CFA® Quantitative Methods
Summary, Syllabus, Topics, and Sample Questions (L1, L2, L3)
In preparation for the 2024 CFA exams, we want to inform you of some minor adjustments in the Quantitative Methods section. Specifically, Volume 1 of the readings will transition to a "pre-requisite." status. This means that while the content won't be directly tested, it remains accessible for your reference and to aid you in meeting the essential knowledge requirements for the CFA Program.
It's important to note that Quantitative Methods plays a significant role in the Level 1 exam and a somewhat smaller role in the Level 2 exam. In contrast, on the Level 3 exam, these foundational concepts are not tested directly. However, it's worth highlighting that the material in Quantitative Methods significantly overlaps with other topics covered in the curriculum, making it a crucial component of your overall CFA preparation.
What to Expect in CFA Level 1 Quantitative Methods?
CFA Level 1 Quantitative Methods curriculum covers understanding and interpreting a normal
distribution and how it relates to quantifying risk. At 6-9% of the exam, Quantitative Methods is given less weight
than Ethics and Financial Statement analysis but is weighted more heavily than most topics. Candidates will also
become familiar with data visualization, probability distributions, sampling and estimation, hypothesis testing, and
regression analysis.
Exam Weighting
The CFA Quantitative Methods topic weighs 6-9%, meaning that approximately
11-16 of the 180 CFA Level 1 exam questions focus on this topic.
| Topic Weight | No. of Learning Modules | No. of Formulas | No. of Questions |
|---|---|---|---|
| 6-9% | 11 | ca. 100 | ca. 16 |
Level 1 Quantitative Methods 2024 Syllabus, Readings, and Changes
While the 2023 CFA Level 1 Quantitative
Methods curriculum emphasizes understanding normal distribution, risk quantification, and various topics like data
visualization and regression, the 2024 syllabus introduces new topics like Rates and Returns and Big Data Techniques,
reflecting contemporary advancements in financial technology. Despite these updates, the core focus on quantitative
analysis remains intact.
Rates and Returns
This topic centers on interpreting interest rates as required rates of return, discount rates, or opportunity costs.
It emphasizes calculating and interpreting different return measurement approaches, comparing money-weighted and
time-weighted rates of return, and evaluating portfolio performance. Additionally, candidates learn to calculate and
interpret annualized return measures, continuously compounded returns, and major return measures with a focus on their
appropriate applications.
Time Value of Money
We can express one of the core concepts of the time value of money through an age-old proverb—a bird in the
hand is worth two in the bush. How does this relate to finance? Money now is worth more than money later: time imposes
risk, which has a cost that detracts from value. Therefore, financial analysts must understand how to factor time into
valuation.
This reading covers how to calculate
- the future value of a cash flow or a series of cash flows or
- the present value of a cash flow or series of cash flows happening in the future
Candidates are introduced to terminology and concepts that will curate a sense of economic intuition for material
covered in later readings.
Statistical Measures of Asset Returns
This topic delves into the intricate world of financial analysis, where we engage in the calculation, interpretation,
and assessment of central tendency and location measures. These crucial statistical tools serve as our compass when
navigating the complex landscape of investment problems.
In addition to these fundamental concepts, we venture into dispersion measures, striving to comprehend their
significance in the context of investment challenges. Furthermore, our exploration expands to encompass the nuanced
understanding and evaluation of skewness and kurtosis, offering us valuable insights into the shape and distribution
of investment data.
Lastly, we aim to unlock the potential of interpreting the correlation between two variables. This vital skill
empowers us to make informed decisions within the framework of addressing intricate investment issues.
Probability Trees and Conditional Expectations
Involves calculating expected values, variances, and standard deviations, showcasing their application to investment
problems. Candidates will also learn to formulate an investment problem as a probability tree and understand the
application of conditional expectations in investment contexts. Furthermore, the focus extends to calculating and
interpreting updated probabilities in investment settings using Bayes’ formula.
Portfolio Mathematics and Simulation Methods
Prospective candidates are required to demonstrate proficiency in several essential quantitative finance skills.
These include the ability to calculate and interpret expected values, variances, standard deviations, covariances, and
correlations of portfolio returns. Additionally, candidates are expected to possess a comprehensive understanding of
risk management, encompassing the definition and evaluation of shortfall risk, the calculation of the safety-first
ratio, and the identification of an optimal portfolio through the application of Roy’s safety-first criterion.
Within the realm of Simulation Methods, candidates will acquire the knowledge and expertise necessary to elucidate
the normal-lognormal relationship and its significance in modeling asset prices. Furthermore, they will gain
proficiency in articulating the principles behind Monte Carlo simulation and mastering the utilization of bootstrap
resampling in the context of investment applications.
Hypothesis Testing
Analysts sift through an avalanche of data to assess the investment environment and develop hypotheses. To test these
hypotheses, analysts will employ statistical inference, allowing them to make judgments about populations based on
smaller sample sizes.
- The reading discusses the three quantities commonly used in investments (mean, variance, and correlation) via a
hypothesis-testing framework.
Simple Linear Regression
The process of determining relationships between variables is an important tool in the analyst’s toolkit. One of
these tools is regression analysis.
- The reading explains the assumptions underlying the simple linear regression model and the roles of independent
variables within that model. - Candidates will also learn to formulate various hypotheses using this tool.
Parametric and Non-Parametric Tests of Independence
This section explains parametric and nonparametric tests concerning the hypothesis that the population correlation
coefficient equals zero. Candidates will determine whether the hypothesis is rejected at a specified level of
significance. Furthermore, the topic includes explaining tests of independence based on contingency table data.
Introduction to Big Data Techniques
Introduced this year in this topic, the segment covers describing aspects of “fintech” relevant for gathering and
analyzing financial data. Candidates will delve into descriptions of Big Data, artificial intelligence, and machine
learning. Additionally, the focus extends to describing applications of Big Data and Data Science in the context of
investment management.
CFA Quantitative Methods Level 1 Sample Questions and Answers
The sample questions are typical of the probing multiple-choice questions on the L1 exam. During the exam, you have
about 90 seconds to read and answer each question, carefully designed to test knowledge from the CFA Curriculum.
UWorld’s question bank is built to expose you to exam-like questions and illustrate and explain the concepts tested
thoroughly.
An analyst determines that the probabilities of two events are as follows:
| Event | Description | Probability |
|---|---|---|
| A | Price of oil increases | 0.33 |
| B | S&P 500 Index increases | 0.49 |
Assuming the events are independent but not mutually exclusive, the probability that at least one of the two events will occur is closest to:
- 0.16
- 0.66
- 0.82
A researcher formulates a null hypothesis stating that a country’s average inflation is 2%. After testing a sample, the researcher fails to reject the null hypothesis when the actual average inflation is not 2%. This most likely results in a:
- Type I error.
- Type II error.
- correct decision.
If a cumulative distribution function of a random variable indicates that P(X < a) is equal to P(X ≤ a), then the distribution is most likely :
- normal.
- discrete.
- binomial.
What to Expect in CFA Level 2 Quantitative Methods?
CFA Level 2 Quantitative Methods builds on the material covered in Level 1 while emphasizing
hypothesis testing. At 5-10% of the exam, Quantitative Methods is one of the less heavily weighted topics. Candidates
will become familiar with tools used to identify relationships among variables and examine fintech, machine learning,
and sentiment analysis related to developing an investment hypothesis.
Exam Weighting
The CFA Quantitative Methods topic has a weight of 5-10% of the total exam
content, so approximately 4-8 of the 88 CFA Level 2 exam questions, or 1-2 of the 22
item sets focus on this topic.
| Topic Weight | No. of Learning modules | No. of Formulas | No. of Questions |
|---|---|---|---|
| 5-10% | 7 | ca. 50 | ca. 4-8 |
Level 2 Quantitative Methods 2024 Syllabus, Readings, and Changes
In 2024, the CFA Level 2 Quantitative Methods
syllabus covers 7 learning modules, constituting approximately 14.3% of the total curriculum.
Notably, several readings have either been consolidated into a single reading or diversified into two or more distinct
readings for enhanced comprehension and coverage.
Basics of Multiple Regression and Underlying Assumptions
Financial analysts typically work with sophisticated statistical models that involve more than one independent
variable. For example, analysts may want to assess particular macroeconomic variables behind the demand for an
individual company’s products or services. To make such assessments, analysts employ multiple linear regression
(linear regression with more than one independent variable) to make such assessments.
- The reading introduces the core principles and models of multiple regression models and the foundational
assumptions applied to and adjusted for real-world situations. - Candidates will learn to diagnose an assumption violation and to adjust to these violations.
- The reading also dives into the role of logistic regression in machine learning for Big Data analysis.
Evaluating Regression Model Fit and Interpreting Results
The reading helps understand:
- How well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures
of goodness of fit - The hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the
results of the joint hypothesis tests - The Calculation and interpretation of a predicted value for the dependent variable, given the estimated regression
model and assumed values for the independent variable
Model Misspecification
The reading helps understanding:
- How model misspecification affects the results of a regression analysis and how to avoid common forms of
misspecification - The types of heteroskedasticity and how it affects statistical inference
- Serial correlation and how it affects statistical inference
- Multicollinearity and how it affects regression analysis
Extensions of Multiple Regression
Financial analysts commonly utilize advanced statistical models that incorporate multiple independent variables. One
such application involves evaluating specific macroeconomic factors that influence the demand for a company’s products
or services. To perform these assessments effectively, analysts rely on the technique known as multiple linear
regression, which enables the analysis of relationships between multiple independent variables and a dependent
variable. This comprehensive approach is explored through four dedicated learning modules, each delving into the
intricate aspects of multiple regression analysis, including:
- The core principles and models of multiple regression models and the foundational assumptions applied to and
adjusted for real-world situations. - The diagnosis of an assumption violation and relevant adjustments.
- The role of logistic regression in machine learning for Big Data analysis.
Time-Series Analysis
A set of these progressive observations is known as a time series, for example, a company’s quarterly sales
over three years.
- The reading explores the two fundamental uses of time-series models: understanding the past and forecasting the
future of a time series. - Candidates will learn to estimate time-series models and how these models explain the changes in a time series
over time.
Machine Learning
Since their introduction in the 1990s, machine learning techniques have become an integral tool in the toolkits of
investment firms. Machine learning (ML) aids analysts in discovering new sources of value and efficiently executing
trades.
- The reading provides an overview of machine learning and essential machine learning algorithms applied to
investing. - Candidates will learn about unsupervised machine learning algorithms, neural networks, deep learning nets, and how
to choose an appropriate ML algorithm for the task.
Big Data Projects
Big data is an umbrella term that refers to data generated by organizations (businesses, financial markets,
governments), individuals (credit cards, social media), sensors, and the Internet of Things. The true impact of big
data on financial analysis is yet to be fully understood, but it has already become an integral part of analysts’
toolkits. Data can aid analysts in developing their hypotheses, and forecasting trends in asset prices, detecting
anomalies, etc.
- Candidates will learn about the concepts that allow analysts to make predictions using structured and unstructured
data. - The reading provides a real-world ‘big data’ project case study that uses sentiment analysis to assess
stock movements.
CFA Quantitative Methods Level 2 Sample Questions and Answers
The sample questions here are typical of the L2 exam’s complexity and depth: formatted as item sets, with a vignette to
deliver a scenario that tests the CFA L2 Curriculum. (On the actual exam, each vignette applies to four questions; we’ve thrown in a couple extra to learn more). And be sure to review the illustrated explanations we’ve provided for each question: UWorld’s question bank is designed to expose you to exam-like questions and explain the concepts tested thoroughly.
Passage
Jae Park, CFA, is a manager of a hedge fund that bases its security selection on advanced quantitative analysis. For
several open job positions with the fund, Park is looking to hire people with scientific and research backgrounds.
Using multiple regression, she would like to evaluate the relationship between the expected salary of the candidates
based on their years of experience (EXP), number of published research papers (PRP), and amount of grant funding
received in their career (GF). The results of that regression are shown below, along with sample critical values. Park
wishes to test the results at a 5% significance level (α = 0.05).
Park also notices that each candidate attended one of five universities. She is considering how to add a variable for
university attended to the regression model and believes dummy variables are the best way to capture this.
Finally, Park suspects that her regression in its current form may violate regression assumptions. Her concern is that
her model might have an artificially large R2 and t-statistics that are understated.
Based on the data in Exhibit 1, the regression is most likely a good predictor of projected salary since:
- it has a high R 2
- the F – statistic has a low p – value.
- most coefficients are statistically significant.
According to Exhibit 1, the most appropriate interpretation of the coefficients is that a higher expected salary will result from:
- more published papers and less grant funding.
- more published papers and more grant funding.
- fewer published papers and more grant funding.
Based on Exhibits 1 and 2, which slope coefficient is most likely to be statistically significant?
- GF
- EXP
- PRP
What to Expect in CFA Level 3 Quantitative Methods?
The CFA Institute does not provide a stand-alone Level 3 Quantitative Methods curriculum. However, the foundational knowledge in the Level 1 and Level 2 curriculum is implicit at CFA Level 3.
Study Tips for CFA Quantitative Methods
-
Create a Solid Foundation
Because they will come up regularly as you move through the curriculum, Quantitative Methods subjects are fundamental knowledge you need to study closely. Candidates can expect to refer back to these readings for refreshers throughout the exam process.
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Lots of Practice and Repetition
CFA Institute suggests the importance of practice, particularly the CFA quant section. UWorld’s Qbank provides over 425 individual questions and explanations (from Level 1 and Level 2) to help you gain fluency with quantitative concepts and practice. Answering as many questions as possible can significantly aid in your grasp of the concepts and the exam's testing formats.
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Start your studies (early) with Quantitative Methods
The order of topics is a common question with most candidates. It is a good idea to start with Quantitative Methods, or at least learn it early in your preparation. These readings introduce fundamental concepts that make up the Level 1 syllabus and that you must grasp to do well on exam day. Additionally, as you go toward your CFA charter, this content will recur throughout the program at every level.
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Prioritize the rationale over memorization of the formula
You would do well to become very familiar with a number of the equations; you'll probably use your calculator a lot when responding to questions on this subject. However, mastering this content involves more than just crunching numbers. While it is important to apply the concepts, it’ll be easier to recall how to do that if you understand the context as well. In some cases, if you really understand the concepts, you won’t have to rely on your calculator at all.
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Level 2 is more conceptual than Level 1, but you’ll still have to do the math
Level 2 Quantitative Methods is more: more complex models, more variables, more formulae, but also more theory and understanding of why the math works. But you will still have to crunch the numbers.
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Put the time in understanding and practicing your itemset/vignette exam technique
Quant is a great place to misread and misuse numbers, so be sure you have a plan for reading through the vignette and then doubling back to find the necessary data—and to ignore the unnecessary data—as you answer specific questions. Always be aware of what the vignette does and doesn’t give you.
-
Be sure to master calculator functions
During the exam, you don’t want to be wishing that you had paid more attention to learning the calculator functions. They can be a huge time saver, keeping you from fumbling and wasting time. And anyways, these are skills that will always come in handy.
For more information, visit our CFA Level 1 Study Guide and CFA Level 2 Study Schedule
