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.
Original language | English (US) |
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Title of host publication | KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
Publisher | Association for Computing Machinery |
Pages | 477-486 |
Number of pages | 10 |
ISBN (Electronic) | 9781450336642 |
DOIs | |
State | Published - Aug 10 2015 |
Event | 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia Duration: Aug 10 2015 → Aug 13 2015 |
Publication series
Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
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Volume | 2015-August |
Other
Other | 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 |
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Country/Territory | Australia |
City | Sydney |
Period | 8/10/15 → 8/13/15 |
Bibliographical note
Publisher Copyright:© 2015 ACM.
Keywords
- Finance
- Online learning
- Structured learning