Discovery Of Multi-component Portfolio
Strategies With Continuous Tuning To The
Changing Market Micro-regimes Using
Input-dependent Boosting
Author(s)
V. V. Gavrishchaka, O. V. Barinova, A. P. Vezhnevets
& M. A. Monina
Abstract
Recently proposed boosting-based optimization offers a generic framework for
the discovery of compact and interpretable portfolios of complimentary trading
strategies with stable (non-resonant) performance over a wide range of market
regimes and robust generalization abilities. Inherent complexity control allows
the framework to work with very large pools of heterogeneous base strategies
with well-established properties. However, in its current version, the framework
outputs a collection of dynamic strategies with fixed parameters and constant
weights defining capital allocations. This excludes any adaptive regime
adjustment or switching on the portfolio level for additional profitability from
regime-specific patterns. In this work we extend boosting-based optimization
framework by including capability to discover portfolio strategies with
continuous and adiabatically smooth adjustment to the current market microregime.
Such regime adaptivity is naturally provided by the input-dependent
boosting. The proposed generalization preserves clarity and interpretability of
the original framework since the dynamic base strategies of the multi-component
portfolio and their optimal parameters remain fixed. However, the weights of the
base strategies are adaptively varied in time according to the implicit rule
discovered by boosting. Operational details of the new framework and
encouraging results are illustrated using real market data. More rigorous
theoretical foundation for the general concept of the boosting-based optimization
is also outlined.
Keywords: adaptive boosting, ensemble learning, regime switching, trading
strategies, portfolio optimisation.
Keywords
adaptive boosting, ensemble learning, regime switching, trading
strategies, portfolio optimisation.
Related Book
Other papers in this volume
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