Abstract
A conventional portfolio selection problem, which is based on a mean-variance model, is difficult to solve by using mathematical programming techniques. This difficulty is caused by the fact that the corresponding mathematical programming problems are large-dimensional one, since almost all variance-covariances of return rates are, typically, not zeros. In this paper, we propose an efficient method for solving a portfolio selection problem, a method which uses a Boltzmann machine. In a real-life problem, it is also important to find the optimal combination of a small number of invested securities out of many securities in a market, because of a limited amount of funds to invest into securities. So we also propose a portfolio selection method to obtain the invest ratio of limited number of securities out of huge number of securities using a multi-stage application of the Boltzmann machine.
| Original language | English |
|---|---|
| Pages (from-to) | 429-437 |
| Number of pages | 9 |
| Journal | International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems |
| Volume | 7 |
| Issue number | 4 |
| Publication status | Published - 1999 Aug |
| Externally published | Yes |
Fingerprint
Keywords
- Boltzmann Machine
- Index Data
- Limited Number of Securities
- Multi-Stage Model
- Portfolio Selection Problem
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
Cite this
Hierarchical decision making in strategic investment by a Boltzmann machine. / Watanabe, Teruyuki; Watada, Junzo; Oda, Kenji.
In: International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, Vol. 7, No. 4, 08.1999, p. 429-437.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Hierarchical decision making in strategic investment by a Boltzmann machine
AU - Watanabe, Teruyuki
AU - Watada, Junzo
AU - Oda, Kenji
PY - 1999/8
Y1 - 1999/8
N2 - A conventional portfolio selection problem, which is based on a mean-variance model, is difficult to solve by using mathematical programming techniques. This difficulty is caused by the fact that the corresponding mathematical programming problems are large-dimensional one, since almost all variance-covariances of return rates are, typically, not zeros. In this paper, we propose an efficient method for solving a portfolio selection problem, a method which uses a Boltzmann machine. In a real-life problem, it is also important to find the optimal combination of a small number of invested securities out of many securities in a market, because of a limited amount of funds to invest into securities. So we also propose a portfolio selection method to obtain the invest ratio of limited number of securities out of huge number of securities using a multi-stage application of the Boltzmann machine.
AB - A conventional portfolio selection problem, which is based on a mean-variance model, is difficult to solve by using mathematical programming techniques. This difficulty is caused by the fact that the corresponding mathematical programming problems are large-dimensional one, since almost all variance-covariances of return rates are, typically, not zeros. In this paper, we propose an efficient method for solving a portfolio selection problem, a method which uses a Boltzmann machine. In a real-life problem, it is also important to find the optimal combination of a small number of invested securities out of many securities in a market, because of a limited amount of funds to invest into securities. So we also propose a portfolio selection method to obtain the invest ratio of limited number of securities out of huge number of securities using a multi-stage application of the Boltzmann machine.
KW - Boltzmann Machine
KW - Index Data
KW - Limited Number of Securities
KW - Multi-Stage Model
KW - Portfolio Selection Problem
UR - http://www.scopus.com/inward/record.url?scp=0347120701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347120701&partnerID=8YFLogxK
M3 - Article
VL - 7
SP - 429
EP - 437
JO - International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
JF - International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
SN - 0218-4885
IS - 4
ER -