Corporate Governance and Value: A Principal Component Approach

2014 ◽  
Author(s):  
Guy Schofield
Keyword(s):  
Corporate Governance ◽  
Principal Component ◽  
Principal Component Approach ◽  
Component Approach

Polarization analysis of high-frequency, three-component seismic data

1991 ◽  
Vol 81 (2) ◽  
pp. 622-642
Author(s):  
K. Bataille ◽  
J. M. Chiu
Keyword(s):  
High Frequency ◽  
Time Windows ◽  
Random Noise ◽  
Polarization State ◽  
Principal Component ◽  
Small Time ◽  
Body Waves ◽  
Local Data ◽  
Principal Component Approach ◽  
Component Approach

Abstract We present a method to determine the polarization of body waves from three-component, high-frequency data and examples of its application. The method is based on the principal component approach. One advantage of this approach is that the polarization state can be determined for small time windows compared with the predominant period of the wave. This is particularly useful for identifying converted waves within the crust. The stability of the result is analyzed with synthetic cases by adding simultaneous arrivals from waves and random noise. The method works well with both synthetic and local data in the detection of the polarization of the wave by separating arrivals from different directions. From the local data, some seismic phases related to crustal conversions are observed that require strong lateral variations.

scholarly journals A principal component approach to measuring investor sentiment in China

2014 ◽  
Vol 14 (4) ◽  
pp. 573-579 ◽  
Author(s):  
Haiqiang Chen ◽  
Terence Tai Leung Chong ◽  
Yingni She
Keyword(s):  
Investor Sentiment ◽  
Principal Component ◽  
Principal Component Approach ◽  
Component Approach

scholarly journals The Evolution of Financial Integration on Selected European Stock Markets: a Dynamic Principal Component Approach

2017 ◽  
Vol 20 (4) ◽  
pp. 45-63 ◽  
Author(s):  
Elżbieta Majewska ◽  
Joanna Olbryś
Keyword(s):  
Stock Markets ◽  
Financial Integration ◽  
Global Financial Crisis ◽  
Principal Component ◽  
The European Union ◽  
Principal Component Approach ◽  
Component Approach ◽  
The Global Financial Crisis ◽  
European Stock Markets ◽  
European Markets

The goal of this paper is to recognize the dynamics of financial integration across the European stock markets over the last two decades. We investigate two groups of markets: (1) three developed European markets in the U.K., France, and Germany; and (2) three emerging Central and Eastern European markets in Poland, the Czech Republic, and Hungary (CEE–3). The evolution of the integration process is analyzed using a dynamic principal component approach. The index of integration serves as a robust measure of integration. The empirical results reveal that the dynamics of integration across the whole group of markets increased significantly following the CEEC–3’s accession to the European Union. An inverted U‑shape in the index of integration has been found in this case. Moreover, the average index of integration was significantly different during the Global Financial Crisis compared to the pre‑crisis period. 

scholarly journals Different Patterns of the SARS-CoV-2 Epidemic across Italian Regions: a Hierarchical Clustering on Principal Components approach

2020 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Guido Basile ◽  
Antonella Agodi
Keyword(s):  
Hierarchical Clustering ◽  
Principal Component ◽  
Control Measures ◽  
Cluster Solution ◽  
Italian Region ◽  
Testing Strategies ◽  
Italian Regions ◽  
Principal Component Approach ◽  
Component Approach ◽  
Optimal Cluster

Abstract Italy has experienced the epidemic of severe acute respiratory syndrome coronavirus 2, which spread at different times and with different intensities throughout its territory. We aimed to identify clusters with similar epidemic patterns across Italian regions. To do that, we defined a set of regional indicators reflecting different domains and employed a hierarchical clustering on principal component approach to obtain an optimal cluster solution. As of 24 April 2020, Lombardy was the worst hit Italian region and entirely separated from all the others. Sensitivity analysis - by excluding data from Lombardy - partitioned the remaining regions into four clusters. Although cluster 1 (i.e. Veneto) and 2 (i.e. Piedmont and Emilia-Romagna) included the most hit regions beyond Lombardy, this partition reflected differences in the efficacy of restrictions and testing strategies. Cluster 3 was heterogeneous and comprised regions where the epidemic started later and/or where it spread with the lowest intensity. Regions within cluster 4 were those where the epidemic started slightly after Veneto, Emilia-Romagna and Piedmont, favoring timely adoption of control measures. Our findings provide policymakers with a snapshot of the epidemic in Italy, which might help guiding the adoption of countermeasures in accordance with the situation at regional level.

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