# 2016 CFA Level 1 Quantitative Methods Review

This course will help you do a quick review of Quantitative Methods in 2 hours
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• Lectures 12
• Length 2.5 hours
• Skill Level All Levels
• Languages English
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Published 10/2015 English

### Course Description

This short review course on CFA Level 1 Quantitative Methods will give you a quick recap of all Quantitative topics for the CFA exam. This course cover the important quantitative techniques essential in financial analysis which are used throughout the CFA Program curriculum.I will cover all important topics including Time Value of Money, Discounted Cash Flow Applications, Probability Concepts, Common Probability Distributions, Sampling and Estimation, Hypothesis Testing, and Technical analysis. In ten short lectures of 10 to 15 mins I will help you revise the Quantitative methods course in around two hours and make you confident of passing the exam.

Section 1 lectures will cover several tools of quantitative analysis such as time value of money, descriptive statistics, and probability. Time value of money calculations are the basic tools used to support corporate finance decisions and to estimate the fair value of fixed income, equity, and other types of securities or investments. After this Discounted Cash Flow Applications and descriptive statistics are reviewed which provide essential tools for describing and evaluating return and risk. In the final lecture of section 1 probability theory concepts are needed to understand investment decision making under conditions of uncertainty.

Section 2 reviews discrete and continuous probability distributions most commonly used to describe the behavior of random variables. Probability theory and calculations are widely used in finance, for example, in the field of investment and project valuation and in financial risk management. Furthermore, this section explains how to estimate different parameters (e.g., mean and standard deviation) of a population if only a sample, rather than the whole population, can be observed. This lecture on Hypothesis testingpresents techniques that are used to accept or reject an assumed hypothesis (null hypothesis) about various parameters of a population. The final lecture goes over the fundamentals of technical analysis and illustrates how it is used to analyze securities and securities markets. Technical analysis is an investment approach that often makes use of quantitative methods.

Be aware that this course is not a substitute for reading the CBOK. I urge you to read it to get a good understanding of Quantitative Methods and then use these lectures for quick and confident review before the exam.

### What are the requirements?

• Should preferably have read Quanititative Methods topic from CFA CBOK.

### What am I going to get from this course?

• Have good knowledge of Quantitative Methods for CFA exam
• Be confident of performing well on Quantiitative Methods portion of the CFA exam

### Who is the target audience?

• CFA Level 1 candidates

### What you get with this course?

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# Curriculum

Section 1: Study Session 1: Basic Concepts
00:55

The candidate should be able to:

• interpret interest rates as required rates of return, discount rates, or opportunity costs;
• explain an interest rate as the sum of a real risk-free rate and premiums that compensate investors for bearing distinct types of risk;
• 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;
• demonstrate the use of a time line in modeling and solving time value of money problems.
18:20

The student would be able to :

• interpret interest rates as required rates of return, discount rates, or opportunity costs;
• explain an interest rate as the sum of a real risk-free rate and premiums that compensate investors for bearing distinct types of risk;
• 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;
• demonstrate the use of a time line in modeling and solving time value of money problems.
12:49

The Student would be able to :

• calculate and interpret the net present value (NPV) and the internal rate of return (IRR) of an investment;
• contrast the NPV rule to the IRR rule, and identify problems associated with the IRR rule;
• calculate and interpret a holding period return (total return);
• calculate and compare the money-weighted and time-weighted rates of return of a portfolio and evaluate the performance of portfolios based on these measures;
• 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.
15:39

The Student would be able to :

• distinguish between descriptive statistics and inferential statistics, between a population and a sample, and among the types of measurement scales;
• define a parameter, a sample statistic, and a frequency distribution;
• calculate and interpret relative frequencies and cumulative relative frequencies, given a frequency distribution;
• describe the properties of a data set presented as a histogram or a frequency polygon;
• calculate and interpret measures of central tendency, including the population mean, sample mean, arithmetic mean, weighted average or mean, geometric mean, harmonic mean, median, and mode;
• calculate and interpret quartiles, quintiles, deciles, and percentiles;
• calculate and interpret
1. a range and a mean absolute deviation and
2. the variance and standard deviation of a population and of a sample;
10:00

The Student would be able to :

• calculate and interpret the proportion of observations falling within a specified number of standard deviations of the mean using Chebyshev’s inequality;
• calculate and interpret the coefficient of variation and the Sharpe ratio;
• explain skewness and the meaning of a positively or negatively skewed return distribution;
• describe the relative locations of the mean, median, and mode for a unimodal, nonsymmetrical distribution;
• explain measures of sample skewness and kurtosis;
• compare the use of arithmetic and geometric means when analyzing investment returns.
16:56

The Student would be able to :

• define a random variable, an outcome, an event, mutually exclusive events, and exhaustive events;
• state the two defining properties of probability and distinguish among empirical, subjective, and a priori probabilities;
• state the probability of an event in terms of odds for and against the event;
• distinguish between unconditional and conditional probabilities; e explain the multiplication, addition, and total probability rules;
• calculate and interpret
1. the joint probability of two events,
2. the probability that at least one of two events will occur, given the probability of each and the joint probability of the two events, and
3. a joint probability of any number of independent events; g distinguish between dependent and independent events;
• calculate and interpret an unconditional probability using the total probability rule;
• explain the use of conditional expectation in investment applications;
• explain the use of a tree diagram to represent an investment problem;
• calculate and interpret covariance and correlation;
• calculate and interpret the expected value, variance, and standard deviation of a random variable and of returns on a portfolio;
• 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.
Section 2: Study Session 2: Application
17:23

The Student would be able to :

• define a probability distribution and distinguish between discrete and continuous random variables and their probability functions;
• describe the set of possible outcomes of a specified discrete random variable;
• interpret a cumulative distribution function;
• calculate and interpret probabilities for a random variable, given its cumulative distribution function;
• define a discrete uniform random variable, a Bernoulli random variable, and a binomial random variable;
• calculate and interpret probabilities given the discrete uniform and the binomial distribution functions;
• construct a binomial tree to describe stock price movement; h calculate and interpret tracking error;
• define the continuous uniform distribution and calculate and interpret probabilities, given a continuous uniform distribution; j explain the key properties of the normal distribution;
• distinguish between a univariate and a multivariate distribution and explain the role of correlation in the multivariate normal distribution;
• determine the probability that a normally distributed random variable lies inside a given interval;
• define the standard normal distribution, explain how to standardize a random variable, and calculate and interpret probabilities using the standard normal distribution;
• define shortfall risk, calculate the safety-first ratio, and select an optimal portfolio using Roy’s safety-first criterion;
• explain the relationship between normal and lognormal distributions and why the lognormal distribution is used to model asset prices;
• distinguish between discretely and continuously compounded rates of return and calculate and interpret a continuously compounded rate of return, given a specific holding period return;
• explain Monte Carlo simulation and describe its applications and limitations;
• compare Monte Carlo simulation and historical simulation.
11:26

The Student would be able to :

• define simple random sampling and a sampling distribution;
• explain sampling error;
• distinguish between simple random and stratified random sampling;
• distinguish between time-series and cross-sectional data;
• explain the central limit theorem and its importance;
• calculate and interpret the standard error of the sample mean;
• identify and describe desirable properties of an estimator;
• distinguish between a point estimate and a confidence interval estimate of a population parameter;
• describe properties of Student’s t-distribution and calculate and interpret its degrees of freedom;
• calculate and interpret a confidence interval for a population mean, given a normal distribution with
1. a known population variance,
2. an unknown population variance, or
3. an unknown variance and a large sample size;
• describe the issues regarding selection of the appropriate sample size, datamining bias, sample selection bias, survivorship bias, look-ahead bias, and timeperiod bias.
19:48

The Student would be able to :

• define a hypothesis, describe the steps of hypothesis testing, and describe and interpret the choice of the null and alternative hypotheses;
• distinguish between one-tailed and two-tailed tests of hypotheses;
• explain a test statistic, Type I and Type II errors, a significance level, and how significance levels are used in hypothesis testing;
• explain a decision rule, the power of a test, and the relation between confidence intervals and hypothesis tests;
• distinguish between a statistical result and an economically meaningful result;
• explain and interpret the p-value as it relates to hypothesis testing;
• identify the appropriate test statistic and interpret the results for a hypothesis test concerning the population mean of both large and small samples when the population is normally or approximately normally distributed and the variance is
• known or
• unknown;
• identify the appropriate test statistic and interpret the results for a hypothesis test concerning the equality of the population means of two at least approximately normally distributed populations, based on independent random samples with
• equal or
• unequal assumed variances;
• identify the appropriate test statistic and interpret the results for a hypothesis test concerning the mean difference of two normally distributed populations;
• identify the appropriate test statistic and interpret the results for a hypothesis test concerning
• the variance of a normally distributed population, and
• the equality of the variances of two normally distributed populations based on two independent random samples;
• distinguish between parametric and nonparametric tests and describe situations in which the use of nonparametric tests may be appropriate.
18:32

The Student would be able to :

• explain principles of technical analysis, its applications, and its underlying assumptions;
• describe the construction of different types of technical analysis charts and interpret them;
• explain uses of trend, support, resistance lines, and change in polarity;
• describe common chart patterns;
• describe common technical analysis indicators (price-based, momentum oscillators, sentiment, and flow of funds);
• explain how technical analysts use cycles;
• describe the key tenets of Elliott Wave Theory and the importance of Fibonacci numbers;
• describe intermarket analysis as it relates to technical analysis and asset allocation
11:41

This video helps you to plan the last month before the exam

10 questions

All questions should be solved in 15 minutes with minimum 80% accuracy.

00:40