<\/p>\n\n\n
LOS<\/p>\n
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<\/a><\/p>\n LOS b: calculate and interpret different approaches to return measurement over time and describe their appropriate uses<\/a><\/p>\n LOS c: compare the money-weighted and time-weighted rates of return and evaluate the performance of portfolios based on these measures<\/a><\/p>\n LOS d: calculate and interpret annualized return measures and continuously compounded returns and describe their appropriate uses<\/a><\/p>\n LOS e: calculate and interpret major return measures and describe their appropriate uses<\/a><\/p>\n LOS a: calculate and interpret the present value (PV) of fixed-income and equity instruments based on expected future cash flows<\/a><\/p>\n 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<\/a><\/p>\n 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<\/a><\/p>\n LOS a: calculate, interpret, and evaluate measures of central tendency and location to address an investment problem<\/a><\/p>\n LOS b: calculate, interpret, and evaluate measures of dispersion to address an investment problem<\/a><\/p>\n LOS c: interpret and evaluate measures of skewness and kurtosis to address an investment problem<\/a><\/p>\n LOS d: interpret the correlation between two variables to address an investment problem<\/a><\/p>\n LOS a: calculate expected values, variances, and standard deviations and demonstrate their application to investment problems<\/a><\/p>\n LOS b: formulate an investment problem as a probability tree and explain the use of conditional expectations in investment application<\/a><\/p>\n LOS c: calculate and interpret an updated probability in an investment setting using Bayes\u2019 formula<\/a><\/p>\n LOS a: calculate and interpret the expected value, variance, standard deviation, covariances, and correlations of portfolio returns<\/a><\/p>\n LOS b: calculate and interpret the covariance and correlation of portfolio returns using a joint probability function for returns<\/a><\/p>\n LOS c: define shortfall risk, calculate the safety-first ratio, and identify an optimal portfolio using Roy\u2019s safety-first criterion<\/a><\/p>\n 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<\/a><\/p>\n LOS b: describe Monte Carlo simulation and explain how it can be used in investment applications<\/a><\/p>\n LOS c: describe the use of bootstrap resampling in conducting a simulation based on observed data in investment applications<\/a><\/p>\n LOS a: compare and contrast simple random, stratified random, cluster, convenience, and judgmental sampling and their implications for sampling error in an investment problem<\/a><\/p>\n LOS b: explain the central limit theorem and its importance for the distribution and standard error of the sample mean<\/a><\/p>\n LOS c: describe the use of resampling (bootstrap, jackknife) to estimate the sampling distribution of a statistic<\/a><\/p>\n LOS a: explain hypothesis testing and its components, including statistical significance, Type I and Type II errors, and the power of a test<\/a><\/p>\n 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<\/a><\/p>\n LOS c: compare and contrast parametric and nonparametric tests, and describe situations where each is the more appropriate type of test<\/a><\/p>\n 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<\/a><\/p>\n LOS b: explain tests of independence based on contingency table data<\/a><\/p>\n 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<\/a><\/p>\n 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<\/a><\/p>\n LOS c: calculate and interpret measures of fit and formulate and evaluate tests of fit and of regression coefficients in a simple linear regression<\/a><\/p>\n 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<\/a><\/p>\n 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<\/a><\/p>\n LOS f: describe different functional forms of simple linear regressions<\/a><\/p>\n LOS a: describe aspects of \u201cfintech\u201d that are directly relevant for the gathering and analyzing of financial data.<\/a><\/p>\n LOS b: describe Big Data, artificial intelligence, and machine learning<\/a><\/p>\n LOS c: describe applications of Big Data and Data Science to investment management<\/a><\/p>\n LOS a: Describe, compare, and interpret returns<\/a><\/p>\n LOS b: describe, compare, and interpret required rates of return, risk-free rates, risk premia, and inflation<\/a><\/p>\n LOS a: Calculate, compare, and interpret different types of returns for financial assets, instruments, and indicators<\/a><\/p>\n LOS a: Calculate and compare money-weighted and time-weighted rates of return<\/a><\/p>\n 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<\/a><\/p>\n LOS a: Calculate and interpret the present value (PV) of fixed-income and equity instruments based on expected future cash flows <\/a><\/p>\n 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<\/a><\/p>\n 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<\/a><\/p>\n LOS a: Calculate, interpret, and evaluate various measures of (1) central tendency and location and (2) dispersion<\/a><\/p>\n LOS b: Describe, interpret, and evaluate measures of skewness and kurtosis<\/a><\/p>\n LOS c: Calculate, interpret, and evaluate covariance and correlation<\/a><\/p>\n LOS d: Calculate, interpret, and evaluate semi-deviation and coefficient of variation<\/a><\/p>\n LOS a: Calculate, interpret, and evaluate unconditional expected values for mean, variance, and covariance<\/a><\/p>\n LOS b: Calculate, interpret, and evaluate the principal moments of key statistical distributions used in finance<\/a><\/p>\n LOS c: Calculate, interpret, and evaluate conditional expectations, variances, and covariances<\/a><\/p>\n LOS d: Formulate investment problems through Bayesian updating<\/a><\/p>\n LOS a: Explain the central limit theorem and the application of confidence intervals and sampling methodologies<\/a><\/p>\n 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<\/a><\/p>\n 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<\/a><\/p>\n LOS a: Calculate, interpret, and evaluate the expected return, variance, standard deviation, covariance, and correlation of portfolio returns<\/a><\/p>\n LOS b: Describe, calculate, and interpret the minimum-variance portfolio and portfolios that lie on the efficient frontier<\/a><\/p>\n LOS c: Explain the selection of an optimal portfolio, given an investor\u2019s risk aversion and the capital allocation line, and how this extends to the market portfolio and the capital market line<\/a><\/p>\n LOS a: Describe historical simulation and explain how it can be used in investment applications<\/a><\/p>\n LOS b: Describe bootstrap resampling, and explain how it can be used in investment applications<\/a><\/p>\n LOS c: Describe Monte Carlo simulation and explain how it can be used in investment applications<\/a><\/p>\n LOS a: Describe, interpret, and explain simple linear regression, including coefficient estimation using the least squares criterion<\/a><\/p>\n LOS b: Describe and compare the assumptions of simple linear regression, identify violations through analyzing residuals, evaluate the estimated model\u2019s goodness-of-fit and regression coefficients, and results of ANOVA estimates<\/a><\/p>\n 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<\/a><\/p>\nLearning Module 2 – Time Value of Money in Finance<\/span><\/strong><\/h3>\n
Learning Module 3 – Statistical Measures of Asset Returns<\/strong><\/h3>\n
Learning Module 4 –<\/strong>\u00a0Probability Trees and Conditional Expectations<\/strong><\/span><\/h3>\n
Learning Module 5 – <\/strong>Portfolio Mathematics<\/strong><\/h3>\n
Learning Module 6 – Simulation Methods<\/span><\/strong><\/h3>\n
Learning Module 7 \u2013 Estimation and Inference<\/strong><\/h3>\n
Learning Module 8 \u2013 Hypothesis Testing<\/strong><\/h3>\n
Learning Module 9 \u2013 Parametric and Non-Parametric<\/strong><\/h3>\n
Learning Module 10 \u2013 Introduction to Linear Regression<\/strong><\/h3>\n
Learning Module 11 <\/span><\/strong>\u2013 <\/strong>Introduction to Big Data Techniques<\/span><\/strong><\/h3>\n
\u00a0<\/h2>\n
2026\/2027 Syllabus<\/h2>\n
Learning Module 1: Returns of Financial Assets and Instruments<\/b><\/h3>\n
Learning Module 2: Types of Financial Returns<\/b><\/h3>\n
Learning Module 3: Benchmarking Returns<\/b><\/h3>\n
Learning Module 4: Time Value of Money in Finance<\/b><\/h3>\n
Learning Module 5: Statistical Characteristics of Asset Returns<\/b><\/h3>\n
Learning Module 6: Statistical Distributions for Financial Asset Prices and Returns<\/b><\/h3>\n
Learning Module 7: Estimation and Hypothesis Testing<\/b><\/h3>\n
Learning Module 8: The Return and Risk of a Financial Portfolio<\/b><\/h3>\n
Learning Module 9: <\/b>Simulation of Financial Asset Prices and Returns<\/b><\/h3>\n
Learning Module 10: Applications of Simple Linear Regression in Finance<\/strong><\/h3>\n