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This course provides a deep and practical introduction to market timing—a strategic approach to portfolio management where exposure to market risk is deliberately adjusted based on anticipated conditions. Aimed at analysts, researchers, students, and self-driven learners, the course builds a strong conceptual and applied foundation using both R and Python.

You’ll begin by examining the financial and econometric underpinnings of market timing, including the Treynor–Mazuy and Henriksson–Merton models. These classic parametric approaches are introduced through clear intuition, mathematical formulation, and interactive visuals that help internalize key mechanics.

The course then walks you through hands-on implementation of both models using replicable toy datasets, interpreted in detail to reveal actionable insights. Rather than just demonstrating code, the material emphasizes deep interpretation of econometric output, bridging econometric modeling and strategic evaluation in real-world finance.

In the final module, you’ll explore a novel behavioral lens using the Kahneman–Tversky value function, illustrating how risk perception and asymmetric responses can inspire new timing strategies grounded in human behavior.

All materials are designed to be reproducible and accessible, even for learners at the entry level of scripting. Whether you're validating a fund's behavior or developing a custom model, this course equips you with tools to test, interpret, and creatively rethink timing ability with rigor, clarity, flexibility, and practical insight.