A Stochastic-Goal Mixed-Integer Programming approach for integrated stock and bond portfolio optimization |
| |
| Authors: | Stephen J. Stoyan Roy H. Kwon |
| |
| Affiliation: | aUniversity of Southern California, Daniel J. Epstein Department of Industrial and Systems Engineering, Los Angeles, CA, USA;bUniversity of Toronto, Department of Mechanical and Industrial Engineering, Toronto, ON, Canada |
| |
| Abstract: | We consider a Stochastic-Goal Mixed-Integer Programming (SGMIP) approach for an integrated stock and bond portfolio problem. The portfolio model integrates uncertainty in asset prices as well as several important real-world trading constraints. The resulting formulation is a structured large-scale problem that is solved using a model specific algorithm that consists of a decomposition, warm-start, and iterative procedure to minimize constraint violations. We present computational results and portfolio return values in comparison to a market performance measure. For many of the test cases the algorithm produces optimal solutions, where CPU time is improved greatly. |
| |
| Keywords: | Stochastic Programming Goal Programming Mixed-Integer Programming Geographical decomposition |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
