20081127-JPMorgan-How Does Reducing Country Bias Impact Risk and Return？

Europe Equity Research 27 November 2008

How does reducing Country bias impact Risk and Return ?

Investigating the effect of Normalisation in Emerging Markets

EM Quantitative Research

Marc MenemAC

(44-20) 7325-7124 [email protected]

J.P. Morgan Securities Ltd.

Steve Malin (852) 2800 8568 [email protected]

J.P. Morgan Securities (Asia Pacific) Limited

Robert Smith (852) 2800 8569 [email protected]

J.P. Morgan Securities (Asia Pacific) Limited

European Quantitative Research Marco Dion (44-20) 7325-8647 [email protected]


Matthew Burgess (44-20) 7325-1496 [email protected]


See page 23 for analyst certification and important disclosures, including non-US analyst disclosures. J.P. Morgan does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision.

Table 1: Performances of the different flavors of the Q-Snap model in the MSCI EM universe

C o u n tr yN e u tr a l

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3 1 -J a n -0 0 1 0 0 . 0 1 0 0 . 0 1 0 0 . 01 1 -N o v -0 8 3 4 9 . 9 3 1 3 . 3 3 7 6 . 4A n n . re tu rn s 1 5 . 3 % 1 3 . 9 % 1 6 . 3 %A n n . S td d e v 5 . 3 % 6 . 0 % 6 . 9 %T -S ta t 2 . 8 8 2 . 3 1 2 . 3 7L a rg e s t d ro p 9 . 6 % 1 0 . 8 % 1 2 . 1 %Hit ra te 5 8 . 4 % 5 5 . 8 % 5 7 . 4 %C o r. u n c s t 8 5 . 8 % 9 4 . 0 %

Source: MSCI, Factset, J.P. Morgan

• We decided to turn a critical eye on Sector Neutral and Country Neutral portfolios and check if the extra investment constraints add value to the strategy.

• We put our generic Quant model – the Q-Snapshot model (The Q-Snapshot Model : Getting the Quant Picture for Emerging Markets – October 27th, 2008) – to the test, and computed returns and correlations over the last 8 years of an Unconstrained flavour, a Country Neutral flavour and a Sector Neutral flavour.

• We find that while the returns over the last 8 years of the Unconstrained model have been 94bps higher, both country neutrality and sector neutrality bring significant advantages in terms of risk profile, reducing the annualised standard deviation from 6.9% to 6.0% and 5.3%.

• Finally we explore the effect of the sector bias on the Country Neutral flavour of the model and find that our model outperforms a synthetic index with the same sector bias by more then 10% year to date.

Figure 1: Returns of the unconstrained flavor of the Q-Snap are higher, but this comes at significant costs in term of risks

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Draw dow ns uncst. neutral Draw dow ns sector neutral Draw dow ns country neutralUnconstrained Sector Neutral Country Neutral


https://mm.jpmorgan.com/servlet/PubServlet?skey=R1BTLTIzODQ3Mi0wLDQzNCxSU0NIX1BOTAA%3D&Name=GPS-238472-0.pdf

https://mm.jpmorgan.com/servlet/PubServlet?skey=R1BTLTIzODQ3Mi0wLDQzNCxSU0NIX1BOTAA%3D&Name=GPS-238472-0.pdf

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Marc Menem (44-20) 7325-7124 [email protected]

Marco Dion (44-20) 7325-8647 [email protected]

Executive Summary It is notoriously difficult to minimise both the sector and country bias of a portfolio in Emerging Markets because the number of securities in each country is so small. This leaves managers with the choice between constructing a sector neutral portfolio or a country neutral portfolio.

We present our methodology for constructing country or sector neutral portfolios and show how each one of these normalisation techniques affects the historic returns of our Generic Quant model in Emerging Markets – both globally and at a regional level.

We find, even with strong sector themes evident in the past year, that the Country Neutral approach has significant advantages over the Sector Neutral approach and we analyse the effects of its sector bias on performance.

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Executive Summary .................................................................2 Building a constrained Quant model ......................................4 Performance of the Q-Snap model..........................................6 Should the model target sector neutrality instead of country neutrality? .................................................................................9 Comparison of the different normalisation flavors by region ....................................10 Risk profiles for different normalisation flavors........................................................11 MSCI EM index.........................................................................................................12 Drilling down to the region level ...............................................................................15 Choosing a normalisation Q-Snap in MSCI Emerging Asia......................................16 Choosing a normalisation Q-Snap in MSCI LATAM ...............................................17 Choosing a normalisation Q-Snap in MSCI CEEMEA .............................................18 Sector bias ..............................................................................19 How does this sector bias affect the performance? ....................................................21 Conclusion..............................................................................22

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Building a constrained Quant model The Q-Snap model is presented in a detailed introduction paper1. Let’s take here a close look at the normalisation step.

• We consider ten different factors from four Families: Value, Growth, Quality and Price

• Each Factor is normalised into a Z-Score, and the Z-Scores are combined into family scores.

• The Family scores are normalised and combined with equal weights into a global score.

• We run a country (or sector) normalisation to cancel the country (or sector) bias of the resulting score or leave it unconstrained.

When we developed this model, we followed a very generic Quant methodology. We tested the performance of a large number of Factors and selected 10 of them with strong returns to integrate in a single Score. We then had to transform them into Z-Scores in order to be able to build linear combinations out of this heterogeneous data.

The Z-Score for a Factor Value is computed by subtracting the average of the universe and dividing by the standard deviation (see example in Table 2). Z-Scores are dimensionless numbers within a fixed range from -3 to 32, and it is possible to combine several Z-Scores with different weights into a single score.

Table 2: Computing a Z-Score: a simple example with the Earnings Yields of 4 stocks Company Name Earnings Yield Z-Score

Stock 1 29.3% (29.2 – 24.9)/4.35 = 1.00 Stock 2 27.7% (27.7 – 24.9)/4.35 = 0.64 Stock 3 22.9% (22.9 – 24.9)/4.35 = -0.46 Stock 4 19.8% (19.8 – 24.9)/4.35 = -1.18 Average 24.9% Standard deviation 4.35%

Source: J.P. Morgan

We combine Factors exploiting the same pricing anomalies into Families. Generally Factors from the same Family have strongly correlated returns, and their Composite performs better. The next step is to combine the Families Composite into the model Score. The reason for combining different Families is diversification of risk: for example if a stock appears cheap from a valuation point of view, it might not be interesting from a Quality point of view. This composition operation is done again by transforming the Family Composite Score into a Z-Score and combining them into the final Composite Q-Score.

1 The Q-Snapshot Model : Getting the Quant Picture for Emerging Markets – October 27th, 2008 2 Because of the properties of the standard deviation, if the distribution of a Factor was normal across the universe, 95% of its Z-Scores would fall in the interval [-3;3]. We truncate the values to constrain them into this interval in order to reduce the effect of outliers.

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Since the Q-Score is computed without any constraint, it might have strong bias towards a particular country or sector. Our normalisation procedure is designed to fix this issue. It is described in Figure 2. The idea is to compute Z-Scores separately for each subgroup of the universe (country/sector), this way the average score is 0 and the average standard deviation will be 1 for subgroup (country/sector).

Figure 2: How to build a Country Neutral Z-Score: a simple Earning Yields example

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An investor choosing stocks based on high EY will favor Thailand stocks on the long side and Mexico on the short side, introducing a Country bias in his portfolio.

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The values are centered around 0 for each country, and they are rescaled to fit in the range [-3,3].

When ranking stocks according to their normalised EY, a stock is compared against its country peers, eliminating the Country bias.

Source: J.P. Morgan

This normalisation could be used with any subdivisions of the universe – we could try for example to build a country/sector neutral model, however this is a not a realistic aim: it is almost impossible to build equally weighted long and short portfolios in healthcare stocks in each one of the Eastern Europe countries.

The Q-Score is designed to diversify the sources of alpha when making investment decisions. Normalisation is a constraining step designed to reduce the exposure of the overall portfolio to a particular risk.

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Performance of the Q-Snap model Previous research by J.P. Morgan Quant has shown that in Emerging Markets the ‘country effect’ on returns is significantly more dominant that the ‘sector effect’. Consequently we have traditionally used the “country normalised” version of the Q-Snap model for Emerging Markets. (See Look Back In Angle 3 – Paper 1 “Who’s Ya Returns Daddy?” Malin, 3-May-2007 for more information)

This section is a detailed view of the year-to-date performance of the country neutralized model.

We measure the performance of the Q-Score as the average returns of the 10% best stocks according to the model against the 10% worst stocks, using a monthly rebalancing of our portfolios. In this paper, we will present the results for the MSCI EM universe its three sub-regions CEEMEA, LatAM and Asia, results for other universes and region are available on request.

Figure 3: YTD performance of the Q-Snap model (Country neutral) in Emerging Markets

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Figure 3 presents the YTD performance of the Q-Snap model. We repeat the same tests in Figure 4 for each region3.

3 (since the CEEMEA and LatAM universes are small, we also tested the model with a 20% threshold and found similar results).

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Figure 4: YTD performance of the Q-Snap Model (Country neutral) in Emerging Markets by region

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The monthly returns chart in Figure 5 is a more convenient way to compare returns of different indices month on month.

Figure 5: Monthly performance of the Q-Snap Model

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CEEMEA has been the most volatile region, after 3 flat months at the start of the year, LatAM performance has been huge – reaching 16% year to date, mostly in November!

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Table 3: Monthly performance of the Q-Snap model (Country neutral)

Q-Snap MSCI EM

Emerging AsiaPAC

LATAM CEEMEA

Jan -2.3% -2.7% -2.2% 0.8% Feb 5.5% 5.9% -0.4% 9.8% Mar -0.8% -1.4% 0.6% 0.9% Apr 2.9% 2.7% 0.6% 5.3% May 2.3% 1.5% 6.3% 4.6% Jun 0.2% 0.2% -1.0% -0.3% Jul 0.2% 0.9% -0.1% -1.7%

Aug -0.2% -0.9% 2.2% -0.5% Sep -2.5% -2.2% 3.3% -10.4% Oct -2.2% -3.5% 2.2% -1.4%

MTD 1.9% 2.9% 4.4% -3.3% YTD 4.8% 3.0% 16.7% 2.4%

Std dev 0.3% 0.4% 0.6% 0.9% Source: MSCI, Factset, J.P. Morgan

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Should the model target sector neutrality instead of country neutrality?

In this section we compare the YTD performance of the Country Neutral and Sector Neutral versions of the Q-Snap model.

Figure 6: YTD Performance of the Country neutral and Sector neutral versions of the Q-Snap model

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The country neutral version of the Q-Snap model outperformed the sector neutral version YTD, however the sector neutral version has been outperforming for the past six months. Could this be a shift in behaviour in emerging markets? We will need to revisit our previous country vs sector correlation analysis to fully investigate the situation regarding country/sector dominance, however given the very strong ‘sector themes’ (Financials, Resources/Energy, Consumer etc) driving markets globally over recent months the result is not that surprising. We leave the question of whether this synchronisation across countries is a temporary or permanent shift open to debate but do highlight that previous analysis by J.P. Morgan Quant concluded that global markets have never been as highly correlated (neither with developed markets nor with each other) as they have become over the last 1-2 years (See Look Back In Angle Part 5 Decoupling - More than dead Malin/Smith 13-Jun-2008 for more information).

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Figure 7: Monthly performances of the Q-Snap model Country Neutral vs Sector Neutral

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Comparison of the different normalisation flavors by region Figure 9 shows that the strong correlation between the constrained and unconstrained indexes has persisted historically. On Figure 8, it seems that in a more diversified universe, the correlation between the sector neutral flavor and the country neutral flavor of the model is stronger. Emerging Asia is a large well diversified universe, and hence the country neutral and sector neutral indices are highly correlated. LatAM and CEEMEA are smaller and less diversified – the sector neutral and country neutral indices are not very correlated.

Figure 8: Q-Snap (sector neutral) performance by Region Country Neutral Sector Neutral

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Risk profiles for different normalisation flavors

In the following sections, we compare the constrained and unconstrained flavors of the Q-Snap model over a longer time period (January 31st 2000 to November 11th 2008). Here is a list of the statistics and charts provided for each one of the strategies:

Summary data

• Annualized returns and annualized standard deviations,

• T-Stat is a measure of the reliability of the results

• Largest drop is computed as the peak to trough variation over the whole history.

• Hit rate is computed as the proportion of days with positive returns

• Correlation with the unconstrained index is a measure of how similar the returns of the constrained index are to the returns of the unconstrained index.

Detailed data

We present in the following pages four charts for each one of the four indexes (MSCI EM, MSCI CEEMEA, MSCI Emerging Asia Pac and MSCI LatAM) to compare the risk profile associated with each one of the constrained indexes to the risk profile of unconstrained index.

• The drawdowns chart shows on each day the level of the index comparing to its highest value. Ideally this chart should be at 100%, because that would mean that the strategy never lost money.

• The two spread charts show the spreads realized by each one of the constrained strategies over the unconstrained strategy. They also show the correlation of each constrained strategy with the unconstrained strategy. These values are computed over a 100 working days rolling window.

• The volatility chart plots the 100 days rolling standard deviation of each of the three indexes.

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MSCI EM index Overall, the Country Neutral flavor of Q-Snap has provided 15.3% returns over the test period (Jan 2000 to Nov 2008), the unconstrained flavor has provided 94bps better returns over the same period, but its standard deviation is higher, its highest drop is higher, its T-Stat is lower, and its hit rate is lower: Our metrics tend to suggest that the country neutral flavor has a better risk profile than the other options.

Table 4: Q-Snap performance for MSCI EM Country

Neutral Sector Neutral

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31-Jan-00 100.0 100.0 100.0 11-Nov-08 349.9 313.3 376.4 Ann. returns 15.3% 13.9% 16.3% Ann. Std dev 5.3% 6.0% 6.9% T-Stat 2.88 2.31 2.37 Largest drop 9.6% 10.8% 12.1% Hit rate 58.4% 55.8% 57.4% Cor. uncst 85.8% 94.0%


Figure 9: Q-Snap Model performance for all flavors of normalisation

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From Figure 10, the strongest periods of drawdowns for the three indexes are in 2001, and then from the end of 2007 until now. As expected, the unconstrained version has the strongest returns, but also the strongest drawdown and the country neutral version is almost always the safest investment.

Figure 11, the spreads of the Country Neutral and Sector Neutral flavors compared to the unconstrained index are almost always negative (except two spikes of outperformance in 2003 and 2004). The constrained indexes have also started to outperform recently (this spread is computed over a 100 day period, so it displays the returns of investments made 100 days earlier). The outperformance of a constrained index seems to accompany a strong drop in correlation.

Finally Figure 12 the country neutral index seems to be the less volatile of the three indexes even if it can experience high spikes. The volatility chart does seem to be a reliable indication of future returns of the index.

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Choosing a normalisation Q-Snap in MSCI EM

Figure 10: Correlation of the constrained and unconstrained models compared to the index level

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Figure 11: Spread and correlation of the country neutral index against the unconstrained index

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Figure 12: Volatility of the constrained and unconstrained indexes

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Sector neutral/Uncst cor. Country neutral/Unc. cor Source: MSCI, Factset, J.P. Morgan

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Drilling down to the region level

The picture is slightly different at the regional level, especially in Asia and LatAM, where the Country Neutral indexes outperform the unconstrained indexes. They have a less attractive risk profile in CEEMEA, with very high drawdowns and standard deviation, while the risk metrics in LatAM are quite attractive. Emerging Asia Pac is very similar to the whole MSCI EM picture, this might be a consequence of the strong weight it has in the index4.

Table 5: Q-Snap performance for MSCI CEEMEA Country


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Table 6: Q-Snap performance for MSCI LatAM Country


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Table 7: Q-Snap performance for MSCI Emerging Asia Pac Country


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The drawdown charts in the next three pages show that CEEMEA and LatAm are extremely volatile, especially with the Country neutral flavor. The effect of the Argentinean devaluation was quite strong, and the Country Neutral model dealt with it best, missing a high boost in performance in the end of 2001, but recovering strongly after the devaluation!

4 See our note: Emerging Markets Factor reference book : Looking for return drivers in bear markets ( 27 Oct 2008)

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Choosing a normalisation Q-Snap in MSCI Emerging Asia

Figure 13: Correlation of the constrained and unconstrained models compared to the index level

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Choosing a normalisation Q-Snap in MSCI LATAM Figure 16: Correlation of the constrained and unconstrained models compared to the index level

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18




Choosing a normalisation Q-Snap in MSCI CEEMEA Figure 19: Correlation of the constrained and unconstrained models compared to the index level

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19




Sector bias In this last section, we examine the year to date effect of the sector bias in the Country Neutral Q-Snap model.

Figure 22: YTD performance of MSCI EM

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Cons Disc Cons Stap Energy Financials Health Care Industrials

IT Materials Telecoms Utilities MSCI EM Source: MSCI, Factset, J.P. Morgan

The year to date spread of Sectors in MSCI EM is huge. Industrials outperformed consumer discretionary by more than 40%. The difference is significant enough to give interesting conclusions.

Figure 24: Sector weights of the Country neutral Q-Snap model Short Portfolio Long Portfolio

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Figure 23: A very bad year for MSCI EM across all sectors!

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20




Figure 24 shows the sector weights in the holdings of the Q-Snap model for the MSCI EM universe. It is worth spending some time examining it, because it shows the relative contraction of sectors driving the performance of the index - in particular the materials sector during the period from September to October.

Figure 25 is easier to read and represents the average weight of each sector in the holdings.

Figure 25: Average sector weights in the Q-Snap model

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Finally Figure 26 provides the summary –year to date the Q-Snap model had long positions in Materials and Energy and financed them with short positions in Industrials and IT.

Figure 26: Net sector weight of the Q-Snap model

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21




How does this sector bias affect the performance? In order to check how the performance of the Country Neutral Q-Snap model is affected by its sector bias we computed a synthetic index. This index is built to have the same sector bias as the model by summing the 10 MSCI EM sector index performances weighted by their net weight in the Q-Snap model (as defined in Figure 26).

The Q-Snap has outperformed this benchmark by more then 10%, showing that its performance is influenced by stock selection above its sector bias.

Figure 27: Performance of the Q-Snap model compared to a synthetic MSCI EM based index with the same sector bias

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The Q-Snap model (Country Neutral) is plotted against the (long only) MSCI EM in Figure 28 for reference.

Figure 28: The Q-Snap model and the MSCI EM index

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22




Conclusion

Overall the Country Neutral flavor of the Q-Snap model seems to be the most suited to deal with the very specific country political and policy risks in Emerging markets. We have shown Country Neutral flavor of our Q-Snap model has the best risk profile at the Global level and at the Region level for all regions except CEEMEA (where an unconstrained model could be the best option).

We also analyzed the sector bias of the Country Neutral flavor and we did not find it introduced a significant effect on the performance of the model.

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