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Overview of Quantitative Methods in CFA Level 1

Key Topics You Need to Focus On

Overview of Quantitative Methods in CFA Level 1

1. Overview of the Subject

วิธีการเชิงปริมาณ is one of the most important and foundational subjects in the Chartered Financial Analyst Level I program.

This subject mainly covers core concepts such as:

  • Time value of money
  • Present and future value of cash flows
  • Probability and statistics
  • Hypothesis testing

A strong understanding of these topics will give you a major advantage when studying more advanced subjects like asset valuation and portfolio management later in the CFA program.

Although the volume of material is relatively large, Quantitative Methods is often considered a “scoring subject” because:

  • The concepts are not overly tricky
  • Many questions are calculation-based
  • It is especially advantageous if you have a solid math background

Exam weight: Approximately 8–12% of the CFA Level I exam

2. Core Topics and Important Concepts

The Quantitative Methods section typically includes several modules (note: curriculum may change slightly each year). Below is a structured overview:

Module 1: Time Value of Money

  • Understand interest rates and their components
  • Calculate the Effective Annual Rate (EAR)
  • Master present value (PV) and future value (FV) of cash flows
  • Use a financial calculator efficiently

Module 2: Organizing, Visualizing, and Describing Data

  • Types and classification of data
  • Frequency distributions
  • Contingency tables and confusion matrices
  • Data visualization techniques
  • Measures of central tendency: mean, median, mode
  • Arithmetic mean vs geometric mean
  • Quantiles and percentiles
  • Dispersion measures: range, variance, standard deviation
  • Coefficient of variation
  • Skewness and kurtosis
  • Correlation coefficient

Module 3: Probability Concepts

  • Random variables and probability fundamentals
  • Conditional vs unconditional probability
  • Addition and multiplication rules
  • Expected value, covariance, correlation
  • Applications in portfolio return and risk
  • Bayes’ formula
  • Factorials, permutations, and combinations

Module 4: Common Probability Distributions

  • Discrete vs continuous random variables
  • Binomial distribution
  • Uniform distribution
  • Normal distribution
  • Lognormal distribution
  • Shortfall risk and Roy’s Safety-First criterion
  • Discrete vs continuous compounding
  • Student’s t-distribution, F-distribution
  • Monte Carlo simulation

Module 5: Sampling and Estimation

  • Random sampling methods
  • Sampling error and stratified sampling
  • Central Limit Theorem
  • Standard error of the mean
  • Confidence intervals
  • Resampling methods (Bootstrap, Jackknife)
  • Sampling bias

Module 6: Hypothesis Testing

  • Hypothesis testing framework
  • Null and alternative hypotheses
  • One-tailed vs two-tailed tests
  • Type I and Type II errors
  • Test statistics and significance level
  • p-value interpretation
  • Testing population parameters (mean, variance, etc.)

Module 7: Introduction to Linear Regression

  • Simple linear regression model
  • Dependent and independent variables
  • Regression equation, slope, and intercept
  • Cross-sectional vs time-series regression
  • Assumptions of regression models
  • Key metrics: SST, RSS, SSE, R²
  • Common hypothesis tests (F-test, t-test)
  • Predicted values and confidence intervals
  • Extensions of linear regression models
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