Systems and methods for providing robust investment portfolios

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Reexamination Certificate

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Reexamination Certificate

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07401041

ABSTRACT:
Robust methods for determining an investment portfolio are based on investment parameters which are assumed to be error bounded rather than precisely known values. A confidence threshold is input based on the measure of confidence in the resulting worst-case portfolio performance that is desired by an investor. Using historical return data, a nominal value for the mean return for each asset, a nominal factor loading vector for each asset and a nominal factor covariance matrix are determined. Uncertainty sets, which define the region within which a parameter is statistically expected to reside are defined for the mean return vector, factor loading matrix and factor covariance matrix. The uncertainty sets are then applied to a robust investment problem of interest, based on investment objectives, such that the worst case market parameters reside within the applied uncertainty sets with a probability set by the selected confidence threshold. Preferably, the uncertainty sets are defined in a manner which enables the robust investment problem of interest to be expressed in the form of a second-order cone problem, which is readily solvable by a number of known techniques.

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Lobo, Miguel Sousa. “Robust and Convex Optimization With Applications in Finance.” Dissertation submitted to the Department of Electrical Engineering and the Committee on Graduate Studies of Stanford University, pp. i-x, 1-104, Mar. 2000.

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