Structured Hedging for Resource Allocations with Leverage

Abstract

Data mining algorithms for computing solutions to online resource allocation (ORA) problems have focused on budgeting resources currently in possession, e.g., investing in the stock market with cash on hand or assigning current employees to projects. In several settings, one can leverage borrowed resources with which tasks can be accomplished more efficiently and cheaply. Additionally, a variety of opposing allocation types or positions may be available with which one can hedge the allocation to alleviate risk from external changes. In this paper, we present a formulation for hedging online resource allocations with leverage and propose an efficient data mining algorithm (SHERAL). We pose the problem as a constrained online convex optimization problem. The key novel components of our formulation are (1) a loss function for general leveraging and opposing allocation positions and (2) a penalty function which hedges between structurally dependent allocation positions to control risk. We instantiate the problem in the context of portfolio selection and evaluate the effectiveness of the formulation through extensive experiments on five datasets in comparison with existing algorithms and several variants.