machine learning and finance iii

8/9/2019 Machine Learning and Finance III

1/31

M a c h i n e l e a r n i n g a n d p o r t f o l i o s e l e c t i o n s .

L s z l G y r

D e p a r t m e n t o f C o m p u t e r S c i e n c e a n d I n f o r m a t i o n T h e o r y

B u d a p e s t U n i v e r s i t y o f T e c h n o l o g y a n d E c o n o m i c s

g y o r f i @ s z i t . b m e . h u

A b s t r a c t

I n t h i s p a p e r t h e s t a t i c p o r t f o l i o a n d t h e d y n a m i c l o g - o p t i m a l p o r t -

f o l i o f o r m e m o r y l e s s a n d f o r s t a t i o n a r y m a r k e t p r o c e s s e s i s s t u d i e d .

W e i l l u s t r a t e t h e i r p e r f o r m a n c e ( a v e r a g e a n n u a l y i e l d ) f o r N Y S E

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2/31

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3/31

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4/31

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5/31

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6/31

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7/31

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8/31

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l i o .

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9/31

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10/31

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11/31

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12/31

6 S e m i - l o g - o p t i m a l p o r t f o l i o .

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13/31

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7 N u m e r i c a l c o m p a r i s o n o f l o g - o p t i m a l a n d s e m i -

l o g - o p t i m a l p o r t f o l i o s .

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j d P H F

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l o k 9 n m D o n d o l m n o n n l l d

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14/31

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15/31

8 D y n a m i c p o r t f o l i o s e l e c t i o n : g e n e r a l c a s e

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21/31

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22/31

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25/31

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27/31

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28/31

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