高盛Goldman Sachs自上而下选股框架--宏观和微观相结合120611

2012611
证券研究报告将宏观观点转化为选股建议的工具 我们推出了一套基于宏观因素描绘、微观层面比较以及对商业周期各阶段投资分析的自上而下选股框架，从而将宏观观点与微观投资建议相关联。

宏观和微观因素对于回报都非常重要 对于亚洲市场上市值占比60%左右的股票而言，宏观因素（如全球经济增速、本地经济增速和金融状况）都是重要的回报推动因素。

此外，估值、每股盈利市场共识预测调整以及技术性指标等微观因素也与随后3个月的回报高度相关。

宏观 + 微观 = 超额收益 对于全球领先指标(GLI)中定义的四阶段商业周期，我们基于各阶段的宏观和微观特征对股票进行筛选。

回溯测试显示，采用这种方法获得的回报有望超过传统买入/持有策略带来的回报。

经济衰退阶段的选股：降低贝塔值和对增长的敏感性；买入受益于政策放松的股票 我们的GLI 指标显示，自4月份以来我们已进入经济衰退阶段。

在此背景下，我们特别关注于受宏观面影响和微观面支撑的程度处于适当水平的股票：NAB 、Orica 、恒安国际、中石油、腾讯控股、长江实业、恒生银行、HDFC 、ITC 、BRI 、LS Corp 、Shinhan 、CIMB 、SM Inv.、JCC 、Delta 、TSMC 和BK Bank 。

我们将定期更新这些股票建议。

对部分市场的部分板块采用自上而下的选股框架 资料来源：FactSet 、MSCI 、CEIC 、高盛全球经济商品和策略研究 刘劲津, CFA
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慕天辉, CFA
+852-2978-1328 timothy.moe@
高盛（亚洲）有限责任公司
Caesar Maasry
+852-2978-7213 caesar.maasry@
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鄧啟志
+852-2978-0722 richard.tang@ 高盛（亚洲）有限责任公司
Sunil Koul
+852-2978-0924 sunil.koul@
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高盛与其研究报告所分析的企业存在业务关系，并且继续寻求发展这些关系。

因此，投资者应当考虑到本公司可能存在可能影响本报告客观性的利益冲突，不应视本报告为作出投资决策的唯一因素。

有关分析师的申明和其他重要信息，见信息披露附录，或参阅/research/hedge.html 。

由非美国附属公司聘用的分析师不是美国FINRA 的注册/合格研究分析师。

本报告仅供分发给高盛机构客户。

Introducing GS top-down stock picking framework
In this report, we introduce a framework to help connect the macro environment to
single stock selection.
Specifically, we believe this framework would be helpful to:
∙Formalize our existing top-down stock picking logic and approach in a
disciplined and statistically-tested manner that is also intuitively
appealing
∙Better comprehend how macro factors influence individual stock returns
and help connect macro trends to actionable stock ideas
∙Expand our implementation focus to include more single stock ideas as
well as theme baskets and derivative overlays
∙Complement our sector analysts’ views and our bottom-up stock
selection processes such as Asia-Pacific Conviction Lists and GS Sustain, which
focus principally on operating returns and industry position
∙Provide a tool which can add further perspective to investors’ own stock
selection processes
The exhibit below summarizes the key building blocks and logic flow in this report.
The key building blocks and approach of our stock-selection framework
Source: FactSet, I/B/E/S, Goldman Sachs Global ECS Research estimates.
Executive summary
Top-down and bottom-up are the two prevalent approaches to securities analysis. However,
neither is perfect— macro-focused investors may overlook micro factors and industry dynamics
while stock pickers may sometimes miss the big picture.
With an objective of combining the two approaches and translating macro views into actionable
stock ideas (and, hopefully, good returns), we introduce a top-down stock selection framework
which builds upon macro factor profiling, micro specific comparisons, and business-
cycle-based investing.
Key conclusions and investment implications in this report are:
1.Macro analysis is important, even to stock pickers. Our regression model shows
select macroeconomic factors are statistically significant return drivers (R²>40%) for
around 250 stocks in Asia, representing 58% of MXAPJ market cap. This
underscores the importance of macro analysis even to bottom-up-
oriented investors. Macro analysis is particularly effective in Hong Kong, China,
Singapore, and sectors including energy, financials, and materials.
2. Some macro variables are more important than others. Investors can be
overwhelmed by macro data, but many macroeconomic variables are highly correlated.
Stock pickers can simplify the process of macro monitoring by focusing on six factors,
namely, market risk, local growth, policy/liquidity, CPI, oil prices, and global
growth.
3.Micro specifics have linked well with ensuing returns. Empirically, micro
considerations such as valuation, consensus EPS and target price changes, and
technical entry levels have shown very strong relationships with ensuing 3-month
returns. These micro considerations complement the macro perspective and address
the extent to which fundamentals are discounted in share prices.
4.Our two-tier stock selection strategy may help performance. We have
established a trading algorithm based on our global leading indicator and its derived
business cycle— expansion, slowdown, contraction, and recovery— to
implement our macro and micro analysis. Backtests of our two-tier strategy
suggest performance can be enhanced to a meaningful extent by
considering these macro and micro factors.
5. Useful tool. This framework is flexible and can complement the investment process for
different types of investors.
Caveats: A tool, not a cure-all
We believe this approach adds value, but we also recognize its limitations.
Regression models are static and assume mean-reversion, and need to be
updated and refined to adapt to changing fundamentals.
Stock ideas: Investing in a contraction phase
The latest GLI reading suggests the global economy has entered a contraction phase. Against
this backdrop, we would:
a. Reduce market risk “beta”;
b. Buy stocks with low sensitivity to local growth;
c. Accumulate policy easing beneficiaries;
d. Own stocks that may outperform in a disinflationary environment;
e. Buy stocks that may benefit from lower oil prices;
f. Overweight stocks that are less sensitive to global growth momentum.
Stocks which have these macro characteristics and have shown favorable micro readings are
shown in Exhibit 1. Exhibit 1: We like these stocks because of their favorable macro exposure and compelling micro profile relative to their regional peers
Stock recommendations for June 2012 (Priced as of June 5)
Note (1): These stocks are rated Buy or Neutral by Goldman Sachs Research except SM Investments which is NC. We use consensus estimates for SM Investments. Note (2): * denotes the stock is on our regional Conviction List. “Tick” indicates stock that ranks top-30 percentile within each factor relative to its market peers and they perform well in our specified macro environment. Revisions and sentiment are based on forward 12-month consensus EPS.
Source: Factset, I/B/E/S, Goldman Sachs Global ECS Research estimates.
Key exhibits for chart lovers
We highlight the important exhibits (takeaways) as follows:
∙ Exhibit 7: Where top-down analysis may be more applicable in terms of markets and
sectors.
∙ Exhibit 8: Stocks that are the most and least sensitive to different macro factors across
markets.
∙ Exhibit 17: The empirical relationships between micro parameters and ensuing returns.
∙ Exhibits 28 to 38: Market summary pages which detail the factor loading for each stock
under our study universe.
Part 1: Mapping stock returns to macro exposure
Defining our study universe
To ensure the practicability of the regression results, we choose to focus on MXAPJ constituents
with at least US$1bn of index market cap, over US$5mn of average daily value traded (ADVT) in
the past 6 months, and at least 5 years of listing history. There are 412 stocks in MXAPJ
which fit these requirements (as of April 30, 2012) and they represent 86% of
MXAPJ index market cap . These stocks are mostly located in Australia, China, Korea
and India , with a sector concentration in financials, IT and telecoms (Exhibits 2 and 3). Exhibit 2: Liquid, large-cap stocks are mostly found in Australia, China, Korea and India Free-float market cap distribution by markets
Exhibit 3: ...and they are concentrated in sectors including financials, IT, and telecoms Free-float market cap distribution by sectors
Source: FactSet, MSCI, Goldman Sachs Global ECS Research. Source: FactSet, MSCI, Goldman Sachs Global ECS Research.
Defining the dependent variables (returns)
We choose 3-month price returns as the dependent variable in our regression model as we
attempt to strike a balance between high frequency trading and the “buy-and-hold” approach.
Returns are quoted in local currency , on our assumption that foreign investors should treat
the currency decisions separately from the stock selection decisions and the impact of FX
changes could be partly reflected/captured in the underlying macro trends (e.g. exports in CAI
and FCI).
Choosing and testing independent variables (macro factors)
While there are a large number of macro variables that equity investors could focus on, we elect
to limit our independent variables to 14 macro factors which theoretically should drive stock prices.
In other words, this is not an exhaustive list of macro variables which might influence stock
returns but what we have found to be generally influential at a market level. See Asia Pacific:
Portfolio Strategy: What macro indicators matter for Asian markets?, May 7, for details.
We also test the statistical significance of
2nd derivatives and lead/lags for each of the factors
in our multi-factor regression model to ensure statistically important relationships will be
accounted for.
Given many of these variables are inter-correlated and are essentially linked to similar sets of
fundamental drivers, we run a correlation matrix to eliminate those with strong directional
relationships to improve the ensuing regression results (Appendix 1). In cases where macro
factors are strongly correlated among each other within a category (e.g. growth), we prefer our
Australia 20%China 23%Hong Kong 8%India 11%Indonesia
2%Korea 16%Malaysia 2%
Philippines 0%
Singapore 5%Taiwan 9%Thailand 4%
Banks
23%
Information
Technology 14%
Telecom
11%
Materials
10%
Energy
9%Industrials
7%
Cons Disc
7%
Cons Stap
6%
Property
6%Insurance and other financial services 4%
Utilities 2%
Health Care
1%
proprietary indexes such as Current Activity Index (CAI), Financial Conditions Index (FCI),
and Global Leading Indicator (GLI) given their broader representation and statistically-tested
robustness (see Exhibit 4).
Following the above steps, we conclude that a significant part of an individual stock’s return
variations can be reasonably explained by 6 broad macro measurements: Market risk (MXAPJ),
domestic growth (CAI), domestic liquidity/policy (FCI), domestic inflation (CPI), oil
prices (WTI), and global growth (GLI).
The final step is to establish linkages between returns and these macro factors by using
regression techniques, namely simple linear and multiple factor regression models .
Recognizing the advantages and deficiencies of these modeling techniques, we choose to base
our stock selection analysis on the former and the market analysis on the latter.
∙ Simple linear regression by a single factor (e.g. six separate regression
models) allows us to estimate the factor loadings for the independent
variables without running into multicollinearity issues. This approach
may work better in capturing the maximum total exposure to a single
factor. That said, it doesn't account for the impact of other significant
variables and therefore is not robust to changes in the relationship
between macro factors over time. Even if we run a number of single
factor regressions, the resulting individual R²s from this are not additive
and we cannot statistically prove the explanatory power of each factor
for returns.
∙ Multiple factor regression with stepwise elimination helps form a quantifiable
relationship (equation) as to what factors are important and to what extent they, when all
treated as independent variables, have historically affected stock returns (when one
factor changes and others are held constant). The drawback is that the (high) correlation
among macro variables lowers the precision of the regression estimates, which is likely
to lead to estimates being very inaccurate for some stocks when the regressions are
carried out across a large universe of single stocks. If we subsequently use the analysis
to pick stocks with the highest/lowest sensitivities there is a risk that we will also end up
maximizing exposure to estimation errors. Exhibit 4: The independent variables in our regression model are representative of the principal macro categories that tend to influence stock returns
Macro variables and the inputs to our regression model
Note: We use the next 3 month yoy growth for CAI (local/US/EU) as they show significantly higher correlations with returns, which provides better indicative power on returns and fits our purpose of mapping returns to macro exposure better. See Appendix 1 for details.
Source: FactSet, I/B/E/S, Goldman Sachs Global ECS Research estimates.
Input specifications
Chosen variables
returns Log P(t)‐Log P(t ‐3)
returns Log P(t)‐Log P(t ‐3) index returns Log P(t)‐Log P(t ‐3)
Log (Avg. of last 3 monthly yoy growth)
growth Log (Avg. of last 3 monthly yoy growth)Log (Avg. of last 3 monthly yoy growth)Log (Avg. of next 3 monthly yoy growth)Log (Avg. of latest 3 mom growth)Local CPI Log (Avg. of last 3 monthly yoy growth)Local CPI Local FCI Log (Avg. of last 3 monthly yoy growth)Local FCI
Log WTI(t)‐Log WTI(t ‐3)Log (Avg. of next 3 monthly yoy growth)Log (Avg. of next 3 monthly yoy growth)
Log (Avg. of last 3 monthly yoy growth)Log (latest 3 mom changes)MXAPJ returns
Local CAI
WTI
GLI
I n
d e p e
n d
e
n t
v a r
i
a b l
e s
Domestic liquidity/policy
Exhibit 5: Our model shows that stock returns can be reasonably explained by 6
market/macro factors
Components our multi-factor regression model
Intercept other factors Source: Goldman Sachs Global ECS Research.
Regression results (1): Understanding where macro analysis may apply
Top-down approach works for some stocks, but not all. Our model has yielded reasonably high R² (over 40%) for 247 stocks out of the total 412 sample universe (58%
of market cap). It also means the remaining 165 stocks could be more sensitive to micro
factors as opposed to macro forces if one takes R² of 40% as the threshold1.
From a market standpoint, the top-down approach seems to work better for Hong Kong, China and Singapore, while the bottom-up study appears more suitable for Australia and the ASEAN-4 markets. We think these results reflect the following:
a. Hong Kong, China (HK-listed) and Singapore have open economies and
free capital markets, meaning that the local stock markets are more sensitive
to global macro forces and capital flows relative to the region.
b. The dynamics between domestic and externally-driven demand for Australia
can differ—currently, the domestic economy is hampered by the (until recently)
strong currency, weak property market and issues in the banking system while
commodity exports remain resilient due to demand from global EMs. This
dichotomy makes a static top-down analysis less effective.
c. The domestic demand component and generally low foreign investors
participation in the ASEAN-4 have resulted in lower returns volatility for these
smaller markets. As such, the top-down framework has to be adjusted by
local factors to make it more applicable and effective to explain returns.
By sector, the average R² is generally low for defensives including telcos, utilities and healthcare stocks, suggesting: a) their share prices are not sensitive to macro
changes, relative to the aggregate market; b), their price sensitivity to the market risk
factor is low as reflected by the regression results; and, c) a micro-focused approach is
required to generate alpha in these sectors.
At the other end of the spectrum, a top-down approach could be effective for energy,
financials, and materials given the high R², which conceptually makes sense as these sectors are closely linked with global dynamics via real demand and financial channels.
1 Note that these R²s are derived from our regression models and the macro factors may not represent the true macro profile for our study universe.
Exhibit 6: Macro factors appear important return drivers (R² more than 40%) for 60% of the
stocks and 58% of market cap in MXAPJ Accumulated distribution of R ² based on our study universe
Source: Goldman Sachs Global ECS Research estimates.
Exhibit 7: Top-down analysis works for select economic groups but not all
Average R² based on our multi-factor regression
Source: FactSet, I/B/E/S, Goldman Sachs Global ECS Research estimates.
Regression results (2): Making sense of it
We ran macro-factor regressions for more than 400 stocks in Asia and the results are organized
by their factor loadings in each market in Exhibit 8 (we show the top-3 stocks under each market
factor only; regression results for the full universe are shown in the country summary pages).
0%10%20%30%
40%
50%60%70%80%
90%
100%0%10%20%30%40%50%60%70%80%90%100%Top ‐down approach may work
better
Macro factors explain >40% of return variations
%of market cap
R2
Bottom ‐up approach may work
better
Macro factors explain<40% of return variations
Bottom 25 percentile Top 25 percentile
Exhibit 8: We group stocks under different macro buckets based on their factor loadings
Stocks with highest/lowest factor loadings by market
Note (*): Highest (positive) coefficients for FCI means stocks have historically performed better relative to their benchmark when financial conditions tighten. Note (1): We rank stocks by their factor loadings based on the output from our simple linear regression model. Multiple factor regression models using specific independent variables are required to estimate the relative significance of variables and their explanatory power on returns.
Note (2): We only show stocks which rank in the top-80 percentile in terms of R² ranking for that particular factor.
Note (3): We exclude Philippines because only 4 stocks satisfy our liquidity requirements.
Source: Goldman Sachs Global ECS Research estimates.
To better interpret and make sense of the regression output, we think it is useful to look at a few
examples below.
Example 1: Samsung Electronics (005930 KR)
The regression R² is low for Samsung Electronics (33%), suggesting the stock’s returns
could be more sensitive to specific micro factors than the high-level macro variables that
we test.
∙While Samsung is perceived by investors as a DM growth proxy, its share does not
appear to be well linked with global growth momentum (GLI is eliminated in the stepwise
regression). This coincides with the stock’s substantial outperformance in the past few
years due to its improved competiveness and strong product cycle (e.g. Galaxy
smartphones).
∙It is positively correlated with domestic growth and liquidity conditions, which seems
normal, although their statistical significance is lower than the market risk factor.
Exhibit 9: The low R² for Samsung suggests that micro factors could be more important in driving the stock price Stepwise regression results (Samsung Electronics)
Source: FactSet, CEIC, MSCI, Goldman Sachs Global ECS Research estimates.
Example 2: ICBC (1398 HK)
∙Our model shows 74% of ICBC’s share price variations can be explained by the four
factors in the stepwise regression model.
∙ICBC’s sensitivity to the market risk factor is high (high beta), partly reflecting the
relatively high growth and policy volatility (or market concerns) in China.
∙It is positively correlated with inflation and policy easing as the bank may benefit from
loan pricing and higher loan quota when these macro conditions move in its favor.
∙The share prices tend to outperform (underperform) the aggregate market when global
growth decelerates (accelerates), as the stock is not as sensitive as the MXAPJ
aggregate to global growth momentum.
Exhibit 10: Policy easing should bode well for ICBC but its GDP growth exposure may not be as high as many have perceived
Stepwise regression results (ICBC)
Source: FactSet, CEIC, MSCI, Goldman Sachs Global ECS Research estimates.
Example 3: WesFarmers (WES AU)
∙ The regression R² is high compared with other Australian stocks in our study sample,
meaning that top-down approach may make more sense for WES relative to other
Australian stocks.
∙ The stock has showed low sensitivity to MXAPJ returns in local currency terms.
∙ Higher CPI tends to bode well for stock returns, as high inflation may allow the company
to raise prices more easily.
∙ While the local exposure of its business operations may suggest high sensitivity to
domestic growth activities, domestic CAI is omitted from the regression. This may reflect
the hybrid structure of the Australian economy as discussed on page 7. Exhibit 11: The market risk sensitivity is generally low for AU stocks due possibly to the omission of FX beta; domestic activities link well with export growth cycles, adding complications when interpreting regression results
Stepwise regression results (WesFarmers)
Source: FactSet, CEIC, MSCI, Goldman Sachs Global ECS Research estimates.
# of obs.60R²
74%
# of obs.142R² 50%
Example 4: TATA Consultancy Services (TCS IN)
∙ It has one of the highest R² (74%) among the Indian stocks in the MXAPJ universe
(average 46%).
∙ The stock can be classified as a low-beta stock given its low return coefficient with
MXAPJ (c.0.67).
∙ The stock has tended to move very closely with GLI momentum (1% of GLI momentum
change has historically led to 15% of share price movement), consistent with TCS’s
business concentration in global developed markets. Exhibit 12: TATA consultancy’s share prices seem very sensitive to global growth given the company’s geographic exposure
Stepwise regression results (TATA Consultancy Services)
Source: FactSet, CEIC, MSCI, Goldman Sachs Global ECS Research estimates.
# of obs.86R² 74%
Part 2: Adding micro overlays—valuation, micro fundamentals and technical indicators—to enhance returns
Part 1 aims to match individual equities’ returns to select macro factors. However, the return
attribution exercise is insufficient to build a sensible trading strategy on its own, because:
a. No market or stock in our sample universe has close to a 100% fit (R²) in our regression
model, suggesting some unobserved or uncaptured variables, which we believe are
mostly micro-related, are also significant performance drivers;
b. Stock returns are essentially a joint function of earnings growth and valuation changes.
Our analysis helps partly explain returns variations using macro factors but does not
take into account the returns that macro drivers have on risk premia and therefore the
willingness of investors to pay for equities for a given level of earnings.
c. In some cases, share prices may have already reflected stock-specific themes/exposure;
and pricing signals (especially shorter-term ones) are difficult to capture in macro-factor
regression models.
As such, we incorporate valuation parameters, micro fundamental variables, and various
technical indicators into our broader framework. We believe these additional considerations
could help us form an objective view on stocks’ micro profile and help answer the questions of
“how much is priced in” and “entry level”, which have shown strong relationships with price
returns (more on this later).
Valuation parameters
We focus on the stock’s current valuations to gauge how the stock’s fundamentals are being
priced by the market and to avoid buying/selling at full/depressed valuation levels.
Specifically, taking the conclusions from our recent work: Asia Pacific: Portfolio Strategy: Global
Strategy Paper: No. 3: AsiaPac Valuation: What works, and when, March 12, 2012, we have
chosen forward P/E, trailing D/Y, and trailing P/B, which are proven to be significant returns
indicators (higher significance to medium-term returns) according to our analysis, as the key
metrics.
While we have also proved that P/CF has strong predictive power on forward returns at a market
level, we deliberately exclude this in our analysis given its short and unstable time series at an
individual stock level.
Micro fundamentals
While we have established relationships between macro factors and stock returns in Part 1,
stocks react to macro forces because they tend to impact earnings or earnings expectations,
which can be quantified and captured by the changes of consensus EPS and expected returns
(target price). In this vein, we look at following indicators to assess stock’s micro dynamics using
consensus data:
∙Earnings revision (magnitude)—percentage of month-on-month forward 12-month
EPS changes;
∙Earnings sentiment (breadth)—percentage of net earnings upgrades/downgrades
versus the total number of consensus estimates (mom);
∙Consensus target price—percentage changes (mom).
Technical indicators
Broadly speaking, technical indicators can be grouped into four categories—momentum,
volatility, trend and volume . Given the numerous forms of technical indicators, we focus on
those that are most commonly used and are comparable from a time series standpoint. The key
objective here is to form a view on short-term entry levels.
1. RSI (momentum): We use 14-day RSI to gauge the near-term price momentum of
particular stocks. We prefer 14-day over longer-dated RSIs given its higher volatility
relative to longer-dated RSIs, which better fits our objective of evaluating entry point for
shorter-term investing.
2. Bollinger bands (volatility): For computational purpose, we look at %b , as opposed
to the actual moving averages and the two bands (upper and lower) to quantify where
the current price is relative to its recent range in standard deviation terms. Low %b
indicates current prices are closer to the lower Bollinger band and vice versa.
3. Moving Average Convergence/Divergence (MACD) (trend): We take the
differentials between MACD line (12 day-26 day exponential moving average (EMA))
and MACD signal line (9-day EMA), which is the histogram on a typical MACD chart, to
gauge the short-term trend and price momentum of the stock. High positive values
reflect strong price momentum in recent trading periods and vice versa.
4. Volume: Most volume-based indicators require a time-series perspective to form trading
signals; hence, an absolute number often does not tell us much. Volume signals can
vary so decision-making is not stable. As such, we exclude volume-related
indicators in our micro score calculation .
Exhibit 13: We use these micro parameters to form a view on the stocks’ fundamentals, aiming to enhance the
risk/reward of our top-down stock recommendations
A summary of micro parameters
Source: FactSet, I/B/E/S, Goldman Sachs Global ECS Research estimates.
The effectiveness of micro overlays—an empirical study
While these micro parameters are commonly regarded as important elements to potential returns,
their actual implications and contribution to returns are unclear to us up to this point.
As such, we test the conditions and formats under which these micro factors matter most to
potential returns, and design our trading strategies accordingly. Input specifications P(t)/fEPS(t)P(t)/tBPS(t)
P(t)/tDPS(t)
fEPS(t)/fEPS(t ‐1)
‐
1{#up rev(t)‐#down rev(t)}/#estimate(t) price TP(t)/TP(t ‐1)‐1
Momentum 14‐day RSI
Volatility Bollinger Bands (%b)
Trend MACD line ‐ MACD signal line Composite micro score
M i
c r
o p
a
r a
m e
t e r s Technical indicators。