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

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 asingle factor in making their investment decision. DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1.MICA(P) 054/04/2013.

Sheng Wang

[email protected]

Miguel-A Alvarez

[email protected]

Javed Jussa

[email protected]

Zongye Chen

[email protected]

Allen Wang

[email protected]

Yin Luo, CFA

[email protected]

North America: +1 212 250 8983Europe: +44 20 754 71684Asia: +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

The Quant View


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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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

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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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Figure 3: Component of the Estimize contributors

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70%

Financial Professional Non Professional

48%

26%

26%

independent research

buy side

sell side Source: Estimize, Compustat, IBES, Russell, S&P, Thomson Reuters, Deutsche Bank

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

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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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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

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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.

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Figure 8: Estimize EPS estimates vs. IBES for longer windows

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43%

All estimates

Estimize more accurate

IBES more accurate

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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

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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

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buy side more accurate sell side more accurate

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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

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IBES more accurate sell side from Estimize more accurate

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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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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

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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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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 [email protected] for more details of the Estimize dataset.

mailto:[email protected]

4 March 2014

The Quant View


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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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.

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Figure 22: Global opportunity set Figure 23: Emerging markets opportunity set

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Source: Bloomberg Finance LP, Compustat, IBES, MSCI, Russell, S&P, Thomson Reuters, Worldscope, 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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Stock selectionopportunity set is greater for small cap stocks



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

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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.

4 March 2014

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Figure 28: Median pairwise correlation for large caps Figure 29: Median pairwise correlation for small caps

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4 March 2014

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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 [email protected] 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

0.90

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




Low Volatility 3M Earnings Revisions Source: Bloomberg Finance LP, Compustat, IBES, MSCI, Russell, S&P, Thomson Reuters, Worldscope, Deutsche Bank


https://eqindex.db.com/gqs/


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

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1.00

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1.15





0.75

0.80

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1.00

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1.10






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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1.00

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1.15





0.85

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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

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1.00

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1.10





0.60

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Figure 38: Emerging Markets: YTD cumulative factor


Figure 39: Developed Markets: YTD cumulative factor


0.88

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1.00

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1.06

1.08





0.86

0.88

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0.92

0.94

0.96

0.98

1.00

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1.04






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

[email protected].

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 IRIDIUM COMMUNICATIONS 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.

Exp

ect

ed

Re

turn

(%

)

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

Sect

or-

Ne

utr

al R

ank

IC

Sector-Neutral Rank IC 12M Avg

(4.0)

(2.0)

0.0

2.0

4.0

6.0

8.0

Act

ive

Re

turn

Active Return 12M Avg



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

400

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1400

(2.0)

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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 [email protected].

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 CBM Group 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

2.0

4.0

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US EU ex UK

Asia ex Japan

Japan EM Canada UK Aus/NZ Global

Ave

rage

Ran

k IC

(%

)

Long-Term Average Rank IC 12M Average Rank IC

(6.0)

(4.0)

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Act

ive

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turn

Active Return 12M Avg




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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400

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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)

0.0

1.0

2.0

3.0

4.0

5.0

Kelly VRP MCRM Momentum Valuation Sentiment CCRM

(6.0)

(5.0)

(4.0)

(3.0)

(2.0)

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0.0

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Kelly VRP MCRM Momentum Valuation Sentiment CCRM



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



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



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



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-1M total 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 CAPM beta, 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 CAPM idosyncratic 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 l ikely 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. Source: Bloomberg Finance LP, Compustat, IBES, MSCI, Russell, S&P, Thomson Reuters, Worldscope, Deutsche Bank

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-1M total 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 l ikely 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. Source: Bloomberg Finance LP, Compustat, IBES, MSCI, Russell, S&P, Thomson Reuters, Worldscope, Deutsche Bank

4 March 2014

The Quant View


Appendix 1

Important Disclosures

Additional information available upon request

For disclosures pertaining to recommendations or estimates made on securities other than the primary subject of this research, please see the most recently published company report or visit our global disclosure look-up page on our website at http://gm.db.com/ger/disclosure/DisclosureDirectory.eqsr

Analyst Certification

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

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

4 March 2014

The Quant View


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