Risk and Portfolio Management Spring Exchange Traded Funds: Gaining Exposure to Sectors

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Julie Gray
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1 Risk ad Portfolio Maagemet Sprig 200 Exchage Traded Fuds: Gaiig Exposure to Sectors

2 Exchage-traded fuds ETF: Ivestmet vehicles similar to mutual fuds but look like stocks -- traded o a exchage -- tradig is similar to stocks (log, short, margi) ETF: ca be viewed as a holdig compay or a fud -- started as idex trackers -- actively maaged ETFs sice mid 2000 s Arbitrage: authorized participats ca create or redeem ETFs i ``creatio uits -- creatio uits: 25K to 00K shares -- APs ofte act as market makers, providig liquidity

3 History Milestoes: 993: first US ETF 998: first Europea ETFs 2008: first actively maaged ETFs History: 989: Idex Participatio Shares, stopped by Chicago Mercatile Exchage 993: SPY Trackig S&P 500 (a.k.a. Spiders or SPDRS, issuer: State Street) 996: BGI creates WEBS (World Equity Bechmark Shares), later called I-Shares 998: Sector SPDRS track 9 sectors of the S&P : 680 ETFs i US with 60B i assets, icrease of 25B i 2 moths Jauary 200: US ETF market breaks the,000 billio mark i AUM

5 Largest issuers

6 ETFs by Asset Class

7 Mai advatages of ETFs Ivestors -- Diversificatio at a affordable price -- Low expese ratios -- Behave like idex mutual fuds but are more flexible -- Limit orders, short-sellig, optios -- Lower fees -- Tax efficiecy: lower turover tha MFs (o eed to sell assets each time someoe sells, less capital gais) -- No ``load (Etrace fees, or exit fees, like may mutual fuds) Professioals -- Used for tradig & hedgig by pros (HF maagers, traders) -- Gai exposure to a sector or coutry easily -- Proxies for market factors for explaiig stock returs

8 20 most active US ETFs (as of Feb 2009) TICKER DESCRIPTION AUM AVG VOL (3m) E RATIO (bps) SPY 0. SP500 93B 366M 8 EFA MSCI Itl Eq 3B 36M 34 GLD Gold 0. oz 2B 3M NA EEM MSCI Emergig Mkts. 9B 00M 72 IVV SP 500 I-Share 5B 7M 9 QQQQ Nasdaq 00 Idex /0 2B 50M 20 IWM Russel B 77M 20 IWF Russel 000 Growth 0B 8M 20 AGG Bod ETF 9.5B 0.7 M 20 IWD Russel 000 Value 9.2B 7M 20 VTI Vaguard MSCI B M 7 DIA Dow B 32M 4 TIP Iflatio-prot bods 8.6B 0.7M 20 LF Fiacial SPDR 7.7B 200M 23 SHY -3 yr Treasurys 7.7B.3M 5 LQD IG Corporates 6.9B.4M 5 MDY Mid-Cap SPDR 6.8B 0M 25 FI Chia ighua B 43M 74 IWB Russel B 9M 5 EWJ Japa 5.6B 32M 52

9 Sector ETFs TICKER DESCRIPTION AUM ($B) * VOL ($M) LF Fiacial SPDR LE Eergy SPDR OIH Oil Services HLDR 5 LK Tech SPDR.8 8 IGE Natural Resources PHO Water Resources LP Cosumer Staples LV Health Care LU Utilities.9 8 MOO Agribusiess GD Gold Miers PPH Pharmaceuticals PBW Clea Eergy IYR Real Estate BBH Biotech HOLDRS LI Idustrials. 2. SMH Semicoductors IYE Eergy Ishare IBB Biotechology a 0.9 IYW Techology ME Metals & Miig RTH Retail RKH Regioal Baks IYT Trasportatio LY Cosumer Discretioary *Feb 2009

10 Coutry ETFs TICKER DESCRIPTION AUM ($B) VOL ($M) EWZ MSCI Brazil EWJ MSCI Japa FI ighua EWT Taiwa.3 2 EWY Korea EWC Caada.8 EWH Hog Kog. 5 EWS Sigapore RS Russia EWA Australia EWW Mexico

11 ETFs as Risk-Factors? I I I, 2,... referece ETFs ΔS S ΔI k β sk + s k Ik -- Ulike eigevectors, ETFs ca have strog correlatios -- Use sparse regressio techiques to map stock to ETFs -- Use stock s sector to match stock to ETF -- I case of several ETFs per sector, use liquidity, highest R-squared

12 PCA Eigeportfolios as Risk Factors i N i i i N i i i s m s R w R V F F F S S + Δ ) ( ) ( eigeportfolio # retur of σ β -- Factors arise directly from data aalysis -- Ucorrelated factors -- Idetificatio problem -- Noise

13 Idetifyig PCA Factors with ETFs by regressio F ΔI m k β k + k Ik Multiple regressio: solve T mi F α α, β t t k β k ΔI I kt kt 2 Sparse regressio: Matchig Pursuit algorithm R-squared statistic R 2 Var Var ( ) ( F ) Good fithigh R-squared

14 Matchig Pursuit Algorithm ( ) ( ) ( ) ( ) () () () ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) β γ β ν γ γ + m m t T t t t i i t t Y Y Y Y T Y E Y Y E Y E E ) ( 2 2 2,... stop if,,,, arg max ) (..., arg max, arg max () 0, depedet variable (target) 0, explaatory variables,..., Fid largest ier product Subtract proectio Iterate, replacig Y by residuals Stop whe gai is egligible

15 Factor # versus SPY daily returs

16 Factor # returs : MP regressio o ETFs IWDRussell 000 (diversified cap weighted)

17 Factor # 2 returs : MP regressio o ETFs Cosumer staples, utilities, health care

18 Factor # 3 returs : MP regressio o ETFs Health care Real estate Regioal baks Fiacials (div.) Oil services

19 Factor # 4 returs : MP regressio o ETFs Retail Cosumer disc Eergy Utilities

January 24, Michael Rechenthin, PhD Frank Kaberna

January 24, Michael Rechenthin, PhD Frank Kaberna January 24, 2017 This week we provide a list of ETFs that are currently exhibiting some of the most liquid options all with a tastytrade liquidity rating of 3 or 4 stars. To dive deeper into the ETFs and