Active Equity Strategies
Active equity strategies represent the origin of quantitative investing. Modern data science technologies and machine learning have taken the field to a completely new level. Unlike many other fields, asset management faces a persistent core challenge: the low replicability of causal relationships and, consequently, limited signal quality extracted from noisy financial data.
While traditional approaches relied on linear models with strong structural assumptions and sparse parameter sets derived from science, modern techniques increasingly utilize highly parameterized, flexible architectures. These models aim to detect subtle patterns and persistent inefficiencies, even under conditions of uncertainty and noise. Our active equity strategies are built on a fully systematic investment approach that combines advanced quantitative modeling with state-of-the-art machine learning and deep learning technologies. The aim is to identify pricing inefficiencies and extract robust, repeatable signals for bottom-up security selection. We apply a multi-factor framework that integrates fundamental, technical and alternative data sources.
We rely on transparent, interpretable (“glassbox”) models, allowing us to understand the drivers of portfolio signals and behavior at any point in time. This clarity enhances the robustness of our investment process and facilitates continuous model refinement. At the same time, we employ deep learning techniques where non-linear, high-dimensional patterns are essential to signal extraction—particularly in unstructured datasets.
Our technology platform allows for consistent signal deployment across scalable portfolios, supported by a rigorous and adaptive risk management framework build on proprietary risk model and optimization modules. Our approach allows for customized ESG integration.
We manage Enhanced, Core and Dynamic (alpha extension) Strategies for Developed Markets. All strategies are designed with the objective of delivering sustainable, risk-adjusted outperformance across diverse market environments.