2022 CFA Program Curriculum Level I Quantitative Methods

✍ Scribed by CFA Institute

• Publisher: Wiley
• Year: 2021
• Tongue: English
• Leaves: 518
• Edition: 1
• Category: Library

✦ Synopsis

Prepare for success on the 2022 CFA Level I exam with the latest official CFA^® Program Curriculum.

The 2022 CFA Program Curriculum Level I Box Set contains all the material you need to succeed on the Level I CFA exam in 2022. This set includes the full official curriculum for Level I and is part of the larger CFA Candidate Body of Knowledge (CBOK).

Highly visual and intuitively organized, this box set allows you to:

• Learn from financial thought leaders.
• Access market-relevant instruction.
• Gain critical knowledge and skills.

The set also includes practice questions to assist with your recall of key terms, concepts, and formulas.

Perfect for anyone preparing for the 2022 Level I CFA exam, the 2022 CFA Program Curriculum Level I Box Set is a must-have resource for those seeking the foundational skills required to become a Chartered Financial Analyst^®.

✦ Table of Contents

How to Use the CFA Program Curriculum
Background on the CBOK
Organization of the Curriculum
Features of the Curriculum
Designing Your Personal Study Program
CFA Institute Learning Ecosystem (LES)
Prep Providers
Feedback
Quantitative Methods
1
Quantitative Methods (1)
1
The Time Value of Money
Introduction
Interest Rates
Future Value of a Single Cash Flow (Lump Sum)
Non-­Annual Compounding (Future Value)
Continuous Compounding, Stated and Effective Rates
5.1 Stated and Effective Rates
Future Value of a Series of Cash Flows, Future Value Annuities
6.1 Equal Cash Flows—Ordinary Annuity
6.2 Unequal Cash Flows
Present Value of a Single Cash Flow (Lump Sum)
Non-­Annual Compounding (Present Value)
Present Value of a Series of Equal Cash Flows (Annuities) and Unequal Cash Flows
9.1 The Present Value of a Series of Equal Cash Flows
9.2 The Present Value of a Series of Unequal Cash Flows
Present Value of a Perpetuity and Present Values Indexed at Times other than t=0
10.1 Present Values Indexed at Times Other than t = 0
Solving for Interest Rates, Growth Rates, and Number of Periods
11.1 Solving for Interest Rates and Growth Rates
11.2 Solving for the Number of Periods
Solving for Size of Annuity Payments (Combining Future Value and Present Value Annuities)
Present Value and Future Value Equivalence, Additivity Principle
13.1 The Cash Flow Additivity Principle
Summary
Practice Problems
Solutions
2
Organizing, Visualizing, and Describing Data
Introduction
Data Types
2.1 Numerical versus Categorical Data
2.2 Cross-­Sectional versus Time-­Series versus Panel Data
2.3 Structured versus Unstructured Data
2.4 Data Summarization
Organizing Data for Quantitative Analysis
Summarizing Data Using Frequency Distributions
Summarizing Data Using a Contingency Table
Data Visualization
6.1 Histogram and Frequency Polygon
6.2 Bar Chart
6.3 Tree-­Map
6.4 Word Cloud
6.5 Line Chart
6.6 Scatter Plot
6.7 Heat Map
6.8 Guide to Selecting among Visualization Types
Measures of Central Tendency
7.1 The Arithmetic Mean
7.2 The Median
7.3 The Mode
7.4 Other Concepts of Mean
Quantiles
8.1 Quartiles, Quintiles, Deciles, and Percentiles
8.2 Quantiles in Investment Practice
Measures of Dispersion
9.1 The Range
9.2 The Mean Absolute Deviation
9.3 Sample Variance and Sample Standard Deviation
Downside Deviation and Coefficient of Variation
10.1 Coefficient of Variation
The Shape of the Distributions
11.1 The Shape of the Distributions: Kurtosis
Correlation between Two Variables
12.1 Properties of Correlation
12.2 Limitations of Correlation Analysis
Summary
Practice Problems
Solutions
3
Probability Concepts
Introduction, Probability Concepts, and Odds Ratios
1.1 Probability, Expected Value, and Variance
Conditional and Joint Probability
Expected Value (Mean), Variance, and Conditional Measures of Expected Value and Variance
Expected Value, Variance, Standard Deviation, Covariances, and Correlations of Portfolio Returns
Covariance Given a Joint Probability Function
Bayes' Formula
6.1 Bayes’ Formula
Principles of Counting
Summary
Practice Problems
Solutions
2
Quantitative Methods (2)
4
Common Probability Distributions
Introduction and Discrete Random Variables
1.1 Discrete Random Variables
Discrete and Continuous Uniform Distribution
2.1 Continuous Uniform Distribution
Binomial Distribution
Normal Distribution
4.1 The Normal Distribution
4.2 Probabilities Using the Normal Distribution
4.3 Standardizing a Random Variable
4.4 Probabilities Using the Standard Normal Distribution
Applications of the Normal Distribution
Lognormal Distribution and Continuous Compounding
6.1 The Lognormal Distribution
6.2 Continuously Compounded Rates of Return
Student’s t-, Chi-­Square, and F-Distributions
7.1 Student’s t-Distribution
7.2 Chi-­Square and F-Distribution
Monte Carlo Simulation
Summary
Practice Problems
Solutions
5
Sampling and Estimation
Introduction
Sampling Methods
2.1 Simple Random Sampling
2.2 Stratified Random Sampling
2.3 Cluster Sampling
2.4 Non-­Probability Sampling
2.5 Sampling from Different Distributions
Distribution of the Sample Mean and the Central Limit Theorem
3.1 The Central Limit Theorem
3.2 Standard Error of the Sample Mean
Point Estimates of the Population Mean
4.1 Point Estimators
Confidence Intervals for the Population Mean and Selection of Sample Size
5.1 Selection of Sample Size
Resampling
Data Snooping Bias, Sample Selection Bias, Look-­Ahead Bias, and Time-­Period Bias
7.1 Data Snooping Bias
7.2 Sample Selection Bias
7.3 Look-­Ahead Bias
7.4 Time-­Period Bias
Summary
Practice Problems
Solutions
6
Hypothesis Testing
Introduction
1.1 Why Hypothesis Testing?
1.2 Implications from a Sampling Distribution
The Process of Hypothesis Testing
2.1 Stating the Hypotheses
2.2 Two-­Sided vs. One-­Sided Hypotheses
2.3 Selecting the Appropriate Hypotheses
Identify the Appropriate Test Statistic
3.1 Test Statistics
3.2 Identifying the Distribution of the Test Statistic
Specify the Level of Significance
State the Decision Rule
5.1 Determining Critical Values
5.2 Decision Rules and Confidence Intervals
5.3 Collect the Data and Calculate the Test Statistic
Make a Decision
6.1 Make a Statistical Decision
6.2 Make an Economic Decision
6.3 Statistically Significant but Not Economically Significant?
The Role of p-Values
Multiple Tests and Interpreting Significance
Tests Concerning a Single Mean
Test Concerning Differences between Means with Independent Samples
Test Concerning Differences between Means with Dependent Samples
Testing Concerning Tests of Variances (Chi-­Square Test)
12.1 Tests of a Single Variance
12.2 Test Concerning the Equality of Two Variances (F-Test)
Parametric vs. Nonparametric Tests
13.1 Uses of Nonparametric Tests
13.2 Nonparametric Inference: Summary
Tests Concerning Correlation
14.1 Parametric Test of a Correlation
14.2 Tests Concerning Correlation: The Spearman Rank Correlation Coefficient
Test of Independence Using Contingency Table Data
Summary
Practice Problems
Solutions
7
Introduction to Linear Regression
Simple Linear Regression
Estimating the Parameters of a Simple Linear Regression
2.1 The Basics of Simple Linear Regression
2.2 Estimating the Regression Line
2.3 Interpreting the Regression Coefficients
2.4 Cross-­Sectional vs. Time-­Series Regressions
Assumptions of the Simple Linear Regression Model
3.1 Assumption 1: Linearity
3.2 Assumption 2: Homoskedasticity
3.3 Assumption 3: Independence
3.4 Assumption 4: Normality
Analysis of Variance
4.1 Breaking down the Sum of Squares Total into Its Components
4.2 Measures of Goodness of Fit
4.3 ANOVA and Standard Error of Estimate in Simple Linear Regression
Hypothesis Testing of Linear Regression Coefficients
5.1 Hypothesis Tests of the Slope Coefficient
5.2 Hypothesis Tests of the Intercept
5.3 Hypothesis Tests of Slope When Independent Variable Is an Indicator Variable
5.4 Test of Hypotheses: Level of Significance and p-Values
Prediction Using Simple Linear Regression and Prediction Intervals
Functional Forms for Simple Linear Regression
7.1 The Log-­Lin Model
7.2 The Lin-­Log Model
7.3 The Log-­Log Model
7.4 Selecting the Correct Functional Form
Summary
Practice Problems
Solutions
Appendices