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Downside Probability is one component of Downside Risk; it is the chance that a portfolio's performance may fall below the goal (for example, there's a 25% chance of not meeting a 10% goal). Please try again.first1Q?| Y#RMNOPcomment1Your answer is incorrect. Standard Deviation measures the dispersion of possible returns around the average return and gauges total variability. It assumes that returns are normally-distributed. Please try again.first1 R?|,2 CSMNOPcomment1Your answer is incorrect. Downside Magnitude measures the amount by which the portfolio will fall short of goal at a certain confidence level (for example, the portfolio will underperform goal by nearly 17% at the 99th percentile confidence level). Please try again.S?|, пGTMNOPcomment1Correct! Downside Risk, measured by downside deviation, is the risk that a portfolio will not meet the investor's goal. Returns above goal are good, and returns below goal are bad. Only bad returns represent risk, and the farther below the goal, the greater the risk.T?| YˊwUpy:  K333,bm' st"A9'trq9<'H'"uA:)9H]t B9!N`]>>>NVõ=Wh K333Hʞʜ>NHʊ>N>W=Xh K333Hʞʜ>NZ邜>NXD=YhSЊ K333fN]NHʊ>N>YLj&Z` K333,@"YYY" Z9@B ?G[Uz)KZ|gggꆆ_,'+îjÖ '4Y@pA?Z\Vp&2EXI05.0 4HO]ut?05ȷL@31#L?L]VpXb~++++P ~k cZ_߅8d[?^N!]* )|w_N!\*)|s?A`Vp&5.0 4HO]ut?05ȷL@31#LaN#\*L6 uHb;[\]^_`a` b\XNmx.skins.halo.DefaultsDefaultsObjectregisterClassR` mx.core.ext.UIObjectExtensions!UIObjectExtensionsObjectregisterClassRHmx.core.UIObjectUIObjectObjectregisterClassRo mxmanagersFocusManagerObjectregisterClass_rootfocusManagerDepthManagerhighestDepthcreateClassObjectNN RNIlNNNNNNNNQONN RPmx.skins.halo.FocusRectFocusRectObjectregisterClassRTmx.managers.FocusManagerFocusManagerObjectregisterClassRf#mx.core.ext.UIComponentExtensions$UIComponentExtensionsObjectregisterClassRNmx.core.UIComponentUIComponentObjectregisterClassRTmx.controls.SimpleButtonSimpleButtonObjectregisterClassREmx.skins.BorderBorderObjectregisterClassR DmxskinsRectBorderObjectsymbolNameSkinElementregisterElementNN=NNNNNRmx.skins.halo.RectBorderRectBorderObjectregisterClassRR!mx.skins.halo.ButtonSkinButtonSkinObjectregisterClassRH"mx.controls.ButtonButtonObjectregisterClassR1mx controlsN RadioButtonN prototypeNadjustFocusRect )5 _parentN focus_mcN iconNameNthemeColorgetStyleRI M _widthNG _heightNG dsetSizeR_xN_yNyNG xN G moveROR2mx.controls.RadioButtonRadioButtonObjectregisterClassR@  Q2cx)AV K333Cb$NNNh]N]NL>4NNNZ銜]]>NN>>N>NNN>NNNNX-NNNNNNNNNN>N]>N>NNN>銜N]>Nc@ 0ĿAdMNOPcomment2Your answer was incorrect. For a given set of asset assumptions, mean-variance optimization finds one efficient frontier; the investor’s goal and time horizon have no effect on which portfolios are deemed “efficient” or “inefficient.”first1dq5L+eMNOPcomment2Correct! For a given set of asset assumptions, mean-variance optimization finds one efficient frontier; the investor’s goal and time horizon have no effect on which portfolios are deemed “efficient” or “inefficient.” first1 eq5O fX] K333h]f+@ W*"gXk K333L>g &hw9Me!K0c(comment2h@ - &i`$ K333,@$YYY" i9@B Hj;[\]^_`a` j\X?kMx]* 5{lMp}]* 5{mMp}]* ʽ6 uHn;[\]kl`m` nI`@  Q3gop:  K333'b&NNNhNN]骜>NN]Nf]N>NdʂN>N>4No@ 0tppl: ` K333,]dʊN]N]Ndʒ>NL]NdʂN]NfN]NHʊ>N>pDqMNOPTcomment3Your answer was incorrect. This is the Sharpe Ratio. Please try again.first1qŨyrMNOP9comment3Your answer was incorrect. Please try again.first1 rrYb @@sMNOPcomment3Correct! The Sortino Ratio, like the Sharpe Ratio, measures how effectively a portfolio delivers return for each unit of risk taken. It is calculated as the difference between the portfolio's return and the goal divided by downside deviation.first1sYb[@`tp: ` K333"]dʊN]N]NNڞ>]NfN]NHʊ>N>tKtup: ` K333,]dʊN]N]Ndʒ>NL]NdʂN]NHʞʜ>NHʊ>N>uK `vp: ` K333"]dʊN]N]NNڞ>]NHʞʜ>NHʊ>N>vKL &wwY7e!K0c(comment3w.J0Hx;[\]kl`m` xI`Hy;[\]^_`a` #y\X&z` K333,@&YYY" (z9@B @ #( Q4{xA: K333Bb(NNNh]N]NL>4NNNL]NN]NN>>Nʒ>NNʊN]N]NNfN]X0NNNNNNNNHʊ>N>N>NN]NN]>N銜]NNN{@ 0ďf+@ W*)|MNOPcomment4Your answer was incorrect. If returns are positively skewed, standard deviation overstates investment risk. Vice versa with negatively skewed distributions, where standard deviation understates the degree of risk taken.first1|ͦm g+@ Wـ}MNOPcomment4Correct! If returns are positively skewed, standard deviation overstates investment risk. Vice versa with negatively skewed distributions, where standard deviation understates the degree of risk taken.first1 }ͧ &~wOe!K0c(comment4~B@.K@H;[\]^_`a` \XH;[\]kl`m` I`&`$ K333,@(YYY" 9@B @   Q5H;[\]^_`a` \X p̀: K333;b*NNNBNNN" N>NNZ邜]NBNNN(N NHʞʜ>NHʊ>N>X;NNNNNNNNNZ邜]NDNN$N NNNPʞNʞ>NN>N]]NNN>> @ 0up /: ` K333.BNNN( NNNNNN銜N>NNN" N>N +@ <3yMNOP9comment5Your answer was incorrect. Please try again.first1 &~gp : ` K333'BN>NNʒ>NN]>NNDNNNN" N>N+@ <ӀLMNOP comment5Correct! Because downside deviation measures risk against the same reference point (i.e., the same goal), portfolio risk can be compared directly. Thus a portfolio with a 4.0% downside deviation is twice as risky as one with 2.0%, assuming the same goal.first1' &w88e!K0c(comment5B@p: K333>B>N]N]Nʒ>>>NNʒ>N>NN]N( NNNNN]NNʞ]N>]NN6X NNN>銜N+@ H;[\]kl`m` I`&` K333,@*YYY" "9@B @  " Q6H;[\]^_`a` \X p:` K3334NNRNNNNʞN]NN>>NNNN銜NfN]X;NNNNNNNNHʊ>N>NNNN]N>]NNNN>>N]>N MNOPcomment6Since a portfolio’s standard deviation is stated in relation to its average return, two portfolios with the same standard deviation are equally risky only if they have the same average return.first1 ͦmͧ &wMe!K0c(comment6B@-f+@ W*g+@ WـH;[\]kl`m` I`P:ɰK333,@|J&`$ K333,@,YYY" 9@B @   Q7pv:H K3338b.NNNHʞʜ>Ndʒ>NNN]NʜN>NNNNN>>ʒ>XNNNNNNNN頜N4N@ 0pOԀB ` K3333Hʞʜ>N`]>>>NNNB]NHʞʜ>NHʊ>N>I;MNOPcomment7Correct! Downside risk takes into account both the chance of failing to earn a target return (called downside probability) and the consequences of failure (average downside deviation).first1VfyMNOP9comment7Your answer was incorrect. Please try again.first1 V5VʅRpI: K333dʒ>NL]NdʂNNNfʜIyRpi: K333Nڞ>NNfN]NHʊ>N>Idp+: K333$f]N>NdʂN>NNfN]NHʊ>N>I &w,`e!K0c(comment7+VH;[\]kl`m` I`H;[\]^_`a` #\X&` K333,@.YYY" (9@B @ #( Q8p:H K333>b0NNNh]N]NL>4NNrNN]N>N>>NNʜ>N]>XNNNNNNNN>NN]NNN@ 0ſoMNOP/comment8Your answer was incorrrect. Calculating downside risk in a spreadsheet is a simplistic method that often gives incorrect results since discrete values are used. For accurate results, the continuous distribution, which considers all possible values along a continuous scale, should be used.first1ͦm\MNOPcomment8Correct! Calculating downside risk in a spreadsheet is a simplistic method that often gives incorrect results since discrete values are used. For accurate results, the continuous distribution, which considers all possible values along a continuous scale, should be used.first1 ͧ &wQe!K0c(comment8B@,@f+@ W*g+@ WـH;[\]^_`a` \XH;[\]kl`m` I`&` K333,@0YYY" 9@B @  Q9*x-AV K333?b2NNNh]N]NL>4NNh]>N>>NNNNNNNNXFNNNNNNNNN>ʂNN]N>N]]NNNN]邜>N>N]>N>NMNOPHcomment9Your answer was incorrect. In our research on five major indices, Investment Technologies found that historically returns have not been normally distributed more than 60% of the time. That means that standard deviation was not an appropriate representation of risk in three out of every five time periods examined.first1ͦmvMNOP6comment9Correct! In our research on five major indices, Investment Technologies found that historically returns have not been normally distributed more than 60% of the time. That means that standard deviation was not an appropriate representation of risk in three out of every five time periods examined.first1 ͧ &wNe!K0c(comment9B@.+@H;[\]^_`a`  \XH;[\]kl`m` I`&` K333,@2YYY" @  Q10(`h K333,@" YYY" @A pĀ:H K333?b" NNNh]N]NL>4NNN`N]Z鞜]N`]NN>>Nh]N>NNNNʃXNNNNNNNNNNNN@ 0ſMNOPcomment10The origins of PMPT can be traced back to Harry Markowitz's work in 1959 when he proposed two risk measures: mean-variance (below-mean semivariance) and downside risk (below-target semivariance). Mean-variance won out over downside risk for practical reasons: it was computationally simpler (there were no PCs in 1959) and if return distributions were assumed to be normal, both measures provided similar results.first1ͦm  ͧ  'wUe!K0c(comment10B@0#z f+@ W*g+@ WـH;[\]^_`a`  \X H;[\]kl`m` I` @ @JFIFHH ExifMM*bj(1r2iHHAdobe Photoshop 7.02005:12:24 14:44:10D(&HHJFIFHH Adobe_CMAdobed            7"?   3!1AQa"q2B#$Rb34rC%Scs5&DTdE£t6UeuF'Vfv7GWgw5!1AQaq"2B#R3$brCScs4%&5DTdEU6teuFVfv'7GWgw ?5DRW~BEeΦ3ʴa߫TPfUѶL+=Ee8Q"iTD˕ GCxC)uFaFmi"&F&f<]sE, y2G)=a0jOVUp[P9ޢ>Xm5Ko>bl# 2VjfYl&6?,[A:4nZ .kΩʁ1w?>^̯9 t$@wY(dEW˨D0t;2X4O W@ng]'U[\:#@?uT*Lry,j@]лa-;S6ÕUĭP-&_Gn|ѸjP1ܦ&M0S*k9*"l) 5"XmEqR] lΊqiv@9oYk*]YJvo1ߤbk ೳz=}c7kqx$emkSH; ?8Ydz}_G6WܷggbPkc5݃1}?*_֫UV[ﯙi;n% qdܗe_j?[k=R^{r4nGo_T~0[evُͥu?*LG~pN"g^npnn~c%H hc+m[DcF{Xֈ;y4Iѹ'v8pvOZ#k)=XHZ"'#:B˝bTAf֐aGsR1|~^8>.XvHR=5ۀkA\ԾW'oLCHx#p#Lr6$GtsF[[,rBhz*Mϵ[;c";J͖nigROF̌Xt}ߴ>sqW+KTaۗjŶ[]ni6z ӭjg___g1G:.7NĦPn|\ֱu_schOI{縼SS&u=gFI>Z>Cٽӫ>"ʠA$m=Igp ;N@܆eao 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