Abstract
Abstract A new model for constructing an integrated stock-bond portfolio is proposed. This serves as an alternative to the popular asset allocation strategy, in which the fund is first allocated to indexes corresponding to diverse asset classes and then allocated to individual assets using appropriate models for each asset class. Our model, to the contrary, determines the allocation of the fund to individual assets in one stage by solving a large-scale mean-variance or mean-absolute deviation model using newly developed technologies in large-scale linear programming and quadratic programming. Computational experiments show that the new approach can serve as a more reliable and less expensive method to allocate the fund to diverse classes of assets.
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