CFA® Level I Exam Quantitative Methods Practice Questions

What our quantitative methods questions cover

• Holding-period, arithmetic, geometric and harmonic mean returns; money-weighted vs time-weighted returns
• Present and future values, annuities, perpetuities, implied returns and implied forward rates
• Dispersion, skewness, kurtosis, covariance and correlation; expected values and Bayes’ formula
• Normal, lognormal, binomial and Student’s t distributions; sampling and confidence intervals
• Hypothesis tests: Type I and Type II errors, p-values, tests of a mean and of a correlation
• Two-asset portfolio risk, the minimum-variance frontier and the optimal portfolio
• Simple linear regression: coefficients, R², the standard error of estimate, ANOVA and prediction
• Historical simulation, bootstrapping and Monte Carlo; machine learning, big data and large language models

This list is our own summary of the topic, written for practice. The official topic outline and learning outcomes are published by CFA Institute for registered candidates.

Ideas the questions test most

1. The geometric mean return is never above the arithmetic mean; the gap widens with volatility. Use the geometric mean for compound growth over time.
2. A money-weighted return is the internal rate of return of the investor’s cash flows and is affected by the timing of contributions. A time-weighted return removes that effect and is the right measure for judging a manager.
3. A Type I error rejects a true null hypothesis; its probability is the significance level. A Type II error fails to reject a false null; power is one minus that probability.
4. In a simple regression, R² is the share of the variation in the dependent variable explained by the independent variable, and equals the squared correlation between the two.
5. Monte Carlo simulation draws from assumed distributions; historical simulation replays past data; bootstrapping resamples the observed data with replacement.
6. In machine learning, supervised learning uses labeled data, unsupervised learning finds structure without labels, and overfitting means a model fits training data well but generalizes poorly.

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Common questions

More Level I topics

• Ethical and Professional Standards · 10–15%
• Economics · 6–9%
• Financial Statement Analysis · 11–14%
• Corporate Finance · 6–9%
• Equities · 11–14%
• Fixed Income · 11–14%
• Derivatives and Risk Management · 6–9%
• Alternative Investments · 6–9%
• Portfolio Construction · 8–12%