Before any mathematics, the industrial context: what a quantitative investment strategy is, how it differs from stock-picking, what the components of the production pipeline are, and how performance is measured. L10's stated purpose is exactly this — "provide the industry contexts and motivations for the quantitative methods and models presented later" — so this note is deliberately lighter on formulas and heavier on the map. Everything introduced here (IC, IR, alpha model, risk model, the optimiser objective) is developed rigorously in the following notes.
Learning objectives. By the end of this note you should be able to:
- contrast the fundamental and quantitative approaches and list the pros/cons of each;
- define the Information Coefficient and explain why multifactor models are unavoidable;
- name the five pipeline stages and the role of each;
- define beta, Jensen's alpha, the Sharpe ratio and the Information Ratio, with the institutional yardsticks;
- explain the P-world / Q-world distinction and locate each part of the course in it.
In this lesson
- 1.1 Two ways to beat a benchmark
- 1.2 Predictive power: the Information Coefficient
- 1.3 The process pipeline
- 1.4 Measuring performance
- 1.5 Information capture
- 1.6 "P" vs "Q"
The full lesson, with worked examples and exercise solutions, is for members.
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