Feature extraction using random matrix theory approach

Author(s):  
Viktoria Rojkova ◽  
Mehmed Kantardzic
2013 ◽  
Vol 88 (2) ◽  
Author(s):  
Martin Schmidt ◽  
Tsampikos Kottos ◽  
Boris Shapiro

2001 ◽  
Vol 299 (1-2) ◽  
pp. 175-180 ◽  
Author(s):  
V. Plerou ◽  
P. Gopikrishnan ◽  
B. Rosenow ◽  
L.A.N. Amaral ◽  
H.E. Stanley

2008 ◽  
Vol 11 (05) ◽  
pp. 655-668 ◽  
Author(s):  
RICARDO COELHO ◽  
PETER RICHMOND ◽  
STEFAN HUTZLER ◽  
BRIAN LUCEY

Correlations of stocks in time have been widely studied. Both the random matrix theory approach and the graphical visualization of so-called minimum spanning trees show the clustering of stocks according to industrial sectors. Studying the correlation between stocks traded in markets of different countries, we show that the random matrix theory approach is able to separate stocks according to their geographical location, provided that they are not strongly correlated. These results are compared with the results from random time series created using the market model, where the main factor is the mean of returns of the stocks of each sector.


2017 ◽  
Vol 118 (4) ◽  
Author(s):  
Huanan Li ◽  
Suwun Suwunnarat ◽  
Ragnar Fleischmann ◽  
Holger Schanz ◽  
Tsampikos Kottos

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