CFA® Level I Quantitative Methods 2025 exam notes

This 290-page exam companion is a comprehensive and methodically structured study guide tailored for the CFA® Level I Quantitative Methods curriculum. Written by a passed CFA Level I candidate, the notes are distilled from direct exam preparation experience, ensuring relevance, clarity, and high exam utility.

Each topic is simplified for practical understanding, combining theoretical rigor with real-world application. With a clear focus on the CFA Institute’s Learning Outcome Statements (LOS), the material includes step-by-step breakdowns, key formulas, conceptual summaries, and practice frameworks that are ideal for review, reinforcement, and mastery.

These notes are specifically designed for busy candidates seeking to optimize their study time while ensuring deep comprehension of Quantitative Methods — one of the most foundational and heavily weighted sections of the CFA exam.

Covered Topics:

• 1. Rate and Return (Page 2):
  Covers nominal vs. real returns, HPR, EAR, and geometric vs. arithmetic mean returns.
• 2. The Time Value of Money in Finance (Page 30):
  In-depth treatment of present and future value, annuities, perpetuities, and TVM equations.
• 3. Statistical Measures of Asset Returns (Page 73):
  Measures of central tendency and dispersion, skewness, kurtosis, and data interpretation.
• 4. Probability Trees and Conditional Expectations (Page 108):
  Probability theory applications including conditional probability, joint distributions, and expected values.
• 5. Portfolio Mathematics (Page 125):
  Portfolio return and risk metrics, correlation, covariance, and diversification impact.
• 6. Simulation Methods (Page 144):
  Introduction to Monte Carlo simulations and how they assist in financial modeling.
• 7. Estimation and Inference (Page 161):
  Sampling distributions, point estimates, confidence intervals, and estimator reliability.
• 8. Hypothesis Testing (Page 177):
  Hypothesis framework, types of errors, significance levels, and common test statistics.
• 9. Parametric and Non-Parametric Tests of Independence (Page 216):
  Use cases and differences between T-tests, Chi-squared tests, and Spearman’s rank.
• 10. Simple Linear Regression (Page 229):
  Understanding regression coefficients, residuals, model assumptions, and interpretation.
• 11. Introduction to Big Data Techniques (Page 280):
  High-level overview of machine learning concepts, AI, and data analytics in modern finance.

Who It’s For:
These notes are perfect for CFA candidates aiming to master Quantitative Methods efficiently, as well as finance professionals and students needing a concise refresher on essential analytical techniques.