CFA Level 1 Volume 1

• Publisher: CFA Institute
• Year: 2023
• Tongue: English
• Leaves: 512
• Category: Library

✦ Table of Contents

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