V1_20110108上海复旦CFA一&cce

V1_20110108上海复旦CFA一&cce

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Unformatted text preview: fiffififififla‘fiifiifllfifi é‘fifi‘fi’zm 1 $1 )3] CFA-fl flight: Iii—iff- 5L Quantitative Methods We : fififiFRM use: 2010.01 flute: let)? CHEF}? Dfiiifil D nit—95': ilfiflflfis&£%5*fit éfifi’fifl (#) 1- imam a‘rtefetnsnsnssussa. fittrtiflm‘is'esécstr. :_. infiefifiénn asmnsseaanm.ennnn 2. sameness-st fi-Ifi -*44i’.-I'] é‘tfifirfilfll int (era. gfedu. net) “$fiifimfi” Mr“) “afifiiiififlw no: in? Hal fittiitssfenfsfifrs;-¥-fi-as Liaise-35+. «slogan assesses Hr} s-‘i is its-Wis? e4nusanmesensnnnax.senseaonssnsme.unsnm {consents t bbs. gfedu. net) $‘LE%}]£51fifiTii. an; {ingssuiziH-Xiiieifi; $1335.81: Meets —~ websites (Eskme aesamfiumm. 3. *filitifi 5% it Heineken 2213391521.- Ififlfl‘i‘ifififilfilfii‘hfi’fliififi “115E139?” #1” -.§.=*: “ifiééfg” FFT'J'M. 3;;th J . 3. Hear i.mfinnminfin.nnnnnannmfi,fime fiffikfid’dfifilfiLt-‘ffifip f’ilfg-Ti'lfiéfiifiii‘. {ii‘fi’dn Lp: Hwn'w. gFtIIII Ine. corn. cnKfiflrAfiifi (Fli-ETfléfififi) *fiffl “fiéfifif‘ifivfs (Tips: Mfififlfimflifimfififififi-Efi 312T stunt-inres-Wemin. hfikfinmsie Kan-l. t‘Frv‘J. k'slTE-ifieFJ-EE-tmfi saints. 1) Messiaenassessments. ifi-nfansaHJeJn-tan't. Mme re)»; 2) ensign-xseksvee. Hansen. ‘I‘éflfififiii’ffiffllikfi, srnflekiflts as. ifi‘idittéflfirz 3) Linei-a-xe-uagze. 3‘45. senses-stance: 4) an)“. Skits; Fifiifififi is??? 2-90 ... “LDL‘HUIJ‘E :Iulzruluar gfihfiliflfi fififisn (:) 4. fii’fllfi- {eaz+-£&ta”vir%$flfit%. Efi'LLfl-j'f-JUHIiliiitifizf-fii. -:Hfié£é‘%?§i~i. «unknown 5. W W121 fifiiiifii‘tflififfiv alfifliifiifiifi‘ i'fi'£‘?—'§i£i%lfil‘3I-‘F‘fi‘ifiiifi: f‘RE‘HEis‘sfiiifrEn fi:#mfi& . fifiCFAJBU'E: http::‘.-"cfa.gfedu.netlf {sits-Leis: httpzttbbsgfedunett frfit‘t‘fiifii issues; 14259969 ‘ issnmsEJ-msnesweetness. I 2. 3. fiefi Ft! fS-ixfi: httpzlnrvmrvgfontineeoment .1 5 . frfiltsts': gtedu@gfedu.net J'Jfifififi‘i'ii :Hilzbt mil-Hi}??? [595} 15% )E Ifléfiél’fié » fil>'<' f-F—tlntm 954% keying j: )g '__'f¥’n;t-% Isl é’dsitidéfl til" ’nliflzazhfl‘tfimneta: 400300-959 3-90 an? $79.“ £33 353 cos-enema Study Session I Ethics & Professional Standaids Study Session 2-3 Study Session 4-6 Quantitative Methods Economic Analysis Study Session 7-10 Financial Statement Analysis Study Session I I Study Session 12 Corporate Finance Portfolio Management Study Session 13~l-l Study Session 15-16 Study Session 1? Equity Analysis Fixed Income Analysis Derivative Investments Study Session 18 4-90 Altenmtive Investments r' x F- 1 fl 3 “}.ULNIu'-RE w'l'ller'JQI Quantitative Methods Jr Study Session 2 — 3 r Estimated weights in exam: 12% 'r Topics Include: r Time Value of Money ‘r Statistical Concepts r Probability Concepts r Common Probability Distributions 'r Sampling and Estimation "r Hypothesis Testing Jr Technical Analysis I”: R 3 I: H 3 5-90 (snowman: wl'.\.£l"IIIJ’I Time Value of Money W'xfififlfi 6-90 (snowman: wl'.\.£l"IIIJ’I Interest Rate 'r Simple Interest r Compound Interest or Interest on Interest neauaunuean=eneennnxnn finrfiiifié'fr’xt fifififi? it it 1626 it: . one nil-H not»: n Peter Minuil at J’ X £4124 enmuennnammaxrnnrrunnennnnua Ell/2E n: ZOUIHF . ll‘i-i-I-'=';2‘:H{t£3ji.l‘ip fifth-‘4' 2,5)J'lL-3e;ua {H nun flaws Ewe-u . in 24 menswear-1e 7% retrain-In alt/2t, iill 375 sari-nu 2000 tri. lull‘i'fil'ai- lnl ens-n a 24 x (1 + 7%)2“"“ “‘3‘” = 2.5068 (rm-Jo it) [5: R a 1-90 4?} fisflfi .I'.\lxI'-Iua9r Effective Annual Rate (EAR) EAR=(1 + periodic rate)"‘ -1 r Where: periodic rate = stated annual rate r m 1n = the number ofcompounding periods per year Example: Computing EAR for u rung of compounding frequencies Using a stated rate of 6%. compute EAR for semiannual, quarterly. monthly, daily compounding. Notes: what is EAR if continuous compounding? I”: R a 3-90 fiaflfi .I'.\lxI'-Iua9r Time line of cash flows Interest Rate 0 j 1 2 3 l l% l l l CF CF CF CF 0 w / 3 Cash Flows Tick marks at ends of periods. ' Time 0 is today; Time 1 is the end of Period 1; or the beginning of Period 2. 90% of getting a Time Value problem correct is setting up the timeline correctly!!! 6'? £3; Fifi 9-90 Fu tu re Values What’s the FV of an initial $100 after 3 years if i = 10% .9 0 1 2 3 l 10% l l I 1-100 ————————————— ——» Fv = ? Finding FV (moving to the right on a time line) is called compounding. ' Compounding involves earning interest on interest for investments of more than one period. 3 ii a a .. H}.DLNIJ'~RE "l'All|'>lIlJ9l' Iii-90 Present Values 6'? £3; Fifi I [-90 Annuities 4* Regular or ordinary annuity is a finite set of sequential cash flows, all with the same value A. which has a first cash flow that occurs one period from new. Jr An annuity due is a finite set ofsequential cash flows. all with the same value A, which has a first cash flow that is paid immediately. 6'? £3; Fifi 12-90 Ordinary Annuity Timeline Time line for an ordinary annuity of $100 for 3 years. $100 $100 $100 [4: K a 13-90 61y» Eitfiflfi "I'.\I£|'NIIJ9F Ordinary Annuity vs. Annuity Due Difference between an ordinagg annuity and an annuity M? Ordinary Annuity 0 1 2 3 I i% l l l PMT PMT PMT Annuity Du 0 1 2 3 PMT PMT PMT PV {5. 3 - 14-90 xi athflfi :l'.\l£|'>lllJ9)' Perpetuity Perpetu ity is a series of constant payments, A, each period fo rerer. fififififififi 0 1 2 3 4 5 6 T P\": an 'n «— P\'_. M] m3 PV‘. :\ r1 1)" PM A-Ll-n'" OTC. Intuition: Present Value ofa perpetuity is the amount that must invested today at the interest rate 1‘ to yield a payment of_-i each year without affecting the value ofihe initial..in¥es.tmcn.n F: a fi E Q E 15-90 HLUL-‘Hd'fiifi "I'.\.£|'NIIJ9I Quantitative Methods Statistical Concepts and Market Returns [4: K a 16-90 61y» Eitfiflfi "I'.\I£|'NIIJ9F 1. Descriptive & Inferential Statistics P160 r Descriptive statistics r lnferential statistics l 190 {gas Eula 2. Population and Sample P160 X,,x2,x3, ..... .ixl,‘ 17()<|._x3._x3 ...... “XKJ pepulation — population parameter sample statistic n ltxlixzfxli.......xnj sample x11x3_x3;.__.____x 13-90 3. Measurement Scales P161 r Nominal scales 'r Ordinal scales r Interval scales r Ratio scales l‘J-Qtl {gas Eula 4. Frequency Distribution P161 r Absolute frequency 'r Relative frequency r Cumulative frequency ir Histogram & Frequency polygon 20-90 =3 l: 5 E ALDL-‘HJ'J’H. \:".||Il'>l:a.' YI' 5. Measures of Central Tendency P166 6. Quantiles P171 f Mean r Median 5' POPUIation mean and sample mean r Qiiartife — the distribution is divided into quarters ’ Arithmetic mean I Qm'miie — the distribution is divided into fifths f weighted mean r DeCi/e — the distribution is divided into quarters ' Geometric mean . . . . . . . . . . “r r Percentile — the distribution is divided into hundredths 'r Harmonic mean f Harmonic mean< Geometric mean< Arithmetic mean _ r LV = (n+1)y/100 r Median r Mode 21-90 ti: if.» 35-3 22-90 5.5.» 35-3 7. Measures of Dispersion P173 8. Chebyshev’s Inequality P176 r Range r X‘J‘fl’iihi 'Efliililiiiliiflv ifiii’i’iHLEJfliUfiiIiiilk/Mlilii’iiifi/Z r Mean absolute deviation (MAD) 1*] li’ili‘ll’E-ifidi 4‘ TI - 1/k2 i X‘J‘ili:i%§k>l s r Variance ’ PUX'HIg k 0) E 1 ' 1/1‘2 k>1 Lr Population variance ( 0 2) 'r Sample variance (53) 2 r Standard deviation Lie a Swim?“ _ _ _ _ . . s deviations r Populanon standard devrahon ( U ) “mm of the mean 'r Sample standard deviation (5) 4 11.90 if.» iii-E 24-90 iii-E .I'.\lxI-uua9r dull. I - u u a 91' 7. Coefficient of Variation (CV) P177 'r Coefficient of variation (CV): relative dispersion CV S . standard dev1atlon of x _ k _ X average value of x 9. Sharpe Ratio P178 r Measurement of excess return per unit of risk PP Ff 0'3, r Sharpe Ratio = where: a = portfolio retum r f = risk-free retum p U = standard deviation of portfolio return 25-90 finfii. 26-90 finfii. 10. Skewness 11. KurtOSiS r Symmetiical and nonsymmetrical distributions _ / Leptokurtie r Positively skewed and negatively skewed 3.. Normal _ _ _ _ Distribution Negatwe-Skewed Symmetric POSItIve-Skewed Mean Median Mode Mean. Median Mode Mode Median Mean \\ F - 7‘“ J / 27-90 finfii. 23-90 finfii. \|'\l£I'NIl.\Y|' \|'\l£I'NIl.\Y|' ll. Kurtosis P181 Quantitative Methods r Sample kurtosis r Excess kurtosis = sample kurtosis — 3 lcptokurlic Mesokurtic Platykurtic (normal distribution) >3 ‘ <3 Probability Concepts Sample kunosis 3 Excess kurtosis >0 a“ a»- 29-90 {@t s a}: a I IDL-‘Hu'Jii. fill-90 I u: IDL-‘Hu'Jii. 1. Basic Concepts P197 2. Two Defining Properties of Probability P197 r Random variable lfllll’i'éjiflf ’ 0g» PUB) g l r Outcome #99 , Event 'uj.i'_1'gi; r If E, E2, . . . . ..,En, is mutually exclusive and exhaustive, then s.__ PE+PE+ . . . . ..+PE=l rMutually exclusive events iilF-iil‘i’l" ( I) ( 2) ( ") f Exhaustive events iiifilil "ii-iii" 3 [-90 331m 32-90 331m \I-tnxu-uu_\gr \I-ian-uu.\$r 3. Three Kinds of Probabilities P198 4. Odds For or Against an Event P198 'r Empirical probability @filfiéfi r Odds for an event v” By analyzing past data - P(E)/(1-P(E)) , Priori probability 5E3QH$ / By using a formal reasoning and inspection process 'r Subjective probability ifllfi$ v” Personal subjectivejudgment r Odds against an event ' (1-P(E))/P(E) 33-90 (ii-E 34-90 \|\l£I'NIl.\Y|' \|\l£ I ' N u . \ or 5. Two Important Rules P199 6. Total Probability Rule P204 r Conditional probability fiEl’IZMéii: P(A|B) r Total Probability Formula r Joint probability Hfifiifliifi: P(AB) "r If an event B must result in one of the mutually , Multiplication rule; P(AB)=9(A|B)X [1(3) exclusive events A1.A:,A3,.y.,Afl , then: [UK and B are mutually exclusive events. then: P(AB)=P(A|B)=P(B|A)=0 f Addition rule: P(A 01‘ B)=P(A)+P(B)-P(AB) lfA v ’ ‘ r - - ,él/ and B are mutuall} exclusne events, then. 1 1 PM or B)=P(A)+P(B) 1 | 35-90 (ii-E 35-90 \|\l£ I ' N u . \ or 1’08):1M)1’(B|AiHIM)I’(BIA2)+-..+1’(z‘l,)1’(8|4,) \|\l£ I ' N u . \ or 6. Total Probability Rule P204 default I ,—--" Ia;1 I\g...- -r.—.Il Nmucrauii A l: [5 SIP» I Companies —.- a. N01 dul‘uult ,1: ‘1 delimile “In” 3i:% ‘3 . ._ . \_\ ;;.- T‘ ulcl'aull ' ." 4.". 37-90 g... «hummus: wl '.\ I I. I ' N u J , I 7. Independent events P203 r The occmrence of A has no influence of on the occurrence of B , P(A|B)=P(A) or P(B|A)=P(B) r P(AB)=P(A)>< P(B) , P(A or B)=P(A)+P(B)-P(A) >< P(B) 38-90 x... «hummus: ‘l '.\ I i I ' N u J 9 I 8. Expected Value and Variance P205 r Expected value: E(X) =in x P(x,)=xl x1003)”: >< P(x3)+ +x” >< PM") 'r Variance: .-v 1 a: = 213M, — E(X)]' l-l 'r Standard deviation: U=d07 39-90 g... «hummus: wl '.\ I I. I ' N u J , I 9. Covariance and Correlation P209 r Covariance: COWX, Y) : E[(X - E(X))(Y - 1300)] r Covariance measures how one random variable moves with another random variable r COV(x,X) = EItX - E(X)}(X — E(X))I = 030:) r Covariance ranges from negative infinity to positive infinity 'r Correlation: p“. = COWXaY) q/Var(X)Var(Y) r Correlation measures the linear relationship between two random variables "r Correlation has no units, ranges from —I to +1 40-90 lg . . .\ «mm r «as: null. I - u u a 9r 10. Bayes’ Formula P218 ; P(AB)=P(A|B) >< m3) =P(B|A) >< P(A) 10. Bayes’ Formula 3035 @Wfifix’ 10(3 | A) P A B = XI’ A W E 1'? -.I:.J-.'H r Example: tilliil 0.3 ‘FW'L‘TJ 0.7 ‘Héii i4) I’E'Ti 131: A imam»; +JL Law; “an 10% finfilikfléfi‘llij 0.3 0,2 ma... Lin Ell-A 0.1 0.9 1M JPA i? IlE'Ji .7-J 1' {iii . IHJ 331';— mii'i I'll] ma ? 41-90 ’35 42-90 (ii-E 11. Counting Problems P220 Quantitative MethOdS r Multiplication rule: n1><n2>< .... .. ><nk r Factorial: n! . n! ’ Labdmg' nllxnglx...xnk! I Common Probablllty H n. "r Combination: =[ .]= _ . . . . _! (n—i )le DlStl'lblltIOIIS . . n! r Permutation: P. : H J' _ 43-90 (ii-E 44-90 (ii-E 1. Basic Concepts P241 r Probability distribution r Discrete random variables r Continuous random variables "r Probability function: p(x)=P(X=x) "r0 a? p(x) «g l r X p(x)=l 'r Probability density function (pdt): f(x) 'r Cumulative distribution function (cdf): F(x)=P(XE§.x) a i: a a .. H}.DLNIJ'~RE "I'AlerIlJ9I 45-90 2. Two Common Discrete Distributions P244 4r Discrete uniform distribution 'r Binomial distribution "r Bernoulli random variable P(Y=1)=p P(Y=0)=1-p 'r Binomial random variable the probability of x successes in n trails ;)(x) = P(X = .r) 2 [:JpWI — p)"" 'r Ex ectations and variances Expectation Bernoulli random variable (Y) i p _ Binomial random variablc_(X} np 46-90 gratin E K... H}.DLNIJ'~RE "I'llerIIJ9I' 3. Continuous Uniform Distribution P249 l.-'n|1'nrm [3. h] Distrihulion mmmmaa=ycpm (B-—+g)=.-1.60' ’{3‘1 4190 ‘ 3.333333 "I'AlerIlJ9I 4. Normal Distribution P251 'r The shape of the density function The normal curve is symmetrical The two halves are idcnliczil ~ ' -.-I|l_\'_ the co Theo . '. t e 00 Elm: etilends to - curve extends to . /” \ Lr Properties: , The mean. median. and mode are equal rxwm,w) Jr Symmetrical distribution: skewnesszt); kurtosis=3 'r A linear combination of nomially distributed random variables is also nomially distributed. 2 Univariate and multivariate distributions 48-90 6'?“ £33 3313 5. Normal Distribution P252 6. Normal Distribution P254 4’ The confidence Intervals r Standard normal distribution 'rN(0,1) orZ 'rStandardization: if X~N (1.1 _, U 3), X ‘*” ~ N(0,1) Xer.“3 then Z = 14-2580 u-‘l.650 it “+1556 “+2586 fz'table u-‘l .960 u+1.960 io— 90% _.i r Example: 950/ X ‘" A'\'(7Uv 9) . compute the probability of X 2 75.9 D First 2 = 159—?“ a L96 . then compute m2 21.96)=l—0.975 = 0.025 99% 49-90 $35 (ii-m 50.90 if; {’55. 7. Roy’s Safety-First Ratio P257 8. Lognormal Distribution P260 -, Shortfall risk; 13(qu RL) ;r If lnX is normal, then X is lognormal r Roy’s safety-first criterion r Right skewed ‘r Minimize P(R],< RL) r Bounded from below by zero "r Maximize S-F-Ratio r Lognormal distribution is used to model asset prices - Minimize P(RI,< R11) <=> Maximize SFR=M 0|, I | | | o 2 a s s 10 I 51-90 335335 52-90 335335 9. Continuously Compounded Return P260 'r Discrete: I? m it. I 'r Continuous: ’1'-i-"'R:,',,i..'._rl“+;) ‘1 =9 " ‘li , W24) = ln(l + HPR) = a“. {J r :HPR Fair” —1 53-90 fi;\ 5 if): 335% 10. Monte Carlo Simulation and Historical Simulation P262 'r Monte Carlo Simulation r Limitations: it is fairly complex and is not an analytic method but a statistical one. and cannot provide the insights that analytic methods can. r Historical Simulation 'r Limitations: the past can not indicate the future and historical simulation catmol address the sort of “what if " questions that Monte Carlo simulation can. 54-90 3.. I Quantitative Methods Sampling and Estimation 55-90 1. Basic Concepts P276 r Sampling and estimation population population parameter SQmPImE-fii cstimati: m sample sample statistic r Simple random sampling vs. Stratified random sampling 'r Sampling error 'r sampling error ofthc mean = sample mean- population mean r Sampling distribution 'r Time—series data vs. Cross-sectional data gait: i? it is 55-9” \._..,’ m.uLm,-'.Hi. -I"II'I'MIIJYI' 2. Central Limit Theorem P278 I For simple random samples of size n from a population with a mean p and a variance 0 3, the sampling distribution of the sample mean approaches Nut , 0 2/n) if the sample size is sufficiently large (n 230). 'r Standard error of the sample mean I Known population variance 0'". 2 am; r Unknown population variance .3". = six/:1 air-90 Q l 3. Point estimation and confidence interval estimate 281 'r Point estimation f —># r Confidence interval estimation ', Levelofsignil‘icance (alpha) 'r Degree ofContidcnce (l—alpha) r Confidence Interval = I Point Estitnate +t- (reliability factor) * Standard error] u . _ 58-90 if} £3?" 55,5. .:-Ian-Mna‘r 4. Properties of an Estimator 281 The Desirable Properties of an Estimator: r Unhiascdncss: expected value ofthe estimator is equal to the parameter that are trying to estimate r Efficiency: for all unbiased estimators. if the sampling dispersion is smaller than any other unbiased estimators. then this unbiased estimator is called efficient. 'r Consistency: the accriracy of the parameter estimate increases as the sample size increases. 59-90 5. t-distribution 282 Symmetrical Degrees of Freedom (dt): 11-] Less peaked than a normal distribution (“fatter tails") As the degrees of freedom gets larger, the shape of t-distribution approaches standard nonnal distribution ‘1"! YV err-90 -!'1-I'I'”Ild‘l 6. Confidence Interval for a Population Mean 28! rl) a known population variance: r2) an unknown population variance and the sample size is small; 'r3) an unknown population variance and the sample size is large. 2)or3) Yet i JOOI’S) u r: J”— e "a" 61-90 .!-1nr|-»u.s\z 7. Five Kinds of Biases P288 Ir Data-mining bias r Rel‘ers to resulls where the statistical significance of the pattern is overestimated because the results were found through data ruining. 7 Sample selection bias r Some data is systematically excluded from the analysis, usually because of the lack ol‘an-‘ailabilitv. 'r Survivorship bias 7 U suallv derives from sample selection for only the existing portfolio are included r Limit-ahead bias r Occurs when a study tests a relationship using sample data that was not :1 available on the test date. ? Time-period bias 'r Time period over which the data is gathered is either too short or too long. 11‘ the time period is too shun, resem'ch results may reflect phenomena specific to that time period. or perhaps even data mining. .!-1nr|-»u.s\z Quantitative Methods Hypothesis Testing a 2 at it 3-9o ts; seam-at I'Ilrl'bruafil 1. Basic Concepts P299 'r Hypothesis: a statement about the value of a population parameter to be tested "r The null hypothesis (H0) and alternative hypothesis (Ha) "r One-tailed test vs. Two-tailed test I One-tailed test H0: t1 330 Ha: ll <0 H0: 1.1 £0 H“: 1-1 >0 r Two-tailed test H0: t1 =0 Ha: t1 #0 . . u. . . ot-oo L.) 3.15. I'Iflrl"flld.l 2. Type I and Type 1] Errors P305 Jr Type I error: reject the null hypothesis when it‘s actually true 'r Type [I error: fail to reject the null hypothesis when it’s actually false 'r Significance level ( a )1 the probability of making a Type I error Significance level =P(Type I error) ‘r Power of a test: the probability of correctly rejecting the null hypothesis when it is false Power ofa test = l—P(Type 11 error) 65-90 Q l 3. Test Statistic and Critical Value P305 'r Test statistic sample statistic - hypothesized value teSt Statlsnc 2 standard error of the sample SlatiStiC 'r Critical value rThe distribution of test statistic (z, t, x 2, F) r Significance level ( CI ) r One-tailed or two-tailed test 66-90 4. Decision Rule P305 "r Reject H0 if |test statisticj>critical value "iv Fail to reject HO if |test statisticl<c1itica1 value 95% 5% - 1.96 1.96 l.6-I{5 Reject Hfl Fail m jom H. Erica Ha Fail to “zinc! Ho Reject H. r We can never say “accept” FLJ r State the conclusion: L1 is (not) significantly different from L1” (fit-90 Q l 5. P —- value testing 309 .—2.5% 2.5l%_. l.(l7% < Negative ol‘ the Positive ol‘ the Critical value for 5% test statistic “Emma‘me level test statistic l 07% 'r P- \-'alue=2.I-l% 63-90 6. Test of Single Population Mean P310 'rHU: ll=l10 r z-test vs. t-test Nonnal population "(~30 Known population variance ( U 2) z-[csl Unknown population variance l-lesl or z-lcsl . _ _ _ Y—«n _ Z = D I Z Statlstlc 0h]; r t-statistic l” ,2 Mil“ six/n 69-91] .l'lll'v>.‘>l—‘|I 7. Test of Differences Between Means P310 r t-test r unknown population variances assumed equal ( U '2: 0 23) I: (E—fg) r unknown population variances not assumed equal (0 13:15 0 23) (Ia-f3) '} 1 1 2 Sb 5 H | + 2 n, 113 Til-90 Q: 31‘ 5? 5. .l'lll'v>.‘>l—‘|I If: 8. Paired Comparisons Test P317 r Two dependent samples r HUI lJ (1:0 "r t-test d f = dl;n-1 " a? 71-90 filififi .l'lll'v>.‘>l—‘|I 9. Test of Single Population Variance P321 . 2: 2 ’ Ho. G 0o 'r The chi—square test ( X 2—test) 2 2 _ (n ‘ Us _ ,cg _—2 dl‘wn-l all 1'2-90 filififi .l'lll'v>.‘>l—‘|I 10. Test of Variances Difference P324 ' . 2: 2 7H“. 0] 0,, "r The F-test S 2 l? :2 ;;%_ df1:q]1—1; dfé::n2—1 'r Always put the larger variance in the numerator (if > sf) "r The rejection region is always the right-side tail, no matter the test is one-tailed or two-tailed \ 73-90 ' El; 5; E 7 uL-‘UJ'J‘E -¢ lltvfll:tl 11. Summary of Hypothesis Testing Assumptions = 'orrna||_\-' dislrihuted population. 'nown population variance Critical value NHL!) - 'onnnlly dislrilauted population. nknmtn population Variance mlupendunl pnpululions. unknown Mean epulnlion variances assumed equal hypothesis testing 101-] ) ltnl +112 —2) ndepcndenl populalions, unknown npulalinn variances not assumed 'qual ~:Irnples nnl indupundml, mired comparisons lest Variance . 'onnally (lislrih uted population hypothesis testina . . “ 1er independent normally u islributed popuialio ns 74-90 12. Parametric and Nonparametric Tests P328 'r Parametric tests: population disnibution and about population parameters 'r Nonparametric tests: no population distribution or not about population parameters .. (:OLIJLNKJ'J‘E IIEDHI:!I nil a T590 3 1: Q E Quantitative Methods Technical Analysis To-QU seas: .__, comm r .J'J‘ti 1. Technical Analysis P339 7 what is technical analysis? r It is the study of collective market sentitnent. as expressed in buying and selling of'assets. 'rThe key assumption of technical analysis 'r It is that market prices reflect both rational and irrational investor behavior. 'r Fundamental Analysis 71-90 "55.? 2. advantages of and challenges to technical analysis P339 r Advantages of technical analysis: 7 It is quick and easy 'r It is not heavily dependent on financial accounting statement. 'r It incorporates psychological as well as economic reasons behind price change. 'r It tells when to buy {not why to buy). I Challenges to technical analysis: ‘r the efficient market hypothesis (EMl-I} ‘r the behavior of past price and market variable may not repeated 'r require too much subjective judgment. 118-90 __’. CnihlJL-‘I l «URI fighfififlfi .'\ IKI'NIIJ9I 3. Different types of technical analysis charts P340 'r Figure 1: A Line Chart 1315 131fl 1305 130D 1295 1290 1235 128i} 121': 127'11 1265 1251} 1255 1250 - 1245 Chart by Metastotk Copyright 6‘ 2008 Investopediacprn 19-90 {ii-E .\l£I'NIIJ9I 4. Different types of technical analysis charts P341 'r Figure 2(a): A Bar Chart 1329 1315 1310 1335 1300 1295 1290 1285 1280 12?5 12?D 1255 1260 1255 1250 1245 Chart by Metastod: Copyright G: 2006 lmestopediaeom a r: a a 311-911 31:; «nun-urns: ‘ .\II.I'NIIJ9I 4. Different types of technical analysis charts P341 "r Figure 2(b): A Candlestick Chart 1315 1310 1305 130D 1296 1290 1235 1280 12?5 12h] 1255 12m] 1266 1250 1245 'lznbe' ' 'Fébmary hibroh ' ' laxil' ' lanai; ' Chart by MetaStodc Copyright (31 2006 lnveslopediaeorn 81-90 filiflfi "l'lllvbilnlli' 5. Different types of technical analysis charts P341 'r Figure 3: Point and Figure Chart (3} Line Chm . .— [0 BO 80 31-33)! I I '13“): i") I!“ 1 [Lil IS-Iul 29-[ul 12 Aug 26 Aug 9 Scp Iii-Sap 31-ch 21 On 82-90 filiflfi "l'lllvbilnlli' 6. The uses of trend, support, and resistant lines, and change in polarity P343 'r Trend ', Uptrend rim": 5: Chung: in Poluky 1* Downlrend W ‘r Trcndlinc an ‘r Breakout ?o 'r Breakdown 6° . 50 7 Support 16\-Bl 4o mghangum supper!- 'r Resistance level 30 r Change in polarity 20 ID 83-90 0:! Nov filiflfi "l'lllvbilnlli' 7. Identify and interpret common chart patterns P344 Lr Reversal Patterns (Jifii'gifii't) Figure 6: Rmml Pane rm 90 ‘I Hrai-and-dmuldeh 84-90 filiflfi "l'lllvbilnlli' 7. Identify and interpret common chart patterns P345 3- TBChnical analySis indicators: Price'based P346 ;r Triangle Continuation Pattem (.2 fl] fié‘il‘ifl) ’ MOVing aVeTage lines (lg/fill I‘lj‘ii‘li‘ii) Figure 7: Triangle Continuation Pattern ' F Bollinger Figure 3: Moving Average and Bollinget Bands 270 — .J 'l p 260 r.lr lrlr Inf Upper Hullingfl hand [r L‘I' m - \ 250 Moving average 240 230 50 10 220 J 10 210 ——-.—————r ———-—..— u-i—. . . . . . . ,.__ _, Apr” AP!” May” May” jun“) I-Aug li-Aug 294mg lZAScp Jase.) loot: 24-0” 1N.» II-Nut' 5.0:..- l‘J-Dn 35-90 3.35% 86-90 3.35% 9. Technical analysis indicators: Oscillators 10. Technical analysis indicators: Non- P346 price-based P349 r Rate of Change Oscillator r Put/call ratio r Relative Strength Index r Volatility index (VIX) r Moving Average Convergence/divergence r Margin debt r Stochastic Oscillator r Short interest ratio 'r Short-term trading index (TRJN) r Mutual fund cash position 'r New equity issuance 31-90 fiéflfi 88-90 fihflfi "l'.\l£|'>lIlJ9)' i-Inlxruun9r 11. Elliott wave theory Figun: 9: Elliott Wave Panama UI d I” (a) pmn I20 3 no I: f 100 I a 3L 4 9n "Lr 2 c 30 ?l] —'—fi_'—'_'_-—"_Y’ Aug Sep Oct Nov 89-90 [h] annucud I 20 HO I00 Cni'huL-‘Hd'..fii. I I. I' u u .\ Y I' again: 90-90 THE END fifififi (Axum f «URL nil-nanr ...
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This note was uploaded on 11/02/2011 for the course FINANCE 612 taught by Professor Liyang during the Spring '11 term at Covenant School of Nursing.

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