on micro data, which performs better in capturing the time points when the market state switches. As a result, two periods have been obtained. There are two GCs in both periods, which are influenced by terms to maturity and ratings. Besides, there are 382 LCs in Period 1 and 166 LCs in Period 2. In the LC portfolios there are two interesting bond collections which are helpful to understand the thoughts of the investors. One is the supper AAA bond collection which is believed to have implicit governmental guarantees by the investors, and the other is the overcapacity industrial bond collection which is influenced by the supply-side reform led by the Chinese government. GCA is expected to be applied to other complex systems.

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