Statistical Analysis of ETF Flows, Prices, and Premiums

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1 Statistical Analysis of ETF Flows, Prices, and Premiums Aleksander Sobczyk ishares Global Investments & Research BlackRock Matlab Computational Finance Conference New York April 9 th, 214 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION

2 Agenda Rapid growth of exchange-traded funds (ETFs) has generated considerable interest in the dynamics of flows, prices, and premiums: Despite over $2 trillion of AUM world-wide as of 213, there is not to our knowledge any previous systematic statistical analysis of ETF flows As ETFs are used by hedge funds and other sophisticated investors to quickly and cheaply express their views across asset classes and regions, flows could be very informative about changes in investor sentiment Concerns have been raised around ETF role in global financial markets, particularly about premiums and discounts to NAV, as well as risks for overall market liquidity Analyzing data that is increasing both in size and complexity (intra-day ETF prices and premiums, ETF taxonomy and classifications, fund holdings information, auxiliary datasets, etc.) requires state-of-the-art tools like Matlab Objectives: Part One: Understand dynamics of ETF flows: Are flows predictable? At what horizons? What drives flow dynamics? How are flows related to past flows and returns within an asset class? Across asset classes? Can flows or sentiment predict returns? Part Two: Analyze the role of ETFs in the global financial markets: Clarify common misconceptions about premiums and discounts to NAV. Provide canonical framework to analyze liquidity and price discovery functions of ETFs. is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 2

3 Part One: Statistical Analysis of ETF Flows ishares Global Investments & Research BlackRock

4 How to Define ETF Flows? Unlike a traditional mutual fund, purchases/sales of ETFs do not necessarily require investors to interact with the fund; offering lower costs and tax efficiency; transactions can also occur throughout the day. While ETFs trade intraday on organized exchanges, like equities, the creation-redemption mechanism allows additional ETF supply/demand through primary market transactions beyond visible secondary market. Creation Process: Authorized Participant delivers a basket of underlying securities to issuer who in turn delivers the ETF units ETF Issuer ETF units Basket of underlying securities Authorized Participant Total ETF Flow in a month (or week) with T trading days = Total Creations Minus Redemptions = S i=1..t days (Shares Out i Shares Out i-1 ) * NAV i is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 4

5 Global ETF Flows and Assets Source: BlackRock ETP Landscape as of December 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 5

6 Predictability of Flows Within Asset Classes Are flows predictable? Simplest approach is to look at autocorrelation in scaled flows within an asset class Examine weekly and monthly frequencies as the daily data is too noisy Consistent patterns emerge: Equity flows are not autocorrelated, indicating little persistence over weekly or monthly horizons However, at weekly and monthly frequencies, both commodity and fixed income flows are strongly positive autocorrelated Autocorrelation is stronger during the period of the financial crisis suggesting that investors sentiment tended to be persistent from week to week with no evidence of contrarian behavior is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 6

7 Flow Change (%) Flow Change (%) Flow Change (%) Monthly Exchange Traded Product (ETP) Flow Changes All US-domiciled ETPs, January 25 July Equity ETPs Year 1.5 Fixed Income ETPs Year 4 Commodities ETPs Flow Change (%) = (1 / T) Flow k / AUM k-1 January 25 - July 213 Flow Change (%) Descriptive Statistics by Asset Class Asset Class Mean Median Std Dev 1st Autocorr Equity Fixed Income ** Commodities * 2 *** Denotes statistical significance at.1% ** Denotes statistical significance at 1% * Denotes statistical significance at 5% Year Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 7

8 Flow Change (%) Flow Change (%) Flow Change (%) Weekly ETP Flow Changes All US-domiciled ETPs, January 25 July Equity ETPs Year Fixed Income ETPs Year 4 Commodities ETPs Flow Change (%) = (1 / T) Flow k / AUM k-1 January 25 - July 213 Flow Change (%) Descriptive Statistics by Asset Class Asset Class Mean Median Std Dev 1st Autocorr Equity Fixed Income *** Commodities *** 2 *** Denotes statistical significance at.1% ** Denotes statistical significance at 1% * Denotes statistical significance at 5% Year Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 8

9 Predictability of Flows Across Asset Classes Are flows predictable? What drives flows? Autocorrelation within an asset class is useful but does not shed light on cross-asset or lead-lag relationships We use autoregression (AR) framework to model flows within a given asset class as a function of past flows and past returns Extend to VAR model to account for cross-asset class effects (note: dimensionality is a challenge) We include year-end effects as this seasonality is important in driving flows Consistent results Equity Equity flows at weekly or monthly horizons are strongly correlated with contemporaneous returns, which is consistent with anecdotal evidence Fixed Income At weekly and monthly frequencies, both fixed income and commodity flows are strongly positive correlated with lagged flows Model fit is especially strong post the financial crisis is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 9

10 Autoregressive Models Equity ETP Flows All Equity US-domiciled ETPs, January 25 July 213 Flows vs. Lagged Flows and Lagged Russell 3 Index Returns Flow Change (%) Asset Class Return (Russell 3 Index) Equities - Monthly Dependent Variable = Independent Flow Change (%) Variable: AR(1) AR(1) AR(2) Constant Flow Lag 1 Month Flow Lag 2 Months Return Lag (Contemporaneous) Return Lag 1 Month Return Lag 2 Months Dummy is_year_end.5 (4.8).37 (1.62).14 (3.45).6 (4.88) (.7) -.33 (1.42).14 (3.57).7 (4.59) (.66) -.69 (.69) -.34 (1.31) -.23 (.9).15 (3.57) Adjusted R-square 11.8% 11.18% 11.21% Observations Flow Change (%) Asset Class Return (Russell 3 Index) Equities - Weekly Dependent Variable = Independent Flow Change (%) Variable AR(1) AR(1) AR(2) Constant Flow Lag 1 Week Flow Lag 2 Weeks Return Lag (Contemporaneous) Return Lag 1 Week Return Lag 2 Weeks Dummy is_year_end.5 (4.95).254 (6.91).12 (3.16).6 (5.5) (.9) -.41 (1.).14 (3.44).6 (4.69) (.87).358 (.71) -.35 (.84).19 (.46).13 (3.28) Adjusted R-square 11.42% 2.21% 1.98% Observations Each column is a autoregressive model; shading indicates statistical significance at 1% level Strong contemporaneous correlation between weekly equity ETP flows and R3K returns Values: Beta ( T-Stat ) Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 1

11 Autoregressive Models Fixed Income All Fixed Income US-domiciled ETPs, January 25 July 213 Flows vs. Lagged Flows and Lagged Barclays AGG Index Returns Flow Change (%) Asset Class Return (Barclays Aggregate Index) Fixed Income - Monthly Dependent Variable = Independent Flow Change (%) Variable AR(1) AR(1) AR(2) Constant Flow Lag 1 Month Flow Lag 2 Months Return Lag (Contemporaneous) Return Lag 1 Month Return Lag 2 Months Dummy is_year_end.14 (11.1).96 (.79) -.3 (.71).1 (5.67).2399 (2.56).213 (1.82) -.4 (1.).11 (5.22).1323 (1.41).441 (.48).262 (2.38).15 (.13) -.4 (.98) Adjusted R-square -.87% 7.38% 4.76% Observations Flow Change (%) Asset Class Return (Barclays Aggregate Index) Fixed Income - Weekly Dependent Variable = Independent Flow Change (%) Variable AR(1) AR(1) AR(2) Constant Flow Lag 1 Week Flow Lag 2 Weeks Return Lag (Contemporaneous) Return Lag 1 Week Return Lag 2 Weeks Dummy is_year_end.15 (15.1) -.15 (.77) -.3 (.74).13 (1.26).1671 (3.58).18 (.9) -.2 (.68).12 (8.58).1413 (3.).579 (1.24).16 (.8).23 (1.2) -.2 (.6) Adjusted R-square -.2% 2.28% 1.99% Observations Strong autocorrelation in fixed income flows at both monthly and weekly horizons Values: Beta ( T-Stat ) Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 11

12 Vector Autoregressive Model (VAR) All US-domiciled ETPs, January 25 July 213 Weekly 6-Dimensional VAR(1) Model solved in Matlab (using vgxvarx): Dependent Variable: 1- Week Lagged Independent Variable: Equity Flow FI Flow Cmd Flow Russell 3 Barclays AGG TR/J CRB Index Flow Change (%) Lag 1 Week Equity Fixed Income Cmd (.48) (.36) -.19 (.7) (2.9).1679 (3.61).275 (1.1) (2.4).32 (.4).3343 (8.15) (3.33) (.3) (2.53).1171 (1.12) -.17 (.15) -.13 (.2) (3.77).7562 (1.18).567 (.17) Asset Returns Lag 1 Week Russell 3 Barclays AGG TR/J CRB Index -.5 (1.7) (1.43).3 (.7) -.13 (.31).16 (.8).81 (2.1).9 (1.35).576 (1.81).42 (.66) -.15 (.19).621 (.23).97 (.18).356 (3.62) (1.1).6 (.64).849 (1.52) -.3 (1.13) (1.31) Strong autocorrelation in fixed income and commodity ETP flows Equity ETP flows are drivers of fixed income and commodity flows Interesting reversal pattern of Russell 3 index on equity ETP flows, consistent with price pressure hypothesis Values: Beta ( T-Stat ) Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 12

13 Equity ETP Flows and Russell 3 Index Reversal All Equity US-domiciled ETPs, January 25 July 213 Russell 3 Index Returns vs. Lagged Flows Flow Change (%) Russell 3 Index Return Equities - Monthly Dependent Variable = Independent Russell 3 Index Return Variable AR(1) AR(1) AR(2) Constant Flow Lag 1 Month Flow Lag 2 Months Return Lag 1 Month Return Lag 2 Months Dummy is_year_end.31 (.65).2329 (2.36).136 (.8).11 (2.16) (3.5).2879 (3.4).26 (1.27).19 (3.39) (3.87) (2.43).2531 (2.52) (.78).231 (1.45) Adjusted R-square 3.94% 13.86% 19.8% Observations Flow Change (%) Russell 3 Index Return Equities - Weekly Dependent Variable = Independent Russell 3 Index Return Variable AR(1) AR(1) AR(2) Constant Flow Lag 1 Week Flow Lag 2 Weeks Return Lag 1 Week Return Lag 2 Weeks Dummy is_year_end.1 (.75) (1.25).39 (.81).21 (1.54) (3.59) -.55 (.11).71 (1.47).24 (1.71) (3.53) (1.4) (.23).741 (1.5).74 (1.53) Adjusted R-square.1% 2.63% 2.78% Observations Russell 3 Index reversal persistent at both monthly and weekly horizon This effect is consistent with price pressure hypothesis Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 13

14 Risk Flow Sentiment Measure Are flows indicative of investor sentiment? With over $2 trillion in worldwide AUM, ETPs are used by sophisticated investors to express their views across asset classes and regions We derived a simple metric that captures shifts in investor sentiment as expressed via primary ETP flows BlackRock Risk Flow Sentiment measure: BlackRock Risk Flow Sentiment measure sorts ETPs within each asset class based on risk of each fund Risk Flow Sentiment is then defined as total dollar inflows/outflows for the riskers group of ETPs less those of safer group, scaled by the dispersion of all flows Our research shows that over longer horizons (i.e. monthly), Risk Flow Sentiment exhibits greater persistence than raw flows alone, consistent with the idea that composition of flows is indicative of investor sentiment BlackRock Risk Flow Sentiment measure has been regularly included in the ETF Landscape starting in July 213 Source: BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 14

15 BlackRock Risk Flow Sentiment Measure Introduced in July s 213 Issue of ETP Landscape BlackRock Risk Flow Sentiment Measure is derived from raw monthly risk flow metric as deviation from one-year moving average Source: BlackRock ETP Landscape as of July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 15

16 Sentiment Flow Sentiment Flow Sentiment Flow Risk Flow Sentiment exhibits statistically significant persistence in all ETP asset classes 5 Equity ETPs Year 2 Fixed Income ETPs Risk Sentiment Flow = (High Low Risk Flow k ) / StdDev(Net Flow k ) Year 2 Commodities ETPs Monthly Sentiment Flows Statistics (1/5-7/13) Asset Class Mean Median Std Dev 1st Autocorr Equities * Fixed Income *** Commodities * Year *** Denotes statistical significance at.1% ** Denotes statistical significance at 1% * Denotes statistical significance at 5% Source: Bloomberg, Thomson Reuters, BlackRock, as at July 31, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 16

17 Part Two: Liquidity and Price Discovery in ETFs ishares Global Investments & Research BlackRock

18 ETFs in Global Financial Markets Key objectives and questions: Clarify common misconceptions about ETF prices, premiums, and liquidity. Provide sound quantitative view into the role that ETFs play as price discovery tools in global financial markets Can the dynamics of ETF prices, volatilities, and premiums be described through systematic econometric framework? Are ETFs efficiently priced? What is the role of liquidity in pricing ETFs? What are the key drivers behind ETF premiums and how do they change with the nature of the ETF exposure (asset class, geography)? Is there a persistence in ETF premiums? is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 18

19 Key Drivers of ETP Market and NAV prices ETF market price p t is the sum of true (unobserved) asset price m t and liquidity shocks (noise) u t : p t = m t + u t True (unobserved) asset price m t follows a martingale process: dm t = m t m t 1 = C + δ t Error correction in liquidity shocks u t is modeled as AR(1) process: u t = φu t 1 + ε t Net Asset Value (NAV) (denoted as n t ) can deviate from true asset price because of various pricing errors g t : n t = m t + γ t To better understand ETF price dynamics, we specifically model one type of error, staleness, arising from using stale quotes in calculation of NAV: n t = 1 θ m t + θn t 1 + γ t Staleness parameter q > indicates to what degree stale NAV quotes were used to determine current NAV. * For more extensive theoretical analysis, see: Madhavan (213) or Engle & Sarkar (22) is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 19

20 ETF Transaction Costs and Spreads The actual transaction price of the ETF at time t is the midquote q mid,t plus or minus the effective bid-ask spread c t : p t = q mid,t + c t 2 p t where p t ( 1,1) Since quotations are two-sided, it is reasonable to posit that midquote q mid,t reflects true asset price m t, and the microstructure noise term u t is interpreted as (c t / 2) p t. For an individual security, in the absence of other costs, the spread arises because order flow is informative and market makers protect themselves against adverse selection. In the context of ETF, the fact that a broad index portfolio is being traded, flow is unlikely to be informative at the individual security level, hence expectation for lower spreads. ETFs indeed offer lower spreads compared to underlying exposure: Average Time-Weighted Bid-Ask Spreads (bps) Source: Bloomberg, BlackRock, and FINRA TRACE as of June 3, 213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 2

21 State-Space Model Representation of ETF Price Dynamics The cointegrated evolution of ETF market and NAV prices can be described in State-Space Model (SSM) form: SSM measurement equation: n t p t = 1 θ 1 φ m t m t 1 + θ φ n t 1 p t 1 + γ t ε t SSM transition equation: m t m t 1 = 1 1 m t 1 m t 2 + C 1 + δ t ETF premium (discount) (or P/D) can be written as: π t = p t n t = u t + θ m t n t 1 γ t Transitory Liquidity NAV Staleness NAV Pricing Errors NAV Measurements Errors (price discovery) Assuming all innovations (e t, g t, and d t ) are i.i.d., the variance of ETF premium can be approximated as: var π var ε 1 φ 2 + θ2 var δ 1 θ 2 + var(γ) The last equation allows to attribute variance of P/D into portions related to transitory liquidity and price discovery. is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 21

22 State-Space Model Estimation: Examples Analyzed three US-listed ETF funds (IVV, EWJ, and HYG) using daily price and NAV data for period 1/1/25 through 12/31/213 Model is solved in Matlab using Kalman Filter and maximum likelihood estimation. ETF Ticker IVV EWJ HYG ETF Name ishares S&P5 ETF ishares MSCI Japan ETF ishares iboxx High Yield Bond ETF q (NAV Staleness) j (Autocorrelation) Fundamental Innovations: St.Dev.(d) (annualized) Price Innovation: St.Dev.(e) (annualized) NAV Innovations: St.Dev.(g) (annualized) Realized St.Dev.(P/D) (annualized) Realized St.Dev.(Px) (annualized) Realized St.Dev.(NAV) (annualized) % Realized P/D variance explained by Transitory Liquidity 35.5% 25.2% 54.3% % Realized P/D variance explained by NAV Staleness 2.6% 1.9% 54.% % Realized P/D variance explained by NAV Pricing Errors 61.4% 52.8% 4.1% Source: Bloomberg, BlackRock for period 1/1/25 12/31/213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 22

23 Drivers of ETF Premiums: Domestic Equity ETFs Analyzed all US-listed, domestic equity ETF funds with above $1MM in AUM (as of 12/31/213) using daily price and NAV data for period 1/1/25 through 12/31/213 Model is solved in Matlab using Kalman Filter and maximum likelihood estimation. ETF AUM ($MM, Log-scale) P/D Volatility (%) Fraction P/D Variance Explained by SSM ETF (ranked from the highest to lowest in AUM) Px Liquid NAV Stale NAV Error Domestic Equity ETFs % Contribution to P/D Variance Mean AUM Weighted Transitory Liquidity 55.5% 52.3% NAV Staleness 7.6% 3.7% NAV Price Errors 37.2% 44.% Source: Bloomberg, BlackRock for period 1/1/25 12/31/213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 23

24 Drivers of ETF Premiums: Developed Markets (ex-us) Equity ETFs Analyzed all US-listed developed markets (ex-us) equity ETF funds with above $1MM in AUM (as of 12/31/213) using daily price and NAV data for period 1/1/25 through 12/31/213 Model is solved in Matlab using Kalman Filter and maximum likelihood estimation. ETF AUM ($MM, Log-scale) P/D Volatility (%) Fraction P/D Variance Explained by SSM ETF (ranked from the highest to lowest in AUM) Px Liquid NAV Stale NAV Error Developed Markets (ex-us) Equity ETFs % Contribution to P/D Variance Mean AUM Weighted Transitory Liquidity 31.7% 25.4% NAV Staleness 16.6% 17.4% NAV Price Errors 47.6% 51.8% Source: Bloomberg, BlackRock for period 1/1/25 12/31/213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 24

25 Drivers of ETF Premiums: Emerging Markets Equity ETFs Analyzed all US-listed emerging markets equity ETF funds with above $1MM in AUM (as of 12/31/213) using daily price and NAV data for period 1/1/25 through 12/31/213 Model is solved in Matlab using Kalman Filter and maximum likelihood estimation. ETF AUM ($MM, Log-scale) P/D Volatility (%) Fraction P/D Variance Explained by SSM ETF (ranked from the highest to lowest in AUM) Px Liquid NAV Stale NAV Error Emerging Markets Equity ETFs % Contribution to P/D Variance Mean AUM Weighted Transitory Liquidity 31.4% 2.3% NAV Staleness 25.7% 43.9% NAV Price Errors 38.1% 34.3% Source: Bloomberg, BlackRock for period 1/1/25 12/31/213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 25

26 Summary and Conclusions Summary of key findings: Described ETF prices, volatilities, and premiums in State-Space Model (SSM) representation. Key drivers of ETF premiums identified as: Transitory liquidity observed ETF market prices (i.e. bid-ask bounce, or liquidity shocks); Staleness and other error measurements in NAV prices. Attributed variance of premiums and discounts into liquidity and NAV error measurements for various categories of ETFs Provided quantitative argument for ETFs serving as price discovery tools, particularly for international ETFs Contributions to Premium/Discount Variance % Transitory Liquidity % NAV Staleness % Nav Price Error 1% 75% 5% 25% % Domestic Equity DM (ex-us) Equity EM Equity Source: Bloomberg, BlackRock, daily data for period 1/1/25 12/31/213. Includes all US-listed ETFs with more than $1MM in AUM as of 12/31/213 is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 26

27 Thank you ishares Global Investments & Research BlackRock

28 Carefully consider the ishares Funds investment objectives, risk factors, and charges and expenses before investing. This and other information can be found in the Funds prospectuses, which may be obtained by calling 1-8-iShares ( ) or by visiting Read the prospectus carefully before investing. Investing involves risk, including possible loss of principal. Transactions in shares of ETFs will result in brokerage commissions and will generate tax consequences. All regulated investment companies are obliged to distribute portfolio gains to shareholders. Although market makers will generally take advantage of differences between the NAV and the trading price of ishares Fund shares through arbitrage opportunities, there is no guarantee that they will do so. The ishares Funds are distributed by BlackRock Investments, LLC (together with its affiliates, BlackRock ). The ishares Funds are not sponsored, endorsed, issued, sold or promoted by Cohen & Steers Capital Management, Inc., European Public Real Estate Association ( EPRA ), FTSE International Limited ( FTSE ), India Index Services & Products Limited, JPMorgan Chase & Co., MSCI Inc., Markit Indices Limited, Morningstar, Inc., The NASDAQ OMX Group, Inc., National Association of Real Estate Investment Trusts ( NAREIT ), New York Stock Exchange, Inc., Russell Investment Group or S&P Dow Jones Indices LLC, nor are they sponsored, endorsed or issued by Barclays Capital Inc. None of these companies make any representation regarding the advisability of investing in the Funds. BlackRock is not affiliated with the companies listed above. 214 BlackRock. All rights reserved. ishares and BLACKROCK are registered trademarks of BlackRock. All other marks are the property of their respective owners. is Not FDIC Insured No Bank Guarantee May Lose Value is-123 FOR INSTITUTIONAL USE ONLY - NOT FOR PUBLIC DISTRIBUTION 28

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