Quantitative Methods – CFA Level 1 Essential Review Summary

Reading 6 – The Time Value of Money

The Time Value of Money is an important concept for the level 1 exam. You will need to be comfortable discounting the Present Value and Future Value of cash flows for individual and ongoing payments in the exam. Your calculator has functions built in to make these calculations easier. Here are the calculations that you need to know:
$$ FV=PV{ \left( 1+\frac { r }{ n } \right) }^{ n\ast t }
$$ PV=\frac { FV }{ { \left( 1+\frac { r }{ n } \right) }^{ n\ast t } }
$$ FV=Future\quad value $$
$$ PV=Present\quad Value $$
$$ r = discount \quad rate $$
$$ n = number \quad or\quad discounting\quad periods\quad per\quad year $$
$$ t = number\quad of\quad years $$

A simple example of this application is if you needed to determine how much you would pay today to receive $1 in one year, assuming your required rate of return was 5%. The formula would look like this:

$$ PV=\frac { $1 }{ \left( 1+.05 \right) } =$0.95 $$

Using this discount rate, $1 one year from now is worth $0.95 today.

You won’t often have to do the formulas written out by hand, but be familiar with them because the logic here applies to many other applications through this topic area. A similar issue that you’ll have to deal with involves ongoing cash flows such as annuities and perpetuities. Both involve finding the current value of a series of cash flows, just that an annuity continues for a certain period of time and a perpetuity never ends. The formulas for these are as follows:

$$ PV\quad Annuity=\frac { \left( 1-{ V }_{ n } \right) }{ r } $$
$$ r=rate\quad of\quad return $$
$$ n=term \quad of \quad annuity $$
$$ V={ \left( 1+r \right) }^{ -1 } $$
$$ PV\quad Annuity\quad Due=\frac { \left( 1-{ V }_{ n } \right) }{ d } $$
$$ d=\frac { r }{ \left( 1+r \right) } $$
$$ PV\quad Perpetuity=\frac { C }{ r } $$
$$ C\quad is\quad amount\quad of\quad each\quad payment $$

Your calculator has built-in functionality for PV and FV equations using the row of buttons labeled “N’, “I/Y”, “PV”, “PMT”, and “FV”. To use these for a problem, you input the value of all the parameters that you know and then have the calculator compute the missing value. To input a value, you type in the number for the value and then press the corresponding value’s button (i.e., to input 10,000 as the PV, you type in 10000, then press the “PV” button). Once you have input all figures you have, you press the CPT button and then the parameter button you want to calculate. Don’t forget to input the interest amount in decimals, so that 5% is entered as 0.05.

Reading 7 – Discounted Cash Flow Applications

The Net Present Value (NPV) and Internal Rate of Return (IRR) methods are both ways of comparing projects with different payoffs to determine where a company should invest its resources. Both methods attempt to make project returns comparable so that a company can tell which one is the best investment.

$$ NPV=\sum _{ }^{ }{ { C }_{ t }{ \left( 1+r \right) }^{ t } } $$
$$ { C }_{ t }\quad is\quad the\quad cash\quad flow\quad at\quad time\quad t $$
$$ r\quad is\quad the\quad discount\quad rate $$
The NPV of a project is the sum of all cash inflows, minus all cash outflows, discounted using a required rate of return so that the final figure is in today’s dollars. A positive NPV means a project will make money for the company, a negative NPV means a company will lose money, and a project with a higher NPV than another project is a more profitable investment.

The IRR is very similar to the NPV. It is the rate of return for a project that gives it an NPV of zero.

$$ NPV=0\quad when\quad r=IRR $$

Expect to see problems where you are calculating these figures for a given project and problems where you are choosing a project based on these figures. For both NPV and IRR, higher values are preferred over lower ones. If you are asked to pick the best project and the two don’t give the same answer, go with the highest NPV. Small projects can have high IRRs but not provide as much value to the company, so the NPV is a better comparison of the overall scale of the return.

The calculator function for IRR and NPV works the same as the rest of the PV and FV. You input the values that you do have using the same value buttons as before and then use the CPT and NPV or IRR buttons to calculate those values.

There are several important types of returns you need to know: Holding Period Return (HPR), Money-Weighted Rate of Return (MWRR), and Time-Weighted Rate of Return (TWRR). The HRP is the simple return over the life of an investment, the MWRR is the IRR of a portfolio, and the TWRR is the geometric mean for a period of HPRs (ie for each year of an investment).

The HPR is the sum of the net gains of an investment divided by the beginning value:

$$ HPR=(Ending\quad value–Beginning\quad value+Income)/Beginning\quad value $$

The MWRR is the discount rate at which: PV of cash outflows = PV of cash inflows

You solve MWRR the same way that you solve other IRR problems using your calculator. Since this method involves finding the PV of each cash flow, it is sensitive to when funds are invested or removed from the portfolio.

$$ The\quad TWRR\quad formula\quad is:\left\{ { \left( 1+{ HPR }_{ 1 } \right) }^{ \ast }{ \left( 1+{ HPR }_{ 2 } \right) { \dots }^{ \ast } }\left( 1+{ HPR }_{ n } \right) \right\} -1 $$

By combining each holding period return into a geometric mean, this method gives a more robust return figure that is not influenced by the timing of cash flows.

There are several ways to calculate a yield on a fixed income security that you need to know. One is the Bank Discount Yield, which is based on the trading price of the bond in comparison to its face value. The formula for this yield is:

$$ { R }_{ BD }=\frac { D }{ F } \ast \frac { 360 }{ t } $$
$$ D=face\quad value-price\left( also\quad known\quad as\quad the\quad Discount \right) $$
$$ F=face \quad value $$
$$ t=time\quad to\quad maturity\quad in\quad days $$
$$ 360\quad is\quad the\quad conventional\quad bank\quad days\quad in\quad a\quad year $$

Another method is the Holding Period Yield, which is the same as the Holding Period Return calculation from earlier in this section:

$$ HPY=(Ending\quad value–Beginning\quad value+Income)/Beginning\quad value $$

The third method is the Effective Annual Yield (EAY). This is an annualized version of the HPY:

$$ EAY={ \left( 1+HPY \right) }^{ \frac { 365 }{ t } }-1 $$

Reading 8 – Statistical Concepts and Market Returns

There are several methods of calculating the average value for a set of numbers as laid out in the curriculum. The formulas you need to know are:

$$ Arithmetic\quad Mean=\frac { \Sigma { X }_{ i } }{ N } $$
$$ Weighted\quad Mean=\sum _{ }^{ }{ { X }_{ i }{ W }_{ i } } $$
$$ Geometric\quad Mean={ \left( { X }_{ 1 }\ast { X }_{ 2 }\ast \dots \ast { X }_{ n } \right) }^{ \frac { 1 }{ n } } $$
$$ Harmonic\quad Mean=\frac { N }{ \sum _{ }^{ }{ \frac { 1 }{ { X }_{ i } } } } $$
$$ N=number\quad of\quad observations $$
$$ W=percentage\quad weight $$


The Arithmetic Mean of the numbers \( { \left\{ 1,2,3,4 \right\} }\) is: \( \frac { 1+2+3+4 }{ 4 } =2.5\)

The Weighted Mean of a portfolio with the following assets is:

{} & Stocks & Bonds \\
Portfolio\quad Weight & 60\% & 40\% \\
Return & 10\% & 6\% \\
$$ \left( 0.6\ast 0.1 \right) +\left( 0.4\ast 0.6 \right) =0.8 $$

The Geometric Mean of the numbers \( \left\{ { 1,2,3,4 } \right\} \) is:
$$ { \left( 1\ast 2\ast 3\ast 4 \right) }^{ \frac { 1 }{ 4 } }=2.21 $$

The Harmonic Mean of the numbers \( \left\{ { 1,2,3,4 } \right\} \) is:
$$ \frac { 4 }{ \frac { 1 }{ 1 } +\frac { 1 }{ 2 } +\frac { 1 }{ 3 } +\frac { 1 }{ 4 } } =1.92 $$

For the same set of numbers, it is always the case that:

Arithmetic Mean > Geometric Mean > Harmonic Mean

In addition to measures of average, there are several measurements of the distribution for datasets that you will need to know.

The Variance is the average of the squared deviations from the mean, and the formula to calculate this for a sample of data looks like this:

$$ { \sigma }^{ 2 }=\frac { \Sigma { \left( { X }_{ i }-\bar { X } \right) }^{ 2 } }{ n-1 } $$
$$ \bar { X }=Mean $$

The Standard Deviation is simply the square root of the variance.

The Mean Absolute Deviation is a measure of the average of the absolute values of deviations from the mean in a data set. We must use the absolute values because a sum of the deviations from the mean in a data set is always 0. This formula is as follows:

$$ MAD=\frac { \Sigma |{ X }_{ i }-\bar { X } | }{ n-1 } $$

Now we’ll calculate these values for the following returns data:

\begin{array} \\
Year\quad 1 & Year\quad 2 & Year\quad 3 & Mean \\
10\% & 7\% & 5\% & 7.3\% \\
$$ Variance={ \sigma }^{ 2 }=\frac { { \left( 0.1-0.073 \right) }^{ 2 }+{ \left( 0.07-0.073 \right) }^{ 2 }+{ \left( 0.05-0.073 \right) }^{ 2 } }{ 2 } =0.00063 $$
$$ Standard\quad Deviation={ \sigma }=\sqrt { 0.00063 } =0.025 $$
$$ MAD=\frac { |0.1-0.073|+|0.07-0.073|+|0.05-0.073| }{ 2 } =0.027 $$
Another important formula related to the previous metrics is the Sharpe Ratio. It measures the risk-adjusted returns of a portfolio. The higher the Sharpe Ratio of a portfolio, the more excess return you are getting for each additional unit of risk. This formula shows up repeatedly in all levels of the CFA curriculum, so it’s important to know it well.

$$ Sharpe\quad Ratio=\frac { \left( { r }_{ p }-{ r }_{ f } \right) }{ { \sigma }_{ p } } $$
$$ { r }_{ p }=portfolio \quad return $$
$$ { r }_{ f }=risk-free\quad rate\quad of\quad return $$
$$ { \sigma }_{ p }=standard\quad deviation\quad of\quad portfolio\quad returns $$

When looking at data sets, there are different ways to categorize the distribution of the data. Two of these methods that you need to know are Kurtosis and Skewness. Kurtosis refers to the degree to which a data set is distributed, relative to the Normal Distribution. There are three categories of kurtosis, as illustrated in the graph below:

A data set would be described as Mesokurtic if it closely resembled a normal distribution, Leptokurtic if it was more tightly clustered around the mean, and Platykurtic if it is more widely distributed. The kurtosis of a normal distribution has a value of 3, so the excess Kurtosis is calculated as:

Excess\quad Kurtosis=\left( \frac { \Sigma { \left( { X }_{ i }-X \right) }^{ 4 } }{ { s }^{ 4 } } \ast \frac { 1 }{ n } \right) -3
$$ s=standard\quad deviation $$
$$ n=number\quad of\quad observations $$

Skewness is another way to describe the distribution of a data set. It refers to how symmetrical a data’s distribution is relative to a completely symmetrical normal distribution. A data set with negative skew has more values to the extreme left side of the distribution, and positive skew indicates the same occurring to the right side of the scale. Either of these conditions would be referred to as the distribution having “fat” tails.

$$ Skewness=\frac { \Sigma { \left( { X }_{ i }-X \right) }^{ 3 } }{ { s }^{ 3 } } \ast \frac { 1 }{ n } $$

Reading 9 – Probability Concepts

There are several concepts involving calculating probabilities you need to know. Most of these involve determining the probability of multiple events occurring, given the probability of each individual event.

The Multiplication Rule applies to the joint probability of multiple events occurring. The formula is as follows:

$$ P\left( AB \right) =P\left( A|B \right) P\left( B \right) $$

The joint probability of both events happening (P(AB)), is the conditional probability of A, assuming B occurs (P(A|B)), times the probability of B (P(B)). Since the probability of both events happening is less than the odds of either one individually, the joint probability will always be lower than either input.

The Addition Rule applies to the probability of multiple events occurring, i.e., A and/or B occurs. That formula is:

$$ P\left( AorB \right) =P\left( A \right) +P\left( B \right) -P\left( AB \right) $$

Since both P(A) and P(B) include the possibility of both events occurring, we subtract the probability of both so that it’s not double-counted.

The Total Probability rule is similar to the addition rule but deals with the combination of conditional probabilities to create the entire probability of an event occurring. The sum of all conditional probabilities for an event adds up to the non-conditional probability of that event, if the conditions are mutually exclusive.

Two important concepts related to statistics in portfolio management are Correlation and Covariance. Correlation refers to the ratio of covariance between two variables and the product of their standard deviations. Covariance is the degree to which two variables move in sync with one another.

The formula to calculate the covariance of two assets (a and b) in a portfolio appears as follows.

$$ { \sigma }_{ { R }_{ a },{ R }_{ b } }=\sum _{ }^{ }{ P\left( { R }_{ a } \right) \left[ { R }_{ a }-E\left( { R }_{ a } \right) \right] } \left[ { R }_{ b }-E\left( { R }_{ b } \right) \right] $$

It is important to understand the nature of covariance, but you should not have to calculate this formula by hand on the exam. You should expect, however, to be calculating correlations, which use the following formula:

$$ Correlation\left( { R }_{ a },{ R }_{ b } \right) =\frac { Covariance\left( { R }_{ a },{ R }_{ b } \right) }{ StdDev\left( { R }_{ a } \right) \ast StdDev\left( { R }_{ b } \right) } $$

Be careful in exam questions involving these equations, because sometimes you will be given the portfolio risk expressed as the Standard Deviation or as the Variance. Don’t forget that the Standard Deviation is the square root of the Variance.

The Correlation value ranges between -1 and 1.

  • 1 means that the two variables move in sync
  • 0 means that they have no relationship
  • -1 means that they move exactly opposite

Bayes’ Formula is a method of calculating an updated probability of an event occurring, given a set of prior probabilities.

$$ P\left( { E }_{ i }|A \right) =\frac { P\left( { E }_{ i } \right) P{ \left( A|{ E } \right) }_{ i } }{ \Sigma P\left( { E }_{ j } \right) P\left( { A|E }_{ j } \right) } $$
$$ P\left( { E }_{ j } \right)=prior\quad probabilities $$
$$ A=an\quad event\quad known\quad to\quad have\quad occurred $$
$$ P\left( { E }_{ i }|A \right)=posterior\quad probability $$

Reading 10 – Common Probability Distributions

There are several types of distributions you will need to understand, and this includes understanding a few common kinds of randomly distributed variables.

A Discrete Uniform Random Variable is one where the probabilities of all outcomes are equal, like the roll of a die. A Bernoulli Random Variable is one that can have only two outcomes. Similarly, a Binomial Random Variable is the number of specific outcomes in a Bernoulli situation that are independent of each other, such as the results of flipping a coin.

One way to calculate potential outcomes using a Bernoulli approach is known as a Binomial Tree:

In this case, each final outcome is seen as successive iterations of Bernoulli events. At each point in time, the stock goes up or down. The tree lists all possible outcomes for this stock, and the probability of each final outcome is the multiplied product of the probability at each step along the way. A good way to catch potential errors when calculating the values of a binomial tree is that each column (a specific point in time) should add up to a probability of 100%.

The curriculum specifies several methods of tracking and managing risk exposures using quantitative means. An important one is known as Roy’s Safety-First Ratio. This is based on the principle that an optimal portfolio is one that minimizes the probability of a returning less than a given threshold level. Achieving a return less than a specified required return is known as Shortfall Risk. The Safety First Ratio is expressed as the excess return of the portfolio (expected returns – threshold returns) divided by portfolio risk (standard deviation):

$$ SF\quad Ratio=\frac { E\left( { R }_{ p } \right) -{ R }_{ L } }{ { \sigma }_{ p } } $$

Computing technology has introduced new methods of performance forecasting, including Monte Carlo Simulation. This method involves building a model of the portfolio and potential outcomes, then running a simulation of what could happen over time (factoring in all available probabilities and covariances of related variables) thousands of times. The output of this method is a distribution of the potential outcomes far more detailed than what a simple linear model could project. This is compared to the other common technique of Historical Simulation. In that approach, a projected portfolio is run through actual historical situations to see how it would have performed. This approach gives a much more limited set of outcomes than the Monte Carlo, but has the benefit of being based on observed market events rather than projected simulations.

Reading 11 – Sampling and Estimation

A core tenet of statistics is using samples of data to find information about the entire population of a data set. It’s often impossible to gather all of the data of a population, so we rely on representative samples and try to make sure they properly represent the population from which they are drawn (like polls given to small groups of people to represent a whole state or country). One major assumption that underpins the validity of this approach is the Central Limit Theorem. This posits that a data sample will have a mean and variance that approach the mean and variance of the population it represents as the sample size becomes sufficiently large. It is widely accepted that a sample of size of at least 30 is usually enough to make the sample representative, but that can vary depending on the skewness of the sample distribution.

A common way (that will definitely appear multiple times on the exam) of analyzing the probability distribution of a data sample is to use the Student’s T-Distribution. This distribution is appropriate for small samples when the population variance is unknown. The formula for calculating a t-statistic is:

$$ t=\frac { x-\mu }{ \frac { S }{ \sqrt { n } } } $$
$$ x = sample \quad mean $$
$$ \mu =population\quad mean $$
$$ S=sample \quad standard \quad deviation $$
$$ n= sample \quad size $$

The t-distribution has an important parameter known as the Degrees of Freedom. The degrees of freedom is calculated as n-1. The t-distribution is symmetrical about its mean and has thicker tails than the normal distribution. The shape of the distribution is dependent on the number of degrees of freedom. As the number of degrees of freedom increases, the distribution becomes more closely gathered around the mean.

A big part of the statistics part of the curriculum deals with the use of Confidence Intervals. A confidence interval is the range of values in which a statistician believes that a certain population parameter lies. They incorporate a trade-off of value range and confidence, such that the smaller the area in which the value is believed to exist, the lower the confidence that it is actually there. There are three primary scenarios for calculating the confidence intervals in the CFA curriculum.

1. A normal distribution with a known variance:

$$ CI=X\pm { z }_{ \frac { \alpha }{ 2 } }\ast \frac { \sigma }{ \sqrt { n } } $$

2. A normal distribution with unknown variance:

$$ CI=X\pm { t }_{ \frac { \alpha }{ 2 } }\ast \frac { S }{ \sqrt { n } } $$

3. A normal distribution with unknown variance and a sample size is large enough:

$$ CI=X\pm { z }_{ \frac { \alpha }{ 2 } }\ast \frac { S }{ \sqrt { n } } $$

When the population variance is known, we can use the more accurate z-score to determine the confidence interval instead of the student’s t-score, but the Central Limit Theorem allows us to also use the z-score when the sample size is large enough.

Reading 12 – Hypothesis Testing

Hypothesis testing is a method of determining the statistical significance of a given data point in a distribution. There are One-Tailed Tests that test the possibility of a change in one direction, and Two-Tailed Tests regarding changes in both positive and negative directions. This method uses calculated test statistics to determine whether a given hypothesis should be accepted or rejected.

The test statistic for the hypothesis testis is calculated as follows:

$$ Test\quad statistic=\frac { Sample\quad statistic-Hypothesized\quad value }{ Standard\quad error\quad of\quad the\quad sample\quad statistic } $$

There are two types of errors that could occur when running a hypothesis test. Type 1 error occurs when we reject a true null hypothesis and Type 2 error occurs when we fail to reject a false null hypothesis. The level of significance (α) represents the probability of making a type 1 error.

Another important component of hypothesis testing is the P-Value. If the calculated P-value is lower than the level of significance, then we can reject the null hypothesis.

The correct hypothesis test for a normal distribution of the random variable is the Z-test, which is calculated as:

$$ z-statistic=\frac { X-{ \mu }_{ 0 } }{ \frac { \sigma }{ \sqrt { n } } } $$
$$ X=sample\quad mean $$
$$ { \sigma }_{ 0 }=Hypothesized\quad mean\quad of\quad the\quad population $$
$$ { \mu }=standard\quad deviation\quad of\quad the\quad population $$
$$ n= sample \quad size $$

For hypothesis testing a population mean when the population variance is unknown and the sample size is large, a T-test is more appropriate:

$$ { t }_{ n-1 }=\frac { X-{ \mu }_{ 0 } }{ \frac { s }{ \sqrt { n } } } $$
$$ X=sample\quad mean $$
$$ { \sigma }_{ 0 }=Hypothesized\quad mean\quad of\quad the\quad population $$
$$ { s }=standard\quad deviation\quad of\quad the\quad sample $$
$$ n= sample \quad size $$

Your testing materials will include tables of significant values for t- and z-scores, so that you can compare to them in determining whether a calculated value exceeds the level necessary to reject a null hypothesis.

There are two classifications of statistical tests that you will need to know, Parametric and Non-Parametric. Parametric tests involve any testing where the given parameter follows a specific distribution. Non-parametric testing does not involve making any assumptions about the distribution of the parameter being studied. An instance where this could be necessary is when there are significant outliers that could cause the mean value of a dataset to be unrepresentative of the data. For this reason, if you wanted to run a test focused on the median of a dataset instead of the mean you would run a non-parametric test.

Reading 13 – Technical Analysis

Technical analysis is the practice of using price and volume data to value stocks. There are a number of concepts you’ll be expected to understand related to technical analysis on the exam. One of these is Trend, which is a when a security consistently keeps moving upward or downward over time. This is the market’s way of reflecting a difference between supply and demand that can persist for a while.

Another important concept is that is Support and Resistance levels. These are price points where market activity tends to keep a security from going below (Support) or above (Resistance). These levels can act as floors or ceilings to the trading level of a security.

Related to the previous concept is that of the Change in Polarity. This is the principle that when a Support or Resistance level is breached, it then becomes its opposite. This can occur when a Resistance level is exceeded and that price point becomes a new Support for trading activity at the new higher level.

In the field of technical analysis, there are a number of patterns that appear on stock charts reflecting price movements that can be used when trying to forecast future movements in stock price. These chart patterns fall into a few categories. Reversal Patterns indicate that a trend is likely to reverse from the direction it has been going. A common example is the “Head and Shoulders” pattern that usually signals the end of an uptrend.

Another category of chart pattern is the Continuation Pattern. These patterns are used to identify trends that will continue after a pause. Many common continuation patterns are based on triangles that highlight trading movement that is converging to a given point in the future.

In addition to chart patterns, there are also indicators used in technical analysis that are used to predict future changes in a security’s price. The four categories of indicators are:

1. Price-Based Indicators

These indicators use information about the security’s past and current price to try and predict future prices. A simple example of this is the moving average, which is the average price of a security over a number of previous time periods. This average removes the oldest observation every time a new observation is added. Bollinger Bands are another common indicator based on price. These are lines that indicate the standard deviations of prices compared to a moving average of the stock’s price.

2. Momentum Oscillators

These are values calculated by finding the difference between the most recent price of a security and a previous price on a specified day in the past. Watching for the calculated value to switch from positive to negative is one indicator of a potential trend reversal

$$ M=\left( V-{ V }_{ x } \right) \ast 100 $$
$$ V=most\quad recent\quad closing\quad price $$
$$ { V }_{ x }=closing\quad price\quad at\quad specified\quad date\quad in\quad past $$

3. Sentiment Indicators

These are various polls that are conducted to gauge the sentiment of individual investors or investment professionals about the state of the equity market.

4. Flow-of-Funds Indicators

These measure the movement of money into and out of rising and declining stocks to identify upward or downward trending behavior. One of the most useful here is the Arms Index:

$$ Arms\quad Index=\frac { \frac { Number\quad of\quad advancing\quad issues }{ Number\quad of\quad declinging\quad issues } }{ \frac { Volume\quad of\quad advancing\quad issues }{ Volume\quad of\quad declinging\quad issues } } $$

Values above 1 indicate more volume in declining stocks, while values below 1 indicate more activity in rising stocks.

Another category in the field of technical analysis used to forecast market price movements is Cycles. These are patterns of market movement that reoccur over various time frames. There are cycles that can happen multiple times throughout a trading day or take many years to complete one occurrence.

Related to the analysis of cycles is the Elliott Wave Theory, developed in the late 1930s. This theory posits that there are repetitive and predictable cycles that can be observed in stock price movements. The theory dictates that there is a pattern of cycles consisting of activity waves. A cycle will contain 5 waves moving in the direction of the main market trend and then 3 waves moving in the opposite direction. The size of each wave is related to the one preceding it based on the Fibonacci number sequence.


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