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CFA Level 1 > Quantitative Methods > Flashcards

Quantitative Methods Flashcards

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1
Q

Compound Interest

A

Interest on interest. Growth in the value of the investment from period to period reflects not only interest earned on the original principal amount, but also on the interest earned on the previous period’s interest earnings

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2
Q

Future Value

A

Projecting the cash flows forward, on the basis of an appropriate compound interest rate (compounding). Amount a current deposit will grow over time when placed in an account paying compound interest
FV = PV(1+1/Y)^N

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3
Q

Present Value

A

Brings the cash flows from an investment back to the beginning of an investment’s life based on the appropriate compound interest rate (discounting). Today’s value of of cash that is to be received some point in the future. Amount of money that must be invested today, at a given rate of return over a period of time, in order to end up with a specified FV

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4
Q

Equilibrium Interest Rates

A

Required rate of return for a particular investment

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5
Q

Market Rate of Return

A

Return that investors and savers require to get them to willingly lend their funds

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6
Q

Discount Rates

A

Interest rates. If you can borrow at 10%, discount payments to be made in the future at that rate in order to get equivalent value in dollars

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7
Q

Opportunity Cost of Current Consumption

A

Earning an additional interest in excess of the interest rate is the opportunity foregone when current consumption is chosen rather than saving (postponing consumption)

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8
Q

Real Risk Free Rate

A

Theoretical rate on a single-period loan that has no expectation of inflation in it. An investor’s increase in purchasing power after adjusting for inflation

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9
Q

Risk Free Rates

A

T-bills: since expected inflation in future periods is not zero

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10
Q

Nominal Risk Free Rates

A

Contain an inflation premium: nominal risk free rate = real risk free rate + expected inflation rate

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11
Q

Default Risk

A

Risk that a borrower will not make the promised payments in a timely manner

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12
Q

Liquidity Risk

A

Risk of receiving less than fair value for an investment if it must be sold for cash quickly

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13
Q

Maturity Risk

A

Prices of longer term bonds are more volatile than those of shorter term bonds (more maturity risk)

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14
Q

Required Interest Rate

A

= nominal risk free rate + default risk premium + liquidity premium + maturity risk premium

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15
Q

Effective Annual Rate (EAR)

A

Rate of interest actually realize as a result of compounding. Annual rate of return actually being earned after adjustments have been made for different compounding periods
(1 + periodic rate)^m - 1 where periodic rate = stated annual rate/m and m = number of compounding periods

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16
Q

Future Value Factor

A

(1+I/Y)^N represents compounding rate on an investment

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17
Q

Annuity

A

Stream of equal cash flows that occurs at equal intervals over a given period

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18
Q

Ordinary Annuity

A

Cash flows that occur at the end of each compounding period

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19
Q

Annuity Due

A

Payments or receipts occur at the beginning of each period

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20
Q

Perpetuity

A

Pays a fixed amount of money at set intervals over an infinite period of time (preferred stock)

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21
Q

PV of a Perpetuity

A

Fixed periodic cash flow divided by the appropriate periodic rate of return

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22
Q

Cash Flow Additivity Principle

A

Present value of any stream of cash flows equals the sum of the present values of the cash flows

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23
Q

Net Present Value

A

Present value of expected cash inflows associated with the project less the present value of the project’s expected cash outflows, discounted at the appropriate cost of capital

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24
Q

Internal Rate of Return

A

Rate of return that equates the PV of an investment’s expected benefits with the PV of its costs. Discount rate for which the NPV of an investment is zero

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25
Q

NPV Decision Rule

A

If a project has positive NPV, the amount goes to the firm’s shareholders. When two projects with positive NPV are mutually exclusive, choose the one with the higher positive NPV

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26
Q

IRR Decision Rule

A

Accept projects with an IRR greater than the firm’s required rate of return

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27
Q

Holding Period Return

A

The percentage change in the value of an investment over the period it is held

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28
Q

Total Return

A

Assets with cash flows such as dividend or interest payments, added to interim cash flows, have total return

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29
Q

Money Weighted Return

A

Internal rate of return on a portfolio, taking into account all cash inflows and outflows.

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30
Q

Time Weighted Rate of Return

A

Measures compound growth. Rate at which $1 compounds over a specified performance horizon. Averaging a set of values over time

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31
Q

Bank Discount Yield

A

Quotes for T-bills, based on the face value of the instrument instead of the purchase price. Not representative of return earned by an investor

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32
Q

Holding Period Yield

A

Total return an investor earns between the purchase date and the sale or maturity date. Actual return an investor will receive if the money market instrument is held until maturity

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33
Q

Effective Annual Yield

A

Annualized value, based on 365 day year, that accounts for compound interest. Annualized HPY on basis of 365 day year that incorporates annual compounding

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34
Q

Money Market Yield

A

Annualized holding period yield, assuming a 360 day year. Makes quote yield on the t-bill comparable to yield quotes for interest bearing money market instruments

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35
Q

Bond Equivalent Yield

A

2 x the semiannual discount rate

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36
Q

Descriptive Statistics

A

Summarize important characteristics of large data sets

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37
Q

Inferential Statistics

A

Procedures used to make forecasts, estimates, or judgments about a large set of data based on the statistical characteristics of a smaller set

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38
Q

Population

A

Set of all possible members of a stated group

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39
Q

Sample

A

Subset of the population of interest. Can be used to describe the population as a whole (N)

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40
Q

Nominal Scales

A

Level of measurement that contains the least information. Observations classified or counted with no particular order (n)

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41
Q

Ordinal Scales

A

Every observation is assigned to one of several categories. Then the categories are ordered with respect to a specified characteristic

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42
Q

Interval Scale

A

Provide relative ranking, plus the assurance that differences between scale values are equal. A measurement of zero does not necessarily indicate the total absence of what we are measuring

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43
Q

Ratio Scales

A

Most refined level. Provide ranking and equal differences between scale values, and they have a true zero point as the origin

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44
Q

Parameter

A

Measure used to describe a characteristic of a population

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45
Q

Sample Statistic

A

Used to measure a characteristic of a sample

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46
Q

Frequency Distribution

A

Tabular presentation of statistical data that aids the analysis of large data sets. Summarize statistical data by assigning it to specified groups or intervals

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47
Q

Interval

A

Class. The set of values that an observation may take on

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48
Q

Absolute Frequency

A

Actual number of observations that fall within a given interval

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49
Q

Modal Interval

A

In frequency distribution, the interval with the greatest frequency

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50
Q

Relative Frequency

A

Divide the absolute frequency of each return interval by the total number of observations (percentage of total observations falling within each interval)

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51
Q

Cumulative Absolute Frequency

A

Sum of the absolute frequencies starting at the lowest interval and progressing through the highest. Sum of the absolute frequencies up to and including the given interval

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52
Q

Cumulative Relative Frequency

A

Sum of the relative frequencies starting at the lowest interval and progressing through the highest. Sum of the relative frequencies up to and including the given interval

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53
Q

Histogram

A

Graphical presentation of the absolute frequency distribution. Bar chart of continuous data that has been classified into a frequency distribution (chosen intervals on horizontal axis, absolute frequencies on vertical)

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54
Q

Frequency Polygon

A

The midpoint of each interval is plotted on the horizontal axis, and the absolute frequency for that interval is plotted on the vertical axis. Each point is connected with a straight line

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55
Q

Measures of Central Tendency

A

Identify the center, or average, of a data set (typical or expected value)

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56
Q

Population Mean

A

All the observed values in the population are summed and divided by the number of observations in the population

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57
Q

Sample Mean

A

Sum of all the values in a sample of a population, divided by the number of observations in the sample. Used to make inferences about the population mean

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58
Q

Arithmetic Means

A

Sum of the observation values divided by the number of observations. All interval and ratio data sets have one, all data values are considered and included, a data set only has one, sum of deviations from the mean is zero

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59
Q

Weighted Mean

A

Different observations may have a disproportionate influence on the mean

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60
Q

Median

A

Midpoint of a data set when the data is arranged in ascending or descending order. Not affected by extreme values

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61
Q

Mode

A

Value that occurs the most frequently in a data set. May have more than one or none (unimodial, bimodial, trimodial)

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62
Q

Geometric Mean

A

Often used when calculating investment returns over multiple periods or when measuring compound growth rates. Only has a solution if product under radical is positive. Always less than or equal to arithmetic mean (can only be equal when observations are all equal)

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63
Q

Harmonic Mean

A

Average cost per shares purchased over time (will be less than geometric mean)

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64
Q

Quantile

A

Measures of location. Value at or below which a stated proportion of the data in a distribution lies (quartile = quarters, quintile = fifths, decile = tenths, percentile = hundredths)

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65
Q

Dispersion

A

Variability around the central tendency

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66
Q

Range

A

Distance between the largest and smallest value in the data set

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67
Q

Mean Absolute Deviation (MAD)

A

Average of the absolute values of the deviations of the individual observations from the arithmetic mean)

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68
Q

Population Variance

A

Average of the squared deviations from the mean

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69
Q

Biased Estimator

A

Systematically underestimating the population parameter, specifically for small samples (that’s why n-1 is used)

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70
Q

Chebyshev’s Inequality

A

For any set of observations, whether sample or population data and regardless of shape of distribution, the percentage of observations that lie within k standard deviations of the mean is at least 1 - 1/k^2 for all k > 1 (minimum percentage of any distribution that lies within a given standard deviation)

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71
Q

Relative Dispersion

A

The amount of variability in a distribution relative to a reference point or benchmark

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72
Q

Coefficient of Variation

A

Measures the amount of dispersion in a distribution relative to the distribution’s mean. Can make a direct comparison of dispersion across different sets of data (measure risk per unit of expected return)

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73
Q

Sharpe Ratio

A

Measures excess return per unit of risk (reward to variability ratio)

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74
Q

Symmetrical Distribution

A

Shaped identically on both sides of the mean, intervals of losses and gains will exhibit the same frequency (mean, median and mode are equal)

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75
Q

Skewness

A

Extent to which a distribution is not symmetrical (outliers in a data set)

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76
Q

Outliers

A

Observations with extraordinarily large values, either positive or negative

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77
Q

Positively Skewed

A

Many outliers in the upper region, or right tail

(mode < media < mean) - mean is most affected by outliers

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78
Q

Negatively Skewed

A

Disproportionately large amount of outliers that fall within its lower (left) tail
(mean < median < mode)

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79
Q

Kurtosis

A

Measure of the degree to which a distribution is more or less “peaked” than a normal distribution

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80
Q

Leptokurtic

A

More peaked than a normal distribution (more returns clustered around the mean and more returns with large deviations from the mean –> fatter tails). Greater percentage of small deviations from the mean and extremely large deviations from the mean (greater likelihood of increased risk)

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81
Q

Platykurtic

A

Less peaked (flatter) than a normal distribution

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82
Q

Mesokurtic

A

Same kurtosis as a normal distribution

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83
Q

Excess Kurtosis

A

Either more or less kurtosis than the normal distribution (normal kurtosis is 3, excess is 0)

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84
Q

Sample Skewness

A

Sum of the cubed deviations from the mean divided by the cubed standard deviation and by the number of observations

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85
Q

Random Variable

A

Uncertain quantity/number

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86
Q

Outcome

A

Observed value of a random variable

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87
Q

Event

A

Single outcome or set of outcomes

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88
Q

Mutually Exclusive

A

Events that cannot both happen at the same time

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89
Q

Exhaustive Events

A

Include all possible outcomes

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90
Q

Empirical Probability

A

Established by analyzing past data (objective)

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91
Q

A priori Probability

A

Determined using a formal reasoning and inspection process (objective)

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92
Q

Subjective Probability

A

Least formal method of developing probabilities, involves use of personal judgment

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93
Q

Unconditional Probability

A

Marginal probability. Probability of an event regardless of the past or future occurrence of other events

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94
Q

Conditional Probability

A

Occurrence of one event affects the probability of the occurrence of another event. Key word is “given”.
P(A | B). Also called likelihood

95
Q

Joint Probability

A

Probability that both events will occur

96
Q

Independent Events

A

Events for which the occurrence of one has no influence on the occurrence of the others

97
Q

Total Probability

A

Relationship between unconditional and conditional probabilities of mutually exclusive and exhaustive events

98
Q

Expected Value

A

Weighted average of the possible outcomes for the variable

99
Q

Covariance

A

Measure of how two assets move together . Expected value of the product of the deviations of the two random variables from their respective expected values

100
Q

Correlation

A

Covariance of two random variables divided by the product of their standard deviations. Strength of the linear relationship between two random variables

101
Q

Bayes’ Rule

A

Update a given set of prior probabilities for a given event in response to the arrival of new information

102
Q

Labeling

A

Situation where there are n items that can receive one of k different labels

103
Q

Combination Formula

A

Binomial, formula for labeling when K = 2

104
Q

Permutation

A

Specific ordering of a group of objects

105
Q

Probability Distribution

A

Probabilities of all possible outcomes for a random variable (must sum to 1)

106
Q

Discrete Random Variable

A

The number of possible outcomes can be counted, and for each possible outcome, there is a measurable and positive probability (eg. number of days it will rain in a given month)

107
Q

Probability Function

A

Specifies the probability that a random variable is equal to a specific value p(x)

108
Q

Continuous Random Variable

A

The number of possible outcomes is infinite, even if lower and upper bounds exist (eg. actual amount of daily rainfall)

109
Q

Discrete Distribution

A

p(x) = 0 when x cannot occur, pr p(x) > 0 when if it can

110
Q

Continuous Distribution

A

p(x) = 0 even though x can occur. X is between two positive values only when x1 and x2 are actual numbers

111
Q

Cumulative Distribution Function

A

Defines the probability that a random variable X takes on a value equal to or less than a specific value (sum, or cumulative value, of the probabilities for the outcomes up to and including a specified outcome)

112
Q

Discrete Uniform Random Variable

A

The probabilities for all possible outcomes for a discrete random variable are equal

113
Q

Binomial Random Variable

A

Number of successes in a given number of trials, whereby the outcome can be either success or failure. Probability of success, p , is constant for each trial, and trials are independent

114
Q

Bernoulli Random Variable

A

Binomial random variable for which the number of trials is 1

115
Q

Binomial Tree

A

All possible combinations of up moves and down moves over a number of successive periods

116
Q

Node

A

All possible values along a binomial tree

117
Q

Continuous Uniform Distribution

A

Range spans between lower limit a and upper limit b (parameters of distribution). Outcomes can only be between a and b

118
Q

Normal Distribution

A

Completely described by its mean and variance (X is normally distributed with mean mu and variance sigma squared). Symmetric about its mean (skew = 0) and kurtosis is normal (3), Tails get very thin but do not reach zero

119
Q

Univariate Distributions

A

Distribution of a single random variable

120
Q

Multivariate Distribution

A

Specifies the probabilities associated with a group of random variables and is meaningful only when the behavior of each random variable in the group is in some way dependent upon the behavior of the others

121
Q

Confidence Interval

A

Range of values around the expected outcome within which we expect the actual outcome to be some specified percentage of the time. (68% within one standard deviation of the mean, 95% within two for normal distribution)

122
Q

Standard Normal Distribution

A

Normal distribution that has been standardized so that it has a mean of zero and a standard deviation of 1

123
Q

Z-value

A

Number of standard deviations a given observation is from the population mean

124
Q

Standardization

A

Process of converting an observed value for a random variable to its z value

125
Q

Z-table

A

Contains values generated using the cumulative density function for a standard normal distribution. Values in the table are the probabilities of observing a z-value that is less than a given value

126
Q

Shortfall Risk

A

Probability that a portfolio value or return will fall below a particular value or return over a given time period

127
Q

Roy’s Safety First Criterion

A

Optimal portfolio minimizes the probability that the return of the portfolio falls below some minimum acceptable level (threshold level)

128
Q

Lognormal Distribution

A

e^x, where x is normally distributed. Skewed to the right, bounded from below by zero (modeling asset prices that never take negative values)

129
Q

Price Relatives

A

End of period price of the asset divided by the beginning price

130
Q

Continuous Compounding

A

Use for shorter and shorter compounding periods (the limit of discrete compounding)

131
Q

Monte Carlo Simulation

A

Repeated generation of one or more risk factors that affect security values, in order to generate a distribution of security values. Specify parameters of the probability distribution, computer generates random values used to draw conclusions about mean and variance (valuation, simulate p&l, VaR)

Limitations: no better than the assumptions, statistical not analytical

132
Q

Historical Simulation

A

Based on actual changes in value or actual changes in risk factors over some prior period. Set of all changes in relevant risk factors over some prior period. Uses actual distribution of risk factors, but past changes may not be indicative of future changes. Not good with “what if” scenarios

133
Q

Simple Random Sampling

A

Method of selecting a sample where each item or person in the population being studied has the same likelihood of being included in the sample

134
Q

Systematic Sampling

A

Selecting every nth number from a population

135
Q

Sample Error

A

Difference between a sample statistic (mean, variance or standard deviation) and its corresponding population parameter

136
Q

Sampling Distribution

A

From the sample statistic. A probability distribution of all possible sample statistics computed from a set of equal-size samples that were randomly drawn from the same population (probability distribution if a statistic from many samples)

137
Q

Stratified Random Sampling

A

Uses a classification system to separate the population into smaller groups based on one or more distinguishing characteristics

138
Q

Stratum

A

Each subgroup of a stratified random sampling. Random sample is taken from each and results are pooled

139
Q

Time Series Data

A

Observations taken over a period of time at specific and equally spaced time intervals

140
Q

Cross-sectional Data

A

Sample of observations taken at a single point in time

141
Q

Longitudinal Data

A

Observations over time of multiple characteristics of the same entity

142
Q

Panel Data

A

Observations over time of the same characteristic for multiple entities

143
Q

Central Limit Theorem

A

For simple random samples of size n from a population with mean mu and finite variance sigma squared, the sampling distribution of the sample mean x bar approaches a normal probability distribution with mean mu and variance sigma squared divided by n

144
Q

Standard Error of the Sample Mean

A

Standard deviation of the distribution of the sample means

145
Q

Unbiased Estimator

A

One for which the expected value of the estimator is equal to the parameter you are trying to estimate (sample mean is unbiased estimator of population mean bc expected value of mean is equal)

146
Q

Efficient Estimator

A

Variance of its sampling distribution is smaller than all the other unbiased estimators of the parameter you are using

147
Q

Consistent Estimator

A

Accuracy of the parameter estimate increases as the sample size increases (standard error falls, bunches more closely around population mean)

148
Q

Point Estimates

A

Single sample values used to estimate population parameters (calculated using the estimator)

149
Q

T-distribution

A

Bell shaped probability distribution that is symmetrical about its mean. Appropriate to use when constructing confidence intervals based on small samples (n<=30) with populations with unknown variance and normal distribution. Flatter, more area under the tails

150
Q

Degrees of Freedom

A

Number of sample observations minus one (define t-distributions). Given the mean, only n - 1 observations can be unique

151
Q

Confidence Interval

A

Estimates result in a range if values within which the actual value of a parameter will lie, given the probability of 1 minus alpha

152
Q

Level of Significance

A

Alpha in a confidence interval

153
Q

Degree of Confidence

A

1 minus alpha in a confidence interval

154
Q

Probabilistic Perspective

A

Repeatedly taking samples, constructing confidence intervals for each sample’s mean, finding that X% of the resulting confidence intervals include the population mean

155
Q

Practical Perspective

A

Being X% confident that the population mean score is between two numbers for things in this population

156
Q

Data Mining

A

Analysts repeatedly use the same database to search for patterns or trading rules until one that “works” is discovered

157
Q

Data Mining Bias

A

Results where the statistical significance of the pattern is overestimated because the results were found through data mining (lack of any economic theory, too many variables tested)

158
Q

Sample Selection Bias

A

Occurs when some data is systematically excluded from the analysis, usually because of lack of availability (nonrandom)

159
Q

Survivorship Bias

A

Databases only include funds currently in existence, not funds that have ceased to exist. The funds that are dropped from the sample have lower returns, so the surviving sample is biased toward better funds (not random), will overestimate returns

160
Q

Look-ahead Bias

A

A study tests a relationship using sample data that was not available on the test date (estimating future values)

161
Q

Time-period Bias

A

If the time period over which the data is gathered is either too short or too long. Might reflect phenomena specific to that time period, or fundamental economic relationships might have changed

162
Q

Hypothesis Testing

A

Statistical assessment of a statement or idea regarding a population. Testing the validity of a statement at a given significance level

163
Q

Null Hypothesis

A

The hypothesis researchers want to reject. It is what’s actually tested and is the basis for selection of test statistics (always includes equal to condition)

164
Q

Alternative Hypothesis

A

What is concluded if there is sufficient evidence to reject the null hypothesis (what you are trying to assess)

165
Q

One-tailed Test

A

One-sided alternative hypothesis (test if something is greater than zero). Level of significance is the amount under the one tail (confidence interval is 100 - 2*significance)

166
Q

Two-tailed Test

A

Two-sided alternative hypothesis (test if return is anything other than zero). Allow for deviations on both sides of the hypothesized value (zero). Level of significance is divided by 2 to find amount under each tail (confidence interval is 100 - significance)

167
Q

Critical Values

A

Rejection points in a two-tailed test. Reject H0 if test statistic > upper critical value or test statistic < lower critical value.

One tailed: Reject H0 if test stat > upper tail or test stat < lower tail

168
Q

Decision Rule

A

Rejection Rule. Two-tailed test at Z = 0.05, reject if test stat < -1.96 or test stat > 1.96

169
Q

Test Statistic

A

Calculated by comparing the point estimate of the population parameter with the hypothesized value of the parameter. Difference between the sample statistic and hypothesized value, scaled standard error of sample stat

170
Q

Type I Error

A

Rejection of the null hypothesis when it is actually true (False Negative)

171
Q

Type II Error

A

Failure to reject the null hypothesis when it is false (False Positive)

172
Q

Significance Level

A

Probability of making a Type I error (designated by alpha). Probability of rejecting a null hypothesis when it’s true

173
Q

Power of a Test

A

Probability of correctly rejecting the null hypothesis when it’s false (1 - probability of Type II Error)

174
Q

Statistical vs Economic Significance

A

Transaction costs, taxes, additional risk could make a strategy that seems statistically significant economically unattractive

175
Q

P-value

A

Probability of obtaining a test statistic that would lead to a rejection of the null hypothesis, assuming the null hypothesis is true. Smallest level of significance for which the null hypothesis can be rejected

One Tailed: probability that lies above the computed test statistic for upper tailed tests, or below test for lower tailed

Two Tailed: probability that lies above the positive value of the computed test statistic plus the probability that lies below the negative value of the test statistic

176
Q

T-test

A

Population variance is unknown, sample is large (n >= 30), sample is small (n < 30) but the distribution of the population is normal

177
Q

Z-test

A

Population is normally distributed with known variance. Compared to the critical z-value corresponding to the significance of the test

178
Q

Pooled Variance (Difference in Means)

A

Used with the t-test for testing the hypothesis that the means of two normally distributed are equal, when the variances of the populations are unknown but assumed to be equal

179
Q

Equality of Population Means

A

T-test when populations are normally distributed and have variances that are unknown and assumed to be unequal, use the sample variance for both populations

180
Q

Paired Comparisons Test (Mean Differences)

A

Test of whether the average difference between monthly returns is significantly different from zero, based on the standard error of the differences in monthly returns. Sample is normally distributed. When sample is dependent

181
Q

Chi Square Test

A

Hypothesis tests concerning the variance of a normally distributed population (true population variance vs hypothesized variance). Values cannot be negative

182
Q

F-test

A

Hypotheses concerned with the equality of the variances of two populations. Normally distributed, independent. Only consider the critical value in the right tail. Right skewed, bounded by zero on the left

183
Q

Parametric Tests

A

Rely on assumptions regarding the distribution of the population and are specific to population parameters (z-test)

184
Q

Nonparametric Tests

A

Do not consider a particular population parameter or have few assumptions about the population that is sampled. Used when there is a concern about quantities other than the parameters of a distribution or when the assumptions pf parametric tests can’t be supported (when you can’t use t-test or z-test, data is ordinal or ranked)

185
Q

Spearman Rank Correlation Test

A

Used when the data are not normally distributed. Large positive value indicates high rank in one year means high rank in second year and vice versa

186
Q

Technical Analysis

A

Study of collective market sentiment, as expressed in buying and selling assets. Prices are determined by the interaction of supply and demand, market price is supply and demand at any instant. Price and volume reflect collective behavior of buyers and sellers (both rational and irrational). Investor behavior is reflected in trends and patterns that tend to repeat and can be identified to forecast price

187
Q

Fundamental Analysis

A

Attempts to determine the intrinsic value of an asset. Uses financial statements

188
Q

Line Charts

A

Show closing prices for each period as a continuous line

189
Q

Bar Charts

A

Add high and low prices for each trading period and often include opening prices. Closing price bar or dash on right of line

190
Q

Candlestick Charts

A

Same data as bar charts, display a box bounded by the opening and closing prices. Clear if closing price higher than opening price, filled if closing price lower than opening

191
Q

Point and Figure Charts

A

Identify changes in direction of price movements. Price on vertical, horizontal is number if changes in direction. Box size is price increment, reversal size is change of direction (three times the box size). X’s increase in box size, O;s decrease (fill in opposite directions in adjacent columns)

192
Q

Volume Charts

A

Displayed below price charts (each period’s volume is a vertical line)

193
Q

Relative Strength Analysis

A

Calculate the ratio of an asset’s closing prices to benchmark values (index, comp asset). Draw a line chart of the ratios. Increasing (positive) is outperforming, Decreasing (negative strength) underperforming

194
Q

Uptrend

A

Prices reaching higher highs and retracing higher lows (demand increasing relative supply)

195
Q

Downtrend

A

Prices declining lower lows and retracing to lower highs (supply is increasing relative to demand –> selling pressure)

196
Q

Trendline

A

Uptrend: line traces increasing lows
Downtrend: connects decreasing highs

197
Q

Breakout/Breakdown

A

Price crosses trendline by a significant amount (out from downtrend, down from an uptrend)

198
Q

Support Level

A

Buying is expected to emerge to prevent further price decreases

199
Q

Resistance Level

A

Selling is expected to emerge to prevent further price increases

200
Q

Change in Polarity

A

Breached resistance levels become support levels and breached support levels become resistance levels

201
Q

Reversal Patterns

A

Occur when a trend approaches a range of prices but fails to continue beyond that range

202
Q

Head and Shoulders Pattern

A

Demand has been driving upward and is fading, especially if each of the highs in the pattern occurs on declining volume. Size can be used to predict price (difference in price between the head and the neckline –> high minus support level). Ensuing downturn price is then neckline - size –> Double Top, Triple Top

Reversal patterns are Inverse Head and Shoulders, double bottom, triple bottom

203
Q

Continuation Patterns

A

Suggest a pause in a trend rather than a reversal

204
Q

Triangles

A

Form when prices reach lower highs and higher lows over a period of time. Trendlines converge when they are projected forward. Buying and selling pressure have become roughly equal temporarily (pennants appear on short term price charts)

205
Q

Rectangles

A

Form when trading temporarily forms a range between a support level and a resistance level (flags appear on short term price charts)

206
Q

Moving Average Lines

A

Smooth the fluctuations in a price chart. Mean of the last n closing prices (larger value of n, smoother the moving average line). Can be viewed as support or resistance levels
Uptrend: price is higher than moving average
Downtrend: price is lower than moving average

207
Q

Golden Cross

A

Short-term average crosses above the long-term average, indicator of an emerging uptrend (buy signal)

208
Q

Dead Cross

A

Short-term average crosses below long-term average, indicator of an emerging downtrend (sell seignal)

209
Q

Bollinger Bands

A

Based on the standard deviation of closing prices over the last n periods. Draw high and low bands a chosen number of deviations (2) above and below the n-period moving average. Move away from each other when volatility increases

210
Q

Overbought

A

Prices at or above upper Bollinger band (too high, likely to decrease in the near term)

211
Q

Oversold

A

Prices at or below lower Bollinger band (too low, likely to increase in the near term)

212
Q

Contrarian Strategy

A

Buy when most traders are selling, sell when most traders are buying. Believe in overbought and oversold markets because most investors buy and sell at wrong times

213
Q

Oscillators

A

Based on the market prices but scaled so that they oscillate around a given value, or between two values. Extreme highs and lows indicate overbought and oversold

214
Q

Convergence

A

Oscillator shows the same pattern as prices (both reaching higher highs). Indicates price trend is likely to continue

215
Q

Divergence

A

Oscillator shows a different pattern than prices (failing to reach higher high). Indicates potential change in price trend

216
Q

Rate of Change Oscillator

A

Momentum, 100 times the difference between the latest closing price and the closing price n periods earlier. Oscillates around zero. Buy when it changes from negative to positive and vice versa. Ratio of current prices to past prices oscillates around 100

217
Q

Relative Strength Index

A

Based on the ratio of total price increases to total price decreases over a selected number of periods. Oscillate between 0 and 100 (>70 is high –> overbought, <30 low –> oversold)

218
Q

Moving Average Convergence/Divergence (MACD)

A

Drawn using exponentially smoothed moving averages, which place greater weight on more recent observations. MACD line is difference between moving averages of the price, signal line is moving average of MACD line. Oscillate around 0, not bounded. MACD crosses above signal line is buy signal, MACD line crosses below signal lines is sell signal

219
Q

Stochastic Oscillator

A

Calculated from the latest closing price and highest and lowest prices reached in a recent period. Sustainable uptrend has prices close nearer to recent high. Sustainable downtrend has prices close nearer to recent low. Bounded by 0 and 100
%K line: difference between latest price and recent low as percentage of difference between recent high and low
%D line: 3 period average of the K line

220
Q

Sentiment Indicators

A

Used to discern the views of potential buyers and sellers (bullish vs. bearish)

221
Q

Put/Call Ratio

A

Put options increase in value when price of asset decreases, call options increase in value when price of asset increases. Volume reflects activity by investors with negative and positive outlooks. Increases in put/call are negative outlook, decreases positive. Contrarian indicator. Extremely high ratio is bearish and could mean oversold, extremely low is bullish and could mean overbought

222
Q

Volatility Index (VIX)

A

Measures the volatility of options on S&P 500 stock index. High levels suggest fears of decline in market. Contrarian indicator

223
Q

Margin Debt

A

Increases in margin debt outstanding suggest aggressive buying by bullish margin investors. As they reach their margin limit, ability to continue buying decrease, which can cause price declines. As price decreases, securities must be sold to meet margin calls, driving prices lower. Increasing margin debit indicates increasing market prices, and vice versa

224
Q

Short Interest Ratio

A

Increases in shares sold short indicate strong negative sentiment. Number of shares investors have borrowed or sold short. Short interest divided by average daily trading volume. High ratio could mean investors expect price to decrease, could imply future buying demand when short sellers must return borrowed shares

225
Q

Arms Index or Short Term trading Index (TRINS)

A

Measure of funds flowing into advancing and declining stocks. Value close to 1 suggests funds are flowing about evenly to advancing and declining stocks. Values greater than 1 suggest majority of volume is in declining stocks, less than 1 volume is in advancing stocks

226
Q

Mutual Fund Cash Position

A

Ratio of mutual funds’ cash to total assets. In uptrends, managers want to invest cash quickly. During downtrends, fund cash balances increase overall fund returns. Cash positions increase when market is falling, decrease when market is rising. Contrarian indicator (accumulating cash indicates future buying power)

227
Q

New Equity Issuance and Secondary Offerings

A

IPO’s and sales of additional shares by issuer add to the supply of stocks. New shares issued when market is high, increases in issuance coincide with market peaks

228
Q

Cycle Theory

A

Study of processes that occur in cycles. 4 year presidential cycles, decennial patterns

229
Q

Kondratieff Wave

A

54 year cycles

230
Q

Elliott Wave Theory

A

Based on a belief that financial market prices can be described by an interconnected set of cycles.
Subminuette: cycle of a few minutes
Grand Supercycle: centuries long cycle

231
Q

Waves

A

Prevailing Uptrend: upward moves in price consist of 5 waves, downward moves occur in 3 waves
Prevailing Downtrend: downward moves have 5 waves, upward moves have 3 waves

232
Q

Fibonacci Ratios

A

Sizes of the waves in Elliott wave patterns, help with estimating price targets. Ratios of consecutive fibonacci numbers converge to 0.618 and 1.618 as the numbers get larger

233
Q

Intermarket Analysis

A

Analysis of the interrelationships among the market values of major asset classes. Relative strength ratios determine outperforming asset classes. Apply relative strength analysis to identify assets within each class outperforming others

CFA Level 1 (7 decks)

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