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Deutsche Bank

QWAFAFEW Presentation

January 2015

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. MICA(P) 072/04/2012

Global Quantitative Strategy

Yin Luo, CFA ▪ 212 250 8983 ▪ [email protected]

Managing Director, Global Head of Quantitative Strategy

Deutsche Bank

Yin Luo, CFA ▪ 1.212.250.8983 ▪ [email protected]

#1 Ranked Global Quant Strategy Team

Source: gettyimages.com, Deutsche Bank Quantitative Strategy

research surveys: America #1; Europe #1; Asia #1 FX quant research #2

All our research can be accessed at: http://eqindex.db.com/gqs

1

New York

— Miguel Alvarez

— Javed Jussa

— Sheng Wang

— Allen Wang

— Gaurav Rohal, CFA

— David Elledge

— Zheyin Zhao

Quant IT

— Sergei Khomiouk

Chile Offshore Support

— Dagoberto Mendez

— Nicolas Magunacelaya

London

— Spyros Mesomeris, PhD

European Head of Quantitative Strategy

— Christian Davies

— Jacopo Capra

— Shan Jiang

— Alison (Shuo) Qu, PhD

— Paul Ward

Quant FX/Commodities

— Caio Natividade

— Vivek Anand

Hong Kong

— Khoi LeBinh

Asian Head of Quantitative Strategy

— Vincent Zoonekynd

— Ada Lau

Mumbai

— Hemant Sambatur

— Yin Luo, CFA

Global Head of Quantitative Strategy

mailto:[email protected]

http://eqindex.db.com/gqs

Deutsche Bank

Yin Luo, CFA ▪ 1.212.250.8983 ▪ [email protected] 2

Introduction to quantitative equity investing Quants look at factors

— A factor is simply a systematic way of ranking (and selecting) stocks. It could be as simple as value (e.g.,

P/E) or momentum (e.g., past 12-month returns).

Source: Bloomberg Finance LP, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank Quantitative Strategy

How do we know it’s a good factor?

And you don’t need to trade it every day The turnover is not too bad

Cheap stocks are (almost) always good

0.6

0.8

1.0

1.2

1.4

1.6

1 2 3 4 5 6 7 8 9 10

Earnings yield, forecast FY1 mean, Quantile average return (%)

(%)

Quantile

20

30

40

50

60

88 90 92 94 96 98 00 02 04 06 08 10 12

Factor turnover, tw o-w ay, (%)

12-month moving average

Earnings yield, forecast FY1 mean

-0.4

0.0

0.4

0.8

1.2

1 2 3 4 5 6 7 8 9 10 11 12

Earnings yield, forecast FY1 mean, Long/short quantile portfolio return decay

(%)

Period

-20

-10

0

10

20

88 90 92 94 96 98 00 02 04 06

Long/short quantile portfolio return (%), Ascending order



(%)

Avg = 1.11%

Std. Dev. = 7.12%

Min = -29.6%

Avg/Std. Dev.= 0.16


Deutsche Bank


-15

-10

-5

0

5

10

01 02 03 04 05 06 07 08 09 10 11 12 13



DB composite options factor

(%)

Avg = 0.82%

Std. Dev. = 2.51%

Min = -10.89%

Avg/Std. Dev.= 0.33

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12

Rel

ativ

e O

pp

ort

unit

y

Stock-specific Country Style Industry Currency

3

But then the 2008 financial crisis changed everything (maybe forever)

Source: Bloomberg Finance LP, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank Quantitative Strategy

The profit from a simple value strategy has fallen by 2/3

And learn to live in a macro dominated environment That’s why we need new factors

Momentum has been even more challenging

-8

-4

0

4

8

12

2008 2009 2010 2011 2012 2013




(%)

Avg = 0.36%

Std. Dev. = 3.46%

Min = -6.94%

Avg/Std. Dev.= 0.1 -60

-40

-20

0

20

40

88 90 92 94 96 98 00 02 04 06 08 10 12



12M-1M total return

(%)

Avg = 1.23%

Std. Dev. = 7.3%

Min = -43.51%

Avg/Std. Dev.= 0.17


Deutsche Bank


26/01/2015 08:18:08 2010 DB Blue template

DB Quant Handbook, Part II — The rapid rise of computing power and wide availability of

off-the-shelf backtesting software provided by many data

vendors have given the impression that quant investing is

easy, or is it?

— In this paper, we discuss the seven common mistakes

investors tend to make when they perform backtesting

and build quant models. Some of these may be familiar to

our readers, but nonetheless, you may be surprised to

see the impact of these biases. The other sins are so

commonplace in both academia and practitioner’s

research that we usually take them for granted.

— There are a few unique features in this research that we

have not seen in other places. We deliberate when to and

when not to remove outliers; discuss various data

normalization techniques; address the intricate issues of

signal decay, turnover, and transaction costs; elaborate

on the optimal rebalancing frequency; illustrate the

asymmetric factor payoff patterns and the impact of short

availability on portfolio performance; answer the question

of “how many stocks should be held in the portfolio”; and

review the tradeoffs of various factor weighting/portfolio

construction techniques. Last but not least, we compare

traditional active portfolio management via multi-factor

models, with the new trend of smart beta/factor portfolio

investing.

Seven sins of quantitative investing

4


Deutsche Bank


I. Survivorship bias

Ignoring inactive companies — Survivorship bias is one of the common mistakes

investors tend to make. Most people are aware of the

survivorship bias, but few understand its significance.

— Practitioners tend to backtest certain investment

strategies using only those companies that are currently

in business, meaning stocks that have left the

investment universe due to bankruptcy, delisting or

being acquired are not included in the backtesting.

Source: Bloomberg Finance LLP, Compustat, IBES, Russell, S&P, Thomson Reuters, Worldscope, Deutsche Bank Quantitative Strategy

5

Survivorship bias

Stocks that have survived perform better than average # of stocks in the US and Europe that have survived until today

0

10

20

30

40

50

60

Russell 3000 index (equally weighted)

Survivor universe (equally weighted)

0

100

200

300

400

500

600

MSCI Europe survivor

0

2

4

6

8

10

12

14

MSCI Europe equally weighted

MSCI Europe survivor universe


Deutsche Bank


Survivorship bias illustrated

Survivorship bias leads to completely opposite conclusions

Source: Bloomberg Finance LLP, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank Quantitative Strategy

6

Merton distance of default factor on the Russell 3000

universe Factor exposure, the Russell 3000 universe Low volatility factor on the proper S&P 500 universe

Merton distance of default factor on the “survivor

universe” Factor exposure, the “survivor universe”

Low volatility factor performance on the current S&P 500

index constituents

0

5

10

15

20

25

Q1 (worst quality/highest credit risk)

Q5 (best quality/lowest credit risk)

0

20

40

60

80

100

120

140

160

180

200

Q1 (worst quality/highest credit risk)


0

1

2

3

4

5

6

7

8

9Quintile 1 (Low Volatility)

Quintile 5 (High Volatility)

0

20

40

60

80

100

120

140

160

180

Quintile 1 (Low Volatility)

Quintile 5 (High Volatility)

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5Q1 (worst quality/highest credit risk)


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5Q1 (worst quality/highest credit risk)



Deutsche Bank


The impact of survivorship bias

1/3 of factors have the opposite signs with the survivorship-biased universe


7

Top 20 factors with the largest return differential Top 20 factors with the opposite signs

0.0% 0.5% 1.0% 1.5%

Realized vol, 1Y daily

Float turnover, 1M

Expected dividend yield

Dividend yield, trailing 12M

Normalized abnormal volume

IBES LTG EPS mean

Short interest/float

Payout on trailing operating EPS

Current ratio

Price-to-52 week high

Merton's distance to default

Operating earnings yield, trailing …

Price to 52-week low

Operating cash flow yield (income …

Skewness, 1Y daily

EBITDA to EV

Earnings yield, FY0

YoY change in # of shares …

Cash flow return on equity


"Survivor" universe vs. correct universe

-1.5% -1.0% -0.5% 0.0% 0.5% 1.0%

Dividend yield, trailing 12M

IBES LTG EPS mean

Short interest/float

Current ratio

Operating earnings yield, trailing …

Operating cash flow yield (income …

Skewness, 1Y daily

EBITDA to EV

Earnings yield, FY0

YoY change in # of shares …


Long-term debt to equity

Return on Equity

IBES FY1 mean EPS growth

# of days to cover short

Altman's z-score

Return on invested capital (ROIC)

IBES 5Y EPS growth

Moving average crossover, 15W-…

IBES FY1 Mean EPS Revision, 3M

Survivor universe Correct universe


Deutsche Bank


II. Look-ahead bias

Using data that were unknown — It is the bias created by using information or data that

were unknown or unavailable as of the time when the

backtesting was conducted. It is probably the most

common bias in the backtesting.

— An obvious example of look-ahead bias lies in

companies’ financial statement data.

— Ideally, we should use point-in-time data for all

backtesting purposes. When PIT data is not available,

we need to make reporting lag assumption.


8

Look-ahead bias

# of days to file quarterly earnings – international companies # of days to file quarterly earnings – US companies

0

4,000

8,000

12,000

16,000

20,000

24,000

28,000

0 10 20 30 40 50 60 70 80 90 100

Fre

qu

en

cy

Mean = 30 days

Median = 28 days

0

500

1,000

1,500

2,000

2,500

3,000

0 10 20 30 40 50 60 70 80 90 100

Fre

qu

en

cy

Mean = 37 days

Median = 35 days


Deutsche Bank


The importance of using PIT data When PIT data is not available, reporting lag assumption is critical


9

The performance of the earnings yield factor, using non-PIT data The performance of the earnings yield factor, using PIT data

The impact of reporting lag assumption, ROE in UK The coverage of UK stocks (S&P BMI index)

IC (

%)

1995 2000 2005 2010 2015

-20

0

20

40

60Spearman rank IC (%), Ascending order12-month moving average

Avg = 8.12%

Std. Dev. = 12.43%Min = -26.28%Max = 47.73%

Avg/Std. Dev. = 0.65

IC (

%)

1995 2000 2005 2010 2015

-20

0

20

40

60 Spearman rank IC (%), Ascending order12-month moving average

Avg = 5.11%

Std. Dev. = 12.82%Min = -30.78%Max = 45.78%

Avg/Std. Dev. = 0.4

0

100

200

300

400

500

600

700

800

PIT coverageTraditional database (non-PIT) coverageS&P BMI UK universe

0%

1%

2%

3%

4%

PIT Non-PIT (no reporting lag

Non-PIT (1M lag)

Non-PIT (2M lag)

Non-PIT (3M lag)


Deutsche Bank


Look-ahead corporate action bias

Look-ahead bias can be tricky — For example, split adjustment factors can potentially

bring look-ahead bias. From time to time, companies

may decide to split their shares (or reverse split), to

improve liquidity or attract certain clientele. For most

modeling purposes, we want everything to be split

adjusted. For example, when we calculate earnings

yield, EPS data typically comes from company financial

statements with low frequency (quarterly, semi-annually,

or annually), while pricing information is from market

data available at least daily. We need to make sure both

EPS and price are split adjusted at the same time.


10

Performance of the top 25 names with the lowest share price

Sharpe ratio Annualized return

0.1

1

10

100

1000

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

The portfolio of 25 “low priced” stocks, based on split adjusted price

The portfolio of 25 “low priced” stocks, based on unadjusted price

S&P 500

0%

5%

10%

15%

20%

25%

30%

Adjusted price Unadjusted price S&P 500

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Adjusted price Unadjusted price S&P 500


Deutsche Bank


III. The sin of storytelling

How long is long enough?


11

Storytelling Earnings yield, 1987-1997, Russell 3000 Earnings yield, 1997-2000, US technology

Earnings yield, 2000-2002, US technology

Earnings yield in US technology sector has never been a

good factor Sharpe ratio

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0

1

2

3

4

5

6

7

0

0.5

1

1.5

2

2.5

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

Entire period Before tech bubble burst

After tech bubble burst


Deutsche Bank


IV. Data mining and data snooping

Data mining is almost avoidable — Universe: S&P 500

— Two factor weighting algorithms

— In-sample model: select the best factor from each of the

six style buckets (value, growth, momentum/reversal,

sentiment, quality, and exotic) from 2009-2014 and then

backtest the same model over the same period.

— Out-of-sample model: from May 31, 2009, at the end of

each month, we use rolling 60 months of data to

construct our multi-factor model, using data available as

of that time.


12

Data mining

Factor weighting – Grinold and Kahn MVO algorithm Factor weighting – equally weighting algorithm


Deutsche Bank


V. Signal decay, turnover, and transaction cost

Need to balance among signal decay,

transaction cost, and model prediction power

— Different stock-selection factors have different

information decay profile. Faster decay signals require

higher turnover to capture their benefit. Higher turnover,

however, is likely to incur greater transaction costs.

— Adding a turnover constraint at the portfolio construction

process is an easy, but not necessarily ideal solution –

turnover constraint can either help or hurt our portfolio

performance.


13

Signal decay

Annualized return with different transaction cost

assumptions, Japan dividend-paying stocks

-2%

0%

2%

4%

6%

8%

10%

12%

No cost 10 bps 20 bps 30 bps

One month reversal

Price to book

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00Turnover 40%

Turnover 80%

Turnover 120%

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Turnover 40% Turnover 80% Turnover 120%

Wealth curve of the N-LASR model, with different

turnover constraints Sharpe ratio of the N-LASR model, with different

turnover constraints


Deutsche Bank


Optimal rebalancing frequency?

Tight turnover constraint ≠ Low rebalancing frequency — Having a tight turnover constraint, however, does not necessarily mean that we should have a very low rebalance frequency. In

many instances, we have heard comments such as “we are long-term value investors; we hold stocks for three to five years;

and therefore, we rebalance once a year”. New information comes in constantly and we should adjust our models and beliefs

accordingly. Even if we have a tight turnover constraint, we may still want to frequently adjust our positions – albeit modestly

each time.


14

Annual versus monthly rebalance for a low turnover value portfolio (36% one-way turnover per year)

0

5

10

15

20

25

30

35

Monthly rebalance

Annual rebalance

Russell 3000


Deutsche Bank


Signal decay at the extreme

Example: one-day reversal factor

— A simple backtesting of the one-day reversal

factor (i.e., buying stocks that have fallen the

most on the previous day) seems to suggest

that short-term reversal to be a great strategy.

— The only problem is that the factor itself can only

be computed after the market closes; therefore,

the earliest time we can trade on the signal is at

the next day’s open.

— If we can calculate the one-day reversal factor

and trade on the same day’s closing price, we

can generate a Sharpe ratio of 1.4x – pretty

good for a single factor model. However, in

reality, we can only trade at the second day’s

open, while Sharpe ratio plummets to merely

0.3x (down almost 80%).


15

Performance of one day reversal

Annualized return and Sharpe ratio

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5Trading at the same day’s close

Trading at the next day’s open

21%

1.41

4% 0.26

Annualized return Sharpe ratio

Trading at the same day’s close Trading at the next day’s open


Deutsche Bank


VI. Outliers – spectacular successes and failures


16

Outliers Aggregate earnings yield, using raw data

Aggregate earnings yield, using winsorized data Aggregate earnings yield for the Korean market Aggregate book-to-market for the Hong Kong market

Outlier control and data normalization

— Traditional outlier control techniques

include: winsorization (capping data

at certain percentiles) and truncation

(removing outliers from data sample).

— Data normalization process is closely

related to outlier control.

— Outliers could contain useful

information, but most of the time, they

don’t.

— Data normalization techniques can

have significant impact on model

performance.

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

Raw data

Winsorizing 1%

Winsorizing 2%

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Raw data Winsorizing Inter-quartile range


Deutsche Bank


Data transformation

Four alternative data transformation techniques


17

The distribution of Indonesia earnings yield – raw data The distribution of Indonesia earnings yield – z-score transformation

The distribution of Indonesia earnings yield – the ranking

transformation

The distribution of Indonesia earnings yield – our proprietary

transformation

De

nsity

-100 -50 0 50 100

0.0

00

.04

0.0

8

De

nsity

-10 -5 0 5 10

0.0

0.4

0.8

1.2

De

nsity

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

De

nsity

-2 -1 0 1 2

0.0

0.1

0.2

0.3

0.4


Deutsche Bank


The impact of data normalization techniques

Our proprietary technique can improve model performance by 11% and reduce signal turnover significantly

— Example: an equally weighted four-factor model (earnings yield, 12-1M price momentum, three-month earnings revision, and ROE


18

Average model performance (rank IC), using different data

normalization techniques

0% 2% 4% 6% 8% 10%

DenmarkGreece

ThailandFinlandNorway

SpainNetherlands

SingaporeSouth Africa

MalaysiaSweden

BrazilSwitzerland

ItalyHong Kong

GermanyFranceChina

CanadaAustralia

KoreaTaiwan

UKJapan

USA

Normalize based on ranking Normalize based on z-score

Average signal serial correlation, using different normalization

techniques

75% 80% 85% 90% 95%

DenmarkGreece

ThailandFinlandNorway

SpainNetherlands

SingaporeSouth Africa

MalaysiaSweden

BrazilSwitzerland

ItalyHong Kong

GermanyFranceChina

CanadaAustralia

KoreaTaiwan

UKJapan

USA

Normalize based on ranking Normalize based on z-score


Deutsche Bank


Do outliers contain any useful information?

Maybe… at least for the price momentum factor


19

Neutralized momentum factor, without the ranking normalization Neutralized momentum factor, with the ranking normalization

Momentum portfolio performance Aggregate net exposure

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%Rank IC

12 month average

Avg = 2.8%

Min=-36%

Max=27%

Avg/ Std. Dev.= 0.31

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%Rank IC

12 month average

Avg = 4.3%

Min=-41%

Max= 46%

Avg/ Std. Dev.= 0.29

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Normalized momentum

Raw momentum

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%Normalized momentum

Raw momentum


Deutsche Bank


Calculating excess returns in event study

Example: dividend announcement

— When we normalize our data, we have to

compute our factors relative to certain universes

or benchmarks. Interestingly, the results can

also be day-and-night depending on which

benchmark we use. We use an event study to

show the impact of benchmark selection bias.

— If we normalize each stock’s return by

subtracting the average return of all dividend-

paying stocks on the same day. On average,

there is no price movement prior to the event

date, i.e., there is probably no leakage of

dividend announcement information.

— However, if we choose the wrong benchmark –

where we use the broad equity market, e.g., the

S&P 500 index, we see stocks actually tend to

go up before the dividend announcement .

— The reason is possibly due to the fact that

dividend-paying stocks tend to earn higher

returns than the broad market. Using the wrong

benchmark makes it impossible to tell whether

the price drift before dividend announcement is

due to the dividend premium or dividend

announcement.


20

Excess return over the equally weighted average of dividend-paying

stocks

Excess return over the S&P 500 index

-1.0%

-0.5%

0.0%

0.5%

1.0%

cum

ula

tive

exc

ess

re

turn

days

ex-date

announcement date

-1.0%

-0.5%

0.0%

0.5%

1.0%

cum

ula

tive

exc

ess

re

turn

days

ex-date

announcement date


Deutsche Bank


VII. The asymmetric payoff pattern and shorting

Alpha from the long and the short — Long portfolio excess return: long the top quartile

stocks (equally weighted) against the average (or

median) return of our investment universe (which is

equivalent to shorting a basket of all stocks in our

universe, equally weighted)

— Short portfolio excess return: short the bottom

quartile stocks (equally weighted) against the average

(or median) return of our investment universe (which is

equivalent to using the proceeds from our short positions

to fund a long portfolio of all stocks in our universe,

equally weighted)


21

Asymmetric patterns

Price momentum Earnings yield

0

1

2

3

4

5

6

7

8Long portfolio excess return

Short portfolio excess return

0

1

2

3

4

5

6

7

8Long portfolio excess return

Short portfolio excess return


Deutsche Bank


Most factors’ payoff patterns are asymmetric

Not all factors are created

equally

— These factors are sorted based

on the spread between “short

excess return” and “long excess

return”.

— The higher up on the list, the

more difficult to capture the

alpha, due to heavier demand for

shorting and likely higher

shorting cost (shorting cost will

be discussed in the next section).

— Value factors generally collect

their premia from the long side,

while price momentum/reversal

and quality factors generate

more alpha from the short side.

Analyst revision factors tend to

show more symmetric payoff

patterns.


22

The asymmetric payoff pattern

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%

EBITDA to EVEarnings yield x IBES 5Y growth

Operating earnings yield, trailing 12MEarnings yield, forecast FY1 meanEarnings yield, forecast FY2 mean

Free cash flow yieldTotal return, 1260D (60M)

Price to BookOperating cash flow yield

IBES FY1 Mean EPS Revision, 3MMean recommendation revision, 3M

Cash flow return on investmentsMoving average crossover, 15W-36W

Asset Turnover# of days to cover short

Sales to EVPrice to SalesGross margin

Short interest/floatYear-over-year quarterly EPS growth

Return on EquityCash flow return on capitalCash flow return on equity

Return on capitalReturn on Assets

YoY change in # of shares outstandingRealized vol, 1Y daily12M-1M total return

Short portfolio excess return Long portfolio excess return


Deutsche Bank


Accounting for short availability

Using DataExplorer’s global stock lending database


23

Coverage for the cost of borrow score (DCBS) from DataExplorer Cost-of-borrow score composition

% of hard-to-borrow names vs. short portfolio performance Performance with and without short constraints, N-LASR model

0

500

1000

1500

2000

2500

3000

3500

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

DCBS=10

DCBS=9

DCBS=8

DCBS=7

DCBS=6

DCBS=5

DCBS=4

DCBS=3

DCBS=2

DCBS=1

Asset Turnover

Sales to EV Cash flow return on equity

# of days to cover short

Short interest/floatPrice to Sales

Target price implied return

Payout on EPS

Abnormal volumeRealized vol, 1Y daily

MomentumPE

Correlation = 20.2%

0%

2%

4%

6%

8%

10%

12%

0% 10% 20% 30% 40% 50% 60%

Sho

rt p

ort

folio

exc

ess

ret

urn

Percentage of hard to borrow names

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Assuming we can short any stock

Assuming we can only short easy-to-borrow stocks


Deutsche Bank


High conviction or diversification

How many stocks do we need to hold

— One popular view in the investment world,

especially a view shared by many fundamental

investors, is that we should fully take advantage

of our “high conviction” ideas; therefore, a more

concentrated portfolio is more desirable than a

portfolio holding hundreds of stocks. On the

other hand, some managers (more likely to be

quant) believe in diversification and typically

hold fairly diversified portfolios.

— Let’s use our N-LASR global stock selection

model (which has shown great live performance)

as an example.

— Without short constraint, as we hold more and

more diversified portfolios, alpha (i.e., active

return) goes down.

— With short constraint, as our portfolio becomes

more diversified, Sharpe ratio also goes up

significantly.


24

Annualized return, long/short N-LASR portfolios

Sharpe Ratio, long/short N-LASR portfolios

0%

10%

20%

30%

40%

50%

25 names 100 names 400 names



0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

25 names 100 names 400 names




Deutsche Bank


A hands-on tutorial

How to build a realistic model

— Five common factors:

— Value (trailing 12 month earnings yield)

— Growth (year-over-year quarterly EPS growth)

— Quality (ROE)

— Momentum (12-1M total return), and

— Sentiment (IBES three-month earnings revision)

— How to avoid the seven sins

— Survivorship bias. We perform our backtesting on the Russell 3000 index universe, using those companies in the index

as of a given point in time.

— Look-ahead bias. We use point-in-time data to calculate all of our factors. Company fundamental data is sourced from

Compustat point-in-time database, which reflects whatever was available at each month end.

— Story telling and data history. We follow the convention for the direction of each factor: buying stocks that are cheaper,

that enjoy higher growth, that are more profitable, that have stronger price momentum, and that have more positive

analyst sentiment. Our backtesting is conducted over the past 20 years, from 1994 to 2014, covering multiple economic

cycles.

— Data mining and data snooping bias. The four factor weighting algorithms are extensively tested across multiple

countries/regions and asset classes.

— Signal decay and turnover. We avoid fast decay factors in this exercise. Portfolio performance is computed after

transaction costs.

— Outlier control. We use our proprietary data normalization technique to transform each factor to a standard normal

distribution, before we combine them together into multi-factor models.

— The asymmetric payoff pattern and shorting cost. We study the impact of short availability in detail in this section.

25


Deutsche Bank


Comparing factor weighting algorithms


26

Average rank IC Risk adjusted rank IC

Grinold & Kahn factor weights Alpha risk parity factor weights Minimum tail dependence factor weights

— Equally weighting

— Grinold & Kahn (i.e.,

mean-variance

optimization)

— Alpha risk parity

— Minimum tail dependence

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Quality Growth Value Momentum Sentiment

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


… and the winner is – our proprietary minimum tail dependence model

0%

1%

2%

3%

4%

5%

6%

0.00

0.10

0.20

0.30

0.40

0.50

0.60


Deutsche Bank


Country and sector neutralization

Risk control at the model building stage

— Traditionally, analysts focus on model building,

while portfolio managers are responsible for risk

control at the portfolio level. In this section, we

show the benefit of adding some risk control at

the alpha model construction stage.

— Company characteristics (e.g., valuation, growth

profile, and profitability) vary greatly from

country to country, and from industry to industry.

A model that ranks stocks regardless of their

country/sector essentially engages in not only

stock selection, but also country/sector rotation.

— One way to make our stock selection model

more robust and less volatile is to control for

country/sector difference via a technique called

neutralization.


27

Average rank IC

Risk adjusted rank IC

0%

1%

2%

3%

4%

5%

6%

Orignal Sector neutral

Equal weight GKW Alpha Risk Parity Min Tail Dependence

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Orignal Sector neutral



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Smart beta investing via factor portfolios

Each factor portfolio is constructed on mean-variance optimization with realistic constraints


28

Annualized return Annualized volatility

Sharpe ratio Maximum drawdown

0%

1%

2%

3%

4%

5%

6%

7%

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-30%

-25%

-20%

-15%

-10%

-5%

0%


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Active portfolio management versus smart beta investing

Pros and cons

— Active portfolio management via multi-factor

models tend to have higher realized risk than

smart beta portfolios. The second stage

optimization in multi factor-portfolios further

reduces risk.

— Active portfolio management via multi-factor

models tend to produce higher Sharpe ratios –

especially with more sophisticated portfolio

construction techniques like alpha risk parity and

minimum tail dependence, as these models are

more efficient than multi factor-portfolios.

— The biggest benefit of smart beta via multi

factor-portfolios is that it empowers asset

owners by providing additional investment

instruments to their asset allocation strategies.

— To add value, active managers need to have

more unique and proprietary factors in their

multi-factor models.


29

Realized portfolio risk

Sharpe ratio

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

Active portfolio management via “multi-factor models”

Smart beta investing via “multi factor-portfolios”


0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Active portfolio management via “multi-factor models”

Smart beta investing via “multi factor-portfolios”



Deutsche Bank


26/01/2015 08:18:11 2010 DB Blue template

Appendix 1 Important Disclosures Additional Information Available upon Request

DOUBLE CLICK IN

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

30


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

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Special Disclosures N/A

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 compensation for providing a specific recommendation or view in this report.

[Yin Luo, Miguel-A Alvarez, Javed Jussa, Sheng Wang, Allen Wang]

Hypothetical Disclaimer

Backtested, hypothetical or simulated performance results discussed herein have inherent limitations. Unlike an actual performance

record based on trading actual client portfolios, simulated results are achieved by means of the retroactive application of a backtested

model itself designed with the benefit of hindsight. Taking into account historical events the backtesting of performance also differs

from actual account performance because an actual investment strategy may be adjusted any time, for any reason, including a

response to material, economic or market factors. The backtested performance includes hypothetical results that do not reflect the

reinvestment of dividends and other earnings or the deduction of advisory fees, brokerage or other commissions, and any other

expenses that a client would have paid or actually paid. No representation is made that any trading strategy or account will or is likely

to achieve profits or losses similar to those shown. Alternative modeling techniques or assumptions might produce significantly different

results and prove to be more appropriate. Past hypothetical backtest results are neither an indicator nor guarantee of future returns.

Actual results will vary, perhaps materially, from the analysis.

31


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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 “Disc losures 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.

3. Country-Specific Disclosures Australia & New Zealand: This research, and any access to it, is intended only for "wholesale clients" within the meaning of the Australian Corporations Act and New Zealand Financial Advisors Act respectively. EU countries: Disclosures relating to our obligations under MiFiD can be found at http://www.globalmarkets.db.com/riskdisclosures. Japan: Disclosures under the Financial Instruments and Exchange Law: Company name - Deutsche Securities Inc. Registration number - Registered as a financial instruments dealer by the Head of the Kanto Local Finance Bureau (Kinsho) No. 117. Member of associations: JSDA, Type II Financial Instruments Firms Association, The Financial Futures Association of Japan, Japan Investment Advisers Association. Commissions and risks involved in stock transactions - for stock transactions, we charge stock commissions and consumption tax by multiplying the transaction amount by the commission rate agreed with each customer. Stock transactions can lead to losses as a result of share price fluctuations and other factors. Transactions in foreign stocks can lead to additional losses stemming from foreign exchange fluctuations. "Moody's", "Standard & Poor's", and "Fitch" mentioned in this report are not registered credit rating agencies in Japan unless “Japan” or "Nippon" is specifically designated in the name of the entity. Russia: This information, interpretation and opinions submitted herein are not in the context of, and do not constitute, any appraisal or evaluation activity requiring a license in the Russian Federation.

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Global Disclaimer The information and opinions in this report were prepared by Deutsche Bank AG or one of its affiliates (collectively "Deutsche Bank"). The information herein is believed to be reliable and has been obtained from public sources believed to be reliable. Deutsche Bank makes no representation as to the accuracy or completeness of such information.

Deutsche Bank may engage in securities transactions, on a proprietary basis or otherwise, in a manner inconsistent with the view taken in this research report. In addition, others within Deutsche Bank, including strategists and sales staff, may take a view that is inconsistent with that taken in this research report.

Opinions, estimates and projections in this report constitute the current judgement of the author as of the date of this report. They do not necessarily reflect the opinions of Deutsche Bank and are subject to change without notice. Deutsche Bank has no obligation to update, modify or amend this report or to otherwise notify a recipient thereof in the event that any opinion, forecast or estimate set forth herein, changes or subsequently becomes inaccurate. Prices and availability of financial instruments are subject to change without notice. This report is provided for informational purposes only. It is not an offer or a solicitation of an offer to buy or sell any financial instruments or to participate in any particular trading strategy. Target prices are inherently imprecise and a product of the analyst judgement.

As a result of Deutsche Bank’s March 2010 acquisition of BHF-Bank AG, a security may be covered by more than one analyst within the Deutsche Bank group. Each of these analysts may use differing methodologies to value the security; as a result, the recommendations may differ and the price targets and estimates of each may vary widely.

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