Discovery of multi-spread portfolio strategies for weakly-cointegrated instruments using boosting-based optimization

DOI

10.2991/jcis.2006.174How to use a DOI?

Keywords

Boosting, Ensemble Learning, Cointegration, Pairs/Spread Trading, Market-Neutral Strategies, Portfolio Optimization

Abstract

Increasing complexity of the modern market dynamics requires new quantitative frameworks for the discovery of stable portfolio strategies. Important requirements include the ability of the coupled and self-consistent optimization of the dynamic strategies and asset allocations as well as robust built-in mechanisms for the strategy complexity control to ensure acceptable out-of-sample performance. Recently introduced boosting-based optimization naturally incorporates all these features. Originally, the framework was described as a generic tool for the discovery of compact portfolio strategies from a given pool of existing financial instruments and base trading strategies. Here I outline the important generalization of this framework that allows simultaneous discovery of new synthetic instruments represented as generalized spreads of existing financial instruments and dynamic trading strategies for each such spread. Detailed arguments and real-market example clarify the essence of this new framework as a powerful generalization of the exiting pairs trading strategies and cointegration-based techniques.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Valeriy Gavrishchaka
PY  - 2006/10
DA  - 2006/10
TI  - Discovery of multi-spread portfolio strategies for weakly-cointegrated instruments using boosting-based optimization
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SP  - 596
EP  - 604
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.174
DO  - 10.2991/jcis.2006.174
ID  - Gavrishchaka2006/10
ER  -