Deutsche Bank Quantitative Strategies Research: The Wisdom Of Crowds, Crowdsourcing Earnings Estimates

Deutsche Bank
Markets Research
North America
United States
Quantitative Strategy
The Quant View
Date
4 March 2014
The wisdom of crowds:
crowdsourcing earnings estimates
Quantitative macro and micro forecasts for the month
________________________________________________________________________________________________________________
Deutsche Bank Securities Inc.
Note to U.S. investors: US regulators have not approved most foreign listed stock index futures and options for US
investors. Eligible investors may be able to get exposure through over-the-counter products. 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) 054/04/2013.
Sheng Wang
sheng.wang@db.com
Miguel-A Alvarez
miguel-a.alvarez@db.com
Javed Jussa
javed.jussa@db.com
Zongye Chen
john.chen@db.com
Allen Wang
allen-y.wang@db.com
Yin Luo, CFA
yin.luo@db.com
North America: +1 212 250 8983
Europe: +44 20 754 71684
Asia: +852 2203 6990
In this report we present our latest quantitative forecasts for the coming
month. Our models are designed to generate both bottom-up stock selection
ideas as well as top-down asset, country, and style allocation calls.
Introducing the crowdsourcing dataset Estimize
Estimize is an online community that allows different types of investors to
contribute their financial forecasts. The contributors include the buy side
investment professionals, individual traders, independent researchers and
students. The merit of the Estimize dataset is based on the diverse group of
contributors and the wisdom of the crowd.
More accurate short-term earnings estimates
Our initial findings show that the more timely Estimize forecasts provide
greater short-term accuracy when compared to IBES, while IBES estimates do
a better job for longer-term forecasts. Specifically, we find Estimize is more
accurate than IBES for estimates taken one-week before the announcement
date, while the sell-side estimates from IBES show greater accuracy for
estimates collected one-month prior to announcement.
Post earnings drift and a corresponding trading strategy
We find that the timelier Estimize forecasts can more accurately identify
earnings surprise which results in a greater capture of the post earnings drift.
We use this finding to construct a daily trading strategy that goes long the
stocks that beat the Estimize consensus and short the stocks that miss.

4 March 2014
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Page 2 Deutsche Bank Securities Inc.
Crowdsourcing earnings
estimates
Introducing the Estimize dataset
Earnings estimates are one of the most widely used financial metrics. They are a
measure of expected company performance and play an important role in many equity
investors’ stock selection strategies. Traditionally, earnings estimates are gathered from
sell-side analysts at institutional brokers and independent research firms. Data vendors
such as Institutional Brokers’ Estimate System (IBES) aggregate these estimates and
offer daily or monthly updates as well as historical datasets. While there are many data
vendors that aggregate sell-side earnings estimates, we have yet to find a reputable
database that collects estimates from buy-side analysts and other types of investors.
In this report, we analyze a new database from the crowdsourced community Estimize
that collects earnings and revenue forecasts from various different types of investors. It
was established in 2011 and has grown rapidly to cover more than 900 US stocks.
What sets it apart is that the community of contributors is varied, ranging across buy-
side investment professions, individual traders, independent researchers and students.
Figure 1 shows the types of the contributors to the database.
Figure 1: Constituents of the contributors of Estimize
Source: Estimize, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank

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Deutsche Bank Securities Inc. Page 3
Estimize allows individuals to contribute their estimates anonymously. The underlying
concept of the community is to capture the “wisdom of the crowds” in order to reflect
investor sentiment and more timely and accurate earnings forecasts. The data structure
consists of two main parts: estimates and contributors. The estimates are made up of
EPS and revenue forecasts across each individual contributor. The data includes the
contributor’s unique ID, a timestamp for which the estimates were created and the
corresponding fiscal quarter of the forecasts. Most estimates cover the current quarter
(FQ1), but the platform allows for estimates up to the fourth fiscal quarter (FQ4). Each
contributor is assigned a unique ID which makes it possible to track the accuracy for
each individual.
Figure 2 shows the percentage of estimates made within one day, one week (including
the first day), one month and one quarter before the earnings announcement. The chart
shows that 40% of the estimates are made within 24 hours of the announcement, and
the majority of the estimates are made within one week. Few estimates are made a
quarter earlier. This is quite different from IBES, where most of the estimates are
entered at least one-month in advance, lending itself more useful to longer horizon
investors.
Figure 2: Percentage of estimates made before the earnings announcement
0%
20%
40%
60%
80%
100%
1 day 1 week 1 month 1 quarter
%ofestimatesmadebeforetheearningsannouncement
Who’s contributing?
Figure 3 shows that more than two-thirds of the estimates are collected from non
financial professionals. Among the financial professionals, half are independent
researchers and the other half are split evenly between buy-side and sell-side analysts.
The sample data shows that the data covers a diverse range of investors and the
information should be complementary to the traditional institutional data sources such as
IBES.

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The Quant View
Figure 3: Component of the Estimize contributors
30%
70%
Financial Professional Non Professional
48%
26%
26%
independent research
buy side
sell side
Unfortunately, as is the case with many of the newer and unique data sources, the
history of the Estimize dataset is relatively short and coverage is less extensive than
that of traditional sell-side estimate databases such as IBES.
In this report, we focus in most part on the EPS estimates from Estimize and begin our
analysis in 2012 since much of the data prior to that is too sparse.
Figure 4 shows the number of stocks covered in the Estimize database that are
members of the Russell 3000 universe. Coverage is defined by the number of unique
tickers which have at least one estimate on some day in a current fiscal quarter during
that month; regardless of whether or not the company reports during that month.
We find a strong seasonal component in the data due to earnings seasons and the fact
that most estimates are not contributed until one week before the actual announcement
(Figure 2). In addition, stock coverage drops quickly as we increase the number of
required contributors.

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Figure 4: Estimize coverage on the Russell 3000 universe
0
200
400
600
800
1000
1200
numberofstockscoveredbyEstimize
>= 1 analyst
>= 3 analysts
>= 10 analysts
Figure 5 shows the median, 25th percentile, and 75th percentile of the market cap
covered by Estimize over time. The coverage consists mainly of large and midcap US
stocks and the distribution of market cap shows to be steady over the sample.
Figure 6 shows the median market cap of the stocks covered by Estimize across
different numbers of contributor (analyst) coverage. As expected, we find that larger
cap stocks which demand more attention are covered by a larger number of
contributors. This is consistent with the traditional institutional databases in that larger
cap companies will have more analyst coverage.
Figure 5: Market Cap of stocks covered by Estimize (US$
Billion)
Figure 6: Median Market Cap of stocks covered by
Estimize across different analyst coverage (US$ Billion)
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40
MarketCap(US$Billion)
25th percentile Median 75th percentile
0
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>= 1 analyst >= 3 analysts >= 10 analysts >= 20 analysts
MarketCap(US$Billion)
Source: Estimize, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank Source: Estimize, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank

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Gauging the accuracy of crowdsourcing?
Comparing estimates
The first question we must address is how it compares to traditional sell-side estimate
data covered by vendors such as IBES. Can it add value beyond these long existing sell
side analyst forecasts?
To get a sense of the accuracy, we compare the last Estimize EPS forecasts with those
from the daily IBES database for stocks that are available in both datasets. We begin by
comparing the average EPS estimates in each database with actual EPS reported on the
announcement date. Figure 7 shows that over the sample, the average estimate across
the Estimize database was closer to the reported number when compared to the IBES
average estimate. In addition, as the Estimize coverage increases, the forecast accuracy
relative to IBES also increases. EPS estimates for stocks with greater than 20 analysts
covering them in Estimize are more accurate 2/3 of the time.
Figure 7: Estimize EPS estimates all estimates vs. IBES
57%
43%
>=1 analyst
Estimize more accurate
IBES more accurate
61%
39%
>=3 analysts
63%
37%
>=10 analysts
65%
35%
>=20 analysts
However, the greater accuracy of Estimize database is in most part due to its timely
updating. Recall that most Estimize estimates are entered a few days prior to the
earnings announcement (Figure 2), while most IBES estimates are entered several
weeks in advance. For a more apples-to-apples comparison, we compare the estimates
at different horizons.
Figure 8 shows the accuracy of the average estimates at various windows before the
announcement date. The results show that one week before announcement the
accuracy across Estimize and IBES is similar. However, when looking at a one-month
window, IBES estimates tend to be more accurate than those in Estimize. This suggest
that sell-side analysts do a better job at predicting earnings over a longer window while
the more timely Estimize data tends to be more accurate within one week of the
announcement.

4 March 2014
The Quant View
Figure 8: Estimize EPS estimates vs. IBES for longer windows
57%
43%
All estimates
Estimize more accurate
IBES more accurate
54%
46%
1 day before
51%
49%
1 week before
46%
54%
1 month before
Professionals vs. non professionals
We further compare the EPS prediction accuracy of finance professionals with non-
professionals to see if the professionals make more accurate predictions. To our
surprise, the data shows that finance professionals slightly underperform non-
professionals (see Figure 9); albeit the difference is too small to make any significant or
sweeping conclusions. One explanation may be that it is due to selection bias in the
Estimize database – i.e. the more accurate professionals do not contribute their
estimates to the database.
We can also compare the accuracy of the estimates from non-professionals to those of
the combination of professionals and non-professionals (see Figure 10). The results show
that there is kind of diversification effect in that combining the two actually results in
better accuracy than any of two individually.
Figure 9: Finance professional vs non-professional Figure 10: Non-professional vs. all Estimize estimates
49%
51%
finance professional more accurate non-professional more accurate
53%
47%
all Estimize more accurate non-professional more accurate
Buy-side vs. sell-side
Recall there is approximately the same number of estimates from buy-side and sell-side
professionals in the Estimize database (see Figure 3). We next investigate whether there
is a significant difference between these two categories in the database. Figure 11 shows
that average estimates for buy-side professions are more accurate than those from the
sell-side in the Estimize dataset. However, due to the limited sample size in the Estimize
buy side and sell side estimates (Estimize started to label the buy side and sell side

4 March 2014
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estimates start in 2013), it may not be statistically significant to make a definite
conclusion. Similar to the results from professionals versus non-professionals above,
this result could be due to selection bias in that the more accurate sell-side analysts are
not contributing their estimates to the database. Nonetheless, Figure 12 shows that
combining the sell-side and buy-side estimates actually increases accuracy suggesting
a sort of diversification benefit from including both types of professionals in the
Estimize database.
Figure 11: Comparing buy side and sell side in Estimize Figure 12: Sell side add value to buy side estimates
56%
44%
buy side more accurate sell side more accurate
49%
51%
buy side more accurate buy side + sell side more accurate
Figure 13 further compares the difference between Estimize sell side and the IBES sell
side. The results show that IBES sell-side estimates are more accurate than those from
Estimize, which lends some credence to our hypothesis that Estimize sell-side data may
have a level of selection bias. In Figure 14, the performance for IBES sell side compared
with buy side estimates are similar as the sell side compared with buy side in Figure 11.
This is as we expected, since IBES are mostly sell side analysts estimates, so they
should have some similarity with the sell side estimates from Estimize.
Figure 13: IBES compared with sell side from Estimize Figure 14: IBES compared with buy side from Estimize
54%
46%
IBES more accurate sell side from Estimize more accurate
58%
42%
buyside more accurate IBES more accurate
Post earnings announcement surprise
Post earnings drift is the return following an earnings announcement that is attributable
to surprise. Typically, companies who beat earnings consensus tend to outperform the
market over subsequent trading while stocks that miss expectations tend to
underperform the market.

4 March 2014
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To analyze the post earnings drift in both the IBES and Estimize datasets we use an
event study. The day one return of the post earnings announcement is calculated using
the open to close price if the earnings was announce before the market opens; and use
next day open to close if the earnings was announce after the market close. The
following day’s returns are all calculated using close to close price returns. The S&P
500 total return index is used as the market return
Figure 15 and Figure 16 show the average excess return to the market for earnings
surprises greater than 10% for both Estimize and IBES estimates. In both cases the
more timely Estimize estimates shows bigger post announcement drift for both beats
and misses. However, in both cases, the cumulative excess return flattens out quickly
after the a few days, due to market efficiency.
Figure 15: Cumulative excess return when estimates beat
earnings by more over 10%
Figure 16: Cumulative excess return when estimates
miss earnings by more than 10%
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EPS beats Estimize
EPS beats IBES
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Excessreturnwhenmissearnings
EPS misses Estimize
EPS misses IBES
Portfolios based on more accurate earnings estimates
Based on the event study from previous session, we would like to examine the
performance of a portfolio based on the same logic: long stocks that beat consensus
and short the stocks that miss.
As we already saw in Figure 15 and Figure 16, the earnings drift occurs mostly during
the first day of trading after the announcement. For simplicity and illustrative purposes,
we construct this portfolio with a one-day holding period, using the open price to close
price (because the earnings announcements almost always occurs after the market
close). We use SP 500 to hedge when there is no holding in one of the two legs. We
call this the Estimize earnings surprise strategy.
Turnover for this strategy is high because the portfolio changes nearly every time it is
traded. Figure 17 shows the wealth curve for this strategy under different levels of
transaction cost. Naturally, the performance drops quickly as we increase transaction
costs. However, even when transaction costs are 15 bps, the net performance is still
attractive.

4 March 2014
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Figure 17: Wealth curve for different transaction cost of the Estimize earnings surprise strategy
0.0
0.5
1.0
1.5
2.0
2.5
3.0
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WealthCurvefordifferenttransactioncost
5 bps
10 bps
15 bps
We compared the same strategy based on the same earnings surprise measure using
the IBES estimates. Figure 18 show the annualized returns and Figure 19 shows the
Sharpe ratio of the two strategies under different transaction costs. For both strategies,
the performance decreases quickly as transaction costs increase. When transaction
cost increases to 10 bps per trade, the performance of the IBES earning surprise
strategy is nearly zero, and it turns negative once we have t-costs increased to 15bps.
In contrast, the Estimize earnings surprise strategy, shows an annualized return of 12%
under the 15bps t-cost scenario.

4 March 2014
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Figure 18: Annualized return for the earnings surprise
strategy for Estimize and IBES with different cost
Figure 19: Sharpe ratio for the earnings surprise strategy
for Estimize and IBES with different cost
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Annualizedreturn
Estimize
IBES
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Estimize
IBES
In conclusion we found multiple benefits to using the Estimize dataset; especially in the
case of short-term applications in which accuracy is essential. Another interesting
byproduct of the analysis was the power of crowdsourcing. We found that some of the
value-added in the Estimize dataset was due to the “wisdom of crowds” effect as more
predictions give way to greater accuracy. Moreover, the diversity of the contributors
provides a greater spectrum of information which can potentially improve investment
strategies based on estimates.
We should also be aware of the potential issues with the Estimize dataset. The main
issue rests on the thin coverage and the short-term nature of the forecasts; especially
when compared to commonly used sell-side estimates data. Also, the short history will
pose a problem when trying to analyze the data across different market and economic
environments.
Please contact us DBEQS.Americas@db.com for more details of the Estimize dataset.

4 March 2014
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Macro update
Turning our attention to the bigger picture, we also take the opportunity to update our
favorite top-down market indicators.
Our favorite market timing indicator
Our Variance Risk Premium (VRP) indicator is a contrarian indicator that measures
market overreaction and underreaction to realized risk. In simple terms, VRP is the
difference between options-implied risk (i.e. the VIX index) and realized risk (i.e. the
actual risk in the market measured historically over the last month). If VRP is high, we
see this as a buying opportunity for risky assets, like equities and high yield bonds.
Why? The intuition is as follows. When VRP is high, VIX has typically shot up
dramatically (i.e. the market is in panic mode). At the same time, realized risk has
probably also risen, but not to the same extent. In other words, the market has
overreacted relative to what the actual, realized data is telling us. Our research shows
that such episodes are good buying opportunities for risky assets on about a three
month horizon. On the other hand, when VRP is low, it tends to be a complacency
indicator: investors are failing to price in rising realized risk in the market, and as a
result we should be selling risky assets like equities.
Our Variance Risk Premium (VRP) indicator is a contrarian indicator that measures
market overreaction and underreaction to realized risk. Today our VRP indicator is
reading 9.1, compared to a long-term average of 14.2. Generally we pay attention to the
VRP when it hits extreme levels (like +/- 2 standard deviations).
Figure 20: Variance Risk Premium (VRP) Figure 21: Recent VRP (lagged) and market returns
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VRP
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S&P 500 Index VRP
High VRP = Buy Risky Assets (e.g. Equities)
Low VRP = Sell Risky Assets (e.g. Equities)
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VRP
S&P500MonthlyReturn
S&P 500 Return VRP (lagged 1M)
High VRP = Buy Risky Assets
Low VRP = Sell Risky Assets
Source: Deutsche Bank Source: Deutsche Bank
The opportunity set for investors
Another metric we keep a close eye on is the so-called “opportunity set” for investors.
Think of this as the total alpha on the table. Our main interest is to understand what is
driving that opportunity, because this can allow us to position our strategies to pick in
the orchard with the juiciest fruit. In Figure 22 we show the opportunity set for global
equity investors, and in Figure 23 we show the same thing for emerging market equity
investors.

4 March 2014
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Figure 22: Global opportunity set Figure 23: Emerging markets opportunity set
0%
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Stock-Specific Global Style Industry Country Currency
0%
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50%
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100%
Stock-Specific EM Global Style Industry Country Currency
Source: Bloomberg Finance LP, Compustat, IBES, MSCI, Russell, S&P, Thomson Reuters, Worldscope,
Deutsche Bank
Deutsche Bank
The key result is the size of the blue portion relative to the other colors. The blue
represents the opportunity explained by stock selection, whereas we can think of the
other colors as representing the opportunity from top-down calls like picking the right
countries, industries, and styles. When the financial crisis exploded in 2008, we moved
into a much more macro-dominated world. As a result, the portion of overall
opportunity that could be explained by individual company characteristics (e.g.
valuation, growth profile, earnings quality, etc.) shrunk sharply; no one cared if a stock
looked good on fundamentals if it was exposed to Europe for example. Needless to say,
such an environment was challenging for quants and non-quants alike, since both
camps tend to use stock specific information to differentiate between stocks.
The small cap opportunity set
We think of the opportunity set as the total available alpha on the table. Our main
interest is to understand what is driving that opportunity, because this can allow us to
position our strategies to pick in the orchard with the juiciest fruit. In Figure 24 we
show the opportunity set for the large cap universe, and in Figure 25 we show the
opportunity set for the small cap universe.
Figure 24: Large cap opportunity set Figure 25: Small cap opportunity set
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RelativeOS(12MAvg)
Stock-Specific Style Industry
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RelativeOS(12MAvg)
Stock-Specific Style Industry
Stock selection
opportunity set is
greater for small
cap stocks
Deutsche Bank
Deutsche Bank
Both charts actually tell a similar story. The key result is the size of the blue portion
relative to the other colors. The blue represents the opportunity explained by stock
selection, whereas we can think of the other colors as representing the opportunity
from top-down calls like picking industries and styles. When the financial crisis
exploded in 2008, we moved into a much more macro-dominated world. As a result,

4 March 2014
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the portion of overall opportunity that could be explained by individual company
characteristics (e.g., valuation, growth profile, earnings quality, etc.) shrunk sharply; no
one cared if a stock looked good on fundamentals if it was exposed to Europe for
example. Needless to say, such an environment was challenging for quants and non-
quants alike, since both camps tend to use stock specific information to differentiate
between stocks.
However, the good news is that both charts show that bottom-up stock picking is
making a strong comeback. The blue area in both charts has reached levels last seen in
2007. The crucial observation is that the relative opportunity coming from stock
selection is higher for small cap stocks. In other words, this universe is particularly
fruitful for managers with skill in picking individual stocks. Note that the relative
opportunity set has remained relatively steady during the past month for small caps.
Valuation spreads
Similar to the opportunity set, valuation spreads allow investors to gauge the level of
stock selection opportunity in the market. Widening valuation spreads typically indicate
more stock-level differentiation and therefore a better environment for stock selection.
On the other hand, narrowing valuation spreads are indicative of lower levels of stock
differentiation. Figure 26 and Figure 27 show the median, 25th percentile, and 75th
percentile of trailing price to earnings for the Russell 1000 and 2000 index constituents.
Interestingly, we see that valuation spreads are wider on a more consistent basis for
small cap stocks. This reinforces the earlier evidence we saw in the opportunity set; the
small cap space is rich with opportunity for skilled stock pickers.
Figure 26: Large cap valuation spreads Figure 27: Small caps valuation spreads
5x
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Trailing12MonthP/ESpread
25th Percentile Median 75th Percentile
Valuation spreads tend to be
narrower on a more consistent basis
5x
10x
15x
20x
25x
30x
35x
40x
45x
50x
Trailing12MonthP/ESpread
Valuation spreads tend to be
wider on a more consistent basis
Deutsche Bank
Deutsche Bank
Keeping an eye on correlations
Closely related to the opportunity set and valuation spreads is the median pairwise
correlation among stocks in the market. This is calculated by taking every possible pair
of stocks, and computing the correlation of their monthly returns based on the past 24
months of data, and then taking the median across all the pairs. Figure 28 shows the
median pairwise correlation for large caps. While it has come down from the peak in
the financial crisis, it is still relatively high compared to its long-term average, so
investors are not yet completely out of the woods. Interestingly, in general median
pairwise correlations for small cap stocks (Figure 29) tend to be lower when compared
to large cap stocks. This tells us that small cap names tend to trade more on their own
merits, rather than being driven by common factors.

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Figure 28: Median pairwise correlation for large caps Figure 29: Median pairwise correlation for small caps
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PairwiseCorrelation
Median pairwise
correlations tend to
be higher
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PairwiseCorrelation
Median pairwise
correlations tend
to be lower
Deutsche Bank
Deutsche Bank

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The DB Quant Dashboard
Which styles have been working around the world?
The DB Quant Dashboard is an easy-to-use cheat sheet that shows which styles have
been working in key markets around the world. We track cumulative factor
performance year-to-date, and highlight what we think are the noteworthy observations
in each region. For those who prefer the previous tabular format (which includes more
factors), you can find those results in the Appendix.
For more details see our website
For the most recent daily factor performance, as well as factor performance delineated
by different universes (e.g., large cap, small cap) and regions, please see our Global
Quantitative Strategy website at https://eqindex.db.com/gqs/. Note that you need a
username and password to log on to this website. If you don’t have login details, please
contact us at DBEQS.Americas@db.com and we’d be happy to set you up.
Figure 30: United States Large Cap (Russell 1000): YTD
cumulative factor performance (Q10-Q1 return spread)
Figure 31: United States Small Caps (Russell 2000): YTD
cumulative factor performance (Q10-Q1 return spread)
0.80
0.85
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0.95
1.00
1.05
31-Dec 5-Jan 10-Jan 15-Jan 20-Jan 25-Jan 30-Jan 4-Feb 9-Feb 14-Feb 19-Feb 24-Feb
Dividend Yield Earnings Yield 12M-1M Momentum
1M Reversal EPS Growth ROE
Low Volatility 3M Earnings Revisions
0.80
0.85
0.90
0.95
1.00
1.05
1.10
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Figure 32: United Kingdom: YTD cumulative factor
performance (Q10-Q1 return spread, local currency)
Figure 33: Europe ex UK: YTD cumulative factor
0.80
0.85
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1.00
1.05
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1.15
0.75
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1.00
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Deutsche Bank
Deutsche Bank
Figure 34: Japan: YTD cumulative factor performance
(Q10-Q1 return spread, local currency)
Figure 35: Asia ex Japan: YTD cumulative factor
0.80
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0.95
1.00
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Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Figure 36: Australia/New Zealand: YTD cumulative factor
Figure 37: Canada: YTD cumulative factor performance
(Q10-Q1 return spread, local currency)
0.80
0.85
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1.00
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0.60
0.65
0.70
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1.00
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1.10
Deutsche Bank
Deutsche Bank
Figure 38: Emerging Markets: YTD cumulative factor
Figure 39: Developed Markets: YTD cumulative factor
0.88
0.90
0.92
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0.96
0.98
1.00
1.02
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1.06
1.08
0.86
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0.96
0.98
1.00
1.02
1.04
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Bottom-up stock selection
QCD U.S. stock selection model
 The QCD model is our flagship stock selection model for U.S. equities.
 The model incorporates a number of unique features including dynamic factor
selection, a non-linear TREE component, and active style and sector rotation.
 For complete details on the model, please see Luo et al., “QCD Model: DB
Quant Handbook”, 22 July 2010.
Current stock recommendations
Figure 40 shows the best 20 buy ideas and sell ideas from today’s model. Note that a
complete ranking for all Russell 3000 stocks is available in spreadsheet format. If you
would like to get a copy of the spreadsheet, please contact us at
DBEQS.Americas@db.com.
Figure 40: Current QCD model stock recommendations
BEST BUY IDEAS (SECTOR NEUTRAL) BEST SELL IDEAS (SECTOR NEUTRAL)
Ticker Name CUSIP GICS Sector
QCD Score
(higher is better long) Ticker Name CUSIP GICS Sector
QCD Score
(lower is better short)
DOW DOW CHEMICAL 260543103 Materials 15.4% BODY BODY CENTRAL CORP 09689U102 Consumer Discretionary -24.0%
FOE FERRO CORP 315405100 Materials 14.4% UNXL UNI-PIXEL INC 904572203 Information Technology -21.9%
FDML FEDERAL-MOGUL CORP 313549404 Consumer Discretionary 12.5% NSM NATIONSTAR MORTGAGE HOLDINGS63861C109 Financials -21.9%
GNTX GENTEX CORP 371901109 Consumer Discretionary 12.3% TWGP TOWER GROUP INTL LTD G8988C105 Financials -21.7%
VZ VERIZON COMMUNICATIONS INC 92343V104 Telecommunication Services 12.2% ACFN ACORN ENERGY INC 4848107 Industrials -21.6%
ALJ ALON USA ENERGY INC 20520102 Energy 11.8% PSMI PEREGRINE SEMICONDUCTOR CORP71366R703 Information Technology -19.6%
INT WORLD FUEL SERVICES CORP 981475106 Energy 10.5% WTSL WET SEAL INC 961840105 Consumer Discretionary -18.8%
AFFX AFFYMETRIX INC 00826T108 Health Care 10.4% BIOL BIOLASE INC 90911108 Health Care -17.3%
MRC MRC GLOBAL INC 55345K103 Industrials 10.0% FCSC FIBROCELL SCIENCE INC 315721209 Health Care -17.0%
T AT&T INC 00206R102 Telecommunication Services 9.9% ACTG ACACIA RESEARCH CORP 3881307 Industrials -16.7%
TEX TEREX CORP 880779103 Industrials 9.4% KIOR KIOR INC 497217109 Energy -15.4%
NYLD NRG YIELD INC 62942X108 Utilities 9.3% FWM FAIRWAY GROUP HOLDINGS 30603D109 Consumer Staples -14.9%
PKI PERKINELMER INC 714046109 Health Care 9.0% VLGEA VILLAGE SUPER MARKET -CL A 927107409 Consumer Staples -12.9%
KMB KIMBERLY-CLARK CORP 494368103 Consumer Staples 8.7% AMRS AMYRIS INC 03236M101 Energy -10.7%
DYN DYNEGY INC 26817R108 Utilities 8.2% MCP MOLYCORP INC 608753109 Materials -9.1%
CNSI COMVERSE INC 20585P105 Information Technology 7.7% ANV ALLIED NEVADA GOLD CORP 19344100 Materials -8.4%
CL COLGATE-PALMOLIVE CO 194162103 Consumer Staples 7.6% NIHD NII HOLDINGS INC 62913F201 Telecommunication Services -8.2%
TTWO TAKE-TWO INTERACTIVE SFTWR 874054109 Information Technology 7.0% IRDM IRIDIUMCOMMUNICATIONS INC 46269C102 Telecommunication Services -6.9%
ETFC E TRADE FINANCIAL CORP 269246401 Financials 6.2% WGL WGL HOLDINGS INC 92924F106 Utilities -3.5%
PZN PZENA INVESTMENT MANAGEMENT 74731Q103 Financials 5.5% SJW SJW CORP 784305104 Utilities -2.1%
Source: Bloomberg Finance LP, Compustat, IBES, MSCI, Russell, S&P, Thomson Reuters, Worldscope, Deutsche Bank
Current sector recommendations
The QCD model also implicitly makes sector predictions. Figure 41 shows the current
ranking of the 10 GICS Level 1 Sectors, ranked from best (most likely to outperform this
month) to worse (least likely to outperform). The bars show the key drivers for each call.

4 March 2014
The Quant View
Figure 41: Current QCD sector recommendations
(1.2)
(1.0)
(0.8)
(0.6)
(0.4)
(0.2)
0.0
0.2
0.4
0.6
0.8
1.0
Industrials Health Care Materials Energy Telecom. Utilities Info. Tech. Cons. Staples Financials Cons. Discr.
ExpectedReturn(%)
Value Growth Momentum Sentiment Quality Technical Industry Tree QCD
Model performance
The QCD model has performed well since inception. Figure 42 shows the pure signal
performance, measured as a monthly sector-neutral rank information coefficient (IC).
Figure 43 shows the performance of an actual model portfolio, after costs, based on a
realistically optimized market-neutral strategy.
Figure 42: Model performance, sector-neutral rank IC Figure 43: Model portfolio active return, after costs
(30.0)
(20.0)
(10.0)
0.0
10.0
20.0
30.0
40.0
Sector-NeutralRankIC
Sector-Neutral Rank IC 12M Avg
(4.0)
(2.0)
0.0
2.0
4.0
6.0
8.0
ActiveReturn
Active Return 12M Avg
Deutsche Bank
Deutsche Bank
Figure 44 shows the cumulative performance of the optimized strategy, and Figure 45
shows the annualized Sharpe ratio (after costs) by calendar year.

4 March 2014
The Quant View
Figure 44: Model portfolio cumulative, after costs Figure 45: Annualized Sharpe ratio, after costs
0
200
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1400
(2.0)
(1.0)
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SharpeRatio
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
N-LASR global stock selection model
 The N-LASR model is our flagship stock selection model for global equities.
 The model is based on a machine learning algorithm called AdaBoost, and is
designed to adaptively learn which factors to use, often in a non-linear way.
 For complete details on the model, please see Wang et al., “Signal Processing:
The Rise of the Machines”, 5 June 2012.
Current stock recommendations
Figure 46 shows the best 20 buy ideas and sell ideas from today’s model. Note that a
complete ranking for all global stocks is available in spreadsheet format. If you would
like to get a copy of the spreadsheet, please contact us at DBEQS.Americas@db.com.
Figure 46: Current N-LASR model stock recommendations
BEST BUY IDEAS BEST SELL IDEAS
Ticker Name SEDOL County
N-LASR Score
(higher is better long) Ticker Name SEDOL County
N-LASR Score
(lower is better short)
1928 HK Sands China Ltd. B5B23W Hong Kong 3.09 005690 KS Pharmicell Co Ltd 698839 Korea -2.43
SAF FP Safran SA B058TZ France 2.65 064260 KS Danal Co B01RWL Korea -2.37
PPG PPG INDUSTRIES INC 2698470 USA 2.53 1903 TT Shihlin Paper Corp 680453 Taiwan -2.29
009240 KS Hanssem Co Ltd 653668 Korea 2.49 4100 JT Toda Kogyo Corp 689350 Japan -2.25
PTTGC TB PTT Global Chemical PCL B67QFW Thailand 2.44 094190 KS ELK Corp/Korea B28VMK Korea -2.24
TNB MK Tenaga Nasional Bhd 690461 Malaysia 2.41 INL IB Indian Infotech & Software Ltd B7F28W India -2.23
AAD GY Amadeus Fire AG 562366 Germany 2.39 8270 HK China Leason CBMGroup Co Ltd B6WVCM China -2.22
STX SEAGATE TECHNOLOGY PLC B58JVZ5 USA 2.38 049550 KS InkTec Co Ltd 651112 Korea -2.21
JAS TB Jasmine International PCL B9GHRJ Thailand 2.31 276 HK Mongolia Energy Corporation Ltd. B02L83 Hong Kong -2.21
9433 JT KDDI Corp 624899 Japan 2.28 1919 JT Yamada SxL Home Co Ltd 649615 Japan -2.21
PNR PENTAIR LTD B8DTTS0 USA 2.27 LIGO SP Lion Gold Corp Ltd B6SZHB Singapore -2.20
MTN SJ MTN Group Ltd 656320 South Africa 2.26 VVUS VIVUS INC 2934657 USA -2.17
TEL TE CONNECTIVITY LTD B62B7C3 USA 2.25 025560 KS Mirae Co 610618 Korea -2.16
LPC IB Lupin Ltd 614376 India 2.23 GARAN TI Turkiye Garanti Bankasi B03MYP Turkey -2.16
HNL. HORIZON NORTH LOGISTICS INC B16TCX4 Canada 2.23 MFRISCOA MM Minera Frisco SAB de CV B3QHKH Mexico -2.16
MDC SJ Mediclinic International Ltd B0PGJF South Africa 2.23 SRPT SAREPTA THERAPEUTICS INC B8DPDT7 USA -2.16
WDC WESTERN DIGITAL CORP 2954699 USA 2.21 BTX BIOTIME INC 2092221 USA -2.16
OCE SJ Oceana Group Ltd 665706 South Africa 2.21 3436 JT Sumco Corp B0M0C8 Japan -2.15
7278 JT Exedy Corp 625041 Japan 2.17 SOCOVESA CI Socovesa SA B284N3 Chile -2.15
CAI IM Cairo Communication SpA 410351 Italy 2.16 JOE ST JOE CO 2768663 USA -2.13
Model performance
The N-LASR model has performed well since inception. Figure 47 shows the average
pure signal performance, measured as a monthly rank information coefficient (IC), in
different regions. Figure 48 shows the performance of a global model portfolio, after
costs, based on a realistically optimized market-neutral strategy.
Figure 47: Regional model performance, average rank IC Figure 48: Global portfolio active return, after costs
0.0
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US EU ex
UK
Asia ex
Japan
Japan EM Canada UK Aus/NZ Global
AverageRankIC(%)
Long-Term Average Rank IC 12M Average Rank IC
(6.0)
(4.0)
(2.0)
0.0
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8.0
ActiveReturn
Active Return 12M Avg
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Figure 49 shows the cumulative performance of the optimized strategy, and Figure 50
shows the annualized Sharpe ratio (after costs) by calendar year.
Figure 49: Global portfolio cumulative, after costs Figure 50: Annualized Sharpe ratio, after costs
0
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1200
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1600
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Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Top-down country rotation
CCRM country rotation model
 Our Composite Country Rotation Model (CCRM) uses three sets of inputs to
dynamically rotate between countries in the MSCI All Country World Index.
 The inputs include top-down macro signals (e.g. VRP, Kelly’s Tail Risk),
aggregate bottom-up fundamental signals (e.g. country-level valuation and
momentum), and lead-lag signals based on economic trade linkages.
 For complete details on the model, please see Luo et al., “Signal Processing:
New Insights in Country Rotation”, 9 February 2012.
Current recommendations
Figure 51 and Figure 52 show the top and bottom third of countries, as ranked currently
by our CCRM model. The bars show what is driving these calls.
Figure 51: Top tercile countries Figure 52: Bottom tercile countries
(4.0)
(3.0)
(2.0)
(1.0)
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Kelly VRP MCRM Momentum Valuation Sentiment CCRM
(6.0)
(5.0)
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(2.0)
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Kelly VRP MCRM Momentum Valuation Sentiment CCRM
Deutsche Bank
Deutsche Bank
Model performance
Figure 53 and Figure 54 show the performance of the model over time.
Figure 53: Long/short quantile portfolio return Figure 54: Model performance with rank IC
-10
-5
0
5
10
03 04 05 06 07 08 09 10 11 12 13 14
Long/short quantile portfolio return (%), Ascending order
12-month moving average
Composite CRM, equally w eighted six-factor model
(%)
Avg = 1.06%
Std. Dev. = 3.19%
Min = -9.51%
Avg/Std. Dev.= 0.33 -80
-40
0
40
80
03 04 05 06 07 08 09 10 11 12 13 14
Pearson IC (%), Ascending order
12-month moving average
Composite CRM, equally w eighted six-factor model
(%)
Avg = 9.34%
Std. Dev. = 28.59%
Min = -61.36%
Avg/Std. Dev.= 0.33
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Top-down asset allocation
Quant Tactical Asset Allocation (QTAA) model
 Our Quantitative Tactical Asset Allocation (QTAA) model uses a model-of-
models methodology to rotate between six asset classes.
 The model uses a wide range of fundamental and market-based factors as
inputs, and dynamically selects a subset of those factors to use at each point in
time.
Quant Tactical Asset Allocation”, 19 September 2011.
Current recommendations and performance
Figure 55 shows the current ranking of our six asset classes, ranked from best to worse
in terms of month-ahead forecast returns. Figure 56 shows the monthly performance of
the QTAA model over time.
Figure 55: Current QTAA forecasts Figure 56: Performance of QTAA model
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Gold
Bond (DB USD Agg. Bond)
Commodities (S&P GSCI)
High Yield (DB USD High Yield)
Equity (S&P 500)
Crude Oil
Forecast Return (%)
-100
-50
0
50
100
05 06 07 08 09 10 11 12 13 14
Model 10 12-month moving average
Cross sectional IC (%)
(%)
Avg = 6.04%
Std. Dev. = 60.35%
Min = -95.54%
Avg/Std. Dev.= 0.1
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Top-down style rotation
Style rotation model
 Our Style Rotation model dynamically rotates between 12 “typical” quant
factors.
 The model uses market-based and macroeconomic inputs to predict month-
ahead factor returns using a backwards stepwise linear regression model.
Style Rotation”, 7 September 2010.
Current recommendations and performance
Figure 57 shows the current ranking of our 12 factors, ranked from best to worse in
terms of month-ahead forecast performance. Figure 58 shows the monthly
performance of the Style Rotation model over time.
Figure 57: Current style rotation forecasts Figure 58: Performance of style rotation model
(4.0) (2.0) 0.0 2.0 4.0 6.0
Size [Ascending]
3M EPS Revision [Ascending]
Earnings Yield [Ascending]
Lottery Factor [Descending]
Sales to Total Assets [Ascending]
IBES 5Y EPS growth [Ascending]
Accruals [Descending]
12M-1M Momentum [Ascending]
CAPM Idio. Vol [Descending]
Net Ext. Financing/NOA [Descending]
Long-Term Debt/Equity [Ascending]
Price to Book [Descending]
Forecast IC (%)
-100
-50
0
50
100
2000 2002 2004 2006 2008 2010 2012 2014
Style IC 12-month moving average
Linear regression model
(%)
Avg = 12.79%
Std. Dev. = 45.19%
Min = -89.51%
Avg/Std. Dev.= 0.28
Deutsche Bank
Deutsche Bank

4 March 2014
The Quant View
Appendix A: Factor performance
Figure 59: US factor performance, measured as rank IC (Russell 3000 universe)
Since Inception
Current Average IC (%) Avg / # of Avg # of Hit Serial
Factor Name Direction1
# of Stocks Last M 12M Avg 3Y Avg Avg Std Dev Std Dev Max Min p-value2
Months Stocks Rate (%) Corr (%)3
1. Value
1 Dividend yield, trailing 12M Ascending 2,971 (6.29) (2.56) 1.69 2.75 14.34 0.19 42.59 (33.26) 0.00 314 2,875 54.14 99.08
2 Expected dividend yield Ascending 2,971 (6.84) (2.38) 1.94 3.00 14.86 0.20 44.46 (33.89) 0.00 314 2,875 53.50 99.30
3 Price-to-operating EPS, trailing 12M, Basic Descending 2,309 (0.56) 2.00 1.54 2.76 10.32 0.27 30.82 (32.28) 0.00 238 2,354 59.24 95.15
4 Operating earnings yield, trailing 12M, Basic Ascending 2,933 (4.97) 1.62 3.64 4.72 12.92 0.37 47.24 (33.30) 0.00 238 2,874 61.76 96.39
5 Earnings yield, forecast FY1 mean Ascending 2,689 (2.85) 2.33 3.06 4.34 12.25 0.35 48.88 (34.61) 0.00 314 2,543 62.42 94.99
6 Earnings yield, forecast FY2 mean Ascending 2,673 (2.24) 2.23 2.08 3.83 11.86 0.32 47.02 (34.31) 0.00 314 2,444 63.38 94.38
7 Earnings yield x IBES 5Y growth Ascending 1,627 4.18 2.45 1.32 1.82 10.39 0.18 41.11 (26.63) 0.01 238 1,916 59.24 93.41
8 Sector-rel Operating earnings yield, trailing 12M, Basic Ascending 2,933 (4.27) 1.88 3.18 4.23 8.34 0.51 28.96 (14.90) 0.00 238 2,872 68.49 95.97
9 Hist-rel Operating earnings yield, trailing 12M, Basic Ascending 2,394 (3.03) (0.06) 0.37 1.56 6.81 0.23 20.73 (18.74) 0.01 144 2,035 60.42 96.95
10 Operating cash flow yield (income stmt def) Ascending 2,971 (2.92) 1.69 2.42 4.02 10.81 0.37 47.14 (32.67) 0.00 314 2,875 64.65 96.01
11 Cash flow yield, FY1 mean Ascending 1,583 (2.76) 1.85 0.19 2.70 17.40 0.16 66.06 (54.29) 0.01 284 779 57.75 95.46
12 Free cash flow yield Ascending 2,840 (1.77) 3.83 3.97 4.83 7.89 0.61 31.93 (22.64) 0.00 277 2,519 74.73 94.66
13 Price-to-sales, trailing 12M Descending 2,895 0.05 2.01 1.15 1.81 10.93 0.17 30.02 (41.46) 0.00 314 2,800 57.01 99.06
14 Price-to-book Descending 2,854 (4.30) (2.52) (1.09) 0.74 10.59 0.07 26.28 (35.75) 0.22 314 2,765 48.73 97.52
15 EBITDA/EV Ascending 2,942 (3.69) 1.57 2.14 4.08 9.67 0.42 39.32 (27.15) 0.00 314 2,822 67.52 95.47
16 Price-to-book adj for ROE, sector adj Descending 2,606 (1.15) 0.11 (0.65) 0.47 8.66 0.05 22.50 (33.21) 0.34 314 2,437 49.04 95.65
2. Growth
17 Hist 5Y operating EPS growth Descending 2,841 5.11 1.60 3.07 1.10 8.59 0.13 30.58 (22.70) 0.06 226 2,736 53.54 97.33
18 Hist 5Y operating EPS acceleration Ascending 2,841 (5.42) (0.13) 0.59 0.79 6.59 0.12 25.31 (16.13) 0.07 226 2,736 53.98 94.84
19 IBES 5Y EPS growth Ascending 2,391 4.95 2.89 2.51 0.98 8.04 0.12 21.65 (27.86) 0.03 314 2,300 54.78 98.25
20 IBES 5Y EPS growth/stability Ascending 2,391 1.96 3.01 2.64 1.39 7.64 0.18 20.64 (19.20) 0.00 314 2,300 57.32 98.60
21 IBES LTG EPS mean Descending 1,882 (2.67) (2.53) (0.55) 1.57 15.69 0.10 37.64 (52.38) 0.08 314 2,145 49.36 97.70
22 IBES FY2 mean DPS growth Ascending 2,110 (1.47) 0.04 1.59 0.84 8.46 0.10 24.12 (21.96) 0.24 141 1,520 50.35 87.33
23 IBES FY1 mean EPS growth Ascending 2,627 2.64 1.98 1.35 1.08 7.41 0.15 20.76 (24.42) 0.01 314 2,521 61.78 88.93
24 Year-over-year quarterly EPS growth Ascending 2,949 3.23 1.75 2.60 2.52 6.95 0.36 23.85 (21.12) 0.00 238 2,879 66.39 81.54
25 IBES FY1 mean CFPS growth Descending 1,118 (4.72) (1.88) (1.00) 0.35 11.05 0.03 38.08 (42.07) 0.62 241 550 50.21 92.74
26 IBES SUE, amortized Ascending 2,526 2.88 2.01 1.77 0.82 6.41 0.13 20.62 (16.30) 0.04 253 1,128 54.55 73.90
3. Price Momentum and Reversal
27 Total return, 1D Descending 2,971 (0.90) 2.17 2.97 4.95 7.13 0.69 15.52 (33.75) 0.00 314 2,876 77.71 1.57
28 Total return, 21D (1M) Descending 2,971 (2.69) 0.17 0.45 1.85 10.80 0.17 29.03 (43.69) 0.00 314 2,875 58.28 0.52
29 Maximum daily return in last 1M(lottery factor) Descending 2,968 (7.50) (0.06) 3.43 4.98 14.83 0.34 39.13 (56.07) 0.00 314 2,750 63.69 53.90
30 21D volatility of volume/price Descending 2,971 3.89 0.82 1.71 0.26 6.56 0.04 24.16 (16.78) 0.48 314 2,865 51.27 56.06
31 Total return, 252D (12M) Ascending 2,826 4.92 2.77 3.11 3.23 13.95 0.23 39.62 (57.00) 0.00 314 2,793 64.97 89.96
32 12M-1Mtotal return Ascending 2,826 4.97 2.95 3.52 4.07 13.06 0.31 37.65 (49.06) 0.00 314 2,793 65.61 88.49
33 Price-to-52 week high Ascending 2,861 (1.64) 1.35 3.50 3.08 17.61 0.17 49.63 (62.50) 0.00 314 1,964 61.46 83.24
4. Sentiment
35 IBES LTG Mean EPS Revision, 3M Ascending 1,862 0.12 0.33 0.84 0.85 3.73 0.23 11.16 (12.06) 0.00 314 2,117 61.78 59.65
36 IBES FY1 Mean EPS Revision, 3M Ascending 2,663 0.25 0.99 1.74 2.88 8.37 0.34 29.96 (33.00) 0.00 314 2,482 66.88 75.18
37 IBES FY1 EPS up/down ratio, 3M Ascending 2,462 0.85 1.39 1.95 3.04 7.79 0.39 27.54 (24.41) 0.00 314 2,346 67.52 79.45
38 Expectation gap, short-term - long-term Descending 2,138 2.39 0.07 1.70 1.18 5.18 0.23 9.60 (19.91) 0.00 314 2,124 57.96 91.10
39 IBES FY1 Mean CFPS Revision, 3M Ascending 1,523 1.10 2.57 2.23 2.02 15.79 0.13 69.38 (75.04) 0.03 283 711 62.54 64.26
40 IBES FY1 Mean SAL Revision, 3M Ascending 2,617 (1.61) 1.32 1.80 1.08 7.74 0.14 27.43 (24.32) 0.04 213 2,186 60.09 71.40
41 IBES FY1 Mean FFO Revision, 3M Ascending 142 (10.32) (2.53) 0.26 2.68 20.89 0.13 71.43 (80.00) 0.03 286 85 56.99 69.24
42 IBES FY1 Mean DPS Revision, 3M Ascending 1,296 0.44 1.70 1.52 0.78 5.15 0.15 14.91 (17.55) 0.08 138 1,012 58.70 62.85
43 IBES FY1 Mean ROE Revision, 3M Ascending 2,122 1.68 1.58 0.93 0.73 6.48 0.11 23.70 (22.19) 0.19 138 1,734 59.42 66.38
44 Recommendation, mean Descending 2,699 3.52 3.13 2.22 0.91 7.48 0.12 21.85 (19.41) 0.06 243 2,679 57.61 94.43
45 Mean recommendation revision, 3M Descending 2,696 (1.43) 0.85 0.40 1.22 4.06 0.30 19.86 (11.55) 0.00 240 2,666 62.92 60.10
46 Target price implied return Ascending 2,657 3.84 2.17 0.11 0.26 16.55 0.02 60.74 (39.59) 0.84 179 2,470 54.19 80.07
47 Mean target price revision, 3M Ascending 2,652 0.64 2.64 2.11 2.39 12.44 0.19 30.14 (41.94) 0.01 176 2,457 63.64 74.99
5. Quality
48 ROE, trailing 12M Ascending 2,834 (1.20) 1.75 3.20 3.79 10.03 0.38 33.42 (29.52) 0.00 238 2,862 64.71 96.34
49 Return on invested capital (ROIC) Ascending 2,922 (2.02) 1.49 3.36 4.09 10.21 0.40 33.02 (31.24) 0.00 238 2,857 68.49 98.04
50 Sales to total assets (asset turnover) Ascending 2,890 2.09 2.60 1.67 1.62 8.66 0.19 22.78 (22.02) 0.00 314 2,815 56.37 99.45
51 Operating profit margin Ascending 2,881 (2.44) (0.23) 0.33 1.15 5.44 0.21 16.98 (14.17) 0.00 314 2,722 59.55 98.44
52 Current ratio Descending 2,279 0.55 0.46 0.86 1.78 10.14 0.18 31.95 (38.66) 0.00 314 2,240 54.14 97.89
53 Long-term debt/equity Ascending 2,829 2.85 0.48 1.76 0.79 9.54 0.08 35.65 (28.14) 0.14 314 2,749 48.73 98.41
54 Altman's z-score Ascending 2,175 (1.51) 0.85 1.79 0.31 9.10 0.03 31.74 (30.44) 0.55 314 2,160 49.04 98.35
55 Merton's distance to default Ascending 2,258 (2.62) 1.22 2.99 3.27 11.67 0.28 33.03 (41.45) 0.00 314 2,336 65.29 95.02
56 Ohlson default model Descending 2,208 (0.98) (0.78) 1.03 2.24 6.33 0.35 16.95 (18.63) 0.00 277 2,127 67.51 98.29
57 Accruals (Sloan 1996 def) Descending 2,138 1.55 (0.30) (0.39) 0.51 4.15 0.12 12.07 (15.48) 0.03 314 2,139 54.78 88.58
58 Firm-specific discretionary accruals Descending 1,186 1.48 (1.07) (0.41) 0.44 3.27 0.13 9.42 (10.87) 0.03 254 2,108 55.51 78.52
59 Hist 5Y operating EPS stability, coef of determination Ascending 2,841 5.28 2.03 1.17 0.88 4.99 0.18 20.01 (12.27) 0.01 226 2,736 53.10 96.93
60 IBES 5Y EPS stability Descending 2,391 (6.13) 0.62 1.57 1.14 8.54 0.13 25.00 (34.33) 0.02 314 2,300 53.50 98.96
61 IBES FY1 EPS dispersion Descending 2,689 1.56 0.82 2.79 1.51 9.01 0.17 31.67 (25.17) 0.00 314 2,543 59.55 84.21
62 Payout on trailing operating EPS Ascending 2,228 (6.45) (4.07) (0.21) 0.65 13.45 0.05 38.55 (30.91) 0.40 314 2,212 49.04 98.94
63 YoY change in # of shares outstanding Descending 2,872 0.23 1.37 3.00 2.56 8.85 0.29 19.53 (46.21) 0.00 314 2,771 60.83 94.19
64 YoY change in debt outstanding Descending 2,177 (1.07) (0.56) (0.18) 0.25 4.07 0.06 13.07 (10.40) 0.28 314 2,220 55.41 90.00
65 Net external financing/net operating assets Ascending 2,956 3.22 0.44 2.23 2.40 8.43 0.29 44.61 (21.76) 0.00 314 2,838 61.15 94.76
66 Piotroski's F-score Ascending 2,971 (0.16) 1.59 2.55 2.89 8.04 0.36 29.20 (27.83) 0.00 314 2,878 67.52 88.31
67 Mohanram's G-score Ascending 536 (0.45) 0.11 1.47 2.55 10.44 0.24 35.27 (32.14) 0.00 226 389 56.19 95.49
6. Technicals
68 # of days to cover short Descending 319 1.35 (0.26) 1.35 2.14 7.28 0.29 33.80 (25.16) 0.00 314 2,033 58.60 91.43
69 CAPMbeta, 5Y monthly Descending 2,970 (6.92) (1.24) 0.24 0.93 13.65 0.07 40.19 (42.70) 0.28 255 2,910 50.59 97.67
70 CAPMidosyncratic vol, 1Y daily Descending 2,970 (4.36) 0.18 4.71 5.05 17.96 0.28 42.60 (60.80) 0.00 302 2,882 61.26 99.16
71 Realized vol, 1Y daily Descending 2,869 (7.11) 0.42 4.65 4.89 18.58 0.26 42.69 (59.63) 0.00 314 2,793 60.51 99.14
72 Skewness, 1Y daily Descending 2,869 (5.15) (0.98) (0.04) 1.18 5.33 0.22 13.93 (22.86) 0.00 314 2,793 56.37 89.76
73 Kurtosis, 1Y daily Descending 2,869 (4.37) (0.21) 0.66 1.38 5.50 0.25 15.28 (15.82) 0.00 314 2,793 61.78 91.51
74 Idiosyncratic vol surprise Descending 2,919 (4.50) 1.98 1.49 2.85 7.93 0.36 22.66 (33.71) 0.00 301 2,863 66.78 87.80
75 Normalized abnormal volume Ascending 2,971 6.14 1.41 2.72 2.25 6.36 0.35 23.10 (16.38) 0.00 314 2,868 65.61 64.23
76 Float turnover, 12M Descending 2,971 (12.99) (1.64) (0.12) 0.16 10.70 0.01 23.53 (26.97) 0.80 303 2,789 47.85 99.76
77 Moving average crossover, 15W-36W Ascending 2,871 2.14 2.28 1.30 2.19 13.05 0.17 46.29 (55.07) 0.00 314 2,536 60.51 90.80
78 Log float-adj capitalization Ascending 2,957 10.06 1.88 3.52 3.46 10.91 0.32 29.53 (40.68) 0.00 314 2,872 61.46 99.42
79 # of month in the database Ascending 2,971 (1.34) (0.30) 1.62 2.14 8.77 0.24 35.61 (23.86) 0.00 314 2,875 56.69 99.99
80 DB composite options factor Ascending 1,969 1.56 (0.47) 0.90 1.39 3.61 0.38 13.99 (13.88) 0.00 151 2,023 66.23 23.96
Note:
1 Direction indicates how the factor scores are sorted. Ascending order means higher factors scores are likely to be associated with higher subsequent stock returns, and vice versa for descending order.
2 P-value indicates the statistical significance of the factor's performance. A smaller p-value suggests that is it more likely the factor's performance is different from zero.
3 This is the autocorrelation of the factor scores over time. Higher serial correlation indicates lower portfolio turnover based on the factor.

4 March 2014
The Quant View
Figure 60: Global factor performance, measured as rank IC (S&P BMI World universe)
Since Inception
Current Average IC (%) Avg / # of Avg # of Hit Serial
Factor Name Direction1
# of Stocks Last M 12M Avg 3Y Avg Avg Std Dev Std Dev Max Min p-value2
Months Stocks Rate (%) Corr (%)3
1. Value
1 Dividend yield, trailing 12M Ascending 10,152 (1.60) (1.00) 2.74 4.14 10.48 0.40 36.88 (23.89) 0.00 290 8,051 63.79 97.99
2 Dividend yield, FY1 Ascending 7,859 0.54 (1.40) 2.15 4.16 10.80 0.39 32.17 (22.90) 0.00 233 5,307 63.09 98.19
3 Dividend yield, FY2 Ascending 7,844 1.00 (1.74) 1.78 4.05 10.86 0.37 33.19 (24.39) 0.00 223 5,269 63.23 98.17
4 Price/Earnings Descending 8,434 (5.34) (2.55) (0.51) 3.90 13.01 0.30 39.66 (50.73) 0.00 283 6,342 61.13 96.34
5 Price-to-FY0 EPS Descending 7,792 (2.67) (1.95) (1.01) 2.82 10.20 0.28 28.98 (37.08) 0.00 290 6,063 61.72 96.41
6 Earnings yield, FY0 Ascending 8,904 (2.15) (0.69) 1.24 3.91 9.13 0.43 31.67 (18.68) 0.00 290 7,047 63.79 96.33
7 Earnings yield, forecast FY1 mean Ascending 8,518 (5.84) (0.78) 1.02 4.59 10.82 0.42 35.35 (22.20) 0.00 290 6,498 63.10 95.69
8 Earnings yield, forecast FY2 mean Ascending 8,471 (5.96) (1.48) (0.32) 4.21 11.81 0.36 37.31 (31.50) 0.00 290 6,326 62.07 95.73
9 Cash flow yield, FY0 Ascending 6,911 (3.86) 1.31 0.52 3.91 6.32 0.62 26.42 (11.80) 0.00 166 5,024 74.70 97.16
10 Cash flow yield, FY1 mean Ascending 5,986 (6.72) (0.42) (1.30) 1.95 9.64 0.20 31.42 (32.01) 0.00 222 4,515 57.66 96.01
11 Price/Sales Descending 9,608 (10.24) 0.78 (0.35) 1.46 9.51 0.15 26.48 (31.59) 0.01 290 7,529 55.86 99.24
12 Price/Book Descending 9,685 (13.25) (3.39) (2.29) 1.08 10.43 0.10 31.56 (37.54) 0.08 290 7,578 56.21 98.35
13 Est Book-to-price, median Ascending 7,446 (10.81) (3.75) (3.01) 0.99 9.79 0.10 30.37 (26.29) 0.19 174 5,470 52.30 98.10
14 EBITDA to EV Ascending 7,792 2.99 7.15 5.83 4.02 10.81 0.37 36.69 (26.20) 0.00 290 4,731 62.76 95.60
15 Sales/EV Ascending 9,475 (9.75) 2.84 1.27 1.97 7.85 0.25 24.81 (20.06) 0.00 290 7,494 61.38 99.00
2. Growth
16 IBES 5Y EPS growth Ascending 8,370 2.70 0.14 1.64 1.07 6.09 0.18 19.09 (21.86) 0.00 290 6,255 58.97 98.06
17 EPS Growth Ascending 9,180 0.46 1.04 1.66 2.03 6.80 0.30 29.72 (28.97) 0.00 274 6,931 64.23 88.48
18 IBES LTG EPS mean Descending 4,858 (0.98) (1.62) 0.10 1.29 11.99 0.11 28.22 (40.36) 0.07 290 4,183 52.76 96.73
19 IBES FY1 mean EPS growth Ascending 7,913 (1.89) 1.01 (0.02) 0.38 6.01 0.06 14.44 (20.10) 0.28 290 6,398 54.83 88.58
20 IBES FY1 mean CFPS growth Descending 5,211 0.82 1.78 1.32 1.74 4.17 0.42 7.47 (11.39) 0.00 166 3,956 65.66 91.79
21 IBES FY2 mean DPS growth Ascending 7,823 1.50 (1.08) (0.25) 2.38 10.89 0.22 38.85 (31.49) 0.00 232 5,151 59.05 88.13
22 Asset growth Descending 9,539 0.37 4.04 1.84 0.70 8.44 0.08 21.57 (27.36) 0.16 290 7,324 53.10 93.73
3. Price Momentum and Reversal
23 Total return, 1D Descending 10,163 (1.47) 2.21 3.93 3.58 7.37 0.49 21.94 (41.58) 0.00 290 8,162 71.03 2.00
24 Weekly Total Return Descending 10,163 (8.02) (1.20) 2.09 2.89 8.68 0.33 30.60 (33.64) 0.00 290 8,161 63.79 1.39
27 12M-1Mtotal return Ascending 9,849 2.82 7.54 6.55 5.07 13.87 0.37 40.96 (42.52) 0.00 290 7,963 69.31 88.82
4. Sentiment
29 IBES LTG Mean EPS Revision, 1M Ascending 4,840 (0.70) 1.29 0.58 0.68 2.55 0.27 7.26 (8.59) 0.00 290 4,145 63.45 0.63
30 IBES LTG Mean EPS Revision, 3M Ascending 4,788 (1.24) 1.67 0.94 0.87 3.30 0.26 11.05 (10.26) 0.00 290 4,090 62.07 60.16
31 IBES FY1 EPS up/down ratio, 1M Ascending 4,296 0.71 3.90 3.58 3.67 5.38 0.68 17.76 (13.76) 0.00 290 4,372 76.90 34.67
32 IBES FY1 EPS up/down ratio, 3M Ascending 7,674 (0.23) 4.04 4.00 3.64 5.76 0.63 17.92 (12.36) 0.00 290 5,869 75.17 78.46
33 IBES FY1 Mean EPS Revision, 1M Ascending 8,373 (1.54) 2.86 2.83 2.86 5.04 0.57 16.50 (12.79) 0.00 290 6,349 72.07 24.05
34 IBES FY1 Mean EPS Revision, 3M Ascending 8,239 (1.81) 3.60 3.76 3.36 6.59 0.51 19.37 (20.12) 0.00 290 6,257 73.45 74.15
35 IBES FY1 Mean CFPS Revision, 3M Ascending 5,698 (2.03) 2.08 2.42 2.45 5.50 0.45 15.81 (23.83) 0.00 212 4,335 76.42 63.87
36 IBES FY1 Mean DPS Revision, 1M Ascending 6,188 (2.26) 1.91 2.62 1.74 4.34 0.40 12.65 (16.63) 0.00 231 4,386 71.86 10.89
37 IBES FY1 Mean DPS Revision, 3M Ascending 6,136 (0.57) 3.75 3.77 2.22 5.79 0.38 19.08 (24.51) 0.00 229 4,326 72.49 65.68
38 IBES FY1 Mean FFO Revision, 1M Ascending 7,653 (2.54) 3.14 3.03 2.22 4.02 0.55 11.73 (8.89) 0.00 158 4,175 77.22 13.49
39 IBES FY1 Mean FFO Revision, 3M Ascending 7,484 (0.92) 4.54 4.19 2.84 5.72 0.50 16.27 (14.53) 0.00 155 4,081 74.19 67.90
40 IBES FY1 Mean ROE Revision, 1M Ascending 8,350 (2.83) 1.76 1.93 1.75 4.05 0.43 13.70 (10.51) 0.00 210 5,452 69.05 14.46
41 IBES FY1 Mean ROE Revision, 3M Ascending 8,220 (3.54) 2.38 2.22 2.16 4.98 0.43 13.57 (13.58) 0.00 208 5,323 69.23 68.63
42 Target price implied return Descending 8,492 5.90 3.21 2.26 0.97 14.38 0.07 55.58 (36.25) 0.37 174 6,356 53.45 82.35
43 Recommendation, mean Descending 8,705 0.38 2.67 2.33 1.80 6.75 0.27 17.41 (16.84) 0.00 243 7,228 65.84 94.49
44 Mean recommendation revision, 3M Descending 8,645 (0.23) 1.90 1.52 1.90 2.90 0.65 10.01 (10.13) 0.00 240 7,206 75.42 60.11
5. Quality
45 Return on Equity Ascending 9,432 6.50 1.26 3.58 4.13 10.08 0.41 30.68 (34.69) 0.00 242 7,714 66.53 97.16
46 return on capital Ascending 9,426 5.74 0.66 3.15 4.37 12.20 0.36 49.47 (34.02) 0.00 290 7,014 64.83 97.98
47 Return on Assets Ascending 9,652 5.81 5.11 5.48 4.73 13.19 0.36 44.20 (30.31) 0.00 290 7,117 63.79 98.16
48 Asset Turnover Ascending 9,623 4.99 8.74 5.13 2.73 16.21 0.17 44.64 (51.55) 0.00 290 7,590 57.93 99.85
49 Gross margin Ascending 8,907 5.88 2.18 2.59 1.84 5.81 0.32 16.60 (13.45) 0.00 290 6,904 62.76 98.90
50 EBITDA margin Ascending 9,763 2.00 4.79 4.13 3.97 13.70 0.29 42.97 (41.30) 0.00 290 7,610 59.66 96.80
51 Berry Ratio Ascending 7,469 6.71 (2.45) (0.13) 2.76 9.28 0.30 29.57 (20.79) 0.00 290 5,349 58.97 97.73
52 IBES FY1 EPS dispersion Descending 8,518 (2.43) 4.83 4.21 0.50 9.49 0.05 32.68 (25.37) 0.37 290 6,498 50.69 87.94
53 IBES 5Y EPS growth/stability Ascending 8,370 4.24 0.32 2.04 1.41 5.95 0.24 18.66 (20.47) 0.00 290 6,254 58.62 98.30
54 YoY change in debt outstanding Descending 7,897 (2.12) 1.52 0.60 0.27 3.89 0.07 11.51 (11.34) 0.23 290 6,319 53.79 91.57
55 Current ratio Descending 7,938 (0.45) (1.38) 0.18 0.57 8.82 0.06 27.86 (27.01) 0.27 290 6,188 48.97 98.52
56 Long-term debt/equity Ascending 9,507 4.67 0.81 1.12 0.79 6.42 0.12 22.37 (18.17) 0.04 290 7,502 54.48 98.90
57 Merton's distance to default Ascending 8,079 0.81 3.90 4.28 2.63 11.04 0.24 31.19 (31.18) 0.00 290 6,499 60.34 93.26
58 Capex to Dep Descending 7,730 1.27 6.71 3.72 1.59 6.49 0.24 22.38 (19.93) 0.00 290 5,186 61.72 96.95
6. Technicals
59 Realized vol, 1Y daily Descending 9,853 6.54 5.10 5.51 5.08 15.22 0.33 29.45 (44.64) 0.00 290 7,970 61.38 98.97
60 Skewness, 1Y daily Descending 9,853 1.67 0.76 1.86 1.61 5.29 0.30 15.03 (32.98) 0.00 290 7,970 63.79 90.04
61 Moving average crossover, 15W-36W Ascending 9,654 5.03 6.05 3.75 3.06 14.47 0.21 37.15 (45.46) 0.00 290 6,980 63.10 91.38
62 Normalized abnormal volume Ascending 10,107 (0.31) 3.99 2.93 2.28 6.50 0.35 20.47 (14.71) 0.00 290 7,929 61.03 66.31
Note:
1 Direction indicates how the factor scores are sorted. Ascending order means higher factors scores are likely to be associated with higher subsequent stock returns, and vice versa for descending order.
2 P-value indicates the statistical significance of the factor's performance. A smaller p-value suggests that is it more likely the factor's performance is different from zero.
3 This is the autocorrelation of the factor scores over time. Higher serial correlation indicates lower portfolio turnover based on the factor.

4 March 2014
The Quant View
Appendix 1
Important Disclosures
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The views expressed in this report accurately reflect the personal views of the undersigned lead analyst(s). In addition,
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or view in this report. Sheng Wang/Miguel-A Alvarez/Javed Jussa/Zongye Chen/Allen Wang/Yin Luo
Hypothetical Disclaimer
Backtested, hypothetical or simulated performance results 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.

4 March 2014
The Quant View
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