
CFA LEVEL I Quantitative Methods
Online Course
Basic Info : Quantitative Methods Level : CFA LEVEL 1 Commitment : 20 hour (10 sessions of 2 hours each) Language : English Schedule Date:
What Will I Learn
When you are done with this course, you will be able to:
 Learn quantitative concepts and techniques used in financial analysis and investment decision making.
 Gain an indepth understanding of the time value of money and discounted cash flow analysis that form the basis for cash flow and security valuation.
 Attain the skills to unravel the complex characteristics of return distributions such as symmetry, skewness, and kurtosis.
 Understand how descriptive statistics is used for conveying important data attributes such as central tendency, location, and dispersion.
Curriculum
Session 1
 Time Value of Money
 • Interpret interest rates as required rates of return, discount rates or opportunity costs
 • Calculate and interpret the effective annual rate, given the stated annual interest rate and the frequency of compounding
 • Solve time value of money problems for different frequencies of compounding
 • Calculate and interpret the future value (FV) and present value (PV) of a single sum of money, an ordinary annuity, an annuity due, a perpetuity (PV only) and a series of unequal cash flows
 • Use of a time line in modelling and solving time value of money problems
Session 2
 Discounted Cash Flows (DCF)
 • Calculate and interpret the net present value (NPV) and the internal rate of return (IRR) of an investment
 • Contrast the NVP rule to the IRR and identify problems associated with the IRR rule
 • Calculate and interpret a holding period return (total return)
 • Calculate and compare the moneyweighted and timeweighted rates of return of a portfolio based on these measure
 • Calculate and interpret the bank discount yield, holding period yield, effective annual yield and money market yield for US Treasury bills and other money market instruments
 • Convert among holding period yields, money market yields, effective annual yields and bond equivalent yields
Session 3
 Statistical Concepts and Market Returns
 • Descriptive statistics and inferential statistics, between a population sample and among the types of measurement scales
 • Define a parameter, a sample statistic and a frequency distribution
 • Relative and cumulative relative frequencies and their calculation
 • Properties of a data set presented as a histogram or a frequency polygon
 • Measures of central tendency including the population mean, sample mean, arithmetic mean, weighted average or mean, geometric mean, harmonic mean, median and mode
 • Quartiles, quintiles, deciles and percentiles
 • Calculate and interpret a range and a mean absolute deviation and the variance and standard deviation of the mean using Chebyshev’s inequality
 • Calculate and interpret the coefficient of variation and the Sharpe ratio
 • Skewness and the meaning of a positively or negatively skewed return distribution
 • The relative locations of the mean, median and mode for a unimodal, nonsymmetrical distribution
 • Measures of sample skewness and kurtosis
 • Use of arithmetic and geometric means when analyzing investment returns
Session 4
 Probability Concepts
 • Random variable, an outcome, an event, mutually exclusive and exhaustive events
 • Properties of probability and empirical subjective and priori probabilities
 • Probability of an event in terms of odd for and against the event
 • Unconditional and conditional probabilities
 • Multiplication, addition and total probability rules
 • Calculate and interpret joint probability of two events, the probability that at least one of the two events will occur given the probability of each and the joint probability of two events, joint probability of any number of independent events
 • Unconditional probability using total probability rule
 • Use of conditional expectation in investment application
 • Use of a tree diagram to represent an investment problem
 • Covariance and correlation
 • Calculate and interpret an updated probability using Bayes’ formula
 • Factorial, combination and permutation concepts
Session 5
 Common Probability Distributors I
 • Probability distribution
 • Discrete and continuous random variables and their probability function
 • Cumulative distribution function
 • Discrete uniform random variable, a Bernoulli random variable and binomial random variable
 • Calculate and interpret probabilities given the discrete uniform and the binomial distribution functions
 • Construction of a binomial tree to describe stock price movement
 • Continuous uniform distribution and its calculation
Session 6
 Common Probability Distributors II
 • Normal distribution
 • Univariate and multivariate
 • The role of correlation in multivariate normal distribution
 • Standard normal distribution and its calculation and how to standardize a random variable
 • Shortfall, calculate the safetyfirst ratio and select an optimal portfolio using Roy’s safetyfirst criterion
 • Normal and lognormal distribution
 • Discretely and continuously compounded rates of return
 • Monte Carlo simulation and historical simulation
Session 7
 Sampling and Estimation I
 • Simple random sampling and a sampling distribution
 • Sampling error
 • Distinguish between simple random and stratified random sampling
 • Time series and crosssectional data
 • Central limit theorem
 • Calculate and interpret the standard error of the mean sample
Session 8
 Sampling and Estimation II
 • Learn the desirable properties of an estimator
 • A point estimate and a confidence interval estimate of a population parameter
 • Describe properties of students tdistribution and calculate and interpret its degrees of freedom
 • Calculate and interpret a confidence interval for a population mean, given a normal distribution with
 • Calculate and interpret covariance given a joint probability function;
 • Calculate and interpret an updated probability using bayes’ formula; identify the most appropriate method to solve a particular counting problem and solve counting problems using factorial, combination, and permutation concepts.
 A known population
 An unknown population variance
 An unknown population variance and a large sample size
 The issues regarding selection of the appropriate sample size, datamining bias, sample selection bias, lookahead bias and timeperiod bias
Session 9
 Hypothesis Testing
 • Define hypothesis, the steps of hypothesis testing
 • Null and alternative hypothesis
 • Onetailed and twotailed tests of hypothesis
 • A test statistic, Type I and Type II errors, a significance level and its uses in a hypothesis
 • A decision rule, the power of a test and the relation between confidence intervals and hypothesis tests
 • A statistical result and an economically meaningful result
 • Explain and interpret the pvalue as it relates to hypothesis testing
 • Identify the appropriate test statistic and interpret the results for a hypothesis test concerning the population mean of large and small samples when the population is normally or approximately normally distributed and the variance is
 Known
 Unknown
 • Identify the appropriate test statistic and interpret the results for a hypothesis test concerning the mean difference of two normally distributed population
 • Parametric and nonparametric tests and describe the situations in which the uses of nonparametric tests may be appropriate
Session 10
 Technical Analysis
 • Principles of technical analysis, its application and its underlying assumptions
 • Construction of different types of technical analysis charts and interpret them
 • Uses of trend, support, resistance lines and changes in polarity
 • Common chart patterns
 • Common technical analysis indicators
 • Explain how technical analysts use cycles
 • The key tenets of Elliot Wave Theory and the importance of Fibonacci numbers
 • Intermarket analysis as it relates to technical analysis and asset allocation
 • Elliot Wave Theory
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