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

Authors:
Stephen J. Stoyan;Roy H. Kwon
Affiliations:
University of Southern California, Daniel J. Epstein Department of Industrial and Systems Engineering, Los Angeles, CA, USA;University of Toronto, Department of Mechanical and Industrial Engineering, Toronto, ON, Canada
Venue:
Computers and Industrial Engineering
Year:
2011

Citing 6
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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.