对投资组合经理和选股者重要的板块策略：推出我们的亚太板块配置模型STAMP-2013-01-08.pdf

2013 年 1 月 8 日 亚太 对投资组合经理和选股者重要 的板块策略 对投资组合经理和选股者重要 的板块策略 证券研究报告 推出我们的亚太板块配置模型 stamp 板块策略在亚洲正变得越发重要板块策略在亚洲正变得越发重要 亚洲股市正在发生变化；相关性分析显示个股的表现越来越与板块同业的表现趋 于一致。亚洲各板块之间的回报离差等于各国指数之间的离差，但板块表现似乎 和宏观指标的相关性更大。 板块策略如何对选股者更有益？板块策略如何对选股者更有益？ 从合适的板块介入对于识别表现出色的股票大有帮助。平均而言，在过去 5 年 中，“最佳板块中位于回报中值的股票”往往和“最差板块中最出色的股票”产 生类似的回报。 推出推出 stamp（板块交易和监控平台）（板块交易和监控平台） 我们的板块配置模型 stamp 包含了 5 个“简化”的宏观观点，以及盈利预测情 绪和相对估值（均为板块回报的良好领先指标）。回溯测试显示，准确的宏观观 点可以创造板块超额收益，表明自上而下的方法和自下而上的方法可以互为补 充，而不是相互替代。 stamp：综合了：综合了 “简化简化”的宏观观点和股票基本面分析的宏观观点和股票基本面分析 资料来源： 高盛全球经济、商品和策略研究 caesar maasry +852-2978-7213 高盛（亚洲）有限责任公司 慕天辉慕天辉, cfa +852-2978-1328 高盛（亚洲）有限责任公司 刘劲津刘劲津, cfa +852-2978-1224 高盛（亚洲）有限责任公司 鄧啟志鄧啟志 +852-2978-0722 高盛（亚洲）有限责任公司 sunil koul +852-2978-0924 高盛（亚洲）有限责任公司 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。 高盛集团 gs asia stamp sector trading and monitoring platform dom. vs. inflation tighterasian gdpexternalvs.finlfx vs.epsrel. growthgrowthgrowth condtnsusdsent.value autos correlation analyses show that stocks are moving more and more closely with their sector peers over time. in addition, regional sector indices offer the same return dispersion as country indices in asia, allowing for ample alpha generation. finally, the relative returns of sectors appear to be more clearly correlated with macro variables as compared with countries returns and respective macro indicators. the history of european markets sets an interesting precedent for asia. looking at stocks correlations with their respective sectors vs. their respective country indices, we find that asia ex- japan appears rather similar to europe of the late 1990s. similarly, we argue against notions that asia is composed of “markets of stocks” as opposed to the “stock markets” that are found in the us and europe, as all three regions exhibit similar average correlations and return dispersions across stocks. consequently, we see little reason for sector strategy to be less important in asia as it is elsewhere. why does sector strategy matter for bottom-up stock-pickers? starting in the right sectors greatly helps identifying stocks that may outperform the benchmark. on average, over the past five years, the “median stock in the best sectors” tends to perform as well as the “best stocks in the worst sectors”. arguably, this implies that an analyst of average ability would have performed as well as an analyst with superior skill, if the weaker analyst were in the “right” sector and the better analyst were in the “wrong” sector. accordingly, it seems that top-down and bottom-up approaches are important complements to one another. driving sector views from macro, eps, and valuation: stamp our sector trading and monitoring platform (stamp) is a framework that adopts a three-pronged approach in formulating quarterly regional sector allocation recommendations: distilling “simplified” macro views. the first leg of the stamp is a model that incorporates five simplified macro inputs: growth, domestic vs. external growth, inflation relative to growth, financial conditions, and currency changes. two main takeaways from this model are: (1) several sectors show up as “consistently defensive” relative to growth variables, but there are fewer “consistently cyclical” sectors. relative growth rates (domestic demand vs. exports, inflation vs. gdp) are necessary to gauge what sort of cycle drives the various pro-growth sectors. (2) a back-test of this model demonstrates that significant alpha may be generated from having accurate, “simplified” views (rising, flat, or falling) of these five macro inputs. in addition, it illustrates that a top-down approach can drive both aggregate sector views as well as identify the areas most ripe for stock-picking. incorporating earnings and relative valuation into the framework: historically, earnings revisions have been highly correlated with sector returns. however, the relationship holds most strongly on a coincident basis (i.e. perfect foresight is necessary). stamp incorporates earnings sentiment (the breadth of analyst revisions) which exhibits a leading relationship with returns (of varying strengths) across the regional sectors. relative valuation has historically provided a positive signal for future relative returns (works better over horizons longer than three months), particularly relative dividend yield and price-to- book ratios. valuation and earnings sentiment are important complements to one another, as most sectors relative returns tend to hold a close relationship with one metric or the other. 2013 年 1 月 8 日 亚太 高盛全球经济、商品和策略研究 4 sector affinity: analytical output organizing our framework according to commonly-used sector groupings of “cyclical”, “commodity”, “financials”, and “defensives” shows several interesting patterns: cyclicals: as the name suggests, the “cyclical” sectors tend to show a strong affinity to growth inputs. the “domestic vs. growth” axis appears important for almost all cyclical areas (except for consumer retail, which shows little macro affinity in general). earnings sentiment is a significant indicator across the cyclical complex. commodity: metals this in turn suggests that sector strategy is gaining in importance for equity investors. alpha opportunity: regional sectors offer the same dispersion as country indices and are more correlated with macro variables. since 2000, the dispersion between the best and worst performing sectors has been nearly identical to the dispersion between the best and worst country indices. in other words, trading sectors offers the same alpha-generating opportunity as trading country indices, which we believe is far more prominent in asia. perhaps more important, however, is that we find regional sector indices are more highly correlated with the “usual” monthly macro data points than country indices (using ip, pmi, retail sales, cpi, and exports). in short, while sector strategy may offer a similar alpha opportunity as country strategy, we believe the implementation of sector views are more straightforward for given a set of basic macro views, which we discuss further on page 12. exhibit 2: stocks are nearly as correlated to regional sectors as to markets, rolling 2-year correlation of monthly returns source: factset, msci, goldman sachs global ecs research 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 dec-96 dec-97 dec-98 dec-99 dec-00 dec-01 dec-02 dec-03 dec-04 dec-05 dec-06 dec-07 dec-08 dec-09 dec-10 dec-11 dec-12 correlation w/ sector correlation w/ country stocks used to move more closely with their country than their sector (0.30) (0.25) (0.20) (0.15) (0.10) (0.05) 0.00 0.05 0.10 dec-96 dec-97 dec-98 dec-99 dec-00 dec-01 dec-02 dec-03 dec-04 dec-05 dec-06 dec-07 dec-08 dec-09 dec-10 dec-11 dec-12 stocks move w/ sector stocks move w/ country stock correlation with sector less stock correlation with country 2013 年 1 月 8 日 亚太 高盛全球经济、商品和策略研究 6 exhibit 3: sector dispersion sectors show stronger correlation with macro variables note: country correlation with respective country indicators; regional sectors with regional market-cap-weighted indicators (since 2004) source: factset, msci, goldman sachs global ecs research asia sector strategy may be similar to strategy in other regions the evolution of the european equity market sets an interesting precedent to the asia story. running a similar correlation analysis as above but for european stocks, we find that country factors seemed much more prominent in europe during the 1990s. however, over the past two decades, stocks have been trading more and more with their sector peers, likely due in part to the integration of the emu. in fact, sector correlation has been greater than country correlation for several periods in the 2000s. although asia is not as financially integrated as europe, we believe the trend of increased sector trading within europe is likely to be repeated in asia in coming years. exhibit 4: in europe, stocks are currently more correlated with markets than sectors, rolling 2-yr correl. of monthly returns + source: factset, msci, goldman sachs global ecs research 0.0000.400.50 china indonesia thailand malaysia philippines telecom services india korea singapore utilities real estate hong kong capital goods consumer staples insurance and we find that the relationships are quite similar across the globe. average correlation of stocks with the sector indices is roughly 0.5 in asia and 0.6 in both the us dispersion is marginally higher in asia, but not significantly in our view. since 2005, return dispersion in asia has averaged 10%, compared with 7% in both the us and europe. on a sector basis, asia shows consistently lower correlations and higher return dispersion across the board, but the magnitude of such differences are not significant. defensive sectors tend to have the lowest correlations in each region, whereas stocks in the cyclical sectors, especially materials, consumer discretionary, and info tech, tend to exhibit the highest return dispersion. in short, the consistent patterns of sector dispersion and correlation, as well as bottom-up stock- level data, supports a view that asian sectors appear to behave quite similarly to sectors in the us this in turn suggests that sector returns are not driven by just a few mega-cap stocks which is sometimes the view espoused by investors in the region. second, as we discuss further, the aggregate returns of sectors may be modeled by macro views, aggregate earnings sentiment, and sector valuation, or, in essence, a top-down approach. accordingly, the analysis also suggests that top-down and bottom-up approaches are important complements to one another. exhibit 6: why sector strategy matters for stock-pickers: starting in the right place helps mean annual returns since 2007 of stocks within top 3, mid 4, and bottom 3 performing gics sectors source: factset, msci, goldman sachs global ecs research -60% -40% -20% 0% 20% 40% 60% 80% 100% 120% best sectors in-line sectors worst sectors 90th %-ile stock 10th %-ile stock 90th %-ile stock 10th %-ile stock the “top stocks“ in the worst sectors give a similar return to the median stock in the best sectors. conversely, the “worst stocks“ in the best sectors give a similar return to the median stock in the worst sectors. howdo sector views help stockpickers? starting in the right sectors greatly increases odds of picking a “winner“ average annual return 2013 年 1 月 8 日 亚太 高盛全球经济、商品和策略研究 9 comparing asia with the us and europe, it appears the single-stock implications of sector strategy may even be more important. similar conclusions hold regarding the ranges of stock returns in the “best” vs. “worst” sectors across the regions, however the ranges appear most distinct in asia. in europe, for example, the worst performing stocks in each sector tend to post similar returns across the board. exhibit 7: sector strategy can help stock-pickers in any region source: factset, msci, goldman sachs global ecs research another way to gauge single-stock implications from sector strategy is to look at the share of stocks in a given sector that beat the regional benchmark, a figure we call “sector consistency”. in asia, for example, telecom services is the most “consistent” sector; when the sector outperforms the regional mxapj index, on average 73% of the stocks in the sector beat the benchmark. on the other hand, financials is the least consistent sector; when that sector outperforms, only 57% of the actual stocks typically post positive excess returns vs. the regional benchmark (since 2005). financials are not a consistent regional sector as they follow country-specific factors such as monetary policy. consistency is a measure of how a top-down view on a sector may translate into “picking winners”. our measures of consistency is almost identical when looking at asia, the us, or europe. exhibit 8: financials appear least “consistent”; stocks within financials do not tend to out- or underperform together source: factset, msci, goldman sachs global ecs research -60% -40% -20% 0% 20% 40% 60% 80% 100% 120% best sectors in-line sectors worst sectors 90th %-ile stock 10th %-ile stock 90th %-ile stock 10th %-ile stock asia sectors- stock “spreads“ sector strategy matters to stock-picking average annual return -50% -25% 0% 25% 50% 75% best sectors in-line sectors worst sectors 90th %-ile stock 10th %-ile stock 90th %-ile stock 10th %-ile stock us sectors- stock “spreads“ larger overlap than in asia -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 60% best sectors in-line sectors worst sectors 90th %-ile stock 10th %-ile stock 90th %-ile stock 10th %-ile stock european sectors-stock “spreads“ the worst stocks in each sector have similar returns sector consistency percentage of stocks within sector that outperform together asia ex-japaneuropeus telecom services73%62%61% utilities69%69%73% info tech67%63%62% energy66%63%76% health care66%63%59% materials65%66%61% consumer discretionary63%65%65% industrials63%62%61% consumer staples61%67%65% financials57%68%61% average65%65%64% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% dec-07 jun-08 dec-08 jun-09 dec-09 jun-10 dec-10 jun-11 dec-11 jun-12 dec-12 jun-13 dec-13 what % of stocks outperformthe asia ex-japan benchmark in . “best sectors“ (avg = 65%) “worst sectors“ (avg = 37%) 2013 年 1 月 8 日 亚太 高盛全球经济、商品和策略研究 10 asia stamp: (s)ector (t)rading (a)nd (m)onitoring (p)latform top-down framework: macro views, earnings, (2) several sectors appear consistently “defensive” relative to growth variables, but there are fewer consistently “cyclical” sectors as a cycle may be driven by a number of different factors (such as local growth, global growth, money flows); and (3) significant alpha may be generated from having basic views (rising, flat, or falling) on these five macro inputs, as our back-test suggests. do earnings and relative valuation matter to sector returns? historically, earnings revisions (changes to eps estimates) have been highly correlated with sector returns. however, the relationship holds most strongly on a coincident basis; i.e. an investor would need to be able to predict future earnings revisions in order to make sector allocation decisions. a more practical strategy may be to use earnings sentiment (the breadth of analyst revisions) which exhibits a leading relationship with sector returns. the linkage between valuation and returns is closest when using long-term returns, though relative valuation, particularly dividend yield and p/b, is an important signal for several sectors on a quarterly basis (see goal - global strategy paper: no. 3 - asiapac valuation: what works, and when, march 12, 2012 for more details). for some sectors where eps sentiment is less effective, valuation provides a stronger signal. exhibit 9: current stamp scores suggest a relatively pro-cyclical sector allocation source: factset, msci, goldman sachs global ecs research current stamp scores based on our macro views and latest data macroepsrelativecurrentcurrent gs viewssentimentvaluationstamp scoreallocation real estate overweight transportation overweight insurance earnings and valuation signals tend to work at different points in time back-testing different approaches to sector strategy suggests that sector alpha is driven by macroeconomic developments, changes to aggregate corporate profit forecasts, and starting valuation stamp combines the three approaches on an equal-weighted basis. although we are only able to test our macro model ex-post, we believe the significant alpha generated by the model may come as a greater surprise, as investors tend to acknowledge that earnings drive stocks but do not necessarily view sectors as coherent groupings of companies that follow basic macroeconomic trends, which the model suggests. that said, we acknowledge the limitations of an ex-post test and would not assume the model would generate such high returns going forward. the model consists of a loading factor (beta) to each macro variable for each sector and then adopts a scoring system of 1 (if macro variable is rising), 0 (if flat), and -1 (if falling). in other words, the model is not a typical regression that relies on specific forecasts; rather it distills each macro driver into this tri-nary scoring system, which, although simple, appears important enough to drive returns. the ex-ante methods of earnings sentiment and starting relative valuation both generate alpha over time to a lesser extent. the two models have a quarterly “hit rate” (historic probability of generating positive excess returns) of 56% and 62%, respectively, with an average annual alpha of 3pp and 5pp. the quarterly excess returns by these two methods are negatively correlated (- 0.25) with each another; suggesting that earnings sentiment and valuation work at different times. there is not a clearly identifiable pattern as to when one or the other method may work; but historically, the valuation strategy is more negatively correlated with benchmark returns implying that valuation works better when market pull back (especially during the global financial crisis). exhibit 10: back-test statistics of various sector strategies hit rate = percentage of quarters strategy generates positive alpha source: factset, msci, goldman sachs global ecs research distilling five “simplified” macro views into sector allocation our macro model incorporates five basic macro views: sector models: in-sample back-tests using current infowith perfect foresightmacro, earnings earningsvaluationearningsmacromacro focus on relative returns source: factset, msci, goldman sachs global ecs research exhibit 14: currently, we recommend a rather pro-cyclical sector tilt, underweighting most