Abstract
This paper is concerned with a portfolio optimization problem under concave and piecewise constant transaction cost. We formulate the problem as nonconcave maximization problem under linear constraints using absolute deviation as a measure of risk and solve it by a branch and bound algorithm developed in the field of global optimization. Also, we compare it with a more standard 0–1 integer programming approach. We will show that a branch and bound method elaborating the special structure of the problem can solve the problem much faster than the state-of-the integer programming code.
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Konno, H., Yamamoto, R. Global Optimization Versus Integer Programming in Portfolio Optimization under Nonconvex Transaction Costs. J Glob Optim 32, 207–219 (2005). https://doi.org/10.1007/s10898-004-2703-x
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DOI: https://doi.org/10.1007/s10898-004-2703-x