2024/2025 Syllabus
Learning Module 1 – Rates and Returns
LOS
LOS a: interpret interest rates as required rates of return, discount rates, or opportunity costs and explain an interest rate as the sum of a real risk-free rate and premiums that compensate investors for bearing distinct types of risk
LOS b: calculate and interpret different approaches to return measurement over time and describe their appropriate uses
LOS c: compare the money-weighted and time-weighted rates of return and evaluate the performance of portfolios based on these measures
LOS d: calculate and interpret annualized return measures and continuously compounded returns and describe their appropriate uses
LOS e: calculate and interpret major return measures and describe their appropriate uses
Learning Module 2 – Time Value of Money in Finance
LOS a: calculate and interpret the present value (PV) of fixed-income and equity instruments based on expected future cash flows
LOS b: calculate and interpret the implied return of fixed-income instruments and required return and implied growth of equity instruments given the present value (PV) and cash flows
LOS c: explain the cash flow additivity principle, its importance for the no-arbitrage condition, and its use in calculating implied forward interest rates, forward exchange rates, and option values
Learning Module 3 – Statistical Measures of Asset Returns
LOS a: calculate, interpret, and evaluate measures of central tendency and location to address an investment problem
LOS b: calculate, interpret, and evaluate measures of dispersion to address an investment problem
LOS c: interpret and evaluate measures of skewness and kurtosis to address an investment problem
LOS d: interpret the correlation between two variables to address an investment problem
Learning Module 4 – Probability Trees and Conditional Expectations
LOS a: calculate expected values, variances, and standard deviations and demonstrate their application to investment problems
LOS b: formulate an investment problem as a probability tree and explain the use of conditional expectations in investment application
LOS c: calculate and interpret an updated probability in an investment setting using Bayes’ formula
Learning Module 5 – Portfolio Mathematics
LOS a: calculate and interpret the expected value, variance, standard deviation, covariances, and correlations of portfolio returns
LOS b: calculate and interpret the covariance and correlation of portfolio returns using a joint probability function for returns
LOS c: define shortfall risk, calculate the safety-first ratio, and identify an optimal portfolio using Roy’s safety-first criterion
Learning Module 6 – Simulation Methods
LOS a: explain the relationship between normal and lognormal distributions and why the lognormal distribution is used to model asset prices when using continuously compounded asset returns
LOS b: describe Monte Carlo simulation and explain how it can be used in investment applications
LOS c: describe the use of bootstrap resampling in conducting a simulation based on observed data in investment applications
Learning Module 7 – Estimation and Inference
LOS a: compare and contrast simple random, stratified random, cluster, convenience, and judgmental sampling and their implications for sampling error in an investment problem
LOS b: explain the central limit theorem and its importance for the distribution and standard error of the sample mean
LOS c: describe the use of resampling (bootstrap, jackknife) to estimate the sampling distribution of a statistic
Learning Module 8 – Hypothesis Testing
LOS a: explain hypothesis testing and its components, including statistical significance, Type I and Type II errors, and the power of a test
LOS b: construct hypothesis tests and determine their statistical significance, the associated Type I and Type II errors, and the power of the test given a significance level
LOS c: compare and contrast parametric and nonparametric tests, and describe situations where each is the more appropriate type of test
Learning Module 9 – Parametric and Non-Parametric
LOS a: explain parametric and non-parametric tests of the hypothesis that the population correlation coefficient equals zero and determine whether the hypothesis is rejected at a given level of significance
LOS b: explain tests of independence based on contingency table data
Learning Module 10 – Introduction to Linear Regression
LOS a: describe a simple linear regression model, how the least squares criterion is used to estimate regression coefficients, and the interpretation of these coefficients
LOS b: explain the assumptions underlying the simple linear regression model, and describe how residuals and residual plots indicate if these assumptions may have been violated
LOS c: calculate and interpret measures of fit and formulate and evaluate tests of fit and of regression coefficients in a simple linear regression
LOS d: describe the use of analysis of variance (ANOVA) in regression analysis, interpret ANOVA results, and calculate and interpret the standard error of estimate in a simple linear regression
LOS e: calculate and interpret the predicted value for the dependent variable, and a prediction interval for it, given an estimated linear regression model and a value for the independent variable
LOS f: describe different functional forms of simple linear regressions
Learning Module 11 – Introduction to Big Data Techniques
LOS a: describe aspects of “fintech” that are directly relevant for the gathering and analyzing of financial data.
LOS b: describe Big Data, artificial intelligence, and machine learning
LOS c: describe applications of Big Data and Data Science to investment management
2026/2027 Syllabus
Learning Module 1: Returns of Financial Assets and Instruments
LOS a: Describe, compare, and interpret returns
LOS b: describe, compare, and interpret required rates of return, risk-free rates, risk premia, and inflation
Learning Module 2: Types of Financial Returns
LOS a: Calculate, compare, and interpret different types of returns for financial assets, instruments, and indicators
Learning Module 3: Benchmarking Returns
LOS a: Calculate and compare money-weighted and time-weighted rates of return
LOS b: Describe the choices and the implications of the different weighting methods used in index construction and management, and calculate, interpret, and explain the value and the returns of an index
Learning Module 4: Time Value of Money in Finance
LOS a: Calculate and interpret the present value (PV) of fixed-income and equity instruments based on expected future cash flows
LOS b: Calculate and interpret the implied return of fixed-income instruments and required return and implied growth of equity instruments given the present value (PV) and cash flows
LOS c: Explain the cash flow additivity principle, its importance for the no-arbitrage condition, and its use in calculating implied forward interest rates, forward exchange rates, and option values
Learning Module 5: Statistical Characteristics of Asset Returns
LOS a: Calculate, interpret, and evaluate various measures of (1) central tendency and location and (2) dispersion
LOS b: Describe, interpret, and evaluate measures of skewness and kurtosis
LOS c: Calculate, interpret, and evaluate covariance and correlation
LOS d: Calculate, interpret, and evaluate semi-deviation and coefficient of variation
Learning Module 6: Statistical Distributions for Financial Asset Prices and Returns
LOS a: Calculate, interpret, and evaluate unconditional expected values for mean, variance, and covariance
LOS b: Calculate, interpret, and evaluate the principal moments of key statistical distributions used in finance
LOS c: Calculate, interpret, and evaluate conditional expectations, variances, and covariances
LOS d: Formulate investment problems through Bayesian updating
Learning Module 7: Estimation and Hypothesis Testing
LOS a: Explain the central limit theorem and the application of confidence intervals and sampling methodologies
LOS b: Explain hypothesis testing and its components, including statistical significance, Type I and Type II errors, and the power of a test; construct appropriate hypothesis tests; and interpret the results
LOS c: Compare and contrast parametric and non-parametric tests, describe situations in which each is the more appropriate type of test, construct appropriate hypothesis tests, and interpret the results
Learning Module 8: The Return and Risk of a Financial Portfolio
LOS a: Calculate, interpret, and evaluate the expected return, variance, standard deviation, covariance, and correlation of portfolio returns
LOS b: Describe, calculate, and interpret the minimum-variance portfolio and portfolios that lie on the efficient frontier
LOS c: Explain the selection of an optimal portfolio, given an investor’s risk aversion and the capital allocation line, and how this extends to the market portfolio and the capital market line
Learning Module 9: Simulation of Financial Asset Prices and Returns
LOS a: Describe historical simulation and explain how it can be used in investment applications
LOS b: Describe bootstrap resampling, and explain how it can be used in investment applications
LOS c: Describe Monte Carlo simulation and explain how it can be used in investment applications
Learning Module 10: Applications of Simple Linear Regression in Finance
LOS a: Describe, interpret, and explain simple linear regression, including coefficient estimation using the least squares criterion
LOS b: Describe and compare the assumptions of simple linear regression, identify violations through analyzing residuals, evaluate the estimated model’s goodness-of-fit and regression coefficients, and results of ANOVA estimates
LOS c: Calculate and interpret predicted values, the standard error of the estimate, and prediction intervals for the dependent variable in a simple linear regression model and describe different functional forms
LOS d: Calculate and interpret the variable estimates of the capital asset pricing model (CAPM)
Learning Module 11: Introduction to Financial Data Science
LOS a: Describe how big data, machine learning, and artificial intelligence are used in financial data science, fintech, and investment management