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(1)

Œ

£

?ù o ÞS X ê s; c" e / n ÇX ~ ¿} º{ ¢] k ù8 ý Ž Ò ÞW Ä] K ¡ à à Å× D; c å ¾ ˔ X ¢ Ž ì ŏ Œ



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Â

Ò â @ /† < Ɠ §  ƒ  õ † < Æ@ /† < Æ Ó ü t o † < Æõ ,  Òí ß – 608-737

(2011¸   8 Z 4 4{ 9  ~ à Î6 £ §, 2011¸   8 Z 4 30{ 9  à º& ñ ‘ : r ~ à Î6 £ §, 2011¸   10 Z 4 5{ 9  > F  S X ‰& ñ )

‘ :

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¨ 8

Š â \     È Ò _  & ñ Õ þ ˜s  r ç ß –s   â õ  # Œ    o | ¨ c à º e ”  . F KÖ 6 x r  © œ\ " f # Œ Q l \ O Õ ªÒ  ¨ \  @ / ô

 Ç Å Òd ” _  à ºe ” Ò  ¦ – РÒ'  } Œ •' Ÿ § > =s  : r`  ¦ & h 6   x # Œ  H “ ¦Ä »u \  ¦ ½ ¨ # Œ r ç ß –\    É r ”   o– РÒ'  l \ O  Õ

ªÒ  ¨ s    ½ + Ë÷ &   Ô  æ õ  ) a  . : £ ¤ y  l \ O Õ ªÒ  ¨ ç ß –_   © œ  ñ ƒ  › ' a$ í `  ¦ ŠҖ Ð 1 l x% i † < Æ& h “   ”   o\  ¦ : Ÿ x K " f ƒ  

½

¨ # Œ, W 1t  r ç ß –ç ß –  \ " f Y O w כ ¹™ è— ¸+ þ A_  1 l x% i † < Æ& h  l \ O Õ ªÒ  ¨+ þ AI \  ¦ Æ Ò& h  ì  r$ 3 ô  Ç .   õ & h Ü ¼– Ð

¿

º  P :  H “ ¦Ä » 7 ˜'   H r ç ß –_  ”   o\    É r ¨ 8 Š ⠁   o\  2 [€  •† < Ê`  ¦ ˜ Ðs “ ¦ e ”  .

Ù þ

˜d ” # Q: à ºe ” Ò  ¦, Y O w כ ¹™ è— ¸+ þ A, } Œ •' Ÿ § > =s  : r,  © œ s  7 £ x Ý ¶  A ™ è

Dynamical Evolution of Multifactor Models in Financial Markets

Gychang Lim · Kyungsik Kim

Department of Physics, Pukyong National University, Busan 608-737 (Received 4 August 2011 : revised 30 August 2011 : accepted 5 October 2011)

We investigate the dynamical evolution of business groups by using the multifactor model in financial markets. The traders can be classified according to the amount of capital employed in investment. Their strategies may change over time because of changes in information and in the environment. The stocks making up a financial market can be partitioned into several business groups, and each business sector is obtained and mapped by using the larger eigenvalue derived from the random matrix theory. In this work, we examine the dynamical evolution of a correlation- based cluster of stocks, which is usually consistent with a business group. By segmenting the whole time series into several overlapping segments, we trace the dynamical evolution of each business group by using the multi-factor model and treat the variation of business groups by the evolution of data. Through our result, we show that the second largest eigenvector is comparatively vulnerable to changes in the environment over time.

PACS numbers: 05.10.-a, 05.45.Df, 89.75.-k, 89.90.+n

Keywords: Financial Times Stock Exchange, Return, Multifactor model, Random matrix theory

E-mail: [email protected]

-966-

(2)

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ü

t s  & h 6   x ÷ &# Q V , >  ƒ  ½ ¨÷ &# Q M ® o   [1–3]. F KÖ 6 x r  © œ\ " f '

Ÿ 0 A  < ʓ É r ½ ¨$ í כ ¹™ è  H = å Se ” \ O s  r ç ß –_  ”   o\     1

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>

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

© œ_   @ / o– Ð “   # Œ È Ò  F K“ É r È Ò \  _ K  > h“   _

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#

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õ  כ ¹1 l x õ  ƒ  › ' a ÷ &# Q ƒ  ½ ¨ ‚  ' Ÿ ÷ &# Q   ) a  “ ¦  « Ñ

 )

a   [4,5].

F

KÖ 6 x r  © œ\ " f  Œ ™F & h  0 A+ « >`  ¦ › ¸& ñ l  0 AK " f ´ ú §“ É r Å Ò d ”

`  ¦ ì ø Í% ò ô  Ç  í ß –½ ¨$ í (portfolio)_  % ò % i s  È Ò ü <   Ø

Ô>  ” > r F ô  Ç . ˜ Ð: Ÿ x  í ß –½ ¨$ í _  $ í ì  r[ þ t“ É r p A 0 A+ « >s  ]

jô  Ç& h Ü ¼– Ð y Œ ™™ èK   Ù ¼– Ð  © œ  ñ q  › ' aº  $ í _  Å Òd ” Ü ¼– Ð

½

¨$ í ô  Ç . # Œç ß – ‚  • ¸ A , ‚  Ó ü t, `  v‚  , Û ¼ € כ Ž õ  ° ú  “ É r  Ò q

t © œ¾ ¡ §(derivative)`  ¦  6   x # Œ 0 A+ « >`  ¦ › ¸] X    H ~ ½ ÓZ O `  ¦ y

© œ½ ¨½ + É Ã º e ”   [6]. Ò q t © œ¾ ¡ § _   A   H F KÖ 6 x r  © œ_  4 Ÿ ¤ ¸ ú š

$ í

`  ¦ 7 £ x r v  9, s  4 Ÿ ¤ ¸ ú š$ í `  ¦ K    l 0 Aô  Ç D h– Ðî  r È Ò



& ñ Õ þ ˜s  € 9 כ ¹  .

>

h“  ,  r , F Z O , ² D G _   l  › ¸f ”  o  1 l x“ É r { 9  © œ6   x

¾

¡

§   , Å Òd ”   , ‚  Ó ü t   , ü @ ¨ 8 ŠÖ  ¦ õ  ° ú  “ É r  r & h  X <s  '

 + þ AI \  ƒ  › ' a r & " f ƒ  ½ ¨÷ &# Q M ® o  . F KÖ 6 x r  © œ\ " f # Œ Q

t  `  v‚  _  J ‡  _  $ í | 9 `  ¦ ³ ð‰ & ³   H ×  æ כ ¹ô  Ç % i ½ + É`  ¦ 



 H : Ÿ x > & h “   € ª œ[ þ t s  ” > r F ô  Ç . Õ ª×  æ \ " f ×  æ כ ¹ô  Ç : Ÿ x > & h 

“

  € ª œ×  æ \     H  r [ þ t  s _  › ' a > \  ¦   ? /  H  l 



© œ › ' a(autocorrelation) s  . >   t F K  t  F KÖ 6 x r  © œ\ 

"

f | 9 é ß –  1 l x \  % ò † ¾ Ó`  ¦ Šҍ  H } Œ •' Ÿ § > =s  : r(random matrix theory) [7]`  ¦  6   x # Œ ´ ú §“ É r ƒ  ½ ¨ [ þ t s       1 l x _   © œ

› '

a› ' a > \  ¦  7 H _  # Œ M ® o   [8–11].  f ”  ö &õ † < Æ, d ”  © œ† < Æ, @ / l

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 Dz D G Å Òd ” r  © œ_  r > \ P  X <s ' – РÒ'  } Œ •' Ÿ § > =_  “ ¦Ä » © œ I

\  @ /6   x ~ ½ ÓZ O `  ¦ • ¸{ 9 ½ + É Ã º e ” Ü ¼ 9, s  Qô  Ç  â Ä º\  „  í ß – r

Ð 3 x ? /l – РÒ'    H‘ : r& h Ü ¼– Ð  r [ þ t ç ß –_  ƒ  › ' a$ í  © œI \  ¦ D

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ô

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–

Ð s  : r x 9 r Ó ý t Y Us ‚   ƒ  ½ ¨\  s z  # Œ¸   1 l x î ß – ×  æ כ ¹ô  Ç

%

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z 

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#

Œ ' ‘ • ¸\ O   H Õ ªÓ ü t } © œ— ¸+ þ A_  ƒ  ½ ¨“   “  '  Å ,   r& h  Õ ªÓ ü t }

© œ, õ † < Æ& h  a ž ?§ 4 › ' a > , Ò q t o† < Æ& h  Õ ªÓ ü t } © œ, / B N1 l x $   Õ ªÓ ü t } © œ õ

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 & ñ & h , 1 l x% i † < Æ& h   1 l x[ þ t“ É r a % v“ É r [ j © œ Õ ªÓ ü t } © œs  : r`  ¦ µ 1 Ï

³

ðô  Ç  7 Hë  H \ " f S X ‰ @ /÷ &# Q ƒ  ½ ¨÷ &# Q M ® o “ ¦, Å Òכ ¹  7 H ] j[ þ t“ É r :

Ÿ

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20] ü < Õ ª_  1 l x « Ñ[ þ t \  _ K " f F KÖ 6 x _  # Œ Q t  Ä »+ þ A[ þ t \ 

"

f  6   x ÷ &# Q  7 H _ ÷ &# Q M ® o  .

‘

: r  7 Hë  H“ É r  © œ s  7 £ x Ý ¶  A ™ è(Financial Times Stock Exchange of Shanghai composites) \ " f D h– Ðî  r ~ ½ ÓZ O Ü ¼– Ð }

Œ

•' Ÿ § > =s  : r õ  ƒ  › ' a r & " f " f– Ð | 9 é ß –`  ¦ s À ҍ  H  r [ þ t z  o

_   © œ  ñ ƒ  › ' a$ í `  ¦ ŠҖ Ð r ç ß –  ç ß –  \  @ /ô  Ç D h– Ðî  r ì  r

$

3 ~ ½ ÓZ O `  ¦ ] jr ô  Ç . ] j2] X \ " f  H ‘ : r ƒ  ½ ¨_  s  : r _  @ / © œ

“

  Y O w כ ¹™ è— ¸+ þ A(multifactor model)õ  } Œ •' Ÿ § > =s  : r`  ¦ • ¸ { 9

ô  Ç . ] j3 © œ\ " f  H  © œ s  7 £ x Ý ¶  A ™ è_  l \ O Õ ªÒ  ¨[ þ t



s _   © œ  ñ ƒ  › ' a$ í `  ¦ ŠҖ Ð r ç ß –  ç ß –  \  @ /ô  Ç Ã ºu ì  r

$ 3

`  ¦  7 H _  “ ¦,  t } Œ • ] X \ " f   õ \  ¦ כ ¹€  •ô  Ç .

II. T Â ] ØX ì Ä 9 0ß O Ë

Y O

w כ ¹™ è — ¸+ þ A“ É r Å Òd ”    _  1 l x% i † < Æ  1 l x \  @ /ô  Ç l 

‘

: r& h “   — ¸+ þ As  9, ‘ : r  7 Hë  H \ " f  H  6 £ § õ  ° ú  s  ³ ð‰ & ³ ) a  .

7

£

¤

r

i

(t) = α

i

+

Ng

X

j=1

β

ij

M

j

(t) + ε

i

(t) , (1)

#

Œl " f N

g

  H כ ¹™ è_  à º, α

i

ü < β

ij

  H z  ´ B > h  à ºs  .  

 É

r Å Òd ”  s \  ¸ ú š6 £ § † ½ Ó 

i

(t)“ É r  © œ  ñƒ  › ' a$ í s  \ O  “ ¦ 

&

ñ ô  Ç . N

g

  H “ §  © œ › ' a› ' a > (cross-correlation)\  @ /ô  Ç } Œ •

(3)

'

Ÿ § > =s  : r [7] Ü ¼– РÒ'  ½ ¨ô  Ç “ ¦Ä »u _  à ºs  . s   7 Hë  H \ 

"

f  H Å Òd ” r  © œ „  ^ ‰\  % ò † ¾ Ó`  ¦ p u   H W 1 7 á x À Ó_   © œ  H

“

¦Ä »u \  ¦ “ ¦ 9ô  Ç .

Ä

ºo   H N

g

\  ¦   & ñ l  0 AK " f “ §  © œ › ' a› ' a >  ' Ÿ § > =_  :

£ ¤$ í ~ ½ Ó& ñ d ” _  K \  ¦ ½ ¨ô  Ç . “ §  © œ › ' a› ' a > _  d ” “   C = 1

L GG

T

(2)

Ü

¼– РÒ'  : £ ¤$ í ~ ½ Ó& ñ d ” “ É r

det(C − λ1) = 0 (3) Ü

¼– Ð Å Ò# Q”   . # Œl " f G  H N × L (N “ É r  r à º, L“ É r { 9  Z >

 à ºe ” Ò  ¦ _  X <s ' à º)“   ½ ©   o à ºe ” Ò  ¦ ' Ÿ § > =s  9, G

T

  H

„

 u ' Ÿ § > =s  . Õ ªo “ ¦ } Œ • © œ › ' a ' Ÿ § > =“ É r C

rm

= 1

L AA

T

(4)

Ü

¼– Ð ³ ð‰ & ³÷ & 9, ¨ î ç  H 0 õ  ì  r í ß – 1`  ¦ ° ú   H $ í ì  r a

ij

`  ¦ ° ú 



 H A  H N × L ' Ÿ § > =s  . Q ≡ L/N(> 1)s  “ ¦& ñ  ) a ° ú כ

“

   â Ä º\  N → ∞, L → ∞_  F G ô  Ç\ " f S X ‰Ò  ¦x 9 • ¸† < Êà º“   P

rm

(λ)  H

P

rm

(λ) = Q 2πσ

2

p(λ

+

− λ)(λ − λ

)

λ (5)

Ü

¼– Ð Å Ò# Qt  9, λ ∈ [λ

, λ

+

], λ

ü < λ

+

  H y Œ •y Œ • “ §  © œ › ' a

› '

a >  ' Ÿ § > = C_  þ j™ è, þ j@ / “ ¦Ä »u s  . 7 £ ¤ λ

±

= σ

2

 1 + 1

Q ± 2 r 1 Q



. (6)

כ

¹™ è[ þ t M

j

  H

M

j

(t) ≡

696

X

k=1

hk|λ

j

ir

k

(t) (7)

Ü

¼– Ð > í ß –÷ & 9, k  H “ ¦Ä »u  λ

j

\  @ /6 £ x ) a “ ¦Ä » 7 ˜'  |λ

j

i _ 

—

¸Ž  H $ í ì  r _  ½ + Ës  . ¢ ¸ô  Ç s [ þ t כ ¹™ è  H Å Òd ” r  © œ\  % ò † ¾ Ó

`

 ¦ p u   H y Œ • l \ O Õ ªÒ  ¨`  ¦    · p . } Œ •  © œ › ' a› ' a > – РÒ' 



© œ › ' a› ' a > \  ¦ ½ ¨Z >  l  0 AK " f  6 £ § õ  ° ú  s  “ §  © œ › ' a› ' a >  _

 ì  r Ÿ í\  ¦  6   x ô  Ç :

C =

Nr

X

i=1

λ

i

i

ihλ

i

| +

Ng

X

j=1

λ

j

j

ihλ

j

| + λ

max

max

ihλ

max

|

= C

r

+ C

g

+ C

m

, (8)

#

Œl " f N

r

ü < N

g

  H y Œ •y Œ • } Œ • Òì  r(random part) õ   © œ › ' a› ' a

>

_   Òì  r \  5 Å q   H “ ¦Ä »u _  à º[ þ t s  . 0 A_  [ j† ½ Ó[ þ t`  ¦

‘

: r  7 Hë  H \ " f  H y Œ •y Œ • C

r

, C

g

, C

m

Ü ¼– Ð & ñ _ K " f • ¸{ 9 ô  Ç .



 " f  6 £ §  © œ\ " f  H d ”  (8)_  “ §  © œ › ' a› ' a > _  ì  r Ÿ í– Ð Â

Ò'  l \ O _   © œ › ' a› ' a > \  ¦ % 3 `  ¦ à º e ” Ü ¼ 9, W 1à Ô0 >ß ¼\  ¦ ½ ¨

$ í

K  l \ O   s _   © œ  ñƒ  › ' a$ í `  ¦ r ç ß – s – Ð  7 H _ ½ + É \ V

&

ñ s  .

Fig. 1. (Color online) The density function of the eigen- values of a correlation matrix C is shown in comparison with the theoretical density P

rm

of a Wishart matrix.

The inset shows that the largest eigenvalues is about 33 times larger than the upper bound λ

+

' 2.9.

Fig. 2. (Color online) The density function of cross cor- relation coefficients are presented for C

r

, C

g

, and C

m

. The critical C

is estimated to be about 0.2. At the value, C

r

a C

g

is well separated.

III. • ¤V  4  ˜ m õ m Í A 0V Ä

€

 $  } Œ •' Ÿ § > =s  : r Ü ¼– РÒ'  “ ¦Ä »u \  @ /ô  Ç “ ¦Ä » 7 ˜' – Ð

³

ð‰ & ³ ) a l \ O Õ ªÒ  ¨ _  ”   o\  @ /ô  Ç  1 l x`  ¦ Æ Ò& h , ì  r$ 3 ô  Ç .

X

<s '   H F KÖ 6 x X <s '   H 2004¸   1 Z 4 Ò'  2008¸   12 Z 4  t

_   © œ s  7 £ x Ý ¶  A ™ è\ " f  A   ) a 696 > h  r _  { 9 Z >  X

<s ' s  .

Figure 1“ É r d ” (8)_  > í ß –\  _ K   © œ s  7 £ x Ý ¶  A ™ è

\

" f  A   ) a 696 > h  r \  @ /ô  Ç “ ¦Ä »u _  ì  r Ÿ í\  ¦ ˜ Ðs 

“

¦ e ”  . ? / Ò_  ¶ ú š{ 9 Õ ªa Ë >\ " f  © œ  H “ ¦Ä »u  λ

max

  H λ

+

' 2.9 _  33C & ñ • ¸ ß ¼ . s [ þ t : Ÿ x > | ¾ ÓÜ ¼– РÒ'  } Œ •' Ÿ 

§ >

=s  : r Ü ¼– РÒ'  “ ¦Ä »u \  ¦ ( Ž É Ó'  ¿ 9 ª ? /? /l \  ¦ : Ÿ x K  ì  r$ 3 

€  , — ¸Ž  H “ ¦Ä »u [ þ t ×  æ \  W 17 á x À Ó_   © œ  H “ ¦Ä »u [ þ t s  r

 © œ„  ^ ‰\  % ò † ¾ Ó (market-wide effect)`  ¦ p } 9   כ Ü ¼– Ð  

« Ñ  ) a  .

Figure 2  H “ §  © œ › ' a› ' a > > à º C

r

, C

g

, C

m

\  @ /ô  Ç ì  r Ÿ í [

þ

t s  . e ” > ° ú כ C

' 0.25 \ " f Ô  æ õ ÷ &# Q" f C

r

õ  C

g

 ¸ ú ˜

(4)

Fig. 3. (Color online) The squared magnitude of each component belonging to the eigenvector |λ

max

> corre- sponding to the largest eigenvalue λ

max

. The low contri- butions of components placed in the center is the cause of the distorted distribution of correlation coefficients ob- served in Figure 2.

Fig. 4. (Color online) The whole stock, represented by the eigenvector corresponding to the largest eigenvalue, are monitored. The presence of a bulk of stocks not ef- fected by the market-wide effect is noticeable. (a) shows the composition of stocks belonging to the eigenvector over time, and (b) shows the change of its composition over time. There is no clear change.

½

¨Z >  ) a  . Å Ò3 l q ½ + É   õ   H C

m

_  ì  r Ÿ í+ þ AI s  9, C

ij

> 2

% ò

% i \ " f C

r

õ  C

g

  * $t   H U  ·“ É r < Ì s    è ß – . Fig.

3“ É r C

r

õ  C

g

 " î Ñ þ ˜y  ¸ ú ˜ ½ ¨Z > ÷ & 9, s  : £ ¤$ í “ É r „  \  ˜ Ð

“

¦  ) a ƒ  ½ ¨ [21–25]\ " f ¶ ú ˜( R˜ Ѐ   ¿ º% ò % i s  ½ ¨ì  r ÷ &  H : £ ¤

$ í

s  F KÖ 6 x r  © œ „  ^ ‰\  % ò † ¾ Ó`  ¦ p } 9   כ Ü ¼– Ð Ò q ty Œ •  ) a  .

y

Œ

• l \ O Õ ªÒ  ¨ _  ”   o  1 l x`  ¦   ? /€   „  ^ ‰X <s ' _  r 

>

\ P \ " f 400> h_  r > \ P `  ¦ ô  Ç [ jà Ԗ Ð • ¸{ 9  # Œ % ò % i t 

³

ð(window index)\  ¦ r > \ P `  ¦ r ç ß –  30{ 9 m ”  ¹ ¡ §f ” { 9  M : 0( r  Œ •{ 9 ), 1(30{ 9 ), 2(60{ 9 ), 3(90{ 9 ), 4(120{ 9 )\ " f W 1 7 á x À

Ó_   © œ  H “ ¦Ä »u [ þ t \ " f    o  ) a l \ O Õ ªÒ  ¨ _  à º\  ¦ ¶ ú ˜ (

R˜ Ð . Figs. 4-7\ " f x» ¡ ¤ _  Å Òd ” t ³ ð(stock index)  H  r



 696> h×  æ \   © œ  H “ ¦Ä »u \  @ /6 £ x ) a “ ¦Ä » 7 ˜' \  5 Å q 



 H y Œ •$ í ì  r _  ] jY  L ß ¼l  |V

k

|

2

s  9, y» ¡ ¤ _  % ò % i t ³ ð  H r 

>

\ P `  ¦ 30{ 9 m ”  ¹ ¡ §f ” { 9  M : 0(r  Œ •{ 9 ), 1(30{ 9 ), 2(60{ 9 ),

Fig. 5. (Color online) The business groups, represented by the eigenvector corresponding to the second largest eigenvalue, are monitored. (a) shows the composition of stocks belonging to the eigenvector over time, and (b) shows the change of its composition over time. The change of composition is most explicit.

Fig. 6. (Color online) The business groups, represented by the eigenvector corresponding to the third largest eigenvalue, are monitored. (a) shows the composition of stocks belonging to the eigenvector over time, and (b) shows the change of its composition over time. There is no clear change.

3(90{ 9 ), 4(120{ 9 )`  ¦    · p . Õ ªo “ ¦ z» ¡ ¤“ É r r ç ß –  0, 1, 2, 3 \     Õ ªÒ  ¨ \   r  ‚ à Ð# Œ(» 1 Ï@)÷ &€   Å Òd ” _  ‚ à Ð

#

Œ(entry of stocks) 1(-1)– Ð & ñ _ ô  Ç ° ú כs  .

Figure 4  H ' Í   P :  © œ  H “ ¦Ä »u \  @ /K " f Õ ªa Ë >(a)  H r

ç ß – \  @ /ô  Ç “ ¦Ä » 7 ˜' \  5 Å q ô  Ç Å Òd ”  $ í ì  r[ þ t s  9, Õ ª a Ë

>(b)  H r ç ß – \  @ /ô  Ç Å Òd ”  $ í ì  r _     os  . r ç ß – 

\

    r  © œ„  ^ ‰\  % ò † ¾ Ó`  ¦ p u t  3 l w  9, " î Ñ þ ˜ô  Ç Å Òd ” 

$ í

ì  r _     o \ O  . ¿ º   P :  © œ  H “ ¦Ä »u \  @ /K " f Fig. 5 \  ˜ Ð# Œï  r  ü < ° ú  s  Õ ªa Ë >(a)  H r ç ß – \  @ /ô  Ç “ ¦ Ä

» 7 ˜' \  5 Å q ô  Ç Å Òd ”  $ í ì  r[ þ t s  9, Õ ªa Ë >(b)  H r ç ß – \  @ / ô

 Ç Å Òd ”  $ í ì  r _     os  . r ç ß – \     r  © œ„  ^ ‰\  % ò

†

¾ Ó`  ¦ p u  9, " î Ñ þ ˜ô  Ç Å Òd ” $ í ì  r _     o Ì º§   .

Figure 6“ É r [ j   P :  © œ  H “ ¦Ä »u \  @ /K " f Õ ªa Ë >(a)  H r

ç ß – \  @ /ô  Ç “ ¦Ä » 7 ˜' \  5 Å q ô  Ç Å Òd ”  $ í ì  r[ þ t s  9, Õ ª a Ë

>(b)  H r ç ß – \  @ /ô  Ç Å Òd ”  $ í ì  r _     os  . r ç ß – 

(5)

Fig. 7. (Color online) The business groups, represented by the eigenvector corresponding to the fourth largest eigenvalue, are monitored. (a) shows the composition of stocks belonging to the eigenvector over time, and (b) shows the change of its composition over time. There is no clear change.

\

    r  © œ„  ^ ‰\  % ò † ¾ Ó`  ¦ p u t  3 l w  9, " î Ñ þ ˜ô  Ç Å Òd ” 

$ í

ì  r _     o \ O  . ¢ ¸ô  Ç Fig. 7“ É r W 1   P :  © œ  H “ ¦Ä » u

\  @ /K " f Õ ªa Ë >(a)  H r ç ß – \  @ /ô  Ç “ ¦Ä » 7 ˜' \  5 Å q ô  Ç Å

Òd ”  $ í ì  r[ þ t s  9, Õ ªa Ë >(b)  H r ç ß – \  @ /ô  Ç Å Òd ”  $ í ì  r _ 



  os  . r ç ß – \     r  © œ„  ^ ‰\  % ò † ¾ Ós  \ O “ ¦, Å Òd ” 

$ í

ì  r _     o \ O  .

IV. + s Ç Â ] Ø

 

 : r& h Ü ¼– Ð  © œ s  7 £ x Ý ¶  A ™ è\ " f Y O w כ ¹™ è— ¸+ þ A\ 

"

f } Œ •' Ÿ § > =s  : r`  ¦  6   x # Œ r ç ß – \  @ /ô  Ç l \ O Õ ªÒ  ¨ _  W

1à Ô0 >ß ¼_  ”   o 1 l x`  ¦  7 H _  % i  .

C

r

õ  C

g

 " î Ñ þ ˜y  ¸ ú ˜ ½ ¨Z > ÷ &  H : £ ¤$ í “ É r C

g

×  æ \    É r l

\ O Õ ªÒ  ¨[ þ t“ É r W 1à Ô0 >ß ¼ | z  ´  , ¿ º   P :  H “ ¦Ä »u 

\

 ¦ ° ú   H l \ O Õ ªÒ  ¨[ þ t ç ß –\  Å Òd ” _  ‚ à Ð# Œ   o e ” # Q 2 [€  •

†

< Ê`  ¦ ˜ Ð% i  .   " f  © œ › ' a› ' a > > à º q & ñ  © œ © œI \  ¦ ° ú 



 H ì  r Ÿ í+ þ AI   H  © œ s  7 £ x Ý ¶  A ™ è_  : £ ¤f ç ×  æ \   s 



.   " f D h– Ðî  r ~ ½ ÓZ O “   } Œ •' Ÿ § > =s  : r`  ¦  6   x # Œ r ç ß –

\  ¦ ß ¼>  › ¸& ñ # Œ l \ O Õ ªÒ  ¨ _  W 1à Ô0 >ß ¼\  @ /ô  Ç ”   o



1 l x`  ¦ ² D G ? /ü @ F KÖ 6 x r  © œ\  & h 6   x q “ §½ + É Ã º e ” `  ¦  כ s  .

:

£

¤ y    É r [ j 7 á x À Ó “ ¦Ä »u ˜ Ð  ¿ º   P :  © œ  H “ ¦Ä » u

_  Å Òd ” $ í ì  r“ É r r ç ß – \     r  © œ„  ^ ‰\  % ò † ¾ Ó`  ¦ p  } 9

  כ s  9, " î Ñ þ ˜ô  Ç Å Òd ” $ í ì  r _     o Ì º§   . Õ ªo “ ¦

‘

: r ƒ  ½ ¨  H “ ¦Ä » 7 ˜'   H > h> h_   r \  @ /ô  Ç & ñ ˜ Ð\  ¦ · ú ˜



˜ Ѝ  H  כ “ É r ~ 1 t  · ú § " f r  © œ „  ^ ‰_   ⠆ ¾ Ó`  ¦ ˜ Ð 9“ ¦ r 

•

¸ô  Ç  כ s  . · ú ¡Ü ¼– Ð  © œ s  F KÖ 6 x r  © œ“ É r  8 ½ ¨^ ‰& h Ü ¼– Ð

ƒ

 ½ ¨K  ^  ¦ u  e ” `  ¦  כ Ü ¼– Ð ó ø Íé ß –  ) a  .   " f Ä ºo _ 

ƒ

 ½ ¨\  ¦ : Ÿ x K  K ‰t ˆ  × ¼  Á »Æ Ò\ O  ˆ  × ¼\  _ ô  Ç  F K _  â ì 2

£

§`  ¦ \ V8 £ ¤ ½ + É Ã º e ” `  ¦  כ Ü ¼– Ð  « Ñ  ) a  .



8¹ ¡ ¤ s  D h– Ðî  r } Œ •' Ÿ § > =s  : r _    õ   H » ¡ ¤' ‘  Ä » W 1à Ô0 >

ß

¼ — ¸+ þ A`  ¦ : Ÿ x K  Ó ü t o † < Æ, Ò q tÓ ü t † < Æ,  â ] j† < Æ,  â % ò † < Æ,   r† < Æ, /

B

N † < Æ x 9 _ † < Æ ì  r  ü < ƒ    ÷ &# Q ƒ  ½ ¨ | ¨ c à º e ”  . s [ þ t — ¸ + þ

A[ þ t“ É r 4 Ÿ ¤ ¸ ú š>  ‰ & ³ © œ_  ƒ  ½ ¨\  à ºu  K $ 3 & h Ü ¼– Ð ×  æ כ ¹ô  Ç

%

i ½ + É`  ¦ ½ + É Ã º e ” Ü ¼ 9, · ú ¡Ü ¼– Ð } Œ •' Ÿ § > =s  : r õ  ƒ  › ' a r †   W 1 à

Ô0 >ß ¼ ¨ 8 Š â \ " f  H " f– Ð | 9 é ß –`  ¦ s À ҍ  H  r [ þ t z o _   © œ  

ñƒ  › ' a$ í `  ¦ ˜ Ð  S X ‰ z  ´y  ƒ  ½ ¨ | ¨ c  כ Ü ¼– Ð  « Ñ  ) a  .

P

c p 8 ý ò k >

s

  7 Hë  H“ É r 2011 † < Ƹ  • ¸ ô  Dz D Gƒ  ½ ¨F é ß –_  l ‘ : rƒ  ½ ¨q  (No. 2009 - 0074635) x 9 l  © œ' õ A F " é ¶ _  ² D Gw n l  © œƒ  ½ ¨

™

è Å Òכ ¹ \ O  “r # 3 t % i  à ºë  H l  © œl Õ ü t > hµ 1 Ï” t " é ¶`  ¦ ~ à Î  Ã

º' Ÿ  ) a ƒ  ½ ¨e ” .

Y

c p w Š à U Ø ”  ô

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Sons, New York, 1995).

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Amaral and H. E. Stanley, Phys. Rev. Lett. 83, 1471 (1999); V. Plerou, P. Gopikrishnan, B. Rosenow, L.

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수치

Fig. 1. (Color online) The density function of the eigen- eigen-values of a correlation matrix C is shown in comparison with the theoretical density P rm of a Wishart matrix.
Fig. 6. (Color online) The business groups, represented by the eigenvector corresponding to the third largest eigenvalue, are monitored
Fig. 7. (Color online) The business groups, represented by the eigenvector corresponding to the fourth largest eigenvalue, are monitored

참조

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