A Stochastic-Goal Mixed-Integer Programming approach for integrated stock and bond portfolio optimization

作者：Stoyan Stephen J^*; Kwon Roy H

来源：Computers & Industrial Engineering, 2011, 61(4): 1285-1295.

DOI：10.1016/j.cie.2011.07.022

• Stochastic Programming
• Goal Programming
• Mixed-Integer Programming
• Geographical decomposition

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.

• 出版日期2011-11