2025 CFA© Program Curriculum Level I Volume 1 - QUANTITATIVE METHODS

✍ Scribed by CFA Institute

• Publisher: CFA Institute
• Year: 2025
• Tongue: English
• Leaves: 360
• Edition: 2025
• Category: Library

✦ Table of Contents

How to Use the CFA Program Curriculum
CFA Institute Learning Ecosystem (LES)
Designing Your Personal Study Program
Errata
Other Feedback
Quantitative Methods
Learning Module 1 Rates and Returns
Introduction
Interest Rates and Time Value of Money
Determinants of Interest Rates
Rates of Return
Holding Period Return
Arithmetic or Mean Return
Geometric Mean Return
The Harmonic Mean
Money-Weighted and Time-Weighted Return
Calculating the Money Weighted Return
Annualized Return
Non-annual Compounding
Annualizing Returns
Continuously Compounded Returns
Other Major Return Measures and Their Applications
Gross and Net Return
Pre-Tax and After-Tax Nominal Return
Real Returns
Leveraged Return
Practice Problems
Solutions
Learning Module 2 Time Value of Money in Finance
Introduction
Time Value of Money in Fixed Income and Equity
Fixed-Income Instruments and the Time Value of Money
Equity Instruments and the Time Value of Money
Implied Return and Growth
Implied Return for Fixed-Income Instruments
Equity Instruments, Implied Return, and Implied Growth
Cash Flow Additivity
Implied Forward Rates Using Cash Flow Additivity
Forward Exchange Rates Using No Arbitrage
Option Pricing Using Cash Flow Additivity
Practice Problems
Solutions
Learning Module 3 Statistical Measures of Asset Returns
Introduction
Measures of Central Tendency and Location
Measures of Central Tendency
Dealing with Outliers
Measures of Location
Measures of Dispersion
The Range
Mean Absolute Deviations
Sample Variance and Sample Standard Deviation
Downside Deviation and Coefficient of Variation
Measures of Shape of a Distribution
Correlation between Two Variables
Scatter Plot
Covariance and Correlation
Properties of Correlation
Limitations of Correlation Analysis
Practice Problems
Solutions
Learning Module 4 Probability Trees and Conditional Expectations
Introduction
Expected Value and Variance
Probability Trees and Conditional Expectations
Total Probability Rule for Expected Value
Bayes' Formula and Updating Probability Estimates
Bayes’ Formula
Practice Problems
Solutions
Learning Module 5 Portfolio Mathematics
Introduction
Portfolio Expected Return and Variance of Return
Covariance
Correlation
Forecasting Correlation of Returns: Covariance Given a Joint Probability Function
Portfolio Risk Measures: Applications of the Normal Distribution
References
Practice Problems
Solutions
Learning Module 6 Simulation Methods
Introduction
Lognormal Distribution and Continuous Compounding
The Lognormal Distribution
Continuously Compounded Rates of Return
Monte Carlo Simulation
Bootstrapping
Practice Problems
Solutions
Learning Module 7 Estimation and Inference
Introduction
Sampling Methods
Simple Random Sampling
Stratified Random Sampling
Cluster Sampling
Non-Probability Sampling
Sampling from Different Distributions
Central Limit Theorem and Inference
The Central Limit Theorem
Standard Error of the Sample Mean
Bootstrapping and Empirical Sampling Distributions
Practice Problems
Solutions
Learning Module 8 Hypothesis Testing
Introduction
Hypothesis Tests for Finance
The Process of Hypothesis Testing
Tests of Return and Risk in Finance
Test Concerning Differences between Means with Dependent Samples
Test Concerning the Equality of Two Variances
Parametric versus Nonparametric Tests
Uses of Nonparametric Tests
Nonparametric Inference: Summary
Practice Problems
Solutions
Learning Module 9 Parametric and Non-Parametric Tests of Independence
Introduction
Tests Concerning Correlation
Parametric Test of a Correlation
Non-Parametric Test of Correlation: The Spearman Rank Correlation Coefficient
Tests of Independence Using Contingency Table Data
Practice Problems
Solutions
Learning Module 10 Simple Linear Regression
Introduction
Estimation of the Simple Linear Regression Model
Introduction to Linear Regression
Estimating the Parameters of a Simple Linear Regression
Assumptions of the Simple Linear Regression Model
Assumption 1: Linearity
Assumption 2: Homoskedasticity
Assumption 3: Independence
Assumption 4: Normality
Hypothesis Tests in the Simple Linear Regression Model
Analysis of Variance
Measures of Goodness of Fit
Hypothesis Testing of Individual Regression Coefficients
Prediction in the Simple Linear Regression Model
ANOVA and Standard Error of Estimate in Simple Linear Regression
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
Practice Problems
Solutions
Learning Module 11 Introduction to Big Data Techniques
Introduction
How Is Fintech used in Quantitative Investment Analysis?
Big Data
Advanced Analytical Tools: Artificial Intelligence and Machine Learning
Tackling Big Data with Data Science
Data Processing Methods
Data Visualization
Text Analytics and Natural Language Processing
Practice Problems
Solutions
Learning Module 12 Appendices A-E
Appendices A-E
Glossary