Generic Regularization of Boosting-Based Algorithms for the Discovery of Regime-Independent Portfolio Strategies from High-Noise Time Series

Abstract

Recently proposed boosting-based optimization offers a generic framework for the discovery of portfolios of complementary base trading strategies with stable combined performance over wide range of market regimes and robust generalization abilities. However, wide variety of market regimes and existence of hard-to-model periods reduces universe of financial instruments and achievable performance ranges for which such portfolio strategies can be found. Recently introduced generic regularization approach based on confusing (noisy) sample removal was shown to be effective for diversification of portfolio strategies discovered by boosting-based optimization. Here we argue and demonstrate that this regularization technique could be also effective in dealing with large periods of excessive volatility and significantly reduced determinism in training data. In the most recent history such situation occurred during current financial crisis. The algorithm for confusing sample removal is outlined and applied to the recent market data in the context of mid-frequency intraday trading.