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

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.