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Making Sense of Risk Metrics

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Risk Metrics for Alternative Funds: Introducing the StatMAP

When it comes to ana­lyz­ing the risks and returns of mutu­al funds, ETFs, SMAs, and hedge funds, finan­cial ana­lysts have an over­whelm­ing num­ber of per­for­mance met­rics avail­able. If one were to pop open Morn­ingstar Direct, Zephyr StyleAD­VI­SOR, or eVest­ment Ana­lyt­ics, there is a del­uge of ratios and sta­tis­tics avail­able.  Sorti­no ratio, Val­ue-at-Risk, track­ing error, kurtosis—who can keep them all straight?  What do these met­rics tell us?  How are they use­ful?

In a pre­vi­ous role as Direc­tor of Research at Zephyr Asso­ciates, I devel­oped a frame­work to orga­nize all the dif­fer­ent met­rics, so it could be eas­i­er for peo­ple to under­stand and keep track of what each met­ric mea­sures. The frame­work has two axes: cat­e­gories of mea­sure­ment and clas­si­fi­ca­tions of risk.

 

Categories of Measurement

The vast major­i­ty of the per­for­mance met­rics avail­able can be clas­si­fied in one of three ways:

  1. Mea­sures of return
  2. Mea­sures of risk
  3. Mea­sures of return-vs-risk trade-off

Gen­er­al­ly speak­ing, the high­er or larg­er the mea­sures of return, the bet­ter. Con­verse­ly, one hopes the val­ues of the var­i­ous risk mea­sures to be as small as pos­si­ble. Final­ly, since return-vs-risk mea­sures are typ­i­cal­ly expressed as ratios with return in the numer­a­tor and risk in the denom­i­na­tor, one would like to see the trade-off ratios like Sharpe ratio and infor­ma­tion ratio to be as large as pos­si­ble.

 

Looking at Risk Holistically

The oth­er axis brings in the many ways we under­stand risk. While volatil­i­ty risk is the more famil­iar one, focus­ing on one aspect risk while ignor­ing oth­ers leaves blind spots in our under­stand­ing of it. To pro­vide a holis­tic view, I pro­pose four broad clas­si­fi­ca­tions:

  1. Risk in terms of volatil­i­ty
  2. Risk rel­a­tive to a bench­mark
  3. Risk in terms of cap­i­tal preser­va­tion
  4. Risk of rare but extreme events, known as tail risk

 

Volatility

This frame­work reflects the evo­lu­tion in think­ing over the last 50–60 years. When Har­ry Markowitz and his con­tem­po­raries devel­oped the ground-break­ing Mod­ern Port­fo­lio The­o­ry, risk was most often described in terms of volatil­i­ty. Because invest­ment returns were often described using long-term aver­ages, volatil­i­ty was used as a cross-check on the valid­i­ty of those long-term aver­ages. Volatil­i­ty was the orig­i­nal mea­sure of risk, and it con­tin­ues to this day as Stan­dard Devi­a­tion and Sharpe Ratio are two of the more wide­spread met­rics used to mea­sure and com­pare funds.

Benchmark-relative Risk

The met­rics that fall under bench­mark risk are cal­cu­lat­ed rel­a­tive to a stan­dard mea­sur­ing stick to high­light val­ue and per­for­mance. Dur­ing the 1980s and 1990s, the most pop­u­lar per­for­mance met­rics were mea­sures like alpha, beta, infor­ma­tion ratio, and cap­ture ratios, all which fall under bench­mark-rel­a­tive risk. Why were they so pop­u­lar dur­ing this time? I believe this was for two rea­sons.

First of all, equi­ty mar­kets enjoyed a remark­able bull run between 1982 and 2000. With the mar­ket per­form­ing so well, so did every­one else. Sec­ond, dur­ing this era, pas­sive invest­ing estab­lished itself as a viable approach. Van­guard and then lat­er the ETF providers promised to match mar­ket returns very afford­ably rather than poten­tial­ly out­per­form at a hefty price. With the bull mar­ket and pas­sive invest­ing as a back­drop, it is no won­der that bench­mark-dri­ven met­rics became pop­u­lar. If one was an active man­ag­er, one had to “prove” added val­ue over a pas­sive option, and met­rics like alpha and infor­ma­tion ratio are designed to do just that.

The short­com­ings of bench­mark-rel­a­tive met­rics were exposed dur­ing the first decade of the new mil­len­ni­um. In the span of less than ten years, we expe­ri­enced the two worst bear mar­kets since World War II. Dur­ing the dot-com bust of 2000-02, mar­kets lost almost 45%, and dur­ing the Finan­cial Cri­sis of 2007-09, mar­kets fell over 50%. In this envi­ron­ment, it was entire­ly pos­si­ble that a man­ag­er out­per­formed its bench­mark and post­ed respectable alphas and infor­ma­tion ratio but still lost 40% of its val­ue.

Capital Preservation Risk

When most investors think of risk, sim­ply “not los­ing mon­ey” is the most like­ly def­i­n­i­tion. Look­ing to met­rics that can mea­sure this is cru­cial for those who care more about pre­serv­ing their wealth that out­per­form­ing a bench­mark. The idea of max­i­miz­ing the excess return-vs-track­ing error rela­tion­ship takes a back­seat to not los­ing 30%, 40%, or 50% of your wealth. Ways of quan­ti­fy­ing risk in terms of cap­i­tal preser­va­tion rep­re­sent the next gen­er­a­tion in risk and per­for­mance mea­sure­ment. Two of which, Pain Index and Pain Ratio, we have already dis­cussed on this blog.

Tail risk

Close­ly relat­ed to cap­i­tal preser­va­tion is the risk of extreme, out­lier events. Com­mon­ly known as “tail risk” or “black swan” events, they are marked by their rar­i­ty and sever­i­ty. Despite their “rar­i­ty,” it is impor­tant to mea­sure how funds do dur­ing these extreme events, so investors can be bet­ter pre­pared for when the event may hap­pen. Fur­ther­more, min­i­miz­ing the impact of these types of events may help avoid the life-alter­ing finan­cial loss­es that can occur. The scope and scale of the Finan­cial Cri­sis of 2007-09 had not been seen since the Great Depres­sion, and who can real­ly say what the future will bring. While quan­ti­fy­ing tail risk is dif­fi­cult, there have been some inno­va­tions on this front.

 

The StatMAP Framework

When we com­bine these con­cepts along two axes, we get what at Zephyr we called “the StatMAP.” Most of the per­for­mance and risk met­rics fall neat­ly into this grid.

StatMAP Framework - Making Sense of Risk Metrics - Swan Blog

Source: Swan Glob­al Invest­ments

There are cer­tain­ly more per­for­mance met­rics out there, but most of them would fit some­where with­in this frame­work.

With this frame­work, it should be eas­i­er for indi­vid­u­als to pick the met­ric that best suits what they want to specif­i­cal­ly mea­sure and com­pare when look­ing at dif­fer­ent funds’ per­for­mances.

While cer­tain met­rics like beta are well estab­lished and well under­stood, many of the new­er, high­er-lev­el sta­tis­tics could use a bit of expla­na­tion. This is espe­cial­ly true of the new­er, post-MPT sta­tis­tics in the “Cap­i­tal Preser­va­tion” and “Tail Risk” columns that are more use­ful for ana­lyz­ing hedge funds and liq­uid alter­na­tives. We have already dis­cussed Pain Index and Pain Ratio, two favorites here at Swan Glob­al Invest­ments. With a focus on mea­sur­ing alter­na­tive invest­ments, some met­rics we will dis­cuss in this series are Omega and Zephyr K-Ratio.

 

About the Author:

Marc Odo, Marc Odo, CFA®, CAIA®, CIPM®, CFP®, Director of Investment Solutions - Swan Global InvestmentsMarc Odo, CFA®, CAIA®, CIPM®, CFP®, Direc­tor of Investor Solu­tions, is respon­si­ble for help­ing clients and prospects gain a detailed under­stand­ing of Swan’s Defined Risk Strat­e­gy, includ­ing how it fits into an over­all invest­ment strat­e­gy. For­mer­ly, Marc was the Direc­tor of Research for 11 years at Zephyr Asso­ciates.

 

Important Notes and Disclosures:

Swan Glob­al Invest­ments, LLC is a SEC reg­is­tered Invest­ment Advi­sor that spe­cial­izes in man­ag­ing mon­ey using the pro­pri­etary Defined Risk Strat­e­gy (“DRS”). SEC reg­is­tra­tion does not denote any spe­cial train­ing or qual­i­fi­ca­tion con­ferred by the SEC. Swan offers and man­ages the DRS for investors includ­ing indi­vid­u­als, insti­tu­tions and oth­er invest­ment advi­sor firms. Any his­tor­i­cal num­bers, awards and recog­ni­tions pre­sent­ed are based on the per­for­mance of a (GIPS®) com­pos­ite, Swan’s DRS Select Com­pos­ite, which includes non-qual­i­fied dis­cre­tionary accounts invest­ed in since incep­tion, July 1997, and are net of fees and expens­es. Swan claims com­pli­ance with the Glob­al Invest­ment Per­for­mance Stan­dards (GIPS®).

All Swan prod­ucts uti­lize the Defined Risk Strat­e­gy (“DRS”), but may vary by asset class, reg­u­la­to­ry offer­ing type, etc. Accord­ing­ly, all Swan DRS prod­uct offer­ings will have dif­fer­ent per­for­mance results due to offer­ing dif­fer­ences and com­par­ing results among the Swan prod­ucts and com­pos­ites may be of lim­it­ed use. All data used here­in; includ­ing the sta­tis­ti­cal infor­ma­tion, ver­i­fi­ca­tion and per­for­mance reports are avail­able upon request. The S&P 500 Index is a mar­ket cap weight­ed index of 500 wide­ly held stocks often used as a proxy for the over­all U.S. equi­ty mar­ket. Index­es are unman­aged and have no fees or expens­es. An invest­ment can­not be made direct­ly in an index. Swan’s invest­ments may con­sist of secu­ri­ties which vary sig­nif­i­cant­ly from those in the bench­mark index­es list­ed above and per­for­mance cal­cu­la­tion meth­ods may not be entire­ly com­pa­ra­ble. Accord­ing­ly, com­par­ing results shown to those of such index­es may be of lim­it­ed use. The adviser’s depen­dence on its DRS process and judg­ments about the attrac­tive­ness, val­ue and poten­tial appre­ci­a­tion of par­tic­u­lar ETFs and options in which the advis­er invests or writes may prove to be incor­rect and may not pro­duce the desired results. There is no guar­an­tee any invest­ment or the DRS will meet its objec­tives. All invest­ments involve the risk of poten­tial invest­ment loss­es as well as the poten­tial for invest­ment gains. Pri­or per­for­mance is not a guar­an­tee of future results and there can be no assur­ance, and investors should not assume, that future per­for­mance will be com­pa­ra­ble to past per­for­mance. All invest­ment strate­gies have the poten­tial for prof­it or loss. Fur­ther infor­ma­tion is avail­able upon request by con­tact­ing the com­pa­ny direct­ly at 970–382-8901 or www.swanglobalinvestments.com128-SGI-032818

By |2018-03-28T17:49:17+00:00March 29th, 2018|Blog|Comments Off on Making Sense of Risk Metrics