2022 CFA Program Curriculum Level I Quantitative Methods

Description

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®. 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