TY - JOUR
T1 - A maximal predictability portfolio using absolute deviation reformulation
AU - Konno, Hiroshi
AU - Morita, Yuuhei
AU - Yamamoto, Rei
N1 - Funding Information:
Acknowledgments The research of the first author was supported in part by the Grant-in-Aid for Scientific Research of the MEXT, Grant Number B18310109.
PY - 2010/1
Y1 - 2010/1
N2 - This paper shows that a large-scale maximal predictability portfolio (MPP) optimization problem can be solved within a practical amount of computational time using absolute deviation instead of squared deviation in the definition of the coefficient of determination. Also, we will show that MPP portfolio outperforms the mean-absolute deviation portfolio using real asset data in Tokyo Stock Exchange.
AB - This paper shows that a large-scale maximal predictability portfolio (MPP) optimization problem can be solved within a practical amount of computational time using absolute deviation instead of squared deviation in the definition of the coefficient of determination. Also, we will show that MPP portfolio outperforms the mean-absolute deviation portfolio using real asset data in Tokyo Stock Exchange.
KW - 0-1 mixed integer programming
KW - Absolute deviation
KW - Fractional programming
KW - Maximal predictability portfolio
KW - Portfolio optimization
UR - http://www.scopus.com/inward/record.url?scp=72349085346&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72349085346&partnerID=8YFLogxK
U2 - 10.1007/s10287-008-0075-2
DO - 10.1007/s10287-008-0075-2
M3 - Article
AN - SCOPUS:72349085346
SN - 1619-697X
VL - 7
SP - 47
EP - 60
JO - Computational Management Science
JF - Computational Management Science
IS - 1
ER -