cfa notes chapter 7

8/7/2019 CFA Notes Chapter 7

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Chapter 7

Statistical Concepts and Returns

1. Intro Return distributions2. Fundamental Concepts

1. Nature ofStatisticsi. Descriptive Statistics: the study ofhow data can be summarized effectively to

describe the importantaspects oflarge data sets.ii. Statistical Inference: Involves makingforecasts, estimates, or judgments

abouta largergroup from the smallergroup actually observed.

2. Populations and Samplesi. Population: as all members ofa specified groupii. Sample: a subsetofa population

iii. Sample Statistic: is a quantity computed from orused to describe a sample3. MeasurementScales

i. Nominal Scales: numerical categories Ifwe assigned integers to mutualfunds thatfollow differentinvestmentstrategies, the number 1 mightreferto

a small-cap value fund, the number 2 to a large-cap value fund, and soonfor

each possible style.

ii. Ordinal Scales: A number assigned to a category torepresentperformancebutitdoes nottell you anythinghow a firm incategory one did relative to

specificfirm B incategory 2. The difference betweenrankings is notnecessarily equal. IEthe probability ofdefaultis notequal between AAA and

AA is notEqual to BBB and BB.iii. Interval scales: These notonly provide ranking butalso assurance thatthe

difference between scale values is equal.

iv. Ratio Scales: This is the strongestform ofmeasurement. They have all thecharacteristics ofinterval measurementscales as well as a true sero pointas

the origin.3. Summarizing Data using Frequency Distribution

i. Frequency Distribution: a tabular display ofdata summarized into arelatively small numberofintervals.

1. Holding Period Returnformulaa.b. Pt=price per share atthe end oftime period tc. P (t-1)= price per share atthe end oftime period t-1d. Dt=cash distributions received duringtime period te. Ithas a rate oftime attached to itand itdoes notmaterwhat

currency itis valued in because the numerator and thedenominatorcancel out.


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ii. Interval: a setofvalues withinwhich anobservationfalls.iii. Absolute Frequency: the actual numberofobservations in a given interval.iv. Relative Frequency: the absolute frequency ofeach interval divided by the

total numberofobservations.v. Cumulative Relative Frequency: the relative frequencies as we move from the

firsttothe lastinterval.4. The Histogram

1. Histogram: a barchartofdata thathave beengrouped into a frequency distribution.2. The frequency polygon and the cumulative frequency distribution

i. Frequency polygon: Return intervals onthe x and frequency onthe y axis5. Measures ofCentral Tendency

easure of Central Tendency: specifies where the data are centered

Measures of Location: include notonly measures ofcentral tendency butother

measures thatillustrate the locationor distribution data.

i. Arithmetic Mean1.

The sum ofthe observations divide by the numberofobservations

ii. Population Mean Formula1. Pg.2842. Is the arithmetic mean value ofa population. For a finite population

the mean is mu, where N is the numberofobservations inthe entire

population and Xi is the ithobservations.

iii. Sample Mean1. Pg 2842. The sample meanor average is the arithmetic mean value ofa samplewhere n is the numberofobservations inthe sample.

iv. Cross sectional is measurementofdata ata specific pointintime and TimeSeries is the measurementofa period oftime.

v. Problem with Arithmetic mean is its sensitivity to extreme values ( IE1,2,3,4,5,6,1000 the meanwould be 146)

2. Mediani. Is the value ofthe middle item ofa data set. (inthe evennumbered data setit

is the meanofthe middle two)

3. The Modei. The mostfrequently occurring value

ii. Modal interval: the interval withthe highestfrequency.4. Weighted Meani. Pg. 293

ii. Where the sum ofthe weights equals 1; thatis sum ofw=15. Geometric Mean

i. Pg 296


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ii. With Xi>0 for i=1,2,3,n6. Geometric Mean Return Formula

i. Pg 297ii. We canuse this equationto solve forthe geometric meanreturnfor any

return data series.

7. Harmonic Meani. Pg 300

ii. Is the value obtained by summingthe reciprocals ofthe observations (1/Xi)then averagingthe sum by dividing itby the numberofobservations and

thentakingthe reciprocal ofthe average.

iii. The mostcommon application is costaveraging8.

The mathematical fact concerning the Harmonic, Geometric, and arithmetic

means is that unless all the observations in a dataset have the same value, theharmonic mean is < the geometric mean which is < arithmetic mean.

6. Other Measures ofLocation: Quantiles (the mostgeneral term for a value ator belowwhicha stated fractionofthe data lies.)

1. Quartiles = 4ths2. Quintiles = 5ths3. Deciles =10ths4. Percentiles = 100ths

ii. Ly=(n+1)(y/100)1. Where y is the percentage pointatwhichwe are dividingthe

distribution and Ly is the location L ofthe percentile Py inthe array

sorted in ascendingorder.2. IfLy is nota whole numberthen Py can be calculated as

a. IfLy is 12.75 and the nextwhole lowernumber is 12 and thenextwhole highernumber is 13 then,

b. Py= X12 + (12.75 12)(X13-X12)Py=

3. LinearInterpolation: estimation anunknown value onthe basis oftwo7. Measures ofDispersions

i. The variability around the central tendency. Ifmeanreturn addressesreward, dispersion addresses risk. IE (range, mean absolute deviation,

variance, and standard deviation)ii. Absolute Dispersion: amountofvariability presentwithoutcomparisonto

any reference pointor benchmark.iii. The Range: the difference betweenthe maximum and minimum values in a

data set.

1. Range= max min2. Mean Absolute Deviation (MAD)

i. Pg 308


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ii. Where Xbar is the sample mean and n is the numberofobservations inthesample.

3. Population Variance and Population Standard Deviationi. Variance: the average ofthe squared deviations around the mean

ii. Standard Deviation: the positive square rootofthe variance.iii. Population Variance Formula

1. Pg 3102. Where mu is the population mean and N is the size ofthe population.

iv. Population Standard Deviation1. Pg 3102. Where mu is the population mean and N is the size ofthe population.

4. Sample Variance & Standard Deviationi.

Sample variance formula

1. Pg 3122. Where Xbar is the sample mean and n is the numberofobservations

inthe sampleii. Sample Standard Deviation Formula

1. Pg 3132. Where Xbar is the sample mean and n is the numberofobservations

inthe sample.

5. Semi Variance, Semi-Deviation, and Related Conceptsi. Variance concepts focus on probability ofan assetincreasing above andbelowthe meanwhen analysts are mostinterested inthe downside riskthey

use semi-variance.

ii. Semi-variance: the averaged squared deviation belowthe mean.1. Formula

iii. Semi-deviation: is the positive square rootofthe semi-variance.iv. Semi Variance Target: has beengivento average squared deviation below a

stated target.

1. Formula: Pg 3176. Chebyschevs Inequalityi. 1-(1/k^2) for all k>1

ii. Importantbecause itapplies to all distributions notjustnormal distributionsAnd itstates thatattwo standard deviations from the meanthe distributionmusthold 75% ofthe data points and atthree SD there mustbe atleast89%

7. CoefficientVariationi. Relative Dispersion: the amountofdispersionrelative to a reference value or

benchmark.


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ii. Justbecause the standard deviationoftwo data sets are the same does notimply an equal value ofrisk. The relative variationcan be huge. IE a standarddeviationof1 millionontwo data sets one with a meanof10 million and one

with a meanof100 millionobviously have dramatically differentimpacts.iii. CoefficientofVariation Formula:

1. CV = s/Xbar2. Where s is the sample standard deviation and Xbar is the sample

mean.

8. Sharpe Ratioi. Formula:

ii. Where Rp bar is the meanreturnofthe portfolio, Rfbar is the meanreturntoa riskfree asset, and sp is the standard deviationofreturnonthe portfolio.

iii. The Sharpe ratio is the inverse ofthe coefficientvariationformula whilesubtractingthe Rffrom the Rp.

iv. Ittells us thatfor each additional unitofriskwe accept, we can expectv. Cautions

1. Can be negative in a bear market2. Decreases ifrisk increases and all else holds3. Sharpe ratioworks bestfor symmetric distributions. Itis overly

optimisticfor distributions thathave a highfrequency ofsmall gains

and very rare butlarge losses and is overly pessimistic aboutsmall

loses butlarge gains (IEoptions)

4. Always ask ifthe Standard Deviation appropriately models aninvestors strategy. Ifitis a non-symmetrical results curve then dont

use the Sharpe ratio.

vi. Symmetry and Skewedness1. Sample Skewness Formula

a. S

b. Where n is the numberofobservations inthe sample and s isthe sample standard deviation.

c. As N becomes sufficiently large youcan approximate through

9. Kurtosis in Returnsi. Kurtosis: the statistical measure thattells us when a distribution is more orless peaked than a normal distribution.

ii. Leptokurtic: a distributionthatis more peaked than a normaliii. Playtokurtic: a distributionthatis less peaked thannormaliv. Mesokurtic: a normal distributionv. Kurtosis Results

1. Playtokurtic


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3. Leptokurtic >0vi. Excess Kurtosis

1. Pg 331

2. Where n is the sample size and s is the sample standard deviation.3. As N becomes largerthe formula approximately equals=

The geometric mean is approximately the arithmetic mean minus halfofthe variance ofreturns.

Rg=

Chapter 7 Concepts to Review:

y 2.3 MeasurementScalesy Pg 302 LinerInterpolation Ly and Py conceptsy Kurtosis

Chapter 8

Probability Concepts

1. Intro2. Probability, Expected Value, and Variance

i. Random Variable: a quantity whose outcomes are uncertain.ii. Event: a specified setofoutcomes

iii. Probability Stated as odds: (IE 15 to 1)1. Formula: E= P(e)/(1-P(e) or B/(A+B) or 1/(15+1)

iv. Pairs Arbitrage Trade: a trade intwoclosely related stocks involvingthe shortsale ofone and the purchase ofthe other.

v. DefinitionofConditional Probability1. Pg 367

2. The conditional probability ofA giventhatB has occurred is equal tothejointprobability ofA and B divided by the probability ofB (assumed notequal to 0)

2. Multiplication Rule for Probability


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i. P(AB)= P(A|B) P(B)ii. The jointprobability ofA and B can be expressed as above.

iii. Addition Rule for Probabilities1. P(A or B) = P(A) + P(B) P(AB)2. IF A and B are mutually exclusive thenthe formula would be

a. P(A or B)= P(A) +P(B)iv. DefinitionofIndependentEvents

1. P(A|B)=P(A) or P(B|A)=P(B)2. The means thatthe occurrence ofone eventdoes notinfluence the

probability ofthe occurrence ofthe second event.

v. Multiplication Rule forIndependentEvents1. P(AB)=P(A)P(B)2. Whentwo events are independentthe jointprobability ofA and B equals

the productofthe individual probabilities ofA and B.

vi. The Total Probability Rule1.

P(A)=P(AS)+P(ASc)

= P(A|S)P(S)+P(A|Sc)P(Sc)2. Pg 375

3. The probability ofany eventP(A) can be expressed as a weightedaverage ofthe probabilities ofthe event, given scenarios (terms such

P(A|S); the weights applied tothese conditional probabilities are therespective probabilities ofthe scenarios {Terms such as P(S)multiplying

P(A|S)} and the scenarios mustbe mutually exclusive and exhaustive.

4. Expected Valuea. Formula Pg377b. E(x)=P(x1)X1+P(x2)X2

5. DefinitionofVariancea. The expected value ofsquared deviations from the random

variables expected value

b. Formula Pg 377

6. Standard Deviationa. The positive square rootofthe varianceb. Formula 379

7. Conditional Expected Valuesa. Pg379b. The e

8. Total Probability Rulea. Pg379b. Where S1, s2, Sn are mutually exclusive and exhaustive scenarios

or events


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3. PortfolioExpected Return and Variance ofReturni. Properties ofExpected Value

1. LetW1 be any constantand Ri be a random variable.a. The epected value ofa constanttime a random variable equals the

constanttimes the expected value ofthe random variable

b. Formula 385c. The expected value ofa weighted sum ofrandom variables equals

the weighted sum ofthe expected values usingthe same weights.

d. Formula 385ii. DefinitionofCovariance

1. Giventworandom variables Ri and Rj the covariance between Ri and Rjis:

a. Pg 386b. This equations states thatthe covariance betweentworandom

variables is the probability weighted average ofthe cross-productsofeachrandom variables deviationfrom its own expected value.

c. Equations 15 and 16

d. We can interpretthe signofCovariance as follows:i. Covariance ofreturns is negative if, whenthe returnononeassetis above its expected value, the returnonthe other

assettends to be below its expected value (an average

inverse relationship betweenreturns)

ii. Covariance ofreturns is 0 ifreturns onthe assets areunrelated

iii. Covariance is positive whenthe returns on both assets tendto be onthe same side (above or belowtheir expected

values)

iv. The covariance ofa random variable with itselfis its ownvariance

1. Formula pg 388e. DefinitionofCorrelationsi. The correlation betweentworandom vriables, Ri and Rj is

defined as p(Ri,Rj)= Cov(Ri,Rj)/sigma(Ri)sigma(Rj) PG389

f. Properties ofCorrelationi. A number between -1 and 1 fortworandom variables

ii. A correlationof0 indicates nocorrelation, -1 are inverselycorrelated, and 1 is a strong positive correlation.


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g. JointProbability Functioni. Formula Pg 392

h. The formula tells us to sum all possible deviationcross productsweighted by the appropriate jointprobability.

2. DefinitionofIndependence for Random Variablesa. Tworandom variables X and Y are independentifand only if

P(X,Y) = P(X)P(Y)

3. Multiplication Rule forExpected Value ofthe ProductofUncorrelatedRandom Variables

a. E(XY)=E(X)E(Y) ifX and Y are uncorrelated4. Bayes Formula

i. {(Probability ofnew informationgiven event)/unconditional probability ofthenew information} x (prior probability ofevent

ii.

Whenthe probabilities are equal, the probability ofinformationgiven an event

equals the probability ofthe eventgiventhe information.5. Principles ofCounting

i. Mutliplication Rule ofCounting1. Ifone taskcan be done innways and a second taskcan be done inn2

ways and a third taskgiventhe firsttwotasks can be done inn3 ways

thenthe numberofways the ktasks can be done is (n1)(n2)(nk)

a. IE N1=4 N2=3 N3=2 then (2)(3)(4) = 24 ways2. Multinomial Formula (general formula for labeling problems).

a. The numberofways thatnobjects can be labeled with k differentlabels, withn1 ofthe firsttype, n2 ofthe second and soonwithn1

+n2+nk=n is gen by

b. Formula 3993. Combination: a listing inwhichthe orderofthe listed items doesnt

matter.

4. Combination Formulaa. The numberofways thatwe canchoose robjects from a total ofn

objects, whenthe order inwhichthe robjects are listed does not

matter is:i. Formula Pg 399

ii.

Concepts for Review Chapter 8

y Covariance IE Section 3


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y Bayes Pg 393

Chapter 9:

Common Probability Distributions

1. Intro1. A probability distribution: specifies the probabilities ofthe possible outcomes ofa

random variable.

2. Discrete Random Variablesi. Random Variable: a quantity whose future outcomes are uncertain.

ii. Discrete Random Variable: a countable numberofvalues even ifitis infinite.iii. Continuous random Variable: a non-countable numberofvalues.iv. Probability Density Functions1. 0 equal toor less than p(x) which is equal toor less than 1 because

probability is a number between 0 and 1

2. The sum ofthe probabilities p(x) over all values ofX equals 1 ifweadd up the probabilities ofall the distinctpossible outcomes ofa

random variable. Thatsum mustequal 1.

v. Binomial Distributions1. Formula Pg 428

2. When p = .5 the results are always symmetricotherwise they areskewed

3. Mean=np4. Variance= p(1-p)

3. Continuous Random Variables1. Pg435

2. ForExample, with a=0 and b= 8 F(x)=1/8 or .1254. Monte Carlo Simulation

Concepts to Review:

y MostofChapter 9


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Chapter 10:

Sampling and Estimation1. Intro2. Sampling

1. Simple Random Samplingi. Parameter: a quantity computed from orused to describe a populationof

data.

ii. Statistic: a quantity computed from orused to describe a sample ofdata.iii. A simple random sample is a subsetofa larger populationcreated in such a

way thateach elementofthe populationhas an equal opportunity ofbeing

selected tothe subset.

iv. Systematic Sampling: selectevery Kth memberuntil we have a sample ofdesired size.

v. SamplingError: the difference betweenthe sample mean and the populationmean.2. Stratified Random Sampling

i. Definition: in stratified random samplingthe populaitn is divided intosubpopulations

ii. A common example would be indexingiii. Benefits:

1. Less dispersion (variance)2. Betterrepresentationofpopulation subdivisions

3. Central LimitTheoremi. Standard Errorofthe Sample Mean

1. Pg 483ii. Sample Variance

1. S2=

Chapter 13:

Elasticity

1. Intro2. Price Elasticity ofDemand

1. Price Elasticity ofDemand: a units-free measure ofthe responsivenessofthe quantity demanded ofa good to a change in its price when all

other influences on buyer plans remainthe same.


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2. Price elasticity ofdemand = %(Q/%(P3. %(Q=(Q/Qaverage4. %(P=(P/Paverage5. Price elasticity ofDemand is a negative number butwe focus onthe

magnitude ofnotthe sign.

6. Perfectly Inelastic= 07. Inelastic Demand = 0


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3. Pg 27 & 28


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Chapter 14:

Efficiency and Equity1. Resource allocation Methodsa. MarketPrice: supply and demand

b. Command: organizational structure do itcause your boss said to.c. Majority Rule:d. Contest:e. First-Come FirstServedf. Lotteryg. Personal Characteristicsh. Force

2. Individual Demand and MarketDemand Pg41

3. Consumer Surplus Pg 42

4. Individual Supply, MarketSupply, and Marginal Social CostPg 43


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a. Supply and Producer Surplus Pg 44

5. Efficiency ofCompetitive Marketsa. Figure 5 Pg 46


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b. Underproduction and Overproduction Pg49

6. Obstacles toEfficiencya. Price and quantity Regulations: Rentcaps are a price regulations and Quantity Regs

are limits to a farms productions

b. Taxes and Subsidies: Taxes increase the price paid by buyers and lowerthe pricereceived by sellers

c. Externalities: a costor benefitthataffects someone otherthanthe selleror buyerofa good

d. Public Goods and Common Resources: a good or service thatis consumedsimultaneously by everyone even ifthey dontpay for it. Commonresource is ownedby noone butused by everyone.

e. Monopoly: a firm thatis the sole providerofa good or service.f. High Transaction Costs: Opportunity costs ofmakingtrades in a market.g.


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Chapter 15

Markets in Action


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Chapter 16

Organization ProductionFour-firm Concentration Ratio: the percentage ofthe value ofsales accounted for by the four

largestfirms in an industry.

Herfindahl Hirschman Index (HHI):the square ofthe percentage marketshare ofeachfirmsummed overthe largest50 firms in a market.

y In perfectcompetitionthe HHI is small.y In a monopoly the HHI is 10,000y Any marketexceeding 1,800 is uncompetitive.

Chapter 18:

Perfect Competition2. Whatis perfectCompetition

a. A firms minimum efficientscale is the smallestquantity ofoutputatwhich longrunaverage costreaches its lowestlevel.

b. Total Rev = Price x Quanityc. Marginal Rev

3. The Firms Decisions in PerfectCompetitiona. ShortRun

i. the time frame inwhich eachfirm has a given plantand the numberoffirms inthe industry is fixed.

ii. Inthe eventofPrice fluctuation a shortrun decision mightbe:1. To produce or shutdown2. Ifproduce thenwhatlevel.

b. Long Runi. The time frame inwhich eachfirm canchange the size ofits plantand decide

whetherto leave the industry

1. Whetherto increase or decrease plantsize2. Whetherto stay or leave the industryc. Profitmaximizingoutput

i. Economic Profit= TR-TCii. Marginal Analysis: marginal revenue compared to marginal cost

iii. IfMR=MC then economic profitis maximizediv. IfMR > MC then should expand

d. Effects ofEntryi. As Firms enter a profitable marketprices decline and industry outputincreases

butindividual firms outputdecrease as they shiftdowntheir supply curve.


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e. Effects ofExiti. Ect.

Chapter 21:

Markets or Factors of Production1. Factor Prices and Incomes

a. Derived Demand: the demand for a factorofproduction (because the demand isderived forthe goods and services produced by the factor.)

b. Marginal Revenue Product: labor is the change intotal revenue thatresults fromemployingone more unitoflabor.

c.

CheckOut:

BureauofLabor Statistics

Chapter 22

Monitoring Jobs and the Price Level2. Jobs and Wages

a. Working Age Population: the total numberofpeople aged 16 and olderthatare notinstitutionalized.

b. Labor Force: the sum ofthe employed and unemployedc. Unemployed musthave made specified efforts tofind a job withinthe lastthirty

days, be waitingto be called backto a job from a layoff, orwaitingto starta new job.d. Major LaborIndicators

i. Unemploymentrate1. Unemploymentrate= (# ofunemployed / laborforce) x 100

ii. Laborforce participationrate1. = (Labor Force / working age population) x 100iii. Employmentto populationratio

1. = (#ofemployed people / Working age population) x 100e. aggregate Hours: total numberofhours workedf. Real Wage Rate: the quantity ofgoods and services thatanhours workcan buy.

Inflation adjusted wage rate.


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3. Unemploymentand Full Employmenta. Types ofUnemployment

i. Frictional: unemploymentthatarises from normal laborturnoverfrompeople entering and leavingthe laborforce and forthe ongoingcreation anddestructionofjobs

ii. Structural Unemployment: this arises whenchanges intechnology orinternational competitionchange the skills needed to perform jobs orchangethe locations ofjobs. Lasts longerthanfrictional.

iii. Cyclical Unemploymentthe fluctuations overthe business cycle.b. Full Employment: whenthere is nocyclical unemploymentand when all

unemploymentis structural orfrictional. The unemploymentrate atfull

employmentis the natural rate of unemployment

c. Potential GDP: the quantity ofreal GDP atfull employment.4. The Consumer Price Index

a. CPI: a measure ofthe average ofthe prices paid by urbanconsumers for a fixedbasketofconsumergoods.

b. Reference Base Period: 100 is at82-84 the average price paid forthe 36 monthperiod.

c. Inflation Rate: the annual percentage change inthe price level.d. CPI Bias

i. Newgoods bias IE a computer is more expensive than a typewriterwas in1982.

ii. Quality Change Bias: improved quality raises pricesiii. Commodity Substitution Bias: people by more chicken instead ofbeefcause

beefprices wentup CPI doesntreflect

Chapter 23:

Aggregate Supply and Aggregate Demand2. The Macroeconomic Long Run and ShortRun

a. Macroeconomic Long Run: a time frame thatis sufficiently longforthe real wagerate tohave adjusted to achieve full employment

b. Macroeconomic shortRun: the period duringwhich some money prices are sticky sothatreal GDP mightbe below above or atpotential GDP and the unemploymentrate

mightbe above belowor atthe natural unemploymentrate.

3. Aggregate supplya. Long Run Aggregate Supply: the relationship betweenthe quantity ofreal GDP

supplied and the price level inthe longrunwhenreal GDP equals potential GDP.

b. ShortRun Aggregate Supply: the relationship betweenthe quantity ofreal GDPsupplied and the price level whenthe money wage rate, the prices ofotherresources and potential GDP remainconstant.


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c. Changes in Potential GDPi. Full-employmentquantity increases

ii. Quantity ofcapital increasesiii. Tech advances.

4. Aggregate Demanda. Y = C +I+ G + X Mb. Where C =real consumption spending, I= investments, G =governmentspending, X

=Exports, and M =Imports

c. Aggregate Demand: the relationship between quantity ofreal GDP demanded andthe price level.

d. WealthEffect: ifprice level rises and all else remains, thenreal wealth decreases.e. SubstitutionEffects: when price levels rise, interestrates rise.f. Changes in Aggregate Demand

i. Expectations: an increase in expected future income increases the amountofconsumptiongoods thatpeople buy today and increases aggregate demand.

ii.

Fiscal Policy: the governments attemptto influence the economy by setting

and changingtaxes, makingtransfer payments, and purchasinggoods andservices.

iii. Monetary Policy: consists ofchanges in interestrates and inthe quantity ofmoney inthe economy.

g. Disposable income: aggregate income minus taxes plus transfer payments5. MacroeconomicEquilibrium

a. InsertFigure 7 through 13b. ShortRun: this occurs whenthe quantity ofreal GDP demanded equals the quantity

ofreal GDP supplied. Fig 8c. Long Run MacEquil: occurs whenreal GDP = Potential GDP (IEwhere AD and LAS

intersect)

d. Business Cycle: Figure 11i. Below Full Employment: a macroeconomic equilibrium inwhich potential

GDP exceeds real GDP

ii. OutputGAP = Potential GDP - real GDPiii. Recessionary Gap = Potential GDP > real GDPiv. Above full employmentequ: a macroeconomic equilibrium inwhichreal GDP

exceeds potential.

v. Inflationary Gap = Potential GDP < Real GDPe. Fluctuations in Aggregate Demand

i. Supply shocks cancreate stagflation.6. MacroEconomic Schools ofThoughta. Classical View

i. Economy is selfregulatingii. Technology is the mostsignificantinfluence on both AD and AS.

1. Increased life ofexistingcapital decreases demand fornewcapitalshifting AD curve tothe left.


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b. New Classicali. Thatbusiness cycle fluctuations are the efficientresponses ofa well

functioning marketeconomy thatis bombarded by shocks thatarise from the

uneven pace oftechnological change.c. Keynesian View

i. Believe thatleftalone, the economy would rarely operate atfull employmentand thatto achieve and maintainfull employmentactive help from fiscal

policy and monetary policy is required.

d. MonetaristViewi. Believe thatthe economy is self-regulating and thatitwill normally operate

atfull employment, provided thatmonetary policy is noterratic and thatthe

pace ofmoney growth is keptsteady.

Chapter 24

Money, The Price Level, and InflationMeans ofPayment

Re Read

Chapter 29

Financial Statement Analysis: An Introduction

Pg 25 Exhibit8

Chapter 30

Financial Analysis Techniques4. Common Ratios Used in Financial Analysis

a. Activity Ratiosi. Inventory Turnover= COGS / Average Inventory

ii. Days ofInventory on Hand = Numberofdays in period / Inventory Turnover


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iii. Receivables Turnover= Revenue / Average Receivablesiv. Pg 322

b. Liquidity Ratios Pg. 329


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c.

cfa notes chapter 7

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