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© Nomura 22 May 2012

Recent Quant Performance and New Investment Ideas for Japanese Equity

Akihiro MURAKAMI Chief Quantitative Strategist, Japan Quant Research Dept Nomura Securities Co Ltd.

1

The performance of Japanese quant funds has been in deadlock since 2011. This could be related to the disaster risk caused by the Great East Japan Earthquake and the financial crisis in Europe. However, E/P factor, the main driver of the quant funds, has been keeping a certain level of positive momentum. We find it a good sign and expect the factor continues to be

effective.

On the other hand, E/P factor tends to be affected adversely by market crash. Based on the experience in 2007 and 2008, starting from the quant turmoil to Lehman's fall, a strategy compensating the weakness of the factor is introduced. (1) Herding avoidance strategies ･Quant funds tend to adopt similar strategies. We need to avoid position unwinding risk lead by

herding. (2) Combination of Alpha strategies for reducing downside risk ･In case market crashes, downside risk has to be hedged by combining a couple of factors.

Summary

2

The performance of Japanese quant funds has been in deadlock

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2001

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End-Dec 2000 = 0% Quant fund : Cumulative return

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1Q 2Q 3Q 4Q 1Q

2011 2012

(%) Quartely return of quant funds since 2011

Quarterly return of quant funds since 2011

Average performance of quant funds We selected 23 funds which seemingly use

quantitative approaches, and calculated the average return to emulate the performance of typical quant fund. Japanese quant funds have been

performed flatly since 2011. We can see more negative quarterly

returns after the earthquake in 2011Q2 and the European financial crisis in Q3.

Note: Average return(Jensen’s alpha) of 23 publicly offered investment trusts that we regard as market-neutral quant funds . The sample period is from January 2001 to April 2012. Source: Nomura

After the earthquake

The financial crisis

3

Quant fund and earnings-based factors

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End-Dec 2000 = 0% Quant fund : Cumulative return

Quant fund average

E/P Boom period E/PSlump period

E/P rehabilitation period

(post-earthquake)

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End-Dec 2000 = 0% Cumulative return

E/P factor

Analyst Revision factor

(yyy

y / m

)

E/P Boom period E/PSlump period

(post-earthquake)


The performance of quant fund and alpha factors. In the long term, the performance of

quant funds is similar to that of E/P or revision factor. Since 2011, ・ E/P factor ・・・ favorable ・ Revision Factor ・・・ unfavorable → Quant fund ・・・ flat

E/P factor has been keeping a certain

level of positive momentum, which is a a good sign as the factor is the main driver of the quant funds.

Note: Universe is TOPIX500. We divided into 5quintiles, with an equal number of stocks in each quintile, by factor value. Portfolios are rebalanced at the beginning of each month. Cumulative spread

return (#5 –#1) is calculated on monthly basis (December2000 = 0%). The sample period is from January 2001 to April 2012. Source: Nomura

4

The current situation of Japan quant investments is reviewed by comparing it with cases at the quant turmoil in 2007 and Lehman‘s fall in 2008.

The factors utilizing analyst forecasts like E/P or revision are the key drivers of quant fund investment.

The following points have to be kept in mind based on the experiences in 2007 and 2008. (1) Any decrease in the reliability on alpha related information ? ・ The quality of profit decreased. ・ Forecasting profits was also difficult.

(2) Any sign of unexpected risk ? ・ Crowded strategies caused the unwinding of positions and the subsequent poor performance. ・ Factor strategies had high market risk. Hedging β risk was not good enough.

Decreased reliability on analyst forecast and quality of profit (negative for E/P or revision factor)

Crowded strategies (under the position unwinding risk)

Unexpected market risk in α strategies (ordinary risk management might not work properly)

Current situation of Japan quant investments

Important check points influencing the performance of quant funds

5

At Lehman‘s fall, the quality of profit deteriorated rapidly and the reliability on valuations based on earnings forecast decreased. The quality of profit is higher currently.

Checking the quality of profit

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0707

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E/P Slump period E/Prehabilitation

period(post-

earthquake)

E/P boom period

Note: The universe, consisting of TOPIX500,is divided into five groups by E/P factor values. Groups are rebalanced monthly (at the start of each month). The difference of median of earnings quality factor between top and bottom quintile is calculated. N/A data for earnings quality has been deleted. The sample period is from January 2001 to April 2012.

Source: Nomura

Relative earnings quality of high E/P (undervalued) stocks to low E/P (overvalued) stocks Low

Quality in high E/P

High Quality in high E/P

6

The dispersion of analyst forecast is monitored as the proxy variable of forecast reliability. At Lehman‘s fall, company profit could not be forecasted easily. The dispersion increased soon after the earthquake but it is stable recently.

Checking the reliability on analyst forecast

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0607

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0707

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0807

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0120

0907

2010

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1007

2011

0120

1107

2012

01

E/P boom period E/P Slump period E/Prehabilitation

period(post-

earthquake)

Note: The chart shows range of forecasts (median value) drawn up by analysts. Dispersion of analyst forecasts calculated by dividing the standard deviation of IFIS next-FY RP forecast by the absolute value of the average figures for forecast profits and multiplying by 100. Universe = TOPIX 500 stocks. The sample period is from January 2001 to April 2012.

Source: Nomura

Dispersion of analysts’ earnings estimates (median value)

High Dispersion

Low Dispersion

7


Decreased reliability on analyst forecast and quality of profit → improved considerably

Crowded strategies

Unexpected market risk in α strategies

Since 2011, negative events continuously occurred for Japanese companies, however the factors based on analyst forecast have been gaining credibility recently. → At the stage where quant funds can benefit from analyst forecast



8

We measure active fund’s holdings in each stock as a percentage of total shares issued and outstanding

Examining the concentrated degree of alpha strategies(1)

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0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0(%)

Low HighShare of stocks held by funds (HoldShr)

Funds may have considerable influence over the share prices of stocks in which they have a particularly high ownership share

Frequency (%)

Distribution of fund’s share ownership ratios Risk Exposure of stocks held heavily by active fund relative to low herding stocks (Jan.2011-Apr.2012)

Note: Shows distribution of TSE-1 stocks confirmed as being owned by active funds as of end-August 2011, based on calculations of funds' holdings in each stock as a percentage of (FFW-adjusted) shares issued and outstanding. For our universe, we selected TSE-1 stocks that owned by at least three funds (f) that had closed their last set of accounts as of the start of each month (t).

Source: Nomura

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0.00

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1.00 E/P

B/P

Revision

MarketValueMerton Default Prob

Specific Risk

Sales Growth

High exposure on E/P and Sales Growth

𝐻𝐻𝐻𝐻𝐻𝐻𝑑𝑑𝑆𝑆ℎ𝑟𝑟𝑖𝑖,𝑡𝑡𝐹𝐹 =∑ 𝑆𝑆ℎ𝑟𝑟 𝑖𝑖

𝑓𝑓𝑓𝑓∈𝐹𝐹 (𝑇𝑇𝐻𝐻𝑡𝑡𝑇𝑇𝐻𝐻 𝑛𝑛𝐻𝐻.𝐻𝐻𝑓𝑓 𝑠𝑠ℎ𝑇𝑇𝑟𝑟𝑎𝑎𝑠𝑠 𝐻𝐻𝑓𝑓 𝑠𝑠𝑡𝑡𝐻𝐻𝑠𝑠𝑠𝑠 𝑖𝑖 ℎ𝑎𝑎𝐻𝐻𝑑𝑑 𝑏𝑏𝑏𝑏 𝐴𝐴𝑠𝑠𝑡𝑡𝑖𝑖𝐴𝐴𝑎𝑎 𝑓𝑓𝑓𝑓𝑛𝑛𝑑𝑑 𝑔𝑔𝑟𝑟𝐻𝐻𝑓𝑓𝑔𝑔 𝐹𝐹)

𝑂𝑂𝑓𝑓𝑡𝑡𝑆𝑆 ℎ𝑟𝑟 𝑖𝑖∗𝐹𝐹𝐹𝐹𝑊𝑊𝑖𝑖 ,𝑡𝑡(𝑁𝑁𝐻𝐻.𝐻𝐻𝑓𝑓 𝑠𝑠ℎ𝑇𝑇𝑟𝑟𝑎𝑎𝑠𝑠 𝑖𝑖𝑠𝑠𝑠𝑠𝑓𝑓𝑎𝑎𝑑𝑑 𝑇𝑇𝑛𝑛𝑑𝑑 𝐻𝐻𝑓𝑓𝑡𝑡𝑠𝑠𝑡𝑡𝑇𝑇𝑛𝑛𝑑𝑑𝑖𝑖𝑛𝑛𝑔𝑔 𝐻𝐻𝑓𝑓 𝑠𝑠𝑡𝑡𝐻𝐻𝑠𝑠𝑠𝑠 𝑖𝑖 𝑇𝑇𝑑𝑑𝑎𝑎𝑓𝑓𝑠𝑠𝑡𝑡𝑎𝑎𝑑𝑑 𝑓𝑓𝐻𝐻𝑟𝑟 𝑡𝑡ℎ𝑎𝑎 𝐹𝐹𝐹𝐹𝑊𝑊)

Note: Sample period is January 2011 through April 2012. We divided the universe into 10 groups of stocks according to the percentage of shares in each stock held by active funds and calculated the difference in the average exposure to seven factors between the group with the highest percentage of shares held by active funds (group #10) and the group with the lowest percentage (group #1).

9

Compared with the peak period in 2008, the exposure is not extremely high. The exposure has been increasing recently. ・・・ If it increases as high as in 2008, we have to keep close watch on the unwinding risk of accumulated shareholdings.

Examining the concentrated degree of alpha strategies(2)

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01

2001

07

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07

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07

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2011

07

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01

E/P boom period E/P Slump period

E/Prehabilitation

period(post-

earthquake)

Note: Universe is TSE-1 stocks. Sample period is January 2001 through April 2012.We divided the universe into 10 groups of stocks according to the percentage of shares in each stock held by active funds and calculated the difference in the average exposure to E/P between the group with the highest percentage of shares held by active funds (group #10) and the group with the lowest percentage (group #1), plotting the data as six-month moving averages. We rebased the cross-sectional average for E/P to 0,and the standard deviation to 1, at the beginning of each month. Also, to mitigate the impact of outliers,we reduced the standard deviation of stocks with a standard deviation of more than ±3σ to ±3σ three times.

Source: Nomura

E/P Exposure of stocks with a high percentage of shares held by active funds

10



Crowded strategies → improved though, we need to focus on the future trends

Unexpected market risk in α strategies



11

β risk is checked, when going long on High E/P(#5) and short on Low E/P(#1). β risk of E/P strategy has been increasing. When markets fall, E/P strategy might not work properly.

Evaluating the market risk of E/P strategy

Note: Universe is TOPIX 500 stocks. The sample period is from January 2000 to April 2012. The universe, consisting of TOPIX500,is divided into five groups by E/P factor values. Groups are rebalanced monthly (at the start of each month). The difference of median of historical (60 months) beta between top and bottom quintile is calculated. N/A data for historical (60 months) beta has been deleted. The sample period is from January 2000 to April 2012.

Source: Nomura

β risk changes of High E/P stocks

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2011

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2012

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E/P Boom period E/P Slump period


(post-earthquake)

E/P strategy is High risk

The difference of Beta between High and Low E/P

E/P strategy Is Low risk

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2001

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(Dec.1999 = 0%) TOPIX Return

downward phase downward phase downward phase

12

Compared with the situation in 2007 and 2008, the current situation is much better.


Crowded strategies → improved though, we need to focus on the future trends

Unexpected market risk in α strategies → increasing again, we have to pay attention

Risk management in the event of market crash is necessary, as we cannot be entirely optimistic about

macro environment. The following two strategies are proposed.

Crowded strategies →“Herding avoidance strategies“

Unexpected market risk in α strategies → “Composite alpha strategies reducing downside risk“

Summary of the current situation


13

Excessive herding is highly likely to lead to (1) position unwinding risk (2) the possibility of share price reversal.

Herding can also occur for factor strategies.

We believe that stable portfolio performance can be achieved by avoiding the unwinding risk associated with herding.

(1) Herding avoidance strategies

HoldShr following accumulation of positions

14

Timing of position unwinding by active funds

We can see that the crowded group in which active funds’ HoldShr is high tends to see a large subsequent decline in HoldShr.

The size of HoldShr for active funds thus probably acts as a signal of position unwinding.

Source: Nomura

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0

20

40

t-1 t+0 t+3 t+6 t+9

(t+0 = 0.0bp)

#1 (low fund share ownership ratio)

#5 (high fund share ownership ratio)

Active funds' share ownerhip ratio (cumulative HoldShr)

No. of months passed (stocks grouped on basis of HoldShr in month 0)Note: Universe is TSE-1 stocks confirmed as being owned by publicly subscribed investment trusts (funds) in their most recent disclosure information. Sample period is January 1999 through August 2011. For each month, we divided the universe into five groups on the basis of HoldShr at the start of each month, then looked at cumulative HoldShr before and after each time point for the group of stocks with the highest HoldShr (group #5) and the group of stocks with the lowest HoldShr (group #1). t+0 is the time at which stocks are grouped on the basis of HoldShr; data are adjusted so that HoldShr at time t+0 is 0.

𝐻𝐻𝐻𝐻𝐻𝐻𝑑𝑑𝑆𝑆ℎ𝑟𝑟𝑖𝑖,𝑡𝑡𝐹𝐹 =∑ 𝑆𝑆ℎ𝑟𝑟 𝑖𝑖

𝑓𝑓𝑓𝑓∈𝐹𝐹 (𝑇𝑇𝐻𝐻𝑡𝑡𝑇𝑇𝐻𝐻 𝑛𝑛𝐻𝐻.𝐻𝐻𝑓𝑓 𝑠𝑠ℎ𝑇𝑇𝑟𝑟𝑎𝑎𝑠𝑠 𝐻𝐻𝑓𝑓 𝑠𝑠𝑡𝑡𝐻𝐻𝑠𝑠𝑠𝑠 𝑖𝑖 ℎ𝑎𝑎𝐻𝐻𝑑𝑑 𝑏𝑏𝑏𝑏 𝐴𝐴𝑠𝑠𝑡𝑡𝑖𝑖𝐴𝐴𝑎𝑎 𝑓𝑓𝑓𝑓𝑛𝑛𝑑𝑑 𝑔𝑔𝑟𝑟𝐻𝐻𝑓𝑓𝑔𝑔 𝐹𝐹)

𝑂𝑂𝑓𝑓𝑡𝑡𝑆𝑆 ℎ𝑟𝑟 𝑖𝑖∗𝐹𝐹𝐹𝐹𝑊𝑊𝑖𝑖 ,𝑡𝑡(𝑁𝑁𝐻𝐻.𝐻𝐻𝑓𝑓 𝑠𝑠ℎ𝑇𝑇𝑟𝑟𝑎𝑎𝑠𝑠 𝑖𝑖𝑠𝑠𝑠𝑠𝑓𝑓𝑎𝑎𝑑𝑑 𝑇𝑇𝑛𝑛𝑑𝑑 𝐻𝐻𝑓𝑓𝑡𝑡𝑠𝑠𝑡𝑡𝑇𝑇𝑛𝑛𝑑𝑑𝑖𝑖𝑛𝑛𝑔𝑔 𝐻𝐻𝑓𝑓 𝑠𝑠𝑡𝑡𝐻𝐻𝑠𝑠𝑠𝑠 𝑖𝑖 𝑇𝑇𝑑𝑑𝑎𝑎𝑓𝑓𝑠𝑠𝑡𝑡𝑎𝑎𝑑𝑑 𝑓𝑓𝐻𝐻𝑟𝑟 𝑡𝑡ℎ𝑎𝑎 𝐹𝐹𝐹𝐹𝑊𝑊)

Performance of stocks in which positions have been accumulated

15

Timing of position unwinding by active funds

The size of HoldShr for active funds thus probably acts not only as a signal of position unwinding

but also as a signal of share price reversal.

Note: Results of Fama-MacBeth regression analysis. Universe is TSE-1 stocks, sample period is January 1999 through August 2011. With We used HoldShr for active funds as the explanatory variable and share price return from t+0 (r(0)) and t+3 (r(3)) as the explained variables. t-values are for the null hypothesis that regression coefficient is 0. *, **, *** and **** represent statistical significance (with two-tailed test) at the 10%, 5%, 1%, and 0.1% levels.

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0

5

10

99/12 00/12 01/12 02/12 03/12 04/12 05/12 06/12 07/12 08/12 09/12 10/12(yy/m)

(end-98/12 = 0%)

Act (lag3)Act (lag0)

Cumulative return

Model Time Coefficient Average Standard deviation t-value

Average/standarddeviation

lag (0) t+0 β(t+0) -0.18 0.89 -2.45 *** -0.69lag (3) t+3 β(t+3) -0.21 0.83 -3.06 *** -0.86

Source: Nomura

16

Factors associated with active fund herding

Risk characteristics of stocks with a high percentage of shares held by active funds (1)

Note: Universe is TSE-1 stocks. Sample period is January 2001 through April 2012. We divided the universe into 10 groups of stocks according to the percentage of shares in each stock held by active funds and calculated the difference in the average exposure to seven factors between the group with the highest percentage of shares held by active funds (group #10) and the group with the lowest percentage (group #1), plotting the data as six-month moving averages. We rebased the cross-sectional average for each factor (E/P, B/P, earnings revisions over the past three months, sales growth, log market cap, volatility, and Merton bankruptcy probability) to 0, and the standard deviation to 1, at the beginning of each month. Also, to mitigate the impact of outliers, we reduced the standard deviation of stocks with a standard deviation of more than ±3σ to ±3σ three times. Source: Nomura

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-0.5

0.0

0.5

1.0

1.5

2.0

01/1 02/1 03/1 04/1 05/1 06/1 07/1 08/1 09/1 10/1 11/1 12/1

σ (0 = neutral)

(yy/m)

Risk indicators (1) Growth (sales growth): tilt towards high-growth stocks

Value (E/P): tilt towards undervalued stocks

Revision: slight tilt towards upwardly revised stocks

Value (B/P): tilt towards overvalued stocks(1) Growth

(2) E/P

(3) Revision

(4) B/P

17

Factors associated with active fund herding

Note: Universe is TSE-1 stocks. Sample period is January 2001 through April 2012. We divided the universe into 10 groups of stocks according to the percentage of shares in each stock held by active funds and calculated the difference in the average exposure to seven factors between the group with the highest percentage of shares held by active funds (group #10) and the group with the lowest percentage (group #1), plotting the data as six-month moving averages. We rebased the cross-sectional average for each factor (E/P, B/P, earnings revisions over the past three months, sales growth, log market cap, volatility, and Merton bankruptcy probability) to 0, and the standard deviation to 1, at the beginning of each month. Also, to mitigate the impact of outliers, we reduced the standard deviation of stocks with a standard deviation of more than ±3σ to ±3σ three times. Source: Nomura

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0.0

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1.0

1.5

2.0

01/1 02/1 03/1 04/1 05/1 06/1 07/1 08/1 09/1 10/1 11/1 12/1

σ (0 = neutral)

(yy/m)

Risk indicators (2)Size (market cap): slight tilt towards large caps

Risk: tilt towards low-risk stocks

Default probability: tilt towards low-risk stocks

(5) Barra specific Risk

(6) Size

(7) Default Prob

Risk characteristics of stocks with a high percentage of shares held by active funds (2)

When we utilize factor strategies based on E/P, growth or revision,

( by going long on stocks with ･･･ )

it is possible that any tilt towards these factors increases the unwinding risk.

Avoiding crowded stocks subject to a high degree of herding might realize stable factor return easily.

Cross-sectional regression analysis, controlling for beta and size.

18

Avoiding quant factor-related unwinding risk (a)

– High Growth

– High E/P

– High Revision

𝑟𝑟𝑖𝑖 ,𝑡𝑡 = 𝛼𝛼𝑖𝑖,𝑡𝑡 + 𝛽𝛽𝑡𝑡 𝐵𝐵𝑎𝑎𝑡𝑡𝑇𝑇𝑖𝑖,𝑡𝑡 + 𝛾𝛾𝑡𝑡 𝐻𝐻𝐻𝐻𝑔𝑔𝑙𝑙𝑙𝑙𝑖𝑖 ,𝑡𝑡 + 𝛿𝛿𝑡𝑡 𝑥𝑥𝑖𝑖,𝑡𝑡 + 𝜖𝜖 𝑖𝑖 ,𝑡𝑡

δ is defined as factor return

– Sales growth factor – E/P factor – Revision factor

19

Factor strategy for avoiding unwinding risk (b)

Limiting the universe to low herding stocks (group #1) generates high risk-adjusted returns and low maximum drawdowns

EPR factor Sales growth factor

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100120

98/1

2

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2

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2

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2

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2

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2

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2

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2

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2

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2

10/1

2 (yy/m)

HoldShr (#5)HoldShr (#1)

E/P factor(98/12 = 0, %) Cumulative return (E/P)

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0

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98/1

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2

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2

10/1

2

(yy/m)


Sales growth rate factor(98/12 = 0, %) Cumulative return (sales growth)

Note: Universe is TSE-1 stocks. Sample period is January 1999 through August 2011. We divided the universe into five groups based on the value of HoldShr (the number of shares held by active funds as a percentage of the free-float adjusted number of shares out), using the groups with the highest and lowest values of HoldShr for Fama-MacBeth regression analysis. For our explanatory variables we used E/P (earnings yield), B/P (net asset yield), sales growth rate, and past-three-month revisions. t-values are for the null hypothesis that regression coefficient is 0. *, **, *** and **** represent statistical significance (with two-tailed test) at the 10%, 5%, 1%, and 0.1% levels. Source: Nomura

TSE-1 results Averagereturn

Standarddeviation t-value Return/risk Maximum

drawdownHoldShr (#5) 0.31 1.21 3.19 *** 0.90 -14.51HoldShr (#1) 0.63 1.29 5.99 **** 1.68 -4.21

TOPIX 500results

Averagereturn


drawdownHoldShr (#5) 0.23 1.78 1.57 0.44 -25.64HoldShr (#1) 0.39 1.61 2.95 *** 0.83 -10.37



drawdownHoldShr (#5) -0.16 1.30 -1.48 -0.41 -39.35HoldShr (#1) 0.16 1.20 1.66 * 0.47 -8.23

TOPIX 500results

Averagereturn


drawdownHoldShr (#5) -0.26 1.39 -2.29 *** -0.65 -52.71HoldShr (#1) 0.06 1.26 0.56 0.16 -11.76

Revision factor

20

Factor strategy for avoiding unwinding risk (c)

BPR factor

Note: Universe is TSE-1 stocks. Sample period is January 1999 through August 2011. We divided the universe into five groups based on the value of HoldShr (the number of shares held by active funds as a percentage of the free-float adjusted number of shares out), using the groups with the highest and lowest values of HoldShr for Fama-MacBeth regression analysis. For our explanatory variables we used E/P (earnings yield), B/P (net asset yield), sales growth rate, and past-three-month revisions. t-values are for the null hypothesis that regression coefficient is 0. *, **, *** and **** represent statistical significance (with two-tailed test) at the 10%, 5%, 1%, and 0.1% levels.

-100

1020304050607080

98/1

2

99/1

2

00/1

2

01/1

2

02/1

2

03/1

2

04/1

2

05/1

2

06/1

2

07/1

2

08/1

2

09/1

2

10/1

2

(yy/m)


Revision factor(98/12 = 0, %) Cumulative return (past 3-M revision)

-30-20-10

01020304050607080

98/1

2

99/1

2

00/1

2

01/1

2

02/1

2

03/1

2

04/1

2

05/1

2

06/1

2

07/1

2

08/1

2

09/1

2

10/1

2

(yy/m)


Reference: B/P factor(98/12 = 0, %) Cumulative return (B/P)

Source: Nomura



drawdownHoldShr (#5) 0.47 1.56 3.73 **** 1.05 -15.57HoldShr (#1) 0.40 1.14 4.32 **** 1.22 -4.49

TOPIX 500results

Averagereturn


drawdownHoldShr (#5) 0.49 1.94 3.13 *** 0.88 -21.36HoldShr (#1) 0.17 1.67 1.23 0.35 -17.75



drawdownHoldShr (#5) 0.23 1.02 2.78 *** 0.78 -8.28HoldShr (#1) 0.46 0.89 6.44 **** 1.81 -2.03

TOPIX 500results

Averagereturn


drawdownHoldShr (#5) 0.13 1.32 1.23 0.34 -14.53HoldShr (#1) 0.13 1.25 1.24 0.35 -9.71

21

Focus on downside risk, the other weak point of E/P factor. (1) Tail risk cannot always be hedged by neutralizing β risk (β=0). (2) Some combinations of strategies in an attempt to diversify risk could increase

downside risk. Whether downside risk can be avoided or not was reviewed by combining E/P with other

factors. If E/P factor continues to be the central part of the strategies, we need to consider the factor selection from perspective of avoiding downside risk.

・ If B/P or low herding strategy is combined with E/P, downside risk can be reduced. ・ If Profit-based strategies is combined with E/P, downside risk can be increased.

(2) Composite alpha strategies reducing downside risk

22

The correlation between E/P factor (long-short) and market returns are especially increasing. When markets fall, alpha strategies might not be effective.

Correlation between some alpha strategies and market return is increasing

Note: (1) Taking the TOPIX 500 as our universe, we divided the stocks into five equally weighted size-based groups for each factor as of the start-month rebalancing. We went long on the group with the highest value for each factor and short on the group with the lowest factor value and calculated the daily return, including dividends, for each. However, we went long on the lowest-value group and short on the highest-value group for the following factors: return reversal, volatility, small cap, accruals, low herding, and net equity finance. (2) Sector allocation was not taken into account when rebalancing. (3) The figure shows the correlation between calculated factor returns and the TOPIX return. (4) Observation periods are January 2000–April 2012 and January 2011–April 2012.

Source: Nomura

Correlation with market return

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

E/P

B/P

Div

iden

d yie

ld

Earn

ings

sur

pris

e

Con

sens

usfo

reca

st re

visi

on

Con

sens

us ra

ting

RO

E

Sale

s gr

owth

Cas

h flo

w y

ield

Ret

urn

reve

rsal

Low

vol

atilit

y

Smal

l cap

Accr

uals

Low

her

ding

Net

equ

ity fi

nanc

e

00/1–11/1–

Negativecorrelation

Positivecorrelation

Correlation between each factor return and the market return

23

It is possible that some factors perform poorly only when the market crushes although the correlation between market return and factors is low. We measure the lower tail dependence as the probability that a relalization of a factor return is the extreme lower of the distribution

conditional on the realization of the market return also being lower of its distribution.

Focus on the differences between correlation and downside risk

𝐶𝐶(𝑓𝑓1,𝑓𝑓2;𝛩𝛩) = 𝑤𝑤1 ∙ 𝐶𝐶𝐿𝐿𝑇𝑇𝐿𝐿(𝑓𝑓1,𝑓𝑓2;𝜃𝜃1) + 𝑤𝑤2 ∙ 𝐶𝐶𝑁𝑁𝑇𝑇𝐿𝐿(𝑓𝑓1,𝑓𝑓2;𝜃𝜃2) + 𝑤𝑤3 ∙ 𝐶𝐶𝑈𝑈𝑇𝑇𝐿𝐿(𝑓𝑓1,𝑓𝑓2;𝜃𝜃3)

CUTD： dependent in the upper tail CNTD： independent - copula CLTD： dependent in the lower tail

Scatter plots of random numbers generated by bivariate copulas ( relation between factor and market return)

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

value of marginal distribution F2

(factor return)

High return

Low return


(market return)

Highreturn

Lowreturn

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00


(factor return)

High return

Low return


(market return)

Highreturn

Lowreturn

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00


(factor return)

High return

Low return


(market return)

Highreturn

Lowreturn

Clayton / Rotated Gumbel Copula Gaussian / Plackett / Frank Rotated Clayton / Gumbel

24

Specifically, we calibrated bivariate models showing the relationship between E/P (B/P) factor and market return There is a stronger “lower tail dependence” between E/P factor and market return than B/P

The differences of downside risk between E/P and B/P

Random numbers generated by estimated joint distribution for factors and market return

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00


(factor return)

High return

Low return


(market return)

Highreturn

Lowreturn

E/P and market return B/P and market return

Note: (1) We estimated the parameters of our combined copula models (see the Appendix for details of models) using daily returns on the P/B and P/E factors and the TOPIX (January 2011–April 2012) and created scatter plots of 10,000 random numbers generated by the model with the most likelihood among 12 models. (2) The model showing the structure of dependence between TOPIX return and P/E factor return is Clayton : Gaussian : rotated Clayton in ratio of 0.28 : 0.69 : 0.03. (3) The model showing the structure of dependence between TOPIX return and P/B factor return is rotated Gumbel : Gaussian : rotated Clayton in ratio of 0.15 : 0.05 : 0.80.

Source: Nomura

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00 value of

marginal distribution F1

(market return)

Highreturn

Lowreturn


(factor return)

High return

Low return

25

There are some factors with high downside risk, though they look market neutral (β = 0). Profit-based strategies tend to have higher downside risk.

The downside Risk and beta Risk - Map

E/P

B/P

Dividend yield

Forecast revision

Consensus rating

ROESales growth

Cash flow yield

Return reversal

Low volatility

Small capAccruals

Low herdingNet equity finance0%

5%

10%

15%

20%

25%

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

Earnings surprise

High risk despitealmost no correlation

Negative correlation Positive correlationAlmost no correlationCorrelation with market return

Probability of historically bad

returns when market drops sharply

High

Low

Note: (1) Using daily returns on each factor and the TOPIX (January 2000–April 2012), we calculated correlation coefficients between each factor’s return and the TOPIX return and also estimated downside risk, plotting the results on a two-dimensional scatter plot.

Source: Nomura

Market correlation versus downside risk for the major strategies ( Jan.2000 - Apr.2012 )

26

Both E/P and B/P are increasingly correlated with market return (High β strategy) . However, downside risk is obviously higher for E/P strategy. Is it a good idea to combine E/P with forecast revision strategy ?

The recent changes of E/P,B/P and revision on the map

E/P

B/PForecast revision

E/P (11/1–)

B/P (11/1–)

Forecast revision(11/1–)

0%

5%

10%

15%

20%

25%

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8



High

Low


Recent changes in risk for the E/P , B/P, and consensus forecast revision factors ( Jan.2011 - Apr.2012 )

Note: Correlation coefficients and downside risk estimated from daily returns on the E/P, B/P, and forecast revision factors and the TOPIX (January 2011–April 2012) are shown on a two-dimensional scatter plot.

Source: Nomura

27

The combination of E/P and revision has lower β, but higher downside risk. The combination of E/P and B/P has higher β, but lower downside. → The combination of E/P and B/P is favorable.

The combination that increase/decrease downside risk

E/P (11/1–)

B/P (11/1–)

Forecast revision(11/1–) E/P x B/P

E/P xForecast revision

0%

5%

10%

15%

20%

25%

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8

Don't combine

Combine



High

Low


Note: we plotted on a coordinate plane correlation coefficients and downside risk estimated using the composite factor combining the E/P factor with the B/P factor, the composite factor combining the E/P factor with the revision index factor, and recent TOPIX daily returns (January 2011–April 2012).

Source: Nomura

Look out for combination that increase/decrease downside risk ( Jan.2011 - Apr.2012 )

28

The composite factors rotation model with minimizing downside risk

Note: (1) At the beginning of each month we estimated the past 24-month returns and downside risks for our universe of 105 composite factors. Next, we divided our universe into five groups based on the size of their downside risk and then calculated the daily factor momentum performance for the composite factors in the top quintile (#5) and those in the other groups (#1–#4). The composite factors we used were those that showed positive cumulative returns over the past 24 months as calculated at the beginning of each month. The chart shows the average performance of composite factors with sizable downside risk and those without such downside risk. (2) Trading costs were not taken into account. (3) This analysis is based on historical data and the results provide no guarantee of future performance. (4) Analysis period is January 2002–April 2012. (5) We used the low-pass filter method to measure upward and downward phases in the market. Shading indicates bear market for TOPIX; no shading indicates bull market.

Source: Nomura

-20

0

20

40

60

80

100

120

02/1 03/1 04/1 05/1 06/1 07/1 08/1 09/1 10/1 11/1 12/1

Group #5 with highest downside risk

Groups other than #5

(end-02/1 = 0%) Cumulative return

(yy/m)

-60

-40

-20

0

20

40

60

80

100

120

02/1 03/1 04/1 05/1 06/1 07/1 08/1 09/1 10/1 11/1 12/1

Downward phase

(end-02/1 = 0%)Cumulative return

(yy/m)

Downward phase Downward phase

TOPIX

The rotation model with minimizing downside risk

No Factor1 E/P2 B/P 3 Div idend y ield4 Earnings surprise5 Consensus forecast rev ision6 Consensus rating7 ROE8 Sales grow th9 Cash flow y ields

10 Return rev ersal11 Low v olatility12 Small caps13 Accruals14 Low herding15 Net equity finance

Methodology 105 composite factors

when 2 factors are chosen

E/P × B/P

E/P × Dividend Yield

E/P × Earnings surprise

E/P × forecast revision

・

・

Pick up the composite factors which has

(1) the positive return (past 24 month )

(2) not top quintile of downside risk

29

The factors recommended to be and not to be combined with E/P are indicated below. The combination that decrease downside risk: B/P, low herding The combination that increase downside risk: Profit-based strategies, return reversal strategy

Don’t mix to avoid the downside risk

No. Factor 1 Factor 2 Correlationcoefficient

Downsiderisk

1 E/P Low volatility -0.55 0.0%2 E/P B/P 0.51 1.2%3 E/P Low herding 0.03 3.9%4 E/P Dividend yield -0.06 4.9%5 E/P Net equity f inance 0.08 9.8%

No. Factor 1 Factor 2 Correlationcoefficient

Downsiderisk

6 E/P Accruals 0.37 10.7%7 E/P Sales grow th 0.38 12.7%8 E/P Cash f low yield 0.49 14.2%9 E/P ROE 0.22 15.1%10 E/P Consensus rating 0.18 15.3%11 E/P Consensus forecast revision 0.20 15.9%12 E/P Return reversal 0.28 16.6%13 E/P Small cap 0.32 16.6%14 E/P Earnings surprise 0.31 19.3%

Note: (1) We created the 14 possible composite factor(composite ratio of 1:1). The table shows the coefficient of correlation with the TOPIX return for each composite factor as well as the downside risk for each estimated using our model. (2) Analysis period is January 2011–April 2012.

Source: Nomura

30

The combination of E/P and B/P is enhanced by herding avoidance strategy ・・・decreasing downside risk

Lastly

Note: Universe is TOPIX500. The performance of the following strategies are calculated. (1) E/P× B/P× Low Herding is composite strategy of E/P ,B/P ,and Low Herding . (2) E/P× B/P is composite strategy of E/P and B/P. (December2010 = 0%). The sample period is from January 2011 to April 2012.

Source: Nomura

-10

-5

0

5

10

15

2020

11/1

2011

/2

2011

/3

2011

/4

2011

/5

2011

/6

2011

/7

2011

/8

2011

/9

2011

/10

2011

/11

2011

/12

2012

/1

2012

/2

2012

/3

2012

/4

▼3.7%

▼1%

+ 1.3%

▼1.5%

EP×BP

EP×BP×Low Herding

-25

-20

-15

-10

-5

0

5

10

2011

/1

2011

/2

2011

/3

2011

/4

2011

/5

2011

/6

2011

/7

2011

/8

2011

/9

2011

/10

2011

/11

2011

/12

2012

/1

2012

/2

2012

/3

2012

/4

▼20%▼7%

TOPIX

31

The performance of Japanese quant funds has been in deadlock since 2011 influenced

by the Great East Japan Earthquake and the financial crisis in Europe. However, we expect E/P factor, the main driver of the quant funds, continues to be

effective. The weakness of E/P factor is its increasing tail risk when markets fall. Based on the

experience in 2007 and 2008, starting from the quant turmoil to Lehman's fall, herding avoidance strategy combined with the factors with less downside risk is recommended.

Conclusion

32

Factor definitions

Appendix (1)

Factor Factor definitions

E/P #5 (undervalued) – #1 (overvalued) Inverse of P/E (priority given to projected next-FY profits; I/B/E/S consensus estimates > Toyo Keizai estimates)

B/P #5 (undervalued) – #1 (overvalued) Inverse of P/B Dividend yield #5 (high) – #1 (low) Calculated using Nikkei dividend projections Earnings surprise #5 (high) – #1 (low) Full-year results surprise Consensus forecast revision

#5 (high) – #1 (low) Estimated recurring profits / average projected recurring profits over past three months – 1 (IFIS consensus estimates > Toyo Keizai estimates)

Consensus rating #5 (large) – #1 (small) IFIS consensus rating / 1-month average – 1

ROE #5 (high) – #1 (low) Forecast profits / shareholders' equity (priority given to projected next-FY profits; IFIS consensus estimates > Toyo Keizai estimates)

Sales growth #5 (high) – #1 (low) Score based on an aggregate of the following growth rates divided by five: (1) from prior year to actual (FY-1→FY0); (2) from actual to current-FY projected (FY0→FY1); and (3) from current-FY projected to next-FY projected (FY1→FY2)

Cash flow yield #5 (high) – #1 (low) Inverse of P/CF (priority given to projected next-FY profits; Nomura estimates > Toyo Keizai estimates) Return reversal #1 (low) – #5 (high) 3-month return Low volatility #1 (low) – #5 (high) Barra specific risk Small cap #1 (small) – #5 (large) Log market cap Accruals #1 (high quality) – #5 (low) (Increase in working capital + growth in net nonliquid operating assets) / total assets

Low herding #1 (low) – #5 (high) Shares held by active funds as % of shares out Active funds defined as all the active (long-only) funds in the Japanese equities category, as categorized by the Investment Trusts Association, Japan

Net equity finance #1 (low) – #5 (high) Change in shares issued and outstanding compared with 24 months earlier

Source: Nomura

33

Types of copula functions

Appendix (2)

Notes: is a standard normal distribution N(0,1) and shows the inverse function. Also, and . In the cases of the Gaussian, Frank, and Plackett copulas, the lower and upper tail dependence coefficients are 0 because they are asymptotically independent. For other copulas also, asymptotically independent dependence coefficients are shown as 0.

Source: Nomura

Copula Function form

Lower tail

dependence

coefficient

Upper tail

dependence

coefficient

• Clayton

2−1/θ 0

• Rotated Gumbel

2− 2−1/θ 0

• Gaussian

0 0

• Frank

0 0

• Plackett

0 0

• Rotated Clayton

0 2−1/θ

• Gumbel

0 2 − 2−1/θ

𝐶𝐶(𝑓𝑓1,𝑓𝑓2; 𝜃𝜃) = �𝑓𝑓1−𝜃𝜃 + 𝑓𝑓2

−𝜃𝜃 − 1�−1/𝜃𝜃

𝐶𝐶(𝑓𝑓1,𝑓𝑓2;𝜃𝜃) = Φ𝜃𝜃(Φ−1(𝑓𝑓1),Φ−1(𝑓𝑓2))

𝐶𝐶(𝑓𝑓1,𝑓𝑓2; 𝜃𝜃) = −𝜃𝜃−1𝐻𝐻𝐻𝐻𝑔𝑔(1 − 𝑎𝑎𝑥𝑥𝑔𝑔(−𝜃𝜃) − (1 − 𝑎𝑎𝑥𝑥𝑔𝑔(−𝜃𝜃𝑓𝑓1))(1 − 𝑎𝑎𝑥𝑥𝑔𝑔(−𝜃𝜃𝑓𝑓2))

1 − 𝑎𝑎𝑥𝑥𝑔𝑔(−𝜃𝜃))

𝐶𝐶(𝑓𝑓1,𝑓𝑓2; 𝜃𝜃) =12

(𝜃𝜃 − 1)−1 �1 + (𝜃𝜃 − 1)(𝑓𝑓1 + 𝑓𝑓2) − ��1 + (𝜃𝜃 − 1)(𝑓𝑓1 + 𝑓𝑓2)�2 − 4𝜃𝜃𝑓𝑓1𝑓𝑓2�1/2�

𝐶𝐶(𝑓𝑓1,𝑓𝑓2; 𝜃𝜃) = 𝑓𝑓1 + 𝑓𝑓2 − 1 + �(𝑓𝑓�1)−𝜃𝜃 + (𝑓𝑓�2)−𝜃𝜃 − 1�−1/𝜃𝜃

𝐶𝐶(𝑓𝑓1,𝑓𝑓2; 𝜃𝜃) = 𝑎𝑎𝑥𝑥𝑔𝑔(−�(− 𝐻𝐻𝐻𝐻𝑔𝑔(𝑓𝑓1))𝜃𝜃 + (− 𝐻𝐻𝐻𝐻𝑔𝑔(𝑓𝑓2))𝜃𝜃�1/𝜃𝜃 )

𝐶𝐶(𝑓𝑓1,𝑓𝑓2;𝜃𝜃) = 𝑓𝑓1 + 𝑓𝑓2 − 1 + 𝑎𝑎𝑥𝑥𝑔𝑔(−�(− 𝐻𝐻𝐻𝐻𝑔𝑔((𝑓𝑓�1))𝜃𝜃 + (− 𝐻𝐻𝐻𝐻𝑔𝑔((𝑓𝑓�2))𝜃𝜃�1/𝜃𝜃 )

Φ Φ−1 𝑓𝑓�1 = 1− 𝑓𝑓1 𝑓𝑓�2 = 1−𝑓𝑓2

34

Nomura Research Reports

Akihiro Murakami, 2012, “Japanese equity quantitative strategy: Alpha strategies for reducing downside risk”, Nomura Quantitative Research Report, May 14, 2012.

Akihiro Murakami, 2011, “Herding avoidance strategies”, Nomura Quantitative Research Report, October 17, 2011.

Reference

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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. 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. 36

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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. 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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. 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