Nomura Global Quantitative Research Monthly 2012-02-16 494848

EQUITY RESEARCH

Factors over the business cycle Strategies for Banks Correlation

Global Quantitative Research Monthly

See Appendix A-1 for analyst certification, important disclosures and the status of non-US analysts.

February 16, 2012

Nomura | Global Quantitative Research Monthly February 16, 2012

2

Contents

Page

Chapter I Quants factor dynamics 4■ We believe mean reversion of factor valuation could continue and we will hold on to our pro-value view for the time

being.

■ Correlation between stock prices has dropped to a lower level, implying a better environment for stock pickers.

■ The net inflows into Asia ex Japan that we have seen YTD undo just around 14% of the mutual fund net outflows in 2011.

■ The value spread of E/P and B/P has been narrowing but the latest level is still standing above the past 24-month average. Likewise, the recent sell-offs in momentum have not fully given up their premium valuation. Judging from the former path of 2009 value rally, we believe there is further to run.

■ Lastly, past experience from the risk-relief rally in 2009 shows that high risk stocks may have further to go, as investors may need time to adopt a lower risk environment. Further, our earnings momentum index has reflected a gradual move away from the 2011 trough and StarMine predicted surprise score also shows a turnaround sign.

Sandy Lee - Head of Equity Quantitative Strategies, Asia ex-Japan

Chapter II Factors for Banks 13■ We analyse the performance of factors within the banks sector, showing how factors within the sector perform over

the cycle. For generalists this should provide tools for stock selection amongst banks; for banks specialists it should provide empirical evidence for why given factors may or may not be effective. For quants, this note should introduce factors that some quants will not generally use and also provide thoughts as to why given factors may be effective.

■ In the banks sector, where it is more difficult to identify an investment style that works in a consistent way over the cycle, it is important to link factor selection into the cycle.

■ To this end we set up an “investment clock” for the sector and identify which factors perform at which stage.

■ We would favour a value + momentum combination at this stage, with a preference of dividend yield as a measure of value. This reinforces our positive view on well capitalised northern European banks such as SHB, STAN and DNB while also capturing some beta from what we rate as the more investable EU17 banks such as SAN.

Inigo Fraser-Jenkins - European Head of Quantitative StrategyJon Peace - co-Head of European Banks Research

Chapter III Business cycle clocks and quant factors 26■ We use the OECD's composite leading indicator (CLI) to divide the economy into phases, in order to measure the

effectiveness of major eight quant factors in each phase. Business cycles tend to rotate in a counter-clockwise direction.

■ Since the CLI is published with a time lag, we analyzed the relationship between economic phases and three-month forward factor returns. We found that factor effectiveness varies depending on the phase. For example, the Sharpe ratio of B/P is high and stable during bust periods, while E/P is high and stable during booms. A projected simulation that switches factors based on economic phase generated positive performance. This suggests that the premium for each factor changes depending on the phase.

Hiromichi Tamura - Japan Head of Equity Quantitative Research

Chapter IV Correlation: stocks go their divergent ways 36■ The fall in pair-wise stock correlation since the start of the year has been the largest that we have seen.■ Falls in correlation such as this tend to be supportive for market returns.■ This also increases the potential return to be earned from stock-picking and we can show that such falls tend to

directly lead to greater inflows for active strategies.■ For quants in particular the fall in correlation should be beneficial. We think that this will lead to a more positive

outlook for quants in coming months.Inigo Fraser-Jenkins - European Head of Quantitative Strategy


3

Foreword The theme running though our research pieces this month is the behaviour of factors over the business cycle. In our Japanese piece we use the OECD leading Indicator to identify the efficacy of factors in phases of the cycle. In our European piece we focus on the performance of strategies within the banks sector. We find that this is the sector that arouses the most controversy at the moment among our clients. It is also a sector in which some quant approaches struggle. We find that the phase of the cycle is key for factor performance, so we set out an investment clock for the sector and relate this to factor performance.

In Asia we make the point that the mean reversion trade of recent months can continue and we hold our pro-value view and think that momentum has further to fall.

Lastly, in our global section we look at stock correlations. We are currently seeing the steepest fall in stock correlations that we have ever seen globally. This should be good news for active managers and quants in particular. We show that flows into active relative to passive funds tend to pick up after a peak in correlation.


4

Quants factor dynamics

• We believe mean reversion of factor valuation could continue and we will hold on to our pro-value view for the time being.

• Correlation between stock prices has dropped to a lower level, implying a better environment for stock pickers.

• The net inflows into Asia ex Japan that we have seen YTD undo just around 14% of the mutual fund net outflows in 2011.

• The value spread of E/P and B/P has been narrowing but the latest level is still standing above the past 24-month average. Likewise, the recent sell-offs in momentum have not fully given up their premium valuation. Judging from the former path of 2009 value rally, we believe there is further to run.

• Lastly, past experience from the risk-relief rally in 2009 shows that high risk stocks may have further to go, as investors may need time to adopt a lower risk environment. Further, our earnings momentum index has reflected a gradual move away from the 2011 trough and StarMine predicted surprise score also shows a turnaround sign.

Value – more to run?

2012 has started with quick and large factor movement. We observed a reversal of return for most factors during the recent risk-on rally. In particular, price momentum factors saw the biggest decline, along with the rebound of high risk and value. YTD a strong return-reversal effect was seen across the region, with 12-month price momentum factors down over 10%. Earnings-momentum indicators such as revision index and StarMine predicted surprise also delivered negative factor returns. In contrast to momentum factors, high risk and value showed a strong rebound. Risk factors rallied some 5-9%, whereas value factors registered positive performance across the board, especially in the emerging Asia region.

Fig. 1: Factor performance comparison (2011 versus year-to-date)

Note: 2011 return figures are annualised performance, while YTD performance runs to 10 February 2012; R/R refers to the average return/standard deviation and rank is based on R/R. Factor returns are generated by calculating the subsequent performance of an equal-weighted portfolio that is long the highest one-third and short the one-third with the lowest scores (rebalanced monthly with country/sector diversified), except for the factors marked with *, which are reverse-based. See Figure 16 for factor definition. Universe is based on constituents in the MSCI Index.

Source: Worldscope, I/B/E/S, StarMine, MSCI, Nomura Quantitative Strategies

FactorReturn R/R Rank Return R/R Rank Return R/R Rank Return Rank Return Rank Return Rank

Market cap * (7.3) (1.7) 18 (9.3) (1.8) 20 (6.7) (1.5) 17 4.5 7 3.3 5 4.8 8Price momentum (1M) (5.7) (0.5) 14 (6.6) (0.7) 13 (5.4) (0.5) 15 (4.8) 18 (5.0) 19 (4.8) 16Price momentum (12M -1M) 14.2 2.4 3 11.4 1.4 5 15.0 2.4 2 (10.6) 21 (9.4) 21 (10.9) 21Volume turnover ratio (4.7) (0.6) 15 (13.2) (1.5) 19 (2.3) (0.3) 14 6.1 2 3.5 4 6.8 3Dividend yield 7.3 2.0 5 11.0 2.5 2 6.4 1.6 5 1.6 10 (3.7) 18 3.0 10Earnings yield 3.7 0.5 10 (0.8) (0.1) 9 5.0 0.7 9 5.4 4 2.5 7 6.2 4B/P (9.6) (2.0) 19 (6.9) (1.4) 18 (10.3) (1.7) 19 5.6 3 0.6 10 7.0 2Cashflow yield (6.4) (1.2) 16 (3.0) (0.5) 11 (7.2) (1.2) 16 4.9 6 1.3 8 5.8 5EBITDA/EV (0.8) (0.2) 12 (6.4) (1.2) 15 0.8 0.1 12 4.2 8 1.1 9 5.0 7Revision index 9.4 1.7 6 9.9 1.6 4 9.4 1.5 6 (3.7) 15 (2.8) 17 (3.9) 15Change in earnings yield 10.5 1.3 7 6.9 1.0 7 11.5 1.3 7 1.6 11 (0.4) 12 2.1 11StarMine predicted surprise 11.3 2.0 4 13.6 2.9 1 10.7 1.7 4 (5.6) 19 (1.0) 14 (6.9) 19Normalised E/P 3.3 0.6 8 (2.1) (0.4) 10 4.7 0.8 8 4.2 9 4.1 3 4.3 9Sales growth (FY2) (1.2) (0.4) 13 (3.2) (0.5) 12 (0.6) (0.2) 13 (1.4) 13 3.0 6 (2.6) 14EPS growth (FY2) (4.4) (2.7) 21 (6.0) (1.2) 16 (3.9) (2.1) 20 (2.1) 14 (0.8) 13 (2.5) 13Return on equity 12.7 4.0 1 9.3 1.4 6 13.6 3.4 1 (4.2) 16 (0.3) 11 (5.1) 18Shareholders’ equity ratio (0.4) (0.1) 11 (3.5) (0.7) 14 0.5 0.1 11 (1.1) 12 (1.7) 15 (1.0) 12Pretax profit margin 10.4 2.5 2 12.6 2.4 3 9.9 2.1 3 (4.3) 17 (1.9) 16 (5.0) 17Volatility (13.8) (1.6) 17 (14.8) (1.4) 17 (13.5) (1.5) 18 8.4 1 7.7 1 8.6 1Estimate dispersion (12.9) (2.4) 20 (13.1) (2.8) 21 (12.9) (2.2) 21 5.4 5 4.1 2 5.7 6Default probability * 6.1 0.6 9 6.4 0.6 8 6.0 0.6 10 (9.2) 20 (8.9) 20 (9.3) 20

2011: APxJ 2011: APxJ DM 2011: APxJ EM YTD: APxJ DM YTD: APxJ EMYTD: APxJ

YTD, a strong return-reversal effect was apparent; momentum suffered, while high risk and value showed a strong rebound

Sandy Lee +852 2252 2101 [email protected] Yasuhiro Shimizu +852 2252 2107 [email protected] Rico Kwan +852 2252 2102 [email protected]

Report previously published on 15 February 2012

mailto:[email protected]�




5

Fig. 2: Performance of reversal, value and risk

Source: Worldscope, I/B/E/S, MSCI, Nomura Quantitative Strategies

Fig. 3: Performance of price and earnings momentum

Source: Worldscope, I/B/E/S, StarMine, MSCI, Nomura Quantitative Strategies

In our Quantitative Outlook 2012 – Navigating the returns maze (8 December, 2011), we mentioned that we expect a mean reversion of factor valuations over the medium term with a positive view on value and reversal strategy using forecast P/E and six-month return reversal factors. We also suggested adding a hedge factor that has relatively low correlation with market returns, such as ROE or dividend yield, to protect against possible downside risk if investors see a need to do so. The recent risk-on rally has brought the outperformance of value-reversal strategy and now triggers the question of whether there is more to run.

We recognize that a number of Asian markets, for instance India, Singapore, and Taiwan, appear overbought or close to overbought on RSI measure from the technical analysis angle (Technical Analysis Focus, 10 February, 2012) and thus have the potential for near-term consolidation. After the recent run, value performance may slow down a bit in the coming month. However, judging from the latest stock correlation, flow of funds, factor valuation, and earnings revision trends, and assuming there is no major shock such as a serious credit event, we believe value could continue to see a re-rating this year and we will hold on to our pro-value view for the moment.

We first observe the correlation between stock prices. In conjunction with the strong factor movement and market performance, the average pair-wise correlation between stock prices has come down to a lower level from the periods of extreme high correlation seen in Q4 2011. We believe falling stock correlation implies a better stock picking environment for both fundamental and quant investors.

Fig. 4: Average pair-wise correlation

Note: Average pair-wise correlation between all the stocks in the MSCI AC APxJ Index, based on daily prices over past one-month. Data run to 31 January, 2012.

Source: Thomson Reuters Datastream, MSCI, Nomura Quantitative Strategies

-25

-20

-15

-10

-5

0

5

10

De

c-1

0

Ma

r-1

1

Jun

-11

Se

p-1

1

De

c-1

1

(%) 6M reversal E/P

B/P Default probability

-5

0

5

10

15

20

De

c-1

0

Ma

r-1

1

Jun

-11

Se

p-1

1

De

c-1

1

(%) 6M return Revision

StarMine PS Rating

0

0.1

0.2

0.3

0.4

0.5

0.6

Jan

-01

Jan

-02

Jan

-03

Jan

-04

Jan

-05

Jan

-06

Jan

-07

Jan

-08

Jan

-09

Jan

-10

Jan

-11

Jan

-12

Correlation

Developed APxJ

Emerging Asia

Stock correlation has moved lower, implying a better environment for stock pickers

Does value has further to run?


6

Secondly, we take a look at the flow of funds tracking mutual fund net subscriptions. YTD net injections into global emerging markets were higher than those into developed markets. This helped drive our 52-week excess flows and relative return measure (emerging Asia to developed Asia) to turn around for the first time in over a year, signalling a reversal in the sentiment towards emerging market equities from mutual fund investors.

Fig. 5: EM excess flows (EM-DM) and 52W forward relative returns

Source: Bloomberg, EPFR Global, Nomura Quantitative Strategies

Asia ex-Japan market equity mutual funds experienced the largest amount of weekly net inflows since June 2011 in the week ended 25 January 2012. YTD Asia ex-Japan equity mutual fund investors invested a net USD2.4bn into equity funds, recording the fifth consecutive week of positive net inflows since the second week of January. We note that the performance of value is positively correlated to the market return and thus closely connected with the flow of funds. The net inflows that we have seen YTD undo just around 14% of the mutual fund net outflows in 2011, suggesting there could be plenty of room for returning inflows in the months ahead.

Fig. 6: Flow of funds through mutual funds (by region) via global investors, equity purchases via foreign investors

Note: MFI = Mutual Fund investors, FI = Foreign investors, EM (MFI): sum of net inflows in Asia ex Japan, EMEA, GEM, and LatAm, DM (MFI): sum of net inflows in developed Europe, International, Pacific, the US, and Japan.

Source: Bloomberg, EPFR Global, Nomura Quantitative Strategies

(30)

(15)

0

15

30

45

60

75 (20)

(10)

0

10

20

30

40

50

Ap

r-04

Sep

-04

Jan

-05

Jun

-05

No

v-05

Mar

-06

Aug

-06

Dec

-06

Ma y

-07

Oct

-07

Feb

-08

Jul-

08

No

v-08

Ap

r-09

Se p

-09

Jan

-10

Jun

-10

Oct

-10

Mar

-11

Au g

-11

Dec

-11

Fo

rwar

d E

xces

s R

etur

n

(%

, 52W

, vs

DM

, MS

CI)

Exc

ess

Flo

w

(% A

UM

in b

ps,

52W

ma,

EM

vs

DM

) Excess flow (bps, % AUM, inverted)Excess return (%, EM - DM)

(40,000)

(30,000)

(20,000)

(10,000)

0

10,000

20,000

30,000

AxJ

(MF

I)

AxJ

(FI)

EM

EA

(MF

I)

GE

M (M

FI)

LatA

m (M

FI)

DE

(MF

I)

Intl

(MF

I)

Pac

(MF

I)

US

(MF

I)

JP (M

FI)

JP (F

I)

EM

: T

ota

l (M

FI)

DM

-T

ota

l (M

FI)

Net

Infl

ow

s (U

SD

mn

)

YTD FY2011

The net inflows into Asia ex Japan that we have seen YTD undo just around 14% of the mutual fund net outflows in 2011

Our 52-week measure on EM relative return and excess flows signals a reversal in the sentiment towards emerging market equities


7

A third reason for keeping hold of our value call is on factor valuation. At end-2011, dispersion of valuations had widened to an extreme (both value and momentum) and thus we highlighted value and reversal as attractive styles in the medium term. From valuation viewpoints, YTD the value spread of E/P and B/P factors has been narrowing. But even after the strong reversal since the beginning of the year, the latest value spread level is still standing above the past 24-month average, near +0.5 standard deviations. Judging from the former path of the risk-relief value rally in 2009, we believe there is further to run, given a complete mean reversion of factor valuation has not yet done.

Fig. 7: E/P spread between Q1 and Q5 by E/P factor

Notes: The spread is the median E/P difference between the top and bottom quintile portfolios by E/P. Moving averages and standard deviations are based on past 24-month data.


Fig. 8: B/P spread between Q1 and Q5 by B/P factor

Notes: The spread is the median B/P difference between the top and bottom quintile portfolios by B/P. Moving averages and standard deviations are based on past 24-month data.


Likewise, the recent sell-offs in momentum have not fully given up their premium valuation. This would suggest that further reversal could still be underway in the medium term.

Fig. 9: E/P spread between Q1 and Q5 by momentum factor

Notes: The spread is the median E/P difference between the top and bottom quintile portfolios by mid-term price momentum factor. Moving averages and standard deviations are based on past 24-month data.


Fig. 10: B/P spread between Q1 and Q5 by momentum factor

Notes: The spread is the median B/P difference between the top and bottom quintile portfolios by mid-term price momentum factor. Moving averages and standard deviations are based on past 24-month data.


+2sd

avg

-2sd

0

2

4

6

8

10

12

14

16

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Dec-

10

Dec-

11

(%)

+2sd

avg

-2sd

0

50

100

150

200

250

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Dec-1

0

Dec-1

1

(%)

+2sd

avg

-2sd

-4

-3

-2

-1

0

1

2

3

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Dec-1

0

Dec-1

1

(%)

+2sd

avg

-2sd

-84

-74

-64

-54

-44

-34

-24

-14

-4

6

16

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Dec-1

0

Dec-1

1

(%)

From factor valuation viewpoint, we believe value could see further re-rating

Further reversal could still be underway for momentum


8

Lastly, in the recent risk-on rally, it was largely the risk re-pricing rather than earnings recovery that was driving the market. The below figure shows that, as the implied volatility has dropped since December 2011, demand for risky assets has resumed and high-risk stocks (by default probability) have quickly re-rated, albeit with a lag. Past experience from the risk-relief rally in 2009 shows that risky stocks could have further to go, as investors may need time to adopt a lower-risk environment.

Fig. 11: HSI Volatility Index and the performance of the default probability factor

Source: Bloomberg, MSCI, Nomura Quantitative Strategies

After the initial stage of risk re-pricing, what lies ahead? We discussed in our Quantitative Outlook 2012 that value-investing generally works when the risk appetite improves and when the earnings outlook is improving. The below figure demonstrates a close relationship between the performance of value style and earnings momentum. We believe value recovery power would be more likely to sustain if the earnings downgrade cycle since 2011 has reached an end and if the earnings outlook starts to improve.

Fig. 12: Relationship between value performance and earnings momentum index

Note: Composite value factor is defined as the sum of the normalized scores of E/P and B/P. Earnings momentum index is defined as: % of companies with +ve Revi,t / % of companies with -ve Revi,t. The revision scores assess how the prevailing consensus estimate differs from the past three-month average


-30

-25

-20

-15

-10

-5

0

5

10

15

0

20

40

60

80

100

120

Jan

-06

Jan

-07

Jan

-08

Jan

-09

Jan

-10

Jan

-11

Jan

-12

(%)(%)HSI Volatility Index (LHS) Default probability* (RHS)

0.0

0.5

1.0

1.5

2.0

2.5

-15

-10

-5

0

5

10

15

20

25

30

35

40

Dec-9

9

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Dec-1

0

Dec-1

1

(%)Composite value (trailing 12M return, LHS)

APxJ Earnings momentum index (RHS)

Risky stocks have quickly re-rated, albeit with a lag

The performance of value is closely connected with the risk appetite and the earnings outlook


9

Our earnings momentum index, which looks at the degree of the revision to consensus forecasts of FY2 EPS and assesses how the prevailing consensus estimate differs from the past three-month average, has reflected a gradual move away from the 2011 trough, suggesting the rate of earnings downgrades has started to slow. We also observe that the median StarMine predicted surprise (forward 12-month), defined as the percentage difference between the SmartEstimates and consensus, has shown a turnaround sign, implying forecasts by top-rated analysts and more recent estimates are improving, and the earnings outlook is potentially to be stabilized.

Fig. 13: Asia earnings momentum index and median StarMIne predicted surprise

Note: Earnings momentum index is defined as: % of companies with +ve Revi,t / % of companies with -ve Revi,t. The indicator looks at the degree of revision to the consensus forecasts of FY2 EPS, and assesses how the prevailing consensus estimate differs from the past three-month average.

Source: I/B/E/S, StarMine, MSCI, Nomura Quantitative Strategies

In conclusion, after examining the latest stock correlation, net fund inflows into the region, factor valuation, and earnings revision trends, we think the mean reversion of factor valuation could continue this year, and we will hold on to our pro-value theme for the time being. We present our updated 2012 outlook quant screen in the below figures, based on the same methodology described in the Quantitative Outlook 2012.

0.0

0.5

1.0

1.5

2.0

2.5

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

Dec-9

8

Dec-9

9

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Dec-1

0

Dec-1

1

(%)

Median StarMine predicted surprise (LHS)

APxJ Earnings momentum index (RHS)

We will hold on to our pro-value view for the time being; we feature the latest long-short stock ideas

The median StarMine predicted surprise (forward 12-month) has shown a turnaround sign, implying forecasts by top-rated analysts and more recent estimates are improving


10

Fig. 14: 2012 Quantitative Outlook screen stock selection: Long companies that are undervalued with a low P/E, high ROE, and have underperformed in the past six months

Note: Data as of 10 February, 2012. Selection of stocks screened from the MSCI Asia-Pacific ex-Japan. Stocks that fall in the top one-third of each market and sector on E/P and ROE factors, and those that fall in the bottom one-third on past six-month return are highlighted.

Source: Worldscope, StarMine, I/B/E/S, MSCI, Nomura Quantitative Strategies

MarketBloomberg code Name Sector

Forecast PER (x)

Forecast ROE (%)

Past 6-month return (%)

StarMine predicted surprise

Australia WOW AU Woolworths Ltd Consumer Staples 12.96 30.47 -3.05 0.00

Australia WPL AU Woodside Petroleum Energy 15.24 17.23 0.63 -0.04

Australia IAG AU Insurance Australia Grp. Financials 9.33 14.53 -5.26 -0.02

Australia QBE AU Qbe Insurance Group Financials 9.56 13.84 -15.16 0.03

Australia FMG AU Fortescue Metals Group Materials 8.30 86.51 -13.39 -0.04

China 900948 CG Inner Mongolia Yitai B Energy 6.96 51.55 -8.70 n.a.

China 3383 HK Agile Property Hldgs Financials 5.48 26.39 -8.54 -0.05

China 3333 HK Evergrande Real Estate Financials 4.81 67.66 -6.43 -0.06

China 1387 HK Renhe Commercial Hldgs Financials 3.46 40.76 -39.87 -0.20

China 200039 CH China Intl Marine B Sz Industrials 5.70 22.78 -10.47 -0.15

China 1101 HK China Rongsheng Heavy In Industrials 5.97 23.34 -12.38 -0.19

China 1313 HK China Resources Cement Materials 7.30 37.45 -15.18 -0.03

China 691 HK China Shanshui Cement Gr Materials 5.48 52.56 -14.75 -0.02

China 200012 CH Csg Holding Co B Materials 4.24 39.33 -20.45 -0.02

Hong Kong 880 HK Sjm Holdings Consumer Discretionary 12.62 48.42 -18.59 -0.03

Hong Kong 11 HK Hang Seng Bank Financials 11.23 24.35 -12.64 -0.02

Hong Kong 293 HK Cathay Pacific Airways Industrials 11.89 9.58 0.26 -0.09

Hong Kong 522 HK Asm Pacific Technology Information Technology 19.14 43.33 33.33 0.02

Hong Kong 8 HK Pccw Telecommunication Services 9.38 63.70 -19.76 -0.13

India HMCL IN Hero Motocorp Consumer Discretionary 14.39 92.50 -3.82 -0.01

India SHTF IN Shriram Transport Fin Financials 8.15 32.55 -9.08 -0.01

India BHEL IN Bharat Heavy Electricals Industrials 9.55 33.08 -26.43 -0.02

India SCS IN Satyam Computer Services Information Technology 9.11 53.75 7.33 -0.01

India SESA IN Sesa Goa Materials 5.89 27.10 0.43 0.01

India GAIL IN Gail India Utilities 11.23 22.60 -6.05 0.01

India TPWR IN Tata Power Co Utilities 12.64 15.88 6.25 -0.05

Indonesia ITMG IJ Indo Tambangraya Megah Energy 9.11 84.00 -6.26 -0.02

Korea 012330 KS Hyundai Mobis Consumer Discretionary 7.74 35.22 -15.43 -0.04

Korea 000270 KS Kia Motors Corp Consumer Discretionary 6.19 45.48 -6.66 0.05

Korea 001450 KS Hyundai Marine & Fire In Financials 6.44 40.21 -6.38 0.05

Korea 029780 KS Samsung Card Co Financials 5.79 14.61 -18.60 -0.12

Korea 042670 KS Doosan Infracore Co Industrials 8.01 33.82 -3.45 -0.21

Korea 009540 KS Hyundai Heavy Industries Industrials 8.31 22.52 -5.19 -0.03

Korea 003600 KS Sk Holdings Industrials 4.28 23.24 -3.04 -0.01

Korea 034730 KS Sk C&C Co Information Technology 10.21 49.45 -13.78 -0.02

Korea 017670 KS Sk Telecom Co Telecommunication Services 6.20 15.63 -12.26 0.00

Malaysia MAXIS MK Maxis Bhd Telecommunication Services 18.44 26.88 7.91 -0.01

Malaysia YTLP MK Ytl Power Int'L Utilities 11.28 14.34 0.00 0.01

Philippines AEV PM Aboitiz Equity Ventures Industrials 11.58 31.69 5.04 -0.06

Philippines TEL PM Phil Long Distance Tel Telecommunication Services 13.40 47.58 14.39 -0.04

Singapore UOB SP United Overseas Bank Financials 11.47 12.43 -5.55 0.00

Taiwan 2884 TT E.Sun Financial Holdings Financials 11.46 10.94 -9.44 -0.05

Taiwan 1802 TT Taiwan Glass Ind'L Corp Industrials 10.61 16.59 -6.13 n.a.

Taiwan 8069 TT E Ink Holdings Information Technology 6.81 28.73 -30.69 -0.12

Taiwan 2498 TT Htc Corp Information Technology 10.05 65.46 -25.66 -0.08

Taiwan 3673 TT Tpk Holding Co Information Technology 8.24 86.71 -36.22 -0.03

Taiwan 3044 TT Tripod Technology Corp Information Technology 10.09 22.82 -9.84 0.00

Taiwan 1314 TT China Petrochemical Dev Materials 9.09 26.50 -17.56 0.02

Taiwan 1704 TT Lee Chang Yung Chem Ind Materials 8.04 31.87 -11.47 n.a.

Thailand KTB TB Krung Thai Bank Financials 8.10 18.11 -13.61 -0.02


11

Fig. 15: 2012 Quantitative Outlook screen stock selection: Short companies that are overvalued with a high P/E, low ROE, and have outperformed in the past six months

Note: Data as of 10 February, 2012. Selection of stocks screened from the MSCI Asia-Pacific ex-Japan. Stocks that fall in the bottom one-third of each market and sector on E/P and ROE factors, and those that fall in the top one-third on past six-month return are highlighted.

Source: Worldscope, StarMine, I/B/E/S, MSCI, Nomura Quantitative Strategies

MarketBloomberg code Name Sector

Forecast PER (x)

Forecast ROE (%)

Past 6-month return (%)

StarMine predicted surprise

Australia STO AU Santos Energy 21.11 8.17 18.21 0.00

Australia DXS AU Dexus Property Group Financials 11.49 7.43 4.65 -0.01

Australia BLD AU Boral Materials 14.67 6.97 15.72 -0.03

China 2357 HK Avichina Ind & Tech H Consumer Discretionary 23.18 4.21 11.76 -0.01

China 1211 HK Byd Co H Consumer Discretionary 39.06 2.32 59.90 0.01

China 857 HK Petrochina Co H Energy 10.53 17.39 15.14 0.05

China 165 HK China Everbright Financials 10.11 8.27 20.42 n.a.

China 392 HK Beijing Enterprises Hldg Industrials 15.40 10.08 24.34 -0.02

China 1919 HK China Cosco Holdings H Industrials -35.58 -2.62 19.18 -0.58

China 2866 HK China Shipping Contain H Industrials -26.34 -2.96 33.33 -0.32

China 347 HK Angang Steel H Materials 29.61 2.36 5.92 -0.47

Hong Kong 494 HK Li & Fung Consumer Discretionary 23.14 22.13 27.21 0.01

Hong Kong 10 HK Hang Lung Group Financials 25.36 4.54 10.66 0.00

Hong Kong 823 HK Link Reit Financials 21.44 5.57 6.81 0.02

Hong Kong 316 HK Orient Overseas Intl Industrials 23.78 2.99 28.09 -0.29

Hong Kong 2038 HK Foxconn International Information Technology 39.44 3.83 47.55 n.a.

India PIHC IN Piramal Healthcare Health Care 20.77 3.01 19.30 n.a.

India WPRO IN Wipro Information Technology 16.96 27.10 33.74 0.00

India RPWR IN Reliance Power Utilities 23.56 7.45 24.17 -0.24

Indonesia ISAT IJ Indosat Telecommunication Services 17.84 9.47 5.71 -0.01

Korea 066570 KS Lg Electronics (New) Consumer Discretionary 18.13 6.90 32.98 0.05

Korea 088350 KS Korea Life Insurance Financials 10.56 10.87 19.75 -0.06

Korea 011200 KS Hyundai Merchant Marine Industrials -27.09 -5.78 16.79 -0.32

Korea 002380 KS Kcc Corp Industrials 14.81 4.27 15.77 0.06

Korea 001300 KS Cheil Industrial Materials 16.11 11.56 1.24 -0.06

Korea 032640 KS Lg Uplus Telecommunication Services 12.10 6.49 20.76 -0.10

Malaysia ULHB MK Uem Land Holdings Financials 32.93 11.53 13.46 -0.04

Philippines GLO PM Globe Telecom Telecommunication Services 13.76 23.97 27.56 0.00

Philippines MER PM Manila Electric Co Utilities 18.99 27.48 4.20 0.02

Singapore CAPL SP Capitaland Financials 18.65 4.59 9.16 -0.06

Singapore CMA SP Capitamalls Asia Financials 23.01 4.29 8.82 0.06

Singapore GLP SP Global Logistic Prop Financials 20.61 1.00 16.77 0.03

Singapore HPHT SP Hutchison Port Trust Industrials 23.22 2.37 16.19 0.00

Singapore NOL SP Neptune Orient Lines Industrials -41.59 -2.13 22.51 -0.07

Taiwan 2834 TT Taiwan Business Bank Financials 15.28 6.86 1.57 n.a.

Taiwan 2885 TT Yuanta Financial Holding Financials 18.53 7.06 1.18 0.00

Taiwan 2603 TT Evergreen Marine Corp Industrials 40.79 2.37 11.77 -0.39

Taiwan 2609 TT Yang Ming Marine Transp Industrials -16.20 -6.72 14.95 -0.36

Taiwan 2353 TT Acer Information Technology 20.51 5.96 22.57 -0.06

Taiwan 3481 TT Chimei Innolux Corp Information Technology -4.56 -4.44 29.37 0.03

Taiwan 2379 TT Realtek Semiconductor Information Technology 15.63 10.93 31.71 0.03

Taiwan 2325 TT Siliconware Precision Information Technology 17.47 9.71 24.76 0.01

Taiwan 2303 TT United Microelectronics Information Technology 17.84 5.19 32.74 0.04

Taiwan 1101 TT Taiwan Cement Corp Materials 12.01 11.94 -3.87 0.00

Thailand BBL/F TB Bangkok Bank Fgn Financials 13.86 10.66 9.17 n.a.


12

Fig. 16: Definition of factors

Note: Factors marked with * are reverse-based.

Source: Nomura Quantitative Strategies

Type Factors Definition

Size, price momentum & Market cap * Log of US$ market cap

Liquidity Price momentum (1M) Past 1-month local currency return

Price momentum (12M -1M) Last 12-month return less the last 1 month return in local currency

Volume turnover ratio Past 1-month trading volume / shares outstanding at month-end

Valuation Dividend yield F12-month DPS / stock price

Earnings yield F12-month EPS / stock price

B/P Actual BPS / stock price

Cashflow yield F12-month cashflow per share / stock price

EBITDA/EV (F12-month net profit + actual interest expense + actual depreciation) / (market cap + interest-bearing debt - cash - short-tern marketable securities)

Revision & earnings yield Revision index (Number of upward analyst revisions - number of downward analyst revisions) / total number of analysts’ estimate

Change in earnings yield F12-month earnings yield - past 3-month average earnings yield

StarMine predicted surprise (SmartEstimate F12-month - consensus mean) / max(divisor, |mean|)

Normalised E/P (F12-month earnings yield - average earnings yield in past 36 months) / standard deviation of the earnings yields in the past 36 months

Growth Sales growth (FY2) FY2 sales / FY1 sales

EPS growth (FY2) FY2 EPS / FY1 EPS

Financial Return on equity F12-month net profit / actual shareholders’ equity

Shareholders’ equity ratio Actual shareholders’ equity / actual total assets

Pretax profit margin F12-month pretax profit / F12-month sales

Risk Volatility Past 36-month price return volatility

Estimate dispersion I/B/ES FY1 consensus EPS standard deviation / absolute value for FY1 consensus EPS

Default probability * Default probability estimated using Merton model


13

Factors for banks

• We analyse the performance of factors within the banks sector, showing how factors within the sector perform over the cycle. For generalists this should provide tools for stock selection amongst banks; for banks specialists it should provide empirical evidence for why given factors may or may not be effective. For quants, this note should introduce factors that some quants will not generally use and also provide thoughts as to why given factors may be effective.

• In the banks sector, where it is more difficult to identify an investment style that works in a consistent way over the cycle, it is important to link factor selection into the cycle.

• To this end we set up an “investment clock” for the sector and identify which factors perform at which stage.

• We would favour a value + momentum combination at this stage, with a preference of dividend yield as a measure of value. This reinforces our positive view on well capitalised northern European banks such as SHB, STAN and DNB while also capturing some beta from what we rate as the more investable EU17 banks such as SAN.

Banks have been in the eye of the storm for the past few years. No sector provokes more extreme views among the clients that we speak to. To this end, we thought it would be useful to study the efficacy of different factors within the sector, or to put it another way, to assess the pros and cons of different approaches to stock picking within the sector. This should appeal to a range of audiences. For generalists, it should cast some light on different ways to think about the sector. For specialists, it should hopefully add some empirical evidence to what they know already about the efficacy of different approaches and it is also aimed at the quants. For the quants, the banks are always a pain. For one, many of the factors that are used blithely across the rest of the market cannot be brought to bear on the sector. It also requires quants to think about accounting, which is never a popular proposition. Perhaps we should add a fourth group, which would be academics. We are always surprised at the number of academic papers that we read that announce that “Financial stocks have been ignored in this study”. Surely leaving out such an important sector should not be done lightly1?

Factors can be used to show strong differentiation between bank stocks, at certain phases in the cycle value or quality for example can be used to draw out strong differences between banks. To use such factors, or course, the trick is to be able to tell which stage of the cycle one is in. We have set out an ‘investment clock’ with particular reference to the banks sector as described below. We show the performance of factors in these various phases.

We can also show that if investors do want to take strong value or quality positions within the sector, say, then it matters how they exactly define ‘value’ or ‘quality’. Banks that are cheap on P/E or dividend yield or price/tangible book have very different properties and tend to perform at different stages in the cycle. Likewise, defining a bank as high quality on its operational, management, earnings or balance sheet quality has large implications. Although we focus on value plus momentum as a strategy that should work in the current environment, we would point out that core tier 1 ratio has been a successful strategy for the sector. It has performed well during the crisis as might be expected, but over the limited history that we have for the factor it has also not lost money at other phases of the cycle.

For the purposes of this note we are assuming that the investment horizon is one quarter, so all our factor portfolios rebalance at that frequency. We detail definitions of the factors used in an appendix. 1 We have published a series of sector-specific quant reports in the past and this adds to that list. For the previous reports please see sector: ‘Telecom Services: The Return of the EBITDA Multiple’, 23 August 2005 ‘Household & Personal Care: A quantitative approach to stock-picking in European HPC’, 12 July, 2005 ‘Selecting Pharma Stocks’, 20 September 2007

Inigo Fraser-Jenkins

+44 20 7102 4658

[email protected]

Jon Peace

+44 20 7102 4452

[email protected] Gerard Alix Guerrini +44 20 7102 5079 [email protected]

We study factors in the banks sector. For quants, this might introduce factors that are not generally used and also offer explanations as to why some factors have worked…

…for banks specialists this hopefully offers a useful empirical analysis on the efficacy of factors over the cycle




14

An investment clock for banks

Given the variability of factor performance over the cycle for the sector, we think it is useful to introduce an ‘investment clock’ for the sector, which we detail below. We then take the periods identified and show the average performance of factors in each of the periods. The performance is expressed on a long-short basis.

• Fig. 17: Investment clock

Source: Datastream, Nomura research

In the ‘investment clock’ above, we illustrate the typical development of the economic cycle and its impact on bank metrics:

• Recovery: Growth rising, unemployment falling, inflation low. Bank ROEs rise, earnings momentum is positive, and NPLs stabilise and begin to fall. Value measures, especially P/E and P/TBV, work well as an investing style.

• Expansion: Growth high, inflation rising, interest rates rising. Bank ROEs high, earnings momentum peaks, NPLs near to lows. Value measures, especially P/E and P/TBV, work well as an investing style.

• Slowdown: Growth, inflation slow from high level, unemployment rises. ROEs stop rising, earnings momentum begins to fall, NPLs begin to rise. Low P/TBV fails as a value investing style, though other measures survive a mild slowdown.

7.0

7.5

8.0

8.5

9.0

9.5

10.

10.

11.

-6

-4

-2

0

2

4

6

8

10

Dec

-91

Au g

-92

Ap

r-93

Dec

-93

Au g

-94

Ap

r-95

Dec

-95

Au g

-96

Ap

r-97

Dec

-97

Au g

-98

Ap

r-99

Dec

-99

Au g

-00

Ap

r-01

Dec

-01

Au g

-02

Ap

r-03

Dec

-03

Au g

-04

Ap

r-05

Dec

-05

Au g

-06

Ap

r-07

Dec

-07

Au g

-08

Ap

r-09

Dec

-09

Au g

-10

Ap

r-11

Dec

-11

Real GDP CPI inf lation Interest rate Unemployment

DT RE EX Recovery EX SD Downturn Recovery EX SD DT Recovery

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

-15

-10

-5

0

5

10

15

20

Dec

-91

Aug

-92

Ap

r-93

Dec

-93

Au g

-94

Ap

r-95

Dec

-95

Au g

-96

Ap

r-97

Dec

-97

Au g

-98

Ap

r-99

Dec

-99

Au g

-00

Ap

r-01

Dec

-01

Au g

-02

Ap

r-03

Dec

-03

Au g

-04

Ap

r-05

Dec

-05

Au g

-06

Ap

r-07

Dec

-07

Au g

-08

Ap

r-09

Dec

-09

Au g

-10

Ap

r-11

Dec

-11

Earnings momentum ROE NPLs

We define an investment clock for the sector


15

• Downturn: Growth and inflation fall more sharply, interest rates fall. ROEs fall sharply, earnings momentum turns negative, NPLs rise more sharply. In sharp contractions where banks generate high losses (eg 2008), all value styles fail. High risk aversion sees quality styles prevail, led by high core Tier 1 and low NPL ratios.

Fig. 18: Performance of factors for banks over the business cycle

Average monthly long-short return of factors within the banks sector in four phases of the cycle since 1991. Source: Nomura Quantitative Strategy

Value

As with the broader market, value does tend to work most of the time for the sector, that is to say, cheap stocks usually go on to outperform expensive stocks. However, at times it can be a very poor strategy. Banks, being more cyclical than many sectors, can see spectacularly poor performance of value at certain phases in the cycle. 2008 was a case in point, where the previous 15 years’ outperformance of the factor within the sector was destroyed in one go. There has been no significant rebound in the factor since then (with the notable exception of dividend yield, of which more below). When does value fail in the sector? Our investment clock shows that the most important time to avoid value is in the ‘expansion’ phase of the cycle, when forward returns from the factor are poor.

Since October 2009, dividend yield has significantly decoupled from other value measures in the sector and has outperformed. In particular, the 52% outperformance of high yielding banks over their low yielding peers in 2011 is interesting as it also stands in stark contrast to the behaviour of that factor across the market more generally. The factor failed within most sectors last year (our sector-neutral dividend yield factor delivered a return of -2.5% over 2011).

Dividend yield came to signal quality in 2011 as it showed which companies were able to continue paying a dividend and those that could not against a backdrop of stress tests and fears of capital raising and dilution.

Median return over cyclesDownturn Recovery Expansion Slowdown

Value Div Yld 0.41 -0.07 -0.22 0.0212m Fwd PE 0.31 0.38 0.16 0.26Trailing PE 0.40 0.21 0.09 0.27Price/tangible book -0.92 0.32 0.22 -0.36

Momentum 12m Price Mom 0.51 0.48 0.00 0.323m Price Mom 0.99 0.15 0.00 0.251m Price Mom (contrarian) -0.83 -0.17 0.00 0.08Earn Mom 0.67 0.42 0.20 0.06

Value + momentum Div Yld+Earn Mom 0.56 0.62 -0.01 0.12Div Yld+Price Mom 1.54 0.39 0.00 0.06P/tang. book + Earn Mom 0.30 0.30 0.24 -0.54P/tang. book + Price Mom 0.70 0.29 0.02 -0.2112m Fwd PE + Earn Mom 0.12 0.50 0.32 0.2512m Fwd PE + Price Mom 1.55 0.04 0.42 0.95Trailing PE + Earn Mom 0.23 0.11 0.00 0.53Trailing PE + Price Mom 1.22 0.04 0.33 1.08

0.78 0.29 0.17 0.28Blended approaches PEG 0.01 0.44 0.10 0.14

PEG + Earn Mom 0.09 0.46 0.55 0.30PEG + Price Mom 1.27 0.00 0.12 0.23PBK/ROE -0.93 0.29 0.32 -1.21PBK/ROE + Earn Mom 0.28 0.39 0.36 -1.28PBK/ROE+ Price Mom 0.45 0.06 0.22 0.25

Qualiity ROE 0.03 -0.29 -0.02 0.28

Capital return (Div to Tang. book) -0.13 0.02 0.00 0.10Core Tier One 0.00 0.09 0.00 0.02Loans to Deposits -0.01 -0.05 0.00 -1.14Non Perf Loans 1.01 -0.50 0.67 0.39Credit rating -0.01 -0.17 -0.32 -0.18(number of shares) 0.08 -0.12 -0.26 0.14(Employees) 0.23 -0.09 -0.46 -0.15

Forward returns from value strategies for banks are poor in expansion phases

Dividend yield has significantly decoupled from other value factors recently


16

P/E (both forward and trailing) has been the most volatile of value factors. It delivered strong returns in 1991-99 and 2000-04 but then lost that outperformance over the rest of the cycle. As with other cyclical sectors, the gains from the factor can prove illusory when the earnings base evaporates.

Fig. 19: Performance of value factors for the banks sector

Figure shows the performance of portfolios screened on 12m forward PE, 12m trailing PE and Dividend Yield which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.

Source: Nomura Quantitative Strategy

Book value has not had much more success than earnings as a base for valuation. True, the sell-off in the factor in cyclical down-turns has been less painful than for P/E, but it can still give up all its gains at certain phases of the cycle. Although book value tends to be a more stable metric than earnings, banks are such highly leveraged companies that both can suffer significant erosion in recessionary phases of the cycle. Investors devalue banks at risk of recapitalisation on the grounds that capital raising will be dilutive to valuation multiples. As stock prices plunge well below book value, the dilutive impact of capital raisings becomes self-reinforcing. We also compared two different price/book factors. The evidence is clear: tangible book consistently ‘outperforms’ total book when used as the basis of valuation within the sector. This should make sense as tangible book is the closest to core Tier 1 capital that underpins banks’ regulatory capital ratios. Many examples exist of banks that have had to impair goodwill incurred through the boom in the post-Lehman recession. Figure 20 shows the efficacy of price/book and price/tangible book, while Figure 21 shows the relative return of the two strategies.

Fig. 20: Performance of price/book and price/tangible book

Figure shows the performance of portfolios screened on price to book and price to tangible book yield which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe. Source: Nomura Quantitative Strategy

0

100

200

300

400

500

600

700

De

c-9

1

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

Div Yld (Q)

12m Fwd PE (Q)

Trailing PE (Q)

0

50

100

150

200

250

300

De

c-9

1

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

PBK

Price to tangible book yield

Tangible book has consistently ‘outperformed’ book


17

Fig. 21: Price/book and price/tangible book compared: relative return of the two factors

Figure shows the ratio of the performance of portfolios screened on price to book and price to tangible book yield which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe. Source: Nomura Quantitative Strategy

Adding catalyst factors Of course, when choosing an individual bank, value by itself is not usually enough. There is hopefully some kind of catalyst to help the avoidance of value traps. One shorthand way of doing this is to use momentum – either price momentum or earnings momentum (here defined as the change in the analyst consensus 12-month forward EPS forecast over the past six months). This value plus momentum approach has returned some strong returns over time, though was a poor way to invest in 2006-10 as earnings and dividend paying capacity evaporated with the financial crisis. Despite this, this is the best candidate for a factor combination in the sector that delivers more consistent performance; it is just important to be mindful that this type of approach does not work well in expansion and slowdown phases as defined in our investment clock.

Interestingly, the fall in efficacy for this combination started in 2005 rather than 2007 when value started to underperform, as in the late stages of the bull market investors rewarded banks that spent their cash on acquisitions rather than returned it to shareholders. However, there has been a significant rebound in the efficacy of this way of investing since 2009. In particular, this has helped improve the performance of the P/E metric, separating banks that were cheap because of superior fundamentals from those that were only superficially cheap because analysts were behind the curve in reducing estimates in a rapidly evolving macro and regulatory environment.

80

90

100

110

120

130

140

150D

ec-

91

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

Value + momentum can be a highly effective strategy in the sector, but it also fails badly at specific phases in the cycle


18

Fig. 22: Performance of value + momentum factors

Figure shows the ratio of the performance of portfolios screened on several blends of ‘value + momentum’ factors using 12m forward PE, 12m trailing PE and Dividend Yield and price to tangible book yield as value factors and the 12m price or earnings momentum as momentum factors. All factors are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


Momentum factors when used by themselves within the sector deliver a very volatile return; of them, 12-month price momentum is the best of the group, Figure 23. This might be expected of a sector where earnings (and losses) are extremely volatile through the cycle, with banks tending to exhibit high betas and materially over- and undershoot fair values.

Fig. 23: Performance of momentum in the banks sector

Figure shows the ratio of the performance of portfolios screened on 1m, 3m and 12m price momentum and the 12m earnings momentum which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


0

100

200

300

400

500

600

700

800

900

1000D

ec-

91

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

Div Yld + Earn Mom

Div Yld + Price Mom

P/TBV + EMOM

P/TBV + PMOM

12m Fwd PE + Earn Mom

12m Fwd PE + Price Mom

Trailing PE + Earn Mom

Trailing PE + Price Mom

0

50

100

150

200

250

300

350

De

c-9

1

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

12m Price Mom1m Price MomEarn Mom (Q)3m Price Mom (Q)


19

Another possible systematic route to go down is to use value with a simple fundamental catalyst (ROE or growth). However, this does not lead to stable performance in the sector, perhaps for the deficiencies in earnings-based measures for this sector described above.

Fig. 24: Value + catalyst factors

Figure shows the ratio of the performance of portfolios screened on 12m trailing PE to growth ratio and price to book to excess ROE which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


One can take a more blended approach and combine both ‘fundamental and technical catalyst factors with value. On this basis, price/book with ROE and price momentum have delivered reasonable returns in the long run, but would have underperformed in recent years.

Fig. 25: Blended approaches to banks’ stock selection

Figure shows the ratio of the performance of portfolios screened on several blends of fundamental and technical factors as 12m trailing PE to growth ratio + momentum and price to book to excess ROE +momentum. All factors are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


0

50

100

150

200

250

300

350

De

c-9

1

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

PEG (Q)

PBK ROE yield (Q)

0

100

200

300

400

500

600

De

c-9

1

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

PEG + Earn Mom

PEG + Price Mom

PBK ROE + Earn Mom

PBK ROE + Price Mom


20

Quality factors

Factors that purport to measure quality have a very broad range of efficacy within the sector. Some have managed a good degree of discrimination in periods when the sector was under stress but some have notably failed to. This shows the importance of deciding exactly which quality factor to use. The two most significant in the recent cycle have been the non-performing loan ratio and core Tier 1. Interestingly, none of them really give a good long-term discriminatory ability over the cycle, though core Tier 1 has delivered relatively consistent returns over the time for which we have the data since 2004.

We would group quality factors for the sector into balance sheet quality (core Tier 1 ratio, NPL ratio, loan/deposit, credit rating) operational quality (ROE, RoRWA) and ‘other’ (management and earnings quality metrics).

Measures of balance sheet quality

If we take the performance of these in turn, we find that core tier 1 ratio has had a strong discriminatory power throughout the financial crisis since 2008 (and interestingly delivering an in-line performance before then and not underperforming in that time), Figure 26. The efficacy of this factor is much greater than for plain Tier 1 ratio.

Fig. 26: Core tier 1 ratio and tier 1 ratio

Figure shows the ratio of the performance of portfolios screened on Core Tier 1 ratio and Tier 1 ratio which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


Non-performing loan ratio has been one of the most important factor differentiators for bank stocks since 2008. NPLs are the major driver of bank profitability and dilution risk, hence this metric performs particularly well as economies enter recession, as seen post Lehman and as increasingly feared under the weight of austerity in late 2011.

0

20

40

60

80

100

120

140

160

180

200

Se

p-0

3

De

c-0

3

Ma

r-0

4

Jun

-04

Se

p-0

4

De

c-0

4

Ma

r-0

5

Jun

-05

Se

p-0

5

De

c-0

5

Ma

r-0

6

Jun

-06

Se

p-0

6

De

c-0

6

Ma

r-0

7

Jun

-07

Se

p-0

7

De

c-0

7

Ma

r-0

8

Jun

-08

Se

p-0

8

De

c-0

8

Ma

r-0

9

Jun

-09

Se

p-0

9

De

c-0

9

Ma

r-1

0

Jun

-10

Se

p-1

0

De

c-1

0

Ma

r-11

Jun

-11

Se

p-1

1

De

c-11

Sep ’03 = 100

Tier 1 Cap Ratio

The Core Tier One

Core Tier 1 ratio has been an effective quality measure, though plain Tier 1 ratio has failed

Non-performing loan ratio has been one of the most effective discriminators between banks in recent years


21

Fig. 27: Performance of non-performing loan factor

Figure shows the ratio of the performance of the portfolio screened on non performing loans to total loans ratio which is defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


The loan/deposit ratio had been an important discriminator within the sector. However, it stopped working in 2002 as easy supply to low cost wholesale funding in the last decade stopped discriminating against those banks with low deposit bases. Loan/deposit ratios are as much a function of national market structure (eg tax deductibility of mortgage interest and degree of development of the covered bond market) as bank-specific features, which is perhaps why this metric did not reverse post Lehman.

Fig. 28: Performance of loan/deposit ratio

Figure shows the ratio of the performance of the portfolio screened on loan-to-deposit ratio which is defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


Credit ratings have worked to some extent within the sector. ‘High quality’ companies on this basis outperformed in the run-up to the peak in credit spreads in late 2002 and again from May 2008-February 2009. The long term alpha of the factor is negative, but it can provide good discrimination in times of stress, Figure 29. Post Lehman, the value of credit ratings has arguably been devalued somewhat as investors take a more direct view on creditworthiness based directly on core Tier 1 ratios and profitability. This could explain the lack of significant performance in the past three years.

0

20

40

60

80

100

120

140

160

180M

ar-

93

De

c-9

3

Se

p-9

4

Jun

-95

Ma

r-9

6

De

c-9

6

Se

p-9

7

Jun

-98

Ma

r-9

9

De

c-9

9

Se

p-0

0

Jun

-01

Ma

r-0

2

De

c-0

2

Se

p-0

3

Jun

-04

Ma

r-0

5

De

c-0

5

Se

p-0

6

Jun

-07

Ma

r-0

8

De

c-0

8

Se

p-0

9

Jun

-10

Ma

r-11

De

c-11

Mar ‘93 = 100

0

50

100

150

200

250

300

350

400

450

Jun

-92

Ma

r-9

3

De

c-9

3

Se

p-9

4

Jun

-95

Ma

r-9

6

De

c-9

6

Se

p-9

7

Jun

-98

Ma

r-9

9

De

c-9

9

Se

p-0

0

Jun

-01

Ma

r-0

2

De

c-0

2

Se

p-0

3

Jun

-04

Ma

r-0

5

De

c-0

5

Se

p-0

6

Jun

-07

Ma

r-0

8

De

c-0

8

Se

p-0

9

Jun

-10

Ma

r-11

De

c-11

Jun ’92 = 100


22

Fig. 29: Performance of credit rating factor

Figure shows the ratio of the performance of the portfolio screened on credit rating which is defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


Operational quality ROE and return on risk-weighted assets RoRWA have not been long-term alpha factors in the sector. They have, however, discriminated between banks at various times of crisis. For banks, ROE and p/book tend to show a strong correlation; thus at stages in the cycle when investors favour value through low P/TBV, they are also implicitly favouring low ROE, Figure 30.

Fig. 30: ROE and return on risk-weighted assets

Figure shows the ratio of the performance of portfolios screened on return on risk weighted assets and ROE pre goodwill which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


0

20

40

60

80

100

120

140D

ec-

91

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

0

50

100

150

200

250

300

Se

p-0

3

Jun

-04

Ma

r-0

5

De

c-0

5

Se

p-0

6

Jun

-07

Ma

r-0

8

De

c-0

8

Se

p-0

9

Jun

-10

Ma

r-11

De

c-11

Return on RWA

ROE PRE GW

ROE and RoRWA have not been long-term alpha factors for the sector


23

Other quality measures The management quality measures that we use have to be indirect proxies2. Here we use change in number of shares and change in employees. These can be thought of as essentially measuring management overconfidence. Across the broader market, the usual interpretation is that companies that increase their number shares often do so to fund an acquisition (which is usually a bad idea); for banks recently this will have signalled a need to raise new capital. Since the Lehman bankruptcy, banks that have increased their number of shares over the previous 12 months have actually outperformed as the probability of bankruptcy is reduced. Presumably, raising capital is less of a stigma than the risk of not raising it.

As with number of shares, companies that increase headcount are usually interpreted as having (over)confident management, and can also be a result of M&A. By contrast, companies that decrease headcount may have costs under control. Starting in the depths of the crisis in October 2009, banks that had reduced headcount over the previous year were rewarded and outperformed through to June 2011. However, more recently they have underperformed; presumably there is a level of headcount that reflects not so much cost control as simply a troubled company, Figure 31.

Fig. 31: Performance of management quality measures

Figure shows the ratio of the performance of portfolios screened on return on y-o-y variation of employees and 12m variation of shares which are defined as the high quartile of stocks relative to the low quartile of stocks. The performance in on an equal-weighted and total return basis. The baskets are rebalanced quarterly. The benchmark universe is the Banks sector in the top 300 stocks in FTSE World Europe universe.


What to buy now?

The current run of macro data and the path of policy recently can make it hard to form a simple interpretation of where we are in an investment clock as it puts us on the cusp of recovery and downturn depending on how one weights the various inputs. So in looking for factors at the moment, we would like to identify those that could work in either environment. Thus we think we are in a phase of the cycle when a value plus momentum approach can work with the value factor of choice being dividend yield. Our dividend yield plus price momentum factor is the one that has, on average, achieved the best returns in this phase in previous cycles.

The stocks that screen as attractive and unattractive on this basis at present are listed below. This reinforces our positive view on well capitalised northern European banks such as SHB, STAN and DNB, while also capturing some beta from the more investable EU17 banks such as SAN. Our underweight positions generally comprise EU17 banks as well as some northern lenders with acknowledged Basel 3 capital shortfalls and/or where investors have concern about the outlook for growth and impairments, including UCG, RIBH, RBS and DANSKE.

2 For more detailed work on alternative management quality measures please see ‘Quantifying Accounting and Management Quality’, 20 May 2010

0

20

40

60

80

100

120

140

160

180

De

c-9

1

De

c-9

2

De

c-9

3

De

c-9

4

De

c-9

5

De

c-9

6

De

c-9

7

De

c-9

8

De

c-9

9

De

c-0

0

De

c-0

1

De

c-0

2

De

c-0

3

De

c-0

4

De

c-0

5

De

c-0

6

De

c-0

7

De

c-0

8

De

c-0

9

De

c-1

0

De

c-11

Dec ’91 = 100

D(Shares)12M C.F

D(Change in employees)

At the cusp of recovery and downturn phases in the cycle we would favour a value plus momentum approach with dividend yield as the favoured measure of value


24

Fig. 32: Stocks that screen as attractive on dividend yield + price momentum


BUYCompany Div yld Price mtm

NATIXIS 9.79 -3.6BANCO SANTANDER S.A. 10.24 -2.8DNB ASA 6.46 -1.7BANCO DE SABADELL S.A. 7.09 -1.5SVENSKA HANDELSBANKEN A 4.41 -0.4STANDARD CHARTERED PLC 2.84 -0.3

SELLCompany Div yld Price mtmUNICREDIT S.P.A. 5.2 -8.6CREDIT AGRICOLE S.A. 9.6 -5.9ERSTE GROUP BANK AG 4.2 -5.3DANSKE BANK A/S 0 -3.5RAIFFEISEN BANK INTERNATIONAL AG 4 -3.2ROYAL BANK OF SCOTLAND GROUP PLC 0 -2.6SKANDINAVISKA ENSKILDA BANKEN AB 3.5 -2.3


25

Appendix: Factor definitions

Price to Book Price to Book Book Value is book value of equity as per last reported accounts

Tangible Book to Price (Book Value - Intangible Assets) to Price

The tangible book is defined as book value less intangible assets

Dividend Yield Dividend to Price Uses past 12 months' dividend paid. Data is source from FTSE

Earnings Momentum 6-month Earnings MomentumPercentage change in the 12-month forward earnings forecast between previous two quarters

Price Momentum 12-month Price Momentum Price returns over past 12 month

12M Price Momentum 12-month Price Momentum Price returns over past 12 month




12M Forward PEPrice to the 12M Forward Earnings

We construct rolling 12-month forward mean consensus EPS from FY1 and FY2 data.This is sourced from IBES.We make a number of corrections to the raw IBES data to correct for changes that have been made to the data; these are accounting changes and currency changes

12M Trailing PEPrice To Earnings (Trailing reported from WS)

Earnings before goodwill and extraordinary items defined as follows: Net income before extraordinary items +depreciation, depletion & amortization + tax-adjusted extraordinary charge (pre-tax) - tax-adjusted extraordinary credit (pre-tax)

PEG Price-To-Earnings to Growth The 12-month trailing PE to long-term growth rate. The growth rate is the consensus long-term expected growth rate sourced from IBES

Price to Book to Excess ROE

Price to Book /(1+(ROE -Bond Yield)/100)

The price to book divided by the difference of the return on equity and the respective bond sovereign rate

Core Tier One Core Tier One Capital RatioDefined as common equity minus intangible assets dividend by the risk weighted assets

Tier One Tier One Capital Ratio Ratio of equity to the risk weighted assets

Non Performing Loans Ratio

Non Performing Loans To Total Loans Ratio

Non performing loans to total loans

Loan-To-Deposit Ratio Loan-To-Deposit Ratio Total loans divided by deposits

Credit Rating Credit Rating Credit Rating from Moodys

Return on Risk Weighted Assets

Net Income to RWA Adjusted net income divided by the risk weighted assets

ROE Pre Goodwill Earnings Per Share to Price

Return is defined as earnings pre goodwill and exceptionals.The value of equity is the average of the past two years' capital employed.Where possible, we adjust the common equity by adding back cumulative written-off goodwill. Data is from Worldscope using last reported accounts

Y-o-y Variation of Employees

Change in Employees One-year change in the number of employees

12m Variation of Shares Change in Number of SharesDefined as, share count at time t compared with the average number of shares in the past 12 months


26

Business cycle clocks and quant factors

• We use the OECD's composite leading indicator (CLI) to divide the economy into phases, in order to measure the effectiveness of major eight quant factors in each phase. Business cycles tend to rotate in a counter-clockwise direction.

• Since the CLI is published with a time lag, we analyzed the relationship between economic phases and three-month forward factor returns. We found that factor effectiveness varies depending on the phase. For example, the Sharpe ratio of B/P is high and stable during bust periods, while E/P is high and stable during booms. A projected simulation that switches factors based on economic phase generated positive performance. This suggests that the premium for each factor changes depending on the phase.

Factor impact varies depending on phase

1. Introduction It is widely recognized that average returns tend to be higher for stocks with high values for factors such as B/P, E/P, and dividend yield, but it is not always the case that these stocks generate high returns. For instance, stocks with high dividend yields are seen as defensives that generate a comparatively high return during periods of economic deterioration. Some factors, like ROE, generate almost no returns on a long-term average basis but at times can produce significant returns. In this report, we examine the relationship between phases of the business cycle and factor returns. Using the OECD's CLI, we look at the returns generated by key factors during phases of the business cycle.

2. Business cycles Figure 33 shows the OECD’s CLI and its change in level versus the previous month (1st diff)3. The CLI covers the wide range of OECD members and is designed to anticipate turning points in the global economy. The trend in economic activity is removed (de-trending), and a CLI level of over 100 implies that economic activity is above trend. Smoothing of the CLI makes economic cycles easy to pinpoint. The latest CLI was published on 13 February 2012 and covers December 2011. The CLI turned to increase in November.

There are various ways in which to separate the economy into phases. The most simple, in our view, is to divide the economy into two periods based on CLI level. As illustrated in Figure 34, we define a CLI level above trend (CLI >= 100) as a boom period and a level below trend (CLI < 100) as a bust period. To determine the relationship between economic phases and share price returns, however, we think the change in the CLI (1st diff) provides important information.

3 In this report, we use the amplitude adjusted CLI for the OECD area, the most generic of the OECD’s CLIs.

Hiromichi Tamura

+81 3 6703 1680

[email protected]

Tomonori Uchiyama

+81 3 6703 1741

[email protected]

Report previously published on 18 January 2012




27

Fig. 33: OECD’s CLI

Note: Based on data for December 2011, announced February 2012.

Source: OECD

Fig. 34: Business cycle phases

Source: Nomura

Fig. 35: Business cycle clocks

Source: Nomura

-3

-2

-1

0

1

90

92

94

96

98

100

102

10490

/1

91/1

92/1

93/1

94/1

95/1

96/1

97/1

98/1

99/1

00/1

01/1

02/1

03/1

04/1

05/1

06/1

07/1

08/1

09/1

10/1

11/1

(yy/m)

Change (1st diff, rhs)

CLI (lhs)

Time

Level

1st dif f

Level1st diff 2nd diff

I (recovery)< 100>= 0>= 0

II (expansion)>= 100>= 0< 0

III (slowdown)>= 100

< 0< 0

IV (downturn)< 100< 0>= 0

Bust BustBoom

1st

Level

2nd

1st diff

I (recovery)

II (expansion)III (slowdown)

IV (downturn)

I (recovery)II (expansion)

III (slowdown) IV (downturn)

(A) Level and 1st diff (B) 1st diff and 2nd diff


28

Here, we divide the business cycle into four phases, as shown in Figure 34. Starting with a trough in the economy, phases move from I (recovery) to II (expansion), III (slowdown), and IV (downturn). Assuming the actual CLI level trends as per Figure 34, 1st diff leads the CLI level by one economic phase. Figure 33 also indicates that 1st diff leads the actual CLI broadly by one phase.

With a CLI trend mirroring that in Figure 34, plotting a cycle with 1st diff on the horizontal axis and the CLI level on the vertical axis results in the business cycle rotating in a counter-clockwise direction, as shown by panel (A) in Figure 35. In our view, an economic-related indicator that warrants attention in addition to the CLI level and its change from the prior month (1st diff) is the change in 1st diff from one month to the next (2nd diff). When the CLI moves as per Figure 34, 1st diff is initially negative and the CLI level is below 100—indicating phase IV (downturn)—but if 2nd diff stays positive, then 1st diff also eventually turns positive and ultimately the CLI level surpasses 100. In other words, 2nd diff acts as a sign. Changing around the axes such that 2nd diff is horizontal and 1st diff vertical, as in panel (B) of Figure 35, also leads the business cycle to rotate in a counter-clockwise direction. However, the four phases of the cycle are positioned differently from those in panel (A). Panel (A) and panel (B) are referred to as business cycle clocks.

Fig. 36: Business cycle clocks based on OECD's CLI (A) Phases based on level and 1st diff Jan 1993-Dec 2011 Latest trend (Apr 2010-Dec 2011

(B) Phases based on 1st diff and 2nd diff Jan 1993-Dec 2011 Latest trend (Apr 2010-Dec 2011

Note: 1st diff represents the difference between current-month and previous-month CLI, while 2nd diff represents the difference between current-month 1st diff and previous-month 1st diff. Based on CLI data for December 2011, announced February 2012. The sample period is Jan 1993–Dec 2011.

Source: Nomura, based on OECD data

90

92

94

96

98

100

102

‐2 ‐1 0 1

Change (1st diff)

Level

Ⅰ

ⅡⅢ

Ⅳ

10/0410/07

10/10

11/0111/04

11/07

11/1011/12

99

100

101

102

103

‐0.75 ‐0.5 ‐0.25 0 0.25 0.5

Change (1st diff)

Level

Ⅰ

ⅡⅢ

Ⅳ

‐2

‐1.5

‐1

‐0.5

0

0.5

1

1.5

‐0.3 0 0.3 0.6

Change (2nd diff)

Change

(1st diff)

Ⅳ

ⅠⅡ

Ⅲ

10/04

10/07

11/01

11/04

11/07

11/10

11/12

‐0.6

‐0.4

‐0.2

0

0.2

0.4

‐0.2 ‐0.1 0 0.1 0.2

Change (2nd diff)

Change

(1st diff)

Ⅳ

ⅠⅡ

Ⅲ


29

Figure 36 shows business cycle clocks based on the actual CLI. Panel (A) shows phases based on level and 1st diff, and panel (B) shows phases based on 1st diff and 2nd diff. The graphs illustrate how the business cycles based on actual data also rotate counter-clockwise. The trough was deep at the time of the Lehman collapse and the wide outward arc of the lines in both graphs makes it difficult to ascertain the trend. The right-hand graphs are based on data in the recent period.

The CLI was at 100.4 as of December 2011 and 1st diff and 2nd diff are positive. The business cycle based on CLI level and 1st diff accordingly shows the economy in expansion (II), while that based on 1st diff and 2nd diff positions the economy in recovery (I).

We hereafter use the three together—level, 1st diff, and 2nd diff. There are eight combinations of these. First we divide boom and bust periods based on CLI level (100 and above being boom and below 100 being bust), and further divide into four phases based on the sign for 1st diff and 2nd diff. Even where the phases for 1st diff and 2nd diff are the same (that is, even where the sign for the relevant 1st diff and 2nd diff is the same), performances of factor returns may differ when the CLI level differs (that is, between bust and boom periods).

3. Factor returns In the remainder of this report, we will analyze the relationship between business cycles and factor returns. Here, we examine eight factors detailed below.

Factor return calculation method

We sort stocks into quintiles based on their factor values (in ascending order) at the end of the preceding month for each of the factors below, and determine monthly returns by subtracting the return of the bottom (#1) quintile from that of the top (#5) quintile for each factor. Returns include dividends.

The universe is TOPIX stocks, and portfolios are equally weighted and rebalanced monthly.

Factor definitions

1. B/P Actual shareholders' equity ÷ market cap

2. E/P Next-FY net profits (consensus forecast) ÷ market cap

3. Dividend yield Current-FY DPS (Nikkei forecast) ÷ share price

4. ROE Next-FY net profits (consensus forecast) ÷ actual shareholders' equity

5. Revision Revision rate for analyst forecasts. Next-FY recurring profits (consensus) minus the average forecast over the past three months, divided by the absolute value of the average forecast over the past three months.

6. Profit growth For recurring profits (consensus forecast), (next-FY – current-FY) ÷ ((|next-FY| + |current-FY|) / 2)

7. Size Log market cap

8. Past 12-month return Return over the past 12 months (incl dividends)


30

Fig. 37: Factor returns

Cumulative factor returns

(annualized)

B/P E/P

Dividend yield

ROE Revision Profit

growth Size

Past 12-month return

Average return (%) 16.83 14.84 7.49 -0.11 14.12 1.94 -6.50 -7.54

Standard deviation (%) 13.59 11.22 11.20 14.18 8.98 9.68 19.98 22.48

Sharpe ratio 1.24 1.32 0.67 -0.01 1.57 0.20 -0.33 -0.34

Note: Sample period is Jan 1993–Nov 2011. Universe is TOPIX stocks. Panel (A) shows cumulative monthly factor returns, starting from end-Dec 1992 as zero. Panel (B) shows a simple regression analysis for each TOPIX stock's monthly return versus the previous-month-end value of each factor.

Source: Nomura

Figure 37 shows cumulative factor returns. Factors for which we observe a large premium over the long term are B/P and E/P, owing to the value stock effect, as well as revision (revision rate to analyst forecasts), all of which have Sharpe ratios in excess of 1. Dividend yield follows these. Size and past 12-month return show negative premiums, as the small cap effect and return reversal effect demonstrate positive premiums over the long term, while we sort quintiles from lowest to highest. ROE and profit growth demonstrate no long-term premium, either positive or negative.

4. Relationship between business cycle and factor returns We will now look at the relationship between the business cycle and three-month forward factor returns. The CLI is published with a two-month time lag; for example, the CLI for August 2011 was published on 10 October 2011. We use the CLI as announced at each point in time, because when new CLI data are announced, minor revisions are made retrospectively to past data. For the period up to and including October 2000, however, we use the November 2000 figure announced in January 2001 because it is not possible to obtain CLI figures released prior to that time.

Figure 38 shows the results of our analysis. The characteristics of the individual factors are as follows.

B/P, E/P The three-month forward Sharpe ratios for B/P and E/P are the opposite of each other, in relation to CLI levels. The Sharpe ratio for B/P is higher during bust periods (CLI < 100), while the Sharpe ratio for E/P is higher during boom periods (CLI >= 100).

Dividend yield For dividend yield, the three-month forward Sharpe ratio is only greater than 1 in the slowdown (III) phase in bust periods (CLI < 100) and the downturn (IV) phase in boom periods (CLI >= 100). These are the two phases in which the three-month forward Sharpe ratio for the TOPIX is negative, reflecting the fact that dividend yield is a defensive factor.

Revision The revision factor is stable in all phases of the business cycle, and the three-month Sharpe ratio for the revision factor is greater than 1 in almost all phases.

-50

0

50

100

150

200

250

300

350

92/1

2

94/1

2

96/1

2

98/1

2

00/1

2

02/1

2

04/1

2

06/1

2

08/1

2

10/1

2

(%)

(yy/m)

B/P

E/P

Dividend yield

ROE

-200

-150

-100

-50

0

50

100

150

200

250

300

92/1

2

94/1

2

96/1

2

98/1

2

00/1

2

02/1

2

04/1

2

06/1

2

08/1

2

10/1

2

(%)

(yy/m)

Revision

Profit growth

SizePast 12-month return


31

ROE, profit growth, size These factors are only effective in certain phases. The three-month forward Sharpe ratio for ROE is less than -1 in the slowdown (III) and downturn (IV) phases of bust periods (CLI < 100), indicating underperformance by high-ROE stocks. However, for profit growth, the three-month forward Sharpe ratio is greater than 1 in the downturn (IV) phase of bust periods (CLI < 100) and less than -1 in the downturn (IV) phase of boom periods (CLI >= 100). Overall, Sharpe ratios are low for the size factor.

Past 12-month return Depending on the phase, there is sometimes a reversal effect and sometimes a momentum effect. In general, there tends to be a three-month reversal effect in bust periods (CLI < 100).

Figure 39 compares factors in each phase of the business cycle. Panel (A) shows the rankings of the various factors in terms of the absolute value of the three-month forward Sharpe ratio, while Panel (B) shows the top three factors in each phase of the business cycle.

B/P is included in the top two factors in every phase in bust periods (CLI < 100). E/P, in contrast, is included in the top two factors in every phase in boom periods (CLI >= 100). Revision is stable and is included in the top three factors in all phases except one.

Dividend yield, ROE, and profit growth are all included in the top three in some phases. However, the Sharpe ratios for ROE and profit growth are negative when they are included in the top three. In other words, these two factors are only included in the top three when stocks with low ROE and low profit growth are recognized as undervalued and therefore outperform the market. The size factor is never included in the top three. Past 12-month return is sometimes included in the top three as a reversal effect and sometimes as a momentum effect.


32

Fig. 38: Sharpe ratios in each phase of business cycle (3-M forward)

Note: Based on CLI published at each time point. However, data up to and including October 2000 are based on CLI for November 2000, published in January 2001. Sample period is Jan 1993–Oct 2011. Returns in %.

Source: Nomura

1.3

3.3

2.2 2.4

0.6 0.9 1.1

0.8

0

1

2

3

4

I II III IV I II III IV

CLI < 100 CLI >= 100

(%)B/P

1.2 0.9

-0.1 0.0

2.3

1.7

2.8 3.1

-1

0

1

2

3

4


CLI < 100 CLI >= 100

(%)E/P

0.8 0.6

1.8

-0.1

0.1

1.0

0.4

1.3

-1

0

1

2


CLI < 100 CLI >= 100

(%) Dividend yield

0.5

-0.4

-1.5 -1.7

0.7

0.2 0.3

0.6

-2

-1

0

1


CLI < 100 CLI >= 100

(%)ROE

1.2

2.5 2.4

-0.1

2.8

1.1

2.1

2.6

-1

0

1

2

3


CLI < 100 CLI >= 100

(%) Revision

0.3

-0.2 -0.6

1.3

0.5

-0.1

0.7

-1.8

-3

-2

-1

0

1

2


CLI < 100 CLI >= 100

(%) Profit growth

0.0

-0.5 -0.9

-1.1

0.2

-0.4 -0.5

0.9

-2

-1

0

1

2


CLI < 100 CLI >= 100

(%)Size

-1.1 -1.5

0.9

-2.1

0.8

-0.4 -0.5

0.8

-3

-2

-1

0

1

2


CLI < 100 CLI >= 100

(%) Past 12-M return


33

Fig. 39: Ranking of Sharpe ratios in each phase of the business cycle (3-M forward)

(A) Ranking CLI<100 (bust) CLI>=100 (boom)

I (recovery) II (expansion) III (slowdown) IV (downturn) I (recovery) II (expansion) III (slowdown) IV (downturn)

1st diff >=0 >=0 <0 <0 >=0 >=0 <0 <0

2nd diff >=0 <0 <0 >=0 >=0 <0 <0 >=0

No. of sample months 36 15 24 25 38 37 38 14

B/P 1 High 1 High 2 High 1 High 5 High 4 High 3 High 6 High

E/P 2 High 4 High 8 Low 8 Low 2 High 1 High 1 High 1 High

Dividend yield 5 High 5 High 3 High 6 Low 8 High 3 High 7 High 4 High

ROE 6 High 7 Low 4 Low 3 Low 4 High 7 High 8 High 8 High

Revision 3 High 2 High 1 High 7 Low 1 High 2 High 2 High 2 High

Profit growth 7 High 8 Low 7 Low 4 High 6 High 8 Low 4 High 3 Low

Size 8 High 6 Low 6 Low 5 Low 7 High 6 Low 5 Low 5 High

Past 12-M return 4 Low 3 Low 5 High 2 Low 3 High 5 Low 6 Low 7 High

(B) Top three factors

Note: Based on CLI published at each time point. However, data up to and including October 2000 are based on CLI for November 2000, published in January 2001. Sample period is Jan 1993–Nov 2011. "High" ("low") next to ranking indicates that stocks with a high (low) value of the factor produce a higher average return than stocks with a low (high) value of the factor.

Source: Nomura

5. Forecast simulation We will now run a simulation involving investment in different factors in different phases of the business cycle, and evaluate its predictive power. Specifically, we will run a simulation in which we invest each month in the three factors with the highest absolute value of the Sharpe ratio in the phase of the business cycle at the time, which is based on the CLI three months previously. Factor return indicates long-short return, and thus we invest in three long-short portfolios.

For our simulation, we need information that enables us to determine the phase of the business cycle at the time, as well as estimates of Sharpe ratios in each phase. We use the following two methods. In both methods, we use the CLI published at each time point. (However, data up to and including October 2000 are based on the CLI for November 2000, published in January 2001.) Estimates for Sharpe ratios in each phase of the business cycle vary.

• Method 1 uses the Sharpe ratios for each phase as estimated from data for the whole sample period of Jan 1993–Nov 2011 (Figure 39). The ranking of the Sharpe ratios is therefore based on future data.

CLI < 100 (bust) CLI >= 100 (boom)

I (Recovery) II (Expansion)

III (Slowdown) IV (Downturn)

I (Recovery)II (Expansion)

III (Slowdown) IV (Downturn)

1. Revision (high)2. B/P (high)3. Dividend yield (high)

1. B/P (high)2. Revision (high)3. Past return (low)

1. B/P (high)2. Past return (low)3. ROE (low)

1. B/P (high)2. P/E (high)3. Revision (high)

1. Revision (high)2. E/P (high)3. Past return (high)

1. E/P (high)2. Revision (high)3. Dividend yield (high)

1. E/P (high)2. Revision (high)3. B/P (high)

1. E/P (high)2. Revision (high)3. Profit growth (low)


34

• Method 2 uses the Sharpe ratio for each phase estimated from CLI data from January 1993 up to three months before the month in question. For this, we divide the sample period into two. We first estimate Sharpe ratios for the period through June 2002, then estimate Sharpe ratios for each phase of the business cycle in the subsequent period, increasing the sample period by one month at a time, and estimating returns from July 2002 onward. This method thus does not use future data.

Figure 40 provides the results of our simulation. Panel (A) shows the return achieved by investing in the top three factors in a ratio of 3:2:1 in line with their rankings, while panel (B) shows the return on investing in the top three factors on an equally weighted basis. The Sharpe ratio, using Method 1, comes to 2.54 for the whole period in panel (A) and to 2.52 for the period from July 2002 onward, both of which are very high values. The equivalent Sharpe ratios for panel (B) are slightly lower.

Figure 41 shows the factors we used for each period. The black and white circles indicate positive and negative Sharpe ratios, respectively. For example, for 12-month past return, a black circle indicates investment in the factor on the basis of a momentum effect, while a white circle indicates investment on the basis of a reversal effect. It is clear from Figure 41 that, while the revision factor is used almost all the time, B/P and E/P tend to be used alternately, depending on whether the economy is in a boom or bust period. 12-month past return also tends to alternate between reversal and momentum.

For comparison purposes, we set the initial value of cumulative return in Method 2 at the value of cumulative return at June 2002 in Method 1 in panels (A) and (B) of Figure 10. The performance of Method 2 is worse than that of Method 1, but the Sharpe ratios are still high, at 1.97 in panel (A) and 1.86 in panel (B).

The results of the analysis presented in this chapter indicate that each factor's premium changes in line with business conditions, and that it is to some extent possible to project future factor returns through the business cycle.


35

Fig. 40: Cumulative return from investing in three factors with highest Sharpe ratio in line with phase of business cycle

(A) Investing in top three factors in 3:2:1 ratio

Monthly return (annualized, %)

Whole period (93/1–11/11)

Second half (02/7–11/11)

Method 1

Average 20.25 21.76

Standard deviation 7.98 8.63

Sharpe ratio 2.54 2.52

Method 2

Average - 17.81

Standard deviation - 9.05

Sharpe ratio - 1.97

(B) Investing in top three factors on equally weighted basis

Monthly return (annualized, %)

Whole period (93/1–11/11)

Second half (02/7–11/11)

Method 1

Average 19.02 20.09

Standard deviation 8.05 8.00

Sharpe ratio 2.36 2.51

Method 2

Average - 16.10

Standard deviation - 8.67

Sharpe ratio - 1.86

Note: Shows return on investment in top three factors in terms of absolute value of Sharpe ratio, for eight phases of business cycle based on 1st and 2nd diffs of three-month prior CLI. Portfolios rebalanced on monthly basis. Based on CLI published at each time point. However, data up to and including October 2000 are based on CLI for November 2000 published in January 2001. In Method 1, the Sharpe ratio for each phase of the business cycle is estimated from data for the whole sample period (Jan 1993–Nov 2011), while in Method 2 the Sharpe ratios are calculated from CLI data from January 1993 through to three months before the month in question. Sample period is Jan 1993–Nov 2011 in Method 1 and Jul 2002–Nov 2011 in Method 2.

Source: Nomura

Fig. 41: Factors used in forecast simulation (Method 1)

Note: Shows top three factors in terms of absolute value of Sharpe ratio, for eight phases of business cycle based on 1st and 2nd diffs of three-month prior CLI. Based on CLI published at each time point. However, data up to and including October 2000 are based on CLI for November 2000 published in January 2001. Sharpe ratios for each phase estimated from data for whole period (Jan–Oct 2011). Black and white circles indicate positive and negative Sharpe ratios, respectively. Sample period is Jan 1993–Nov 2011.

Source: Nomura

-50

0

50

100

150

200

250

300

350

400

92/1

2

94/1

2

96/1

2

98/1

2

00/1

2

02/1

2

04/1

2

06/1

2

08/1

2

10/1

2

(%)

(yy/m)

Method 1

Method 2

02/6

-50

0

50

100

150

200

250

300

350

400

92/1

2

94/1

2

96/1

2

98/1

2

00/1

2

02/1

2

04/1

2

06/1

2

08/1

2

10/1

2

(%)

(yy/m)

Method 1

Method 2

02/6

93/0

1

94/0

1

95/0

1

96/0

1

97/0

1

98/0

1

99/0

1

00/0

1

01/0

1

02/0

1

03/0

1

04/0

1

05/0

1

06/0

1

07/0

1

08/0

1

09/0

1

10/0

1

11/0

1

(yy/m)

B/P

E/P

Dividend yield

ROE

Revision

Earnings growth rate

Size

Past return


36

Correlation: stocks go their divergent ways • The fall in pair-wise stock correlation since the start of the year has been the largest

that we have seen.

• Falls in correlation such as this tend to be supportive for market returns

• This also increases the potential return to be earned from stock-picking and we can show that such falls tend to directly lead to greater inflows for active strategies.

• For quants in particular the fall in correlation should be beneficial. We think that this will lead to a more positive outlook for quants in coming months.

There is understandable excitement with a rally that has taken the S&P back to its 2011 highs and also delivered a significant recovery in other regions. A major consequence of this is the sharp drop in correlation that has accompanied this rally. On some measures this is the largest and fastest fall in correlation that we have ever seen. Our US colleagues recently examined this in some detail (see January’s historic correlation collapse, 6 February 2012). Here we discuss some of the consequences of this fall:

• Stock picking becomes a more profitable exercise as correlation falls;

• Flows into active funds relative to passive tend to pick up following a fall in correlation; and

• Falls in correlation tend to be supportive for equity index levels.

We think that this fall also supports the case that we have made before4 that there has not been a structural increase in correlation in recent years. Rather, we have just witnessed some large cycles in the measure.

Fig. 42: Correlation of global stocks (25 day rolling average pairwise correlation)


Predicting correlation can be difficult. However, there is a strong mean-reverting tendency from peaks and also there is a tendency for implied volatility to lead measures of correlation. The last three major peaks in correlation have exactly coincided with peaks of volatility and peaks in the two series have coincided over the past two decades, Figure 43. However falls in volatility have tended to lead falls in correlation. This is a lead-lag relationship similar to that which exists at the market level that we have discussed in recent research (see Value and Volatility, January 15 2012). The VIX has fallen from 34.5 to 21.1 since 25 November 2011. Despite the small up-tick in the VIX since 3 February, this is consistent with a low level of correlation.

4 Please see: ‘Is stock picking dead? (or why is correlation so high?)’, 11 October 2010

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Ap

r-0

0

Oct

-00

Ap

r-0

1

Oct

-01

Ap

r-0

2

Oct

-02

Ap

r-0

3

Oct

-03

Ap

r-0

4

Oct

-04

Ap

r-0

5

Oct

-05

Ap

r-0

6

Oct

-06

Ap

r-0

7

Oct

-07

Ap

r-0

8

Oct

-08

Ap

r-0

9

Oct

-09

Ap

r-1

0

Oct

-10

Ap

r-11

Oct

-11

Correlation

Inigo Fraser-Jenkins

+44 20 7102 4658

[email protected] Saurabh Katiyar +44 20 7102 9135 [email protected]

2012 has seen the fastest fall in correlation that we have experienced



37

Fig. 43: Correlation and implied volatility

Source: Nomura Quantitative Strategy, Datastream

Implications for active vs passive funds

When correlations between stocks increase, investors seem to behave in a rational way and increase their allocations into passive funds at the expense of active funds. In higher correlation environments, the potential value-added from active managers is reduced so there is less incentive to pay active fees. However, we see that after the last two peaks in correlation, over the following 6-9 months there was a significant net flow into active funds. As we show in Figure 44, the recent cycle of correlation has been similar to those that we have seen in the past. As correlation rose in H2 2011, active funds lost out to passive funds. We would anticipate that the reduction in correlation that we have seen will benefit active managers in H1 2012.

Fig. 44: Flows into active vs passive funds before and after peaks in stock correlation

Source: Nomura Quantitative Strategy, EPFR

0

0.2

0.4

0.6

0.8

1

1.2

0

10

20

30

40

50

60

70

80

90Ja

n-9

0

Jan

-91

Jan

-92

Jan

-93

Jan

-94

Jan

-95

Jan

-96

Jan

-97

Jan

-98

Jan

-99

Jan

-00

Jan

-01

Jan

-02

Jan

-03

Jan

-04

Jan

-05

Jan

-06

Jan

-07

Jan

-08

Jan

-09

Jan

-10

Jan

-11

Jan

-12

Index

CBOEVIX VIX (LHS)

CBOEVIX Median 63-Day Correlation of S&P 500 Stocks to the S&P 500 Index (RHS)

-20,000

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

Series2

Series3

Series4

Flows into active strategies tend to pick up following a fall in correlation


38

Implications for quants

Correlation has implications for active managers in general, but also is of importance to quant managers in particular. For several years now we have run our ‘Meta Model’ that tries to model the 12-month future performance of quants relative to the market. This models the future performance as a function of factor dispersion and stock correlation. The spike in correlation in Q4 2011 pushed our alpha forecast for quants down to zero – ie predicting an in-line return from quant managers. We find that for a 12-month prediction of quant returns a 75-day correlation is most appropriate. This has only just started to adjust, but barring any major event, this should follow the short-term correlation shown above. Therefore we would expect that we will be able to become more bullish on the outlook for quants in coming months.

Implications for the market

Falling correlation tends to be supportive of equity index levels. The level of correlation has tended to have a negative correlation with index levels over several decades. However, we think it is most relevant to focus in on the last two large peaks in correlation in 2008 and 2010 as they have the most similar characteristics to the recent elevated level of correlation. Correlation fell sharply from both these peaks. We show in Figure 45 the equity rally that occurred over the time that it took 75-day correlation to fall from peak to trough in these last two cycles, in this case for the global market. Although the market response so far has been similar to that which we saw in 2010, the fall in 75-day correlation so far has been much smaller and the peak level reached was higher. The abrupt fall in 25-day correlation we think means that 75-day correlation will follow, which should be supportive of future equity returns.

Fig. 45: Fall in 75-day correlation and market returns in recent cycle


We can also show the relationship between n day forward returns and correlation level on a continuous basis over the past dozen years. Here the look-back window for the calculation of the correlation depends on how far forward one wants to forecast the market. For up to 25-day forward returns a 25-day look-back on the correlation is most relevant, but for longer holding periods a longer look-back window is required. The relationship continues to strengthen out to a one year forward horizon. We conclude that the recent fall in correlation should be supportive of equity indices in 2012.

index level mkt return, % correl levelchange in correlation

peak 01/12/2008 237.7 0.38trough 03/08/2009 312.3 31.4 0.20 -0.19

peak 17/08/2010 330.0 0.39trough 26/01/2011 388.9 17.9 0.17 -0.23

peak 22/09/2011 318.0 0.43now 15/02/2012 373.6 17.5 0.33 -0.10

Small fall so far

The fall in correlation should be supportive for equity index levels


39

Fig. 46: Correlation of x day trailing correlation measure and n day forward market returns

Figure shows the correlation coefficient between x day trailing average pairwise correlation and n day forward market returns April 2000-January 2012 for a global universe. Source: Nomura Quantitative Strategy

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 50 100 150 200 250 300

75 day

25 day


40

Nomura Global Quant Team contacts

EQUITY RESEARCH

London

Ian Scott +44 207 102 2959 [email protected]

Inigo Fraser-Jenkins +44 207 102 4658 [email protected]

Shanthi Nair +44 207 102 4518 [email protected]

Mark Diver +44 207 102 2987 [email protected]

Saurabh Katiyar +44 207 102 1935 [email protected]

Rohit Thombre +44 207 102 5461 [email protected]

Robertas Stancikas +44 207 102 3127 [email protected]

Gerard Alix Guerrini +44 207 102 5079 [email protected]

Maureen Hughes +44 207 102 4659 [email protected]

Yamini Patel +44 207 102 4257 [email protected]

Tokyo

Hiromichi Tamura +81 3 6703 1680 [email protected]

Tomonori Uchiyama +81 3 6703 1741 [email protected]

Yoko Ishige +81 3 6703 3914 [email protected]

Akihiro Murakami +81 3 6703 1746 [email protected]

Mami Ode +81 3 6703 1743 [email protected]

Naoko Kato +81 3 6703 3912 [email protected]

Hong Kong

Sandy Lee +852 2252 2101 [email protected]

Yasuhiro Shimizu +852 2252 2107 [email protected]

Rico Kwan, CFA +852 2252 2102 [email protected]

Tacky Cheng +852 2252 2105 [email protected]

Desmond Chan +852 2252 2110 [email protected]

Flora Lam +852 2252 1397 [email protected]

New York

Joseph J Mezrich +1 212 667 9316 [email protected]

Yasushi Ishikawa +1 212 667 1562 [email protected]

Junbo Feng +1 212 667 9016 [email protected]

Gan Jiang +1 212 667 1073 [email protected]

London

Ashish Gupta +44 20 7103 2448 [email protected]

New York

Amit Manwani +1 212 667 9809 [email protected]

London

Bhavik Shah +44 20 710 39988 [email protected]

Sarah English +44 20 710 35485 [email protected]

Norman Pfeifer +44 20 710 39988 [email protected]

Danial Rohani +44 20 710 39988 [email protected]

New York

William O'Brien +1 212 667 2081 [email protected]

Ethan Brodie +1 212 667 2123 [email protected]

Gregory Giordano +1 212 667 9408 [email protected]

Anushree Laturkar +1 212 667 9806 [email protected]

Peter Holowesko +1 212 667 1692 [email protected]

Tokyo

Aaron Kugan +81 3 3272 7996 [email protected]

US QUANTITATIVE DESK STRATEGY

LIQUID MARKET ANALYTICS

QUANT SOLUTIONS GROUP


41

Appendix A-1

Analyst Certification

We, Sandy Lee, Inigo Fraser-Jenkins, Hiromichi Tamura and Jon Peace, hereby certify (1) that the views expressed in this Research report accurately reflect our personal views about any or all of the subject securities or issuers referred to in this Research report, (2) no part of our compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this Research report and (3) no part of our compensation is tied to any specific investment banking transactions performed by Nomura Securities International, Inc., Nomura International plc or any other Nomura Group company.


42

Important Disclosures Online availability of research and conflict-of-interest disclosures Nomura research is available on www.nomuranow.com, Bloomberg, Capital IQ, Factset, MarkitHub, Reuters and ThomsonOne. Important disclosures may be read at http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx or requested from Nomura Securities International, Inc., on 1-877-865-5752. If you have any difficulties with the website, please email [email protected] for help. The analysts responsible for preparing this report have received compensation based upon various factors including the firm's total revenues, a portion of which is generated by Investment Banking activities. Unless otherwise noted, the non-US analysts listed at the front of this report are not registered/qualified as research analysts under FINRA/NYSE rules, may not be associated persons of NSI, and may not be subject to FINRA Rule 2711 and NYSE Rule 472 restrictions on communications with covered companies, public appearances, and trading securities held by a research analyst account. Any authors named in this report are research analysts unless otherwise indicated. Industry Specialists identified in some Nomura International plc research reports are employees within the Firm who are responsible for the sales and trading effort in the sector for which they have coverage. Industry Specialists do not contribute in any manner to the content of research reports in which their names appear. Marketing Analysts identified in some Nomura research reports are research analysts employed by Nomura International plc who are primarily responsible for marketing Nomura’s Equity Research product in the sector for which they have coverage. Marketing Analysts may also contribute to research reports in which their names appear and publish research on their sector. Distribution of ratings (US) The distribution of all ratings published by Nomura US Equity Research is as follows: 35% have been assigned a Buy rating which, for purposes of mandatory disclosures, are classified as a Buy rating; 11% of companies with this rating are investment banking clients of the Nomura Group*. 59% have been assigned a Neutral rating which, for purposes of mandatory disclosures, is classified as a Hold rating; 2% of companies with this rating are investment banking clients of the Nomura Group*. 6% have been assigned a Reduce rating which, for purposes of mandatory disclosures, are classified as a Sell rating; 0% of companies with this rating are investment banking clients of the Nomura Group*. As at 31 December 2011. *The Nomura Group as defined in the Disclaimer section at the end of this report. Distribution of ratings (Global) The distribution of all ratings published by Nomura Global Equity Research is as follows: 47% have been assigned a Buy rating which, for purposes of mandatory disclosures, are classified as a Buy rating; 40% of companies with this rating are investment banking clients of the Nomura Group*. 43% have been assigned a Neutral rating which, for purposes of mandatory disclosures, is classified as a Hold rating; 45% of companies with this rating are investment banking clients of the Nomura Group*. 10% have been assigned a Reduce rating which, for purposes of mandatory disclosures, are classified as a Sell rating; 21% of companies with this rating are investment banking clients of the Nomura Group*. As at 31 December 2011. *The Nomura Group as defined in the Disclaimer section at the end of this report. Explanation of Nomura's equity research rating system in Europe, Middle East and Africa, US and Latin America The rating system is a relative system indicating expected performance against a specific benchmark identified for each individual stock. Analysts may also indicate absolute upside to target price defined as (fair value - current price)/current price, subject to limited management discretion. In most cases, the fair value will equal the analyst's assessment of the current intrinsic fair value of the stock using an appropriate valuation methodology such as discounted cash flow or multiple analysis, etc. STOCKS A rating of 'Buy', indicates that the analyst expects the stock to outperform the Benchmark over the next 12 months. A rating of 'Neutral', indicates that the analyst expects the stock to perform in line with the Benchmark over the next 12 months. A rating of 'Reduce', indicates that the analyst expects the stock to underperform the Benchmark over the next 12 months. A rating of 'Suspended', indicates that the rating, target price and estimates have been suspended temporarily to comply with applicable regulations and/or firm policies in certain circumstances including, but not limited to, when Nomura is acting in an advisory capacity in a merger or strategic transaction involving the company. Benchmarks are as follows: United States/Europe: Please see valuation methodologies for explanations of relevant benchmarks for stocks (accessible through the left hand side of the Nomura Disclosure web page: http://go.nomuranow.com/research/globalresearchportal);Global Emerging Markets (ex-Asia): MSCI Emerging Markets ex-Asia, unless otherwise stated in the valuation methodology. SECTORS A 'Bullish' stance, indicates that the analyst expects the sector to outperform the Benchmark during the next 12 months. A 'Neutral' stance, indicates that the analyst expects the sector to perform in line with the Benchmark during the next 12 months. A 'Bearish' stance, indicates that the analyst expects the sector to underperform the Benchmark during the next 12 months. Benchmarks are as follows: United States: S&P 500; Europe: Dow Jones STOXX 600; Global Emerging Markets (ex-Asia): MSCI Emerging Markets ex-Asia. Explanation of Nomura's equity research rating system in Japan and Asia ex-Japan STOCKS Stock recommendations are based on absolute valuation upside (downside), which is defined as (Target Price - Current Price) / Current Price, subject to limited management discretion. In most cases, the Target Price will equal the analyst's 12-month intrinsic valuation of the stock, based on an appropriate valuation methodology such as discounted cash flow, multiple analysis, etc. A 'Buy' recommendation indicates that potential upside is 15% or more. A 'Neutral' recommendation indicates that potential upside is less than 15% or downside is less than 5%. A 'Reduce' recommendation indicates that potential downside is 5% or more. A rating of 'Suspended' indicates that the rating and target price have been suspended temporarily to comply with applicable regulations and/or firm policies in certain circumstances including when Nomura is acting in an advisory capacity in a merger or strategic transaction involving the subject company.

http://www.nomuranow.com/�

http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx�



http://go.nomuranow.com/research/globalresearchportal�


43

Securities and/or companies that are labelled as 'Not rated' or shown as 'No rating' are not in regular research coverage of the Nomura entity identified in the top banner. Investors should not expect continuing or additional information from Nomura relating to such securities and/or companies. SECTORS A 'Bullish' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a positive absolute recommendation. A 'Neutral' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a neutral absolute recommendation. A 'Bearish' rating means most stocks in the sector have (or the weighted average recommendation of the stocks under coverage is) a negative absolute recommendation. . Target Price A Target Price, if discussed, reflect in part the analyst's estimates for the company's earnings. The achievement of any target price may be impeded by general market and macroeconomic trends, and by other risks related to the company or the market, and may not occur if the company's earnings differ from estimates.


44

Disclaimers This document contains material that has been prepared by the Nomura entity identified at the top or bottom of page 1 herein, if any, and/or, with the sole or joint contributions of one or more Nomura entities whose employees and their respective affiliations are specified on page 1 herein or identified elsewhere in the document. Affiliates and subsidiaries of Nomura Holdings, Inc. (collectively, the 'Nomura Group'), include: Nomura Securities Co., Ltd. ('NSC') Tokyo, Japan; Nomura International plc ('NIplc'), UK; Nomura Securities International, Inc. ('NSI'), New York, US; Nomura International (Hong Kong) Ltd. (‘NIHK’), Hong Kong; Nomura Financial Investment (Korea) Co., Ltd. (‘NFIK’), Korea (Information on Nomura analysts registered with the Korea Financial Investment Association ('KOFIA') can be found on the KOFIA Intranet at http://dis.kofia.or.kr ); Nomura Singapore Ltd. (‘NSL’), Singapore (Registration number 197201440E, regulated by the Monetary Authority of Singapore); Capital Nomura Securities Public Company Limited (‘CNS’), Thailand; Nomura Australia Ltd. (‘NAL’), Australia (ABN 48 003 032 513), regulated by the Australian Securities and Investment Commission ('ASIC') and holder of an Australian financial services licence number 246412; P.T. Nomura Indonesia (‘PTNI’), Indonesia; Nomura Securities Malaysia Sdn. Bhd. (‘NSM’), Malaysia; Nomura International (Hong Kong) Ltd., Taipei Branch (‘NITB’), Taiwan; Nomura Financial Advisory and Securities (India) Private Limited (‘NFASL’), Mumbai, India (Registered Address: Ceejay House, Level 11, Plot F, Shivsagar Estate, Dr. Annie Besant Road, Worli, Mumbai- 400 018, India; Tel: +91 22 4037 4037, Fax: +91 22 4037 4111; SEBI Registration No: BSE INB011299030, NSE INB231299034, INF231299034, INE 231299034, MCX: INE261299034); Banque Nomura France (‘BNF’), regulated by the Autorité des marches financiers and the Autorité de Contrôle Prudentiel; NIplc, Dubai Branch (‘NIplc, Dubai’); NIplc, Madrid Branch (‘NIplc, Madrid’) and NIplc, Italian Branch (‘NIplc, Italy’). This material is: (i) for your private information, and we are not soliciting any action based upon it; (ii) not to be construed as an offer to sell or a solicitation of an offer to buy any security in any jurisdiction where such offer or solicitation would be illegal; and (iii) based upon information from sources that we consider reliable, but has not been independently verified by Nomura Group. Nomura Group does not warrant or represent that the document is accurate, complete, reliable, fit for any particular purpose or merchantable and does not accept liability for any act (or decision not to act) resulting from use of this document and related data. To the maximum extent permissible all warranties and other assurances by Nomura group are hereby excluded and Nomura Group shall have no liability for the use, misuse, or distribution of this information. Opinions or estimates expressed are current opinions as of the original publication date appearing on this material and the information, including the opinions and estimates contained herein, are subject to change without notice. Nomura Group is under no duty to update this document. Any comments or statements made herein are those of the author(s) and may differ from views held by other parties within Nomura Group. Clients should consider whether any advice or recommendation in this report is suitable for their particular circumstances and, if appropriate, seek professional advice, including tax advice. Nomura Group does not provide tax advice. Nomura Group, and/or its officers, directors and employees, may, to the extent permitted by applicable law and/or regulation, deal as principal, agent, or otherwise, or have long or short positions in, or buy or sell, the securities, commodities or instruments, or options or other derivative instruments based thereon, of issuers or securities mentioned herein. Nomura Group companies may also act as market maker or liquidity provider (as defined within Financial Services Authority (‘FSA’) rules in the UK) in the financial instruments of the issuer. Where the activity of market maker is carried out in accordance with the definition given to it by specific laws and regulations of the US or other jurisdictions, this will be separately disclosed within the specific issuer disclosures. This document may contain information obtained from third parties, including ratings from credit ratings agencies such as Standard & Poor’s. Reproduction and distribution of third party content in any form is prohibited except with the prior written permission of the related third party. Third party content providers do not guarantee the accuracy, completeness, timeliness or availability of any information, including ratings, and are not responsible for any errors or omissions (negligent or otherwise), regardless of the cause, or for the results obtained from the use of such content. Third party content providers give no express or implied warranties, including, but not limited to, any warranties of merchantability or fitness for a particular purpose or use. Third party content providers shall not be liable for any direct, indirect, incidental, exemplary, compensatory, punitive, special or consequential damages, costs, expenses, legal fees, or losses (including lost income or profits and opportunity costs) in connection with any use of their content, including ratings. Credit ratings are statements of opinions and are not statements of fact or recommendations to purchase hold or sell securities. They do not address the suitability of securities or the suitability of securities for investment purposes, and should not be relied on as investment advice. Any MSCI sourced information in this document is the exclusive property of MSCI Inc. (‘MSCI’). Without prior written permission of MSCI, this information and any other MSCI intellectual property may not be reproduced, re-disseminated or used to create any financial products, including any indices. This information is provided on an "as is" basis. The user assumes the entire risk of any use made of this information. MSCI, its affiliates and any third party involved in, or related to, computing or compiling the information hereby expressly disclaim all warranties of originality, accuracy, completeness, merchantability or fitness for a particular purpose with respect to any of this information. Without limiting any of the foregoing, in no event shall MSCI, any of its affiliates or any third party involved in, or related to, computing or compiling the information have any liability for any damages of any kind. MSCI and the MSCI indexes are services marks of MSCI and its affiliates. Investors should consider this document as only a single factor in making their investment decision and, as such, the report should not be viewed as identifying or suggesting all risks, direct or indirect, that may be associated with any investment decision. Nomura Group produces a number of different types of research product including, among others, fundamental analysis, quantitative analysis and short term trading ideas; recommendations contained in one type of research product may differ from recommendations contained in other types of research product, whether as a result of differing time horizons, methodologies or otherwise. Nomura Group publishes research product in a number of different ways including the posting of product on Nomura Group portals and/or distribution directly to clients. Different groups of clients may receive different products and services from the research department depending on their individual requirements. Figures presented herein may refer to past performance or simulations based on past performance which are not reliable indicators of future performance. Where the information contains an indication of future performance, such forecasts may not be a reliable indicator of future performance. Moreover, simulations are based on models and simplifying assumptions which may oversimplify and not reflect the future distribution of returns. Certain securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. The securities described herein may not have been registered under the US Securities Act of 1933 (the ‘1933 Act’), and, in such case, may not be offered or sold in the US or to US persons unless they have been registered under the 1933 Act, or except in compliance with an exemption from the registration requirements of the 1933 Act. Unless governing law permits otherwise, any transaction should be executed via a Nomura entity in your home jurisdiction. This document has been approved for distribution in the UK and European Economic Area as investment research by NIplc, which is authorized and regulated by the FSA and is a member of the London Stock Exchange. It does not constitute a personal recommendation, as defined by the FSA, or take into account the particular investment objectives, financial situations, or needs of individual investors. It is intended only for investors who are 'eligible counterparties' or 'professional clients' as defined by the FSA, and may not, therefore, be redistributed to retail clients as defined by the FSA. This document has been approved by NIHK, which is regulated by the Hong Kong Securities and Futures Commission, for distribution in Hong Kong by NIHK. This document has been approved for distribution in Australia by NAL, which is authorized and regulated in Australia by the ASIC. This document has also been approved for distribution in Malaysia by NSM. In Singapore, this document has been distributed by NSL. NSL accepts legal responsibility for the content of this document, where it concerns securities, futures and foreign exchange, issued by their foreign affiliates in respect of recipients who are not accredited, expert or institutional investors as defined by the Securities and Futures Act (Chapter 289). Recipients of this document in Singapore should contact NSL in respect of matters arising from, or in connection with, this document. Unless prohibited by the provisions of Regulation S of the 1933 Act, this material is distributed in the US, by NSI, a US-registered broker-dealer, which accepts responsibility for its contents in accordance with the provisions of Rule 15a-6, under the US Securities Exchange Act of 1934. This document has not been approved for distribution in the Kingdom of Saudi Arabia (‘Saudi Arabia’) or to clients other than 'professional clients' in the United Arab Emirates (‘UAE’) by Nomura Saudi Arabia, NIplc or any other member of Nomura Group, as the case may be. Neither this document nor any copy thereof may be taken or transmitted or distributed, directly or indirectly, by any person other than those authorised to do so into Saudi Arabia or in the UAE or to any person located in Saudi Arabia or to clients other than 'professional clients' in the UAE. By accepting to receive this document, you represent that you are not located in Saudi Arabia or that you are a 'professional client' in the UAE and agree to comply with these restrictions. Any failure to comply with these restrictions may constitute a violation of the laws of Saudi Arabia or the UAE. NO PART OF THIS MATERIAL MAY BE (I) COPIED, PHOTOCOPIED, OR DUPLICATED IN ANY FORM, BY ANY MEANS; OR (II) REDISTRIBUTED WITHOUT THE PRIOR WRITTEN CONSENT OF A MEMBER OF NOMURA GROUP. If this document has been distributed by electronic transmission, such as e-mail, then such transmission cannot be guaranteed to be secure or error-free as information could be intercepted, corrupted, lost, destroyed, arrive late or incomplete, or contain viruses. The sender therefore does not accept liability for any errors or omissions in the contents of this document, which may arise as a result of electronic transmission. If verification is required, please request a hard-copy version. Nomura Group manages conflicts with respect to the production of research through its compliance policies and procedures (including, but not limited to, Conflicts of Interest, Chinese Wall and Confidentiality policies) as well as through the maintenance of Chinese walls and employee training. Additional information is available upon request and disclosure information is available at the Nomura Disclosure web page: http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx

http://go.nomuranow.com/research/globalresearchportal/pages/disclosures/disclosures.aspx�

Documents

Nomura Global Quantitative Research Monthly 2012-02-16 494848