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Deutsche Bank Markets Research

North America

United States

Synthetic Equity & Index Strategy

US ETF Insights

Date

9 June 2016

Portfolio Implementation Ideas with ETF Flow Risk-o-Meter

Tactical Asset Allocation made easy with EFRoM portfolios

________________________________________________________________________________________________________________

Deutsche Bank Securities Inc.

Deutsche Bank does and seeks to do business with companies covered in its research reports. Thus, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1.MCI (P) 057/04/2016.

Author

Sebastian Mercado, CFA

Strategist

(+1) 212 250-8690

[email protected]

Related research Date

ETF Flow Risk-o-Meter 05 May 2016

Source: Deutsche Bank

Use our Risk-o-Meter signal for implementing tactical trades or overlays in multi asset, balanced, smart beta, or sector portfolios.

ETF Flow Risk-o-Meter (EFRoM) Portfolios for performance enhancement We use our recently developed EFRoM signal for tactically constructing ETF portfolios. Each portfolio is built using two investment pillars: a risk-on and a risk-off one. Positive MoM EFRoM signal changes suggest an increase in risk and a corresponding positioning on Risk-on assets; while negative changes suggest a reduction of risk with its respective positioning on Risk-off assets.

Not your usual 60/40 portfolio A multi asset portfolio using the EFRoM signal to tactically switch between a risk-on and a risk-off basket can enhanced significantly the risk-return profile of a traditional multi asset portfolio, both on a long-only and a long/short format. In addition, a traditional 60/40 balanced long-only portfolio can also enhance its performance with as little as a 5% tactical allocation budget.

Use Minimum Volatility and Momentum for Smart Beta tactical asset allocation Minimum Volatility and Momentum can enhance performance of broad equity positions when used in a tactical asset allocation EFRoM strategy.

Use Utilities, Financials, and Materials for Sector tactical asset allocation Utilities, and Financials or Materials can enhance performance of broad equity positions when used in a tactical asset allocation EFRoM strategy.

ETF Flow Risk-o-Meter recommends a reduction of risk in June The EFRoM level decreased in May suggesting a reduction of risk in June; therefore the EFRoM portfolios should be positioned on the Risk-Off trade.

Figure 1: ETF Flow Risk-o-Meter suggests reduction of risk in June

(4.0)

(3.0)

(2.0)

(1.0)

-

1.0

2.0

3.0

4.0

(2.5)

(2.0)

(1.5)

(1.0)

(0.5)

-

0.5

1.0

1.5

2.0

2.5

No

v-1

4

Dec-1

4

Jan

-15

Feb

-15

Mar-

15

Ap

r-1

5

May-1

5

Ju

n-1

5

Ju

l-1

5

Au

g-1

5

Sep

-15

Oct-

15

No

v-1

5

Dec-1

5

Jan

-16

Feb

-16

Mar-

16

Ap

r-1

6

May-1

6

Ris

k-o

-Mete

r M

oM

Ch

an

ge

Ris

k-o

-Mete

r

MoM Chg [rhs]

ETF Flow Risk-o-meter

AD

DR

ISK

RE

DU

CE

RIS

K

RIS

KO

NR

ISK

OFF

Source: Deutsche Bank, Bloomberg Finance LP.

9 June 2016

US ETF Insights


Table Of Contents

EFRoM Portfolios ................................................................. 3 Methodology behind construction of EFRoM portfolios ...................................... 3 Multi Asset Portfolios: Not your usual 60/40 ....................................................... 4 Equity Smart Beta Portfolios ................................................................................ 7 Equity Sector Portfolios ........................................................................................ 9

Appendix A ........................................................................ 11 EFRoM Methodology and Applications ............................................................. 11

Appendix B ........................................................................ 13 List of ETFs from EFRoM portfolios ................................................................... 13

9 June 2016

US ETF Insights


EFRoM Portfolios

Methodology behind construction of EFRoM portfolios

The EFRoM portfolios use our recently developed ETF Flow Risk-o-Meter

signal1 for tactical asset allocation. The main concept is that month-over-

month changes of the EFRoM signal can provide guidance for the expected

future returns of risky assets. Thus a positive MoM change of the signal

suggests that risky assets should see a favorable month ahead and, therefore,

investors should increase their exposure to risky assets. On the other hand, a

negative MoM change of the signal suggests that risky assets should see a

challenging month ahead and, therefore, investors should reduce their

exposure to risky assets.

Each portfolio’s investment universe is composed of two blocks: a risk-on

group of investments, and a risk-off one. The risk-on group has a more

aggressive risk-return profile and should do well when the economic outlook,

investor sentiment, and markets are positive; while the risk-off group has a

more defensive risk-return profile and should do well when the

macroeconomic environment seems uncertain, markets are under pressure,

and investors seek the protection of safe haven assets. Each group could range

from one to several ETFs. The majority of products have been selected using a

criteria that would allow for efficient tactical trading of the portfolio, such as

liquidity (volume and cost), and ability to short2; however, investors may use

other ETFs suitable for implementing each asset class as they see fit.

We provide ideas for both long-only and long/short portfolio implementations.

For the long-only portfolios, whenever the EFRoM MoM signal change

suggests adding exposure to risky assets, the portfolio will be invested in the

Risk-on investment set, while it will be invested in the Risk-off investment set if

the signal suggests a reduction in risky asset exposure. On the other hand, for

the long/short implementations the portfolio will take a long position in the

risk-on assets and a short position in the risk-off assets when the signal implies

an increase in risk; while during periods when the signal suggests a reduction

of risk, the portfolio will take a long position on the risk-off basket and a short

position on the risk-on basket.

Furthermore, some portfolios are fully tactical, while other portfolios have a

strategic core and a tactical satellite overlay. The tactical component of the

portfolio allocation is fully invested according to the EFRoM signals. Portfolio

allocations are evaluated and adjusted on a monthly basis, if necessary. In this

report, we present different implementations covering multi asset, traditional

60/40, sector, and smart beta portfolios.

Performance presented in this report is back-tested performance covering the

period of time between Dec 31st, 2014 and May 31st, 2016.

1 See Appendix A for more details on the methodology.

2 A list of the ETFs used in this report and their characteristics is provided on Appendix B.

9 June 2016

US ETF Insights


Multi Asset Portfolios: Not your usual 60/40

60/40 Multi Asset portfolio with moderate and aggressive EFRoM

implementation

The 60/40 multi asset portfolios provide access to a diversified portfolio

including 60% of global equity exposure, and 40% of non-equity exposure. The

global equity exposure is broken down into 30% to US equities, 25% to DM Intl

equities, and 5% to EM equities; while the non-equity exposure is broken down

into 5% Gold and 35% to bonds. Furthermore, we defined a Risk-on basket

with exposure to the target asset classes offering higher sensitivity to risky

asset classes; while at the same time we defined a Risk-off basket with a more

defensive profile. The risk-on basket should outperform the risk-off basket

during times when investors are increasing their risk appetite, while the

opposite should happen when investors are reducing their risk appetite.

On the risk-on side we implement the Gold and Bonds allocations with ETFs

offering exposure to Gold miners and High Yield Corporate debt, respectively;

while for equities we use Large Caps in the US and broad equity exposures

outside the US. Meanwhile, on the risk-off side we implement the Gold and

Bonds allocations with ETFs offering exposure to Physical Gold and

Intermediate US Treasuries, respectively; while for equities we use ETFs

employing a minimum volatility strategy. The former implementation, which

we would consider moderate in terms of risk, can be slightly adjusted to obtain

a more aggressive risk-return profile. More specifically, by switching the US

Large Caps for US Small Caps, and Gold Miners for Small Cap Gold Miners on

the risk-on side; and switching the Intermediate US Treasuries for Long US

Treasuries on the risk-off side, investors can access a wider breadth of returns

which can allow the tactical signal to boost returns in an absolute and in a risk-

adjusted basis (Figure 2).

Figure 2: 60/40 Multi Asset portfolio constituents and allocation details

Asset Class Weight Focus Ticker Focus Ticker

Multi Asset - Moderate Implementation

US Equities 30% US Large Caps SPY US Min Vol USMV

DM Intl Equities 25% DM Intl Broad EFA DM Intl Min Vol EFAV

EM Equities 5% EM Broad EEM EM Min Vol EEMV

Gold 5% Gold Miners GDX Physical Gold GLD

Bonds 35% High Yield Credit HYG UST Intermediate IEF

Multi Asset - Aggressive Implementation

US Equities 30% US Small Caps IWM US Min Vol USMV

DM Intl Equities 25% DM Broad EFA DM Intl Min Vol EFAV

EM Equities 5% EM Broad EEM EM Min Vol EEMV

Gold 5% Gold Small Miners GDXJ Physical Gold GLD

Bonds 35% High Yield Credit HYG UST Long TLT

Risk-on Risk-off

Source: Deutsche Bank.

During the backtesting period, we observed that both Long and Long/Short

implementations for the Moderate and Aggressive portfolios outperformed a

traditional 60%/40% portfolio benchmark and their individual risk-on and risk-

off baskets. The improvement was not only on an absolute return basis, but

also on a risk-adjusted basis (Figure 3 to Figure 7).

9 June 2016

US ETF Insights


Figure 3: 60/40 Multi Asset performance details – Moderate & Aggressive

Risk/Return Metrics

Multi Asset EFRoM

60/40

Bmk Long

60/40 EFRoM

Long

60/40 EFRoM

Long/Short

Risk-on

Basket Long

Risk-off

Basket Long

60/40 EFRoM

Long

60/40 EFRoM

Long/Short

Risk-on

Basket Long

Risk-off

Basket Long

Annualized Total Return 1.2% 10.8% 13.2% 1.8% 6.1% 14.0% 20.3% 0.9% 6.4%

Annualized Volatility 9.3% 9.3% 6.9% 12.2% 7.2% 9.8% 9.8% 12.7% 7.5%

Sharpe Ratio (RF=0) 0.13 1.16 1.91 0.15 0.85 1.42 2.06 0.07 0.85

Maximum Drawdown 11.4% 7.3% 4.1% 16.5% 5.8% 6.8% 5.6% 19.1% 6.8%

Moderate Aggressive

Source: Deutsche Bank, Bloomberg Finance LP, FactSet

Figure 4: 60/40 Multi Asset performance vs. Constituents

Risk-on and Risk-off Baskets - Moderate

Figure 5: 60/40 Multi Asset performance vs. Benchmark -

Moderate

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Add Risk Reduce Risk

60/40 EFRoM Long 60/40 EFRoM Long/Short

Risk-on Basket Long Risk-off Basket Long

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60/40 Bmk Long Source: Deutsche Bank, Bloomberg Finance LP, FactSet

Source: Deutsche Bank, Bloomberg Finance LP, FactSet. Note: 60/40 Benchmark is 60% Global Equities (ACWI) and 40% Aggregate Bonds (AGG) rebalanced on a monthly basis.

Figure 6: 60/40 Multi Asset performance vs. Constituents

Risk-on and Risk-off Baskets - Aggressive

Figure 7: 60/40 Multi Asset performance vs. Benchmark -

Aggressive

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Risk-on Basket Long Risk-off Basket Long

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60/40 Bmk Long Source: Deutsche Bank, Bloomberg Finance LP, FactSet

Source: Deutsche Bank, Bloomberg Finance LP, FactSet. Note: 60/40 Benchmark is 60% Global Equities (ACWI) and 40% Aggregate Bonds (AGG) rebalanced on a monthly basis.

9 June 2016

US ETF Insights


Traditional 60/40 portfolio with tactical EFRoM overlay

The traditional 60/40 portfolio with tactical EFRoM overlay seeks to represent a

common balanced portfolio allocating 60% to Global Equities, and 40% to

Aggregate Bonds, but with an additional allocation tilt driven by the monthly

tactical EFRoM signal. We present several variations of this strategy to

illustrate the benefits of the tactical signal at different levels of allocation

budget. More specifically we provide guidance for implementations using a

5%, 15%, 25%, or unconstrained tactical budget. For example, an investor with

a 15% tactical budget would fluctuate anywhere between 45%/55%

Equity/Bonds and 75%/25% Equity/Bonds.

Furthermore, we provide two implementation alternatives: the first option

involves tilting the allocation weights of the diversified positions; while the

second one uses more concentrated positions (e.g. US Large Caps, Long Term

US Treasuries) for the implementation of the tactical overlays. These portfolios

are provided on a long-only basis (Figure 8).

Figure 8: Traditional 60/40 portfolio with tactical EFRoM tilt and overlay

constituents and allocation details

Asset Class Weight Ticker Weight Ticker

60/40 - Diversified Allocations +/- 5%/15%/25%/Unconstrained

Global Equities (60%) 65%-85% ACWI (60%) 55%-35% ACWI

Agg Bonds (40%) 35%-15% AGG (40%) 45%-65% AGG

60/40 - Extra Overlay +/- 5%/15%/25%/Unconstrained

Global Equities 60% ACWI 55%-35% ACWI

US Equities (extra) 5%-25% SPY 0% SPY

Agg Bonds 35%-15% AGG 40% AGG

UST Long (extra) 0% TLT 5%-25% TLT

Risk-on Risk Off


We observed that a tactical signal based on the EFRoM methodology

increased the absolute returns and risk-adjusted returns of all the tilted and

unconstrained portfolios during the backtesting period relative to a neutral

60/40 portfolio benchmark rebalanced on a monthly basis (Figure 9 to Figure

11).

Figure 9: Traditional 60/40 portfolio with tactical EFRoM tilt and overlay performance details

Risk/Return Metrics

60/40 EFRoM

60/40

Neutral

60/40 +/-5%

EFRoM tilt

60/40 +/-15%

EFRoM tilt

60/40 +/-25%

EFRoM tilt

Eqty/Bond

Unconstrained

60/40 +/-5%

EFRoM tilt

60/40 +/-15%

EFRoM tilt

60/40 +/-25%

EFRoM tilt

Eqty/Bond

Unconstrained

Annualized Total Return 1.2% 2.4% 4.8% 7.3% 13.9% 3.0% 6.6% 10.3% 26.3%

Annualized Volatility 9.3% 9.0% 8.7% 8.7% 9.3% 8.9% 8.4% 8.6% 13.8%

Sharpe Ratio (RF=0) 0.13 0.27 0.56 0.83 1.49 0.34 0.79 1.20 1.90

Maximum Drawdown 11.4% 10.1% 7.9% 7.1% 5.9% 9.3% 7.1% 5.6% 10.3%

Extra OverlayDiversified Allocations


9 June 2016

US ETF Insights


Figure 10: Traditional 60/40 EFRoM performance vs.

Neutral Benchmark – Diversified allocations

Figure 11: Traditional 60/40 EFRoM performance vs.

Neutral Benchmark – Extra Overlay

85

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Add Risk Reduce Risk60/40 Neutral 60/40 +/-5% EFRoM tilt60/40 +/-15% EFRoM tilt 60/40 +/-25% EFRoM tiltEqty/Bond Unconstrained

85

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135

145

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Add Risk Reduce Risk60/40 Neutral 60/40 +/-5% EFRoM tilt60/40 +/-15% EFRoM tilt 60/40 +/-25% EFRoM tiltEqty/Bond Unconstrained

Source: Deutsche Bank, Bloomberg Finance LP, FactSet Note: 60/40 Benchmark is 60% Global Equities (ACWI) and 40% Aggregate Bonds (AGG) rebalanced on a monthly basis.

Source: Deutsche Bank, Bloomberg Finance LP, FactSet Note: 60/40 Benchmark is 60% Global Equities (ACWI) and 40% Aggregate Bonds (AGG) rebalanced on a monthly basis.

Equity Smart Beta Portfolios

We studied the behavior for different smart beta strategies such as minimum

volatility, momentum, size, quality, and value during periods of increasing and

decreasing risk appetite, and we found that minimum volatility and momentum

had the most distinctive behavior compared to the other three factor strategies

which seemed to be less dependent on risk cycles. Minimum volatility clearly

presented a more defensive profile more suitable for periods when investors

are unloading risk, while momentum exhibited a more aggressive profile

suitable for periods when investors are loading up on risk. Therefore, we

decided to implement a Smart Beta portfolio using our EFRoM tactical signal

that would work as a 0/100% binary signal for the long-only portfolios, and a

100/-100% signal for the long/short portfolios. Thus whenever the EFRoM

signal recommends an increase in risk the long-only portfolio would take a full

position on a Momentum ETF, while in the case of a reduction of risk the

portfolio would take a full position on a Minimum Volatility ETF. For the

long/short portfolio the allocations would be similar with the addition of a short

position of equal size on the other ETF (Figure 12)

Figure 12: Smart Beta portfolio constituents and allocation details


Minimum Volatility/Momentum - Long Only

US Min Volatility 0% USMV 100% USMV

US Momentum 100% MTUM 0% MTUM

Minimum Volatility/Momentum - Long/Short

US Min Volatility -100% USMV 100% USMV

US Momentum 100% MTUM -100% MTUM

Risk-on Risk Off


9 June 2016

US ETF Insights


Both, long and long/short, smart beta strategies outperformed a US equity

benchmark on an absolute and risk-adjusted basis during the backtesting

period. However, only the long-only strategy outperformed the constituents

(US Momentum and US Min Volatility) on both absolute and risk-adjusted

basis; nevertheless the long/short strategy still looked very attractive from a

risk-adjusted perspective despite not being able to outperform the portfolio

constituents. We believe that the long/short strategy was somewhat limited on

its upside given the low dispersion of returns among the two smart beta

strategies tracking a very similar asset class (i.e. US Equities); still the

long/short strategy could be an attractive low risk strategy (Figure 13 to Figure

15).

Figure 13: Smart Beta EFRoM portfolio performance details

Risk/Return Metrics

Smart Beta EFRoM

EFRoM Smart

Beta Long

EFRoM Smart

Beta Long/Short

US

Equity

US Minimum

Volatility

US

Momentum

Annualized Total Return 12.1% 6.9% 3.3% 8.8% 7.6%

Annualized Volatility 13.7% 6.7% 15.5% 12.8% 16.1%

Sharpe Ratio (RF=0) 0.88 1.02 0.22 0.69 0.47

Maximum Drawdown 9.5% 5.2% 13.0% 9.5% 12.9%


Figure 14: Smart Beta EFRoM portfolio performance vs.

constituents

Figure 15: Smart Beta EFRoM portfolio performance vs.

benchmark

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EFRoM Smart Beta Long EFRoM Smart Beta Long/Short

US Min Vol US Momentum

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EFRoM Smart Beta Long EFRoM Smart Beta Long/Short

US Equity Source: Deutsche Bank, Bloomberg Finance LP, FactSet

Source: Deutsche Bank, Bloomberg Finance LP, FactSet. Note: US Equity benchmark is US Large Caps

9 June 2016

US ETF Insights


Equity Sector Portfolios

We reviewed the behavior for different equity sectors during periods of

increasing and decreasing risk appetite, and we found that Utilities, Financials,

and Materials had the most distinctive behavior compared to the other sectors

which seemed to be less dependent on risk cycles. Utilities clearly presented a

more defensive profile more suitable for periods when investors are unloading

risk; this came as no surprise to us given that this sector is usually considered

a bond proxy among equities. On the other hand, Financials and Materials

exhibited a more aggressive profile suitable for periods when investors are

loading up on risk. Therefore, we decided to implement two Sector rotation

portfolios using our EFRoM tactical signal that would work as a 0/100% binary

signal for the long-only portfolios, and a 100/-100% signal for the long/short

portfolios. Thus whenever the EFRoM signal recommends an increase in risk

the long-only portfolio would take a full position on a Financials or a Materials

ETF, while in the case of a reduction of risk the portfolio would take a full

position on a Utilities ETF. For the long/short portfolio the allocations would be

similar with the addition of a short position of equal size on the other ETF

(Figure 16).

Figure 16: Sector EFRoM portfolio constituents and allocation details


Utilities/Financials - Long Only

Utilities 0% XLU 100% XLU

Financials 100% XLF 0% XLF

Utilities/Financials - Long/Short

Utilities -100% XLU 100% XLU

Financials 100% XLF -100% XLF

Utilities/Materials - Long Only

Utilities 0% XLU 100% XLU

Materials 100% XLB 0% XLB

Utilities/Materials - Long/Short

Utilities -100% XLU 100% XLU

Materials 100% XLB -100% XLB

Risk-on Risk Off


Both pairs of long and long/short sector strategies outperformed a US equity

benchmark and their constituents on an absolute and risk-adjusted basis

during the backtesting period. We also noticed a significant performance

enhancement in the long/short portfolios, most likely due to the wide

dispersion of returns between risk-on and risk-off sectors (Figure 17 to Figure

21)

9 June 2016

US ETF Insights


Figure 17: Sector EFRoM portfolio performance details

Risk/Return Metrics

Sector EFRoM

EFRoM Sector

Uti/Fin Long

EFRoM Sector

Uti/Fin Long/Short

EFRoM Sector

Uti/Mat Long

EFRoM Sector

Uti/Mat Long/ShortUS Equity US Utilities

US

Financials

US

Materials

Annualized Total Return 20.2% 34.7% 23.0% 40.3% 3.3% 6.1% -1.1% -0.7%

Annualized Volatility 16.9% 19.5% 17.1% 19.9% 15.5% 16.4% 18.5% 19.5%

Sharpe Ratio (RF=0) 1.19 1.78 1.35 2.03 0.22 0.37 -0.06 -0.03

Maximum Drawdown 12.8% 15.5% 12.8% 17.2% 13.0% 15.7% 22.3% 26.9% Source: Deutsche Bank, Bloomberg Finance LP, FactSet

Figure 18: Sector EFRoM portfolio performance vs.

constituents – Utilities/Financials


benchmark – Utilities/Financials

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etu

rn


EFRoM Sector Uti/Fin Long EFRoM Sector Uti/Fin Long/Short

US Utilities US Financials

70

80

90

100

110

120

130

140

150

160

170

Dec-1

4

Jan

-15

Feb

-15

Mar-

15

Ap

r-15

May-1

5

Ju

n-1

5

Ju

l-1

5

Au

g-1

5

Sep

-15

Oct-

15

No

v-1

5

Dec-1

5

Jan

-16

Feb

-16

Mar-

16

Ap

r-16

May-1

6

No

rmalize

d L

evel (1

00

) -

To

tal R

etu

rn


EFRoM Sector Uti/Fin Long EFRoM Sector Uti/Fin Long/Short

US Equity Source: Deutsche Bank, Bloomberg Finance LP, FactSet.

Source: Deutsche Bank, Bloomberg Finance LP, FactSe.t Note: US Equity benchmark is US Large Caps

(SPY)


constituents – Utilities/Materials


benchmark – Utilities/Materials

70

80

90

100

110

120

130

140

150

160

170

180

Dec-1

4

Jan

-15

Feb

-15

Mar-

15

Ap

r-15

May-1

5

Ju

n-1

5

Ju

l-15

Au

g-1

5

Sep

-15

Oct-

15

No

v-1

5

Dec-1

5

Jan

-16

Feb

-16

Mar-

16

Ap

r-16

May-1

6

No

rmalize

d L

evel (1

00

) -

To

tal R

etu

rn


EFRoM Sector Uti/Mat Long EFRoM Sector Uti/Mat Long/Short

US Utilities US Materials

70

80

90

100

110

120

130

140

150

160

170

180

Dec-1

4

Jan

-15

Feb

-15

Mar-

15

Ap

r-15

May-1

5

Ju

n-1

5

Ju

l-15

Au

g-1

5

Sep

-15

Oct-

15

No

v-1

5

Dec-1

5

Jan

-16

Feb

-16

Mar-

16

Ap

r-16

May-1

6

No

rmalize

d L

evel (1

00

) -

To

tal R

etu

rn


EFRoM Sector Uti/Mat Long EFRoM Sector Uti/Mat Long/Short

US Equity Source: Deutsche Bank, Bloomberg Finance LP, FactSet.

Source: Deutsche Bank, Bloomberg Finance LP, FactSe.t Note: US Equity benchmark is US Large Caps

(SPY)

9 June 2016

US ETF Insights


Appendix A

EFRoM Methodology and Applications

The ETF Flow Risk-o-Meter (EFRoM) signal combines selected individual

TAARSS3 signals and our past experience in an effort to provide a unique

indicator of investors’ risk appetite in the market.

Defining the asset classes and risk factors

We first identify 14 asset classes which are relevant to the anatomy of a

risk-on or risk-off trade, then we assigned risk factors to each of these asset

classes. Those asset classes more likely to receive inflows during a risk-off

trade are assigned a negative risk factor, while those asset classes more likely

to receive inflows during a risk-on trade are assigned a positive risk factor.

Furthermore, the magnitude of the risk factors varies depending on the nature

of the asset class; thus more defensive asset classes have lower risk factors

than more aggressive asset classes (Figure 22).

Figure 22: Selected TAARSS Asset Classes and Risk Factors

Asset Class

US

Treasurie

s

Gold Corp IG Sr Loans Corp HY EM Debt

US

Defensive

s

US LargeBroad

Comdty

US

Domestic

Cycl.

DM Intl

US

Global

Cycl.

US Small EM

Risk Factor -1 -1 -0.5 0.5 0.5 0.5 0.5 0.5 1 1 1 1 1.5 1.5

Risk Appetite

EquitiesCommoditiesFixed Income

LESS RISK MORE RISK


Defining the allocation measure

The next step is to determine the interest in the particular trade or allocation.

We obtain this measure by leveraging the TAARSS signals for the respective

14 asset classes. Each TAARSS signal measures the magnitude, direction, and

consistency of a particular asset allocation trend.

Calculating the ETF Flow Risk-o-Meter (EFRoM) Level

Finally, we calculate the ETF Flow Risk-o-Meter Level by multiplying each

TAARSS signal by its respective risk factor, and aggregating these results into

a single measure by adding them up.

(1) EFRoM Level = SUM [i] {TAARSS[i] x Risk Factor [i]} ,

i=1 to 14 representing the different asset classes in the model

3 Refer to “US ETF Insights: ETF Flow Risk-o-Meter (EFRoM)” published on May 5, 2016 for additional

details on this methodology. Our TAARSS strategy focuses on identifying relative strength signals across

asset classes based on the magnitude and consistency of ETF flow trends, and it was first introduced in

“Tactical Asset Allocation (TAA) insights from ETF Flows” published on May 14, 2014.

9 June 2016

US ETF Insights


Figure 23 depicts the monthly levels and changes of the ETF Flow Risk-o-Meter

for the past 18 months. A positive Risk-o-Meter measure suggests that

investors are on risk-on mode and have a relatively high appetite for risk; while

a negative reading suggests that investors are on risk-off mode and have a

relatively low appetite for risk.

Figure 23: ETF Flow Risk-o-Meter (EFRoM)

(4.0)

(3.0)

(2.0)

(1.0)

-

1.0

2.0

3.0

4.0

(2.5)

(2.0)

(1.5)

(1.0)

(0.5)

-

0.5

1.0

1.5

2.0

2.5

No

v-1

4

Dec-1

4

Jan

-15

Feb

-15

Mar-

15

Ap

r-1

5

May-1

5

Ju

n-1

5

Ju

l-1

5

Au

g-1

5

Sep

-15

Oct-

15

No

v-1

5

Dec-1

5

Jan

-16

Feb

-16

Mar-

16

Ap

r-1

6

Ris

k-o

-Mete

r M

oM

Ch

an

ge

Ris

k-o

-Mete

r

MoM Chg [rhs]

ETF Flow Risk-o-meter

AD

DR

ISK

RE

DU

CE

RIS

K

RIS

KO

NR

ISK

OFF

Source: : Deutsche Bank, Bloomberg Finance LP

Practical applications of the ETF Flow Risk-o-Meter

The level of the Risk-o-Meter is not a leading or lagging indicator, but rather a

concurrent indicator. It should not be used to try to predict the performance of

risky assets, but rather as a tool to measure and understand the level of

investors’ risk appetite in the market at a specific point in time. However, we

found that the month-over-month changes of the Risk-o-Meter level had some

predictive power in terms of forecasting the performance of risky asset;

although more in terms of the direction of the performance of risky assets than

the actual magnitude of such performance.

9 June 2016

US ETF Insights


Appendix B

List of ETFs from EFRoM portfolios

Figure 24: List of ETFs used in EFRoM portfolios and select characteristics

5-Day $ 5-Day bps

Equities

Global Equities ACWI 0.33% 5,622 89 63 152 0.01 1.8

US Large Caps SPY 0.09% 181,436 17,787 7,485 25,272 0.01 0.5

US Small Caps IWM 0.20% 26,631 3,227 165 3,392 0.01 0.9

US Min Volatility USMV 0.15% 13,465 144 728 871 0.01 2.3

US Momentum MTUM 0.15% 1,325 14 2,327 2,341 0.03 4.4

US Financials XLF 0.14% 16,985 868 1,207 2,075 0.01 4.2

US Materials XLB 0.14% 2,732 267 504 771 0.01 2.2

US Utilities XLU 0.14% 8,291 709 596 1,305 0.01 2.0

DM Intl Broad EFA 0.33% 59,907 1,389 1,823 3,212 0.01 1.7

DM Intl Min Volatility EFAV 0.20% 7,364 68 336 404 0.02 3.1

EM Broad EEM 0.69% 22,161 2,176 447 2,624 0.01 3.0

EM Min Volatility EEMV 0.25% 3,589 39 28 68 0.02 3.3

Gold Miners GDX 0.52% 8,048 2,089 62 2,152 0.01 4.3

Gold Small Miners GDXJ 0.54% 2,886 566 18 584 0.01 2.9

Fixed Income

Aggregate Bonds AGG 0.08% 37,236 263 n.a. n.a. 0.01 0.9

UST Intermediate IEF 0.15% 9,554 209 n.a. n.a. 0.01 0.9

UST Long TLT 0.15% 8,656 915 n.a. n.a. 0.01 0.8

High Yield Credit HYG 0.50% 14,587 1,078 n.a. n.a. 0.01 1.2

Commodities

Physical Gold GLD 0.40% 35,240 1,119 n.a. n.a. 0.01 0.9

Implied

Liq. $M

Total

Liq. $

Avg. Bid/Ask SpreadsTicker TER AUM $M

20D ADV

$MAsset Class

Source: Deutsche Bank, Bloomberg Finance LP. Data as of Jun 7, 2016

9 June 2016

US ETF Insights


Appendix 1

Important Disclosures

Additional information available upon request

*Prices are current as of the end of the previous trading session unless otherwise indicated and are sourced from local exchanges via Reuters, Bloomberg and other vendors . Other information is sourced from Deutsche Bank, subject companies, and other sources. For disclosures pertaining to recommendations or estimates made on securities other than the primary subject of this research, please see the most recently published company report or visit our global disclosure look-up page on our website at http://gm.db.com/ger/disclosure/DisclosureDirectory.eqsr

Analyst Certification

The views expressed in this report accurately reflect the personal views of the undersigned lead analyst(s). In addition, the undersigned lead analyst(s) has not and will not receive any compensation for providing a specific recommendation or view in this report. Sebastian Mercado

Equity rating key Equity rating dispersion and banking relationships

Buy: Based on a current 12- month view of total share-holder return (TSR = percentage change in share price from current price to projected target price plus pro-jected dividend yield ) , we recommend that investors buy the stock.

Sell: Based on a current 12-month view of total share-holder return, we recommend that investors sell the stock

Hold: We take a neutral view on the stock 12-months out and, based on this time horizon, do not recommend either a Buy or Sell.

Newly issued research recommendations and target prices supersede previously published research.

46 %51 %

3 %

51 % 43 %

15 %0

50100150200250300350400450500

Buy Hold Sell

North American Universe

Companies Covered Cos. w/ Banking Relationship

Regulatory Disclosures

1.Important Additional Conflict Disclosures

Aside from within this report, important conflict disclosures can also be found at https://gm.db.com/equities under the

"Disclosures Lookup" and "Legal" tabs. Investors are strongly encouraged to review this information before investing.

2.Short-Term Trade Ideas

Deutsche Bank equity research analysts sometimes have shorter-term trade ideas (known as SOLAR ideas) that are

consistent or inconsistent with Deutsche Bank's existing longer term ratings. These trade ideas can be found at the

SOLAR link at http://gm.db.com.

http://gm.db.com/ger/disclosure/DisclosureDirectory.eqsr

https://gm.db.com/equities

http://gm.db.com/

9 June 2016

US ETF Insights


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9 June 2016

US ETF Insights


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US ETF Insights


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Research

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