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This report has been prepared by UBS Securities Asia Limited. ANALYST CERTIFICATION AND REQUIRED DISCLOSURES BEGIN ON PAGE 21. UBS 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.

Global Research 15 July 2015

Quantitative Monographs The madness of crowds

Institutional crowded trades result in mid- to long-term performance reversals After an initial one to two months, the performance of stocks that are heavily bought or sold by institutional investors tends to reverse. Heavily sold names start to outperform whilst crowdedly bought names start to underperform. The reversal trend could last for 12 months post the initial transactions. This phenomenon has become much stronger and more consistent in recent years.

Valuation plays a key role in differentiating winners and losers Large-scale sell-offs of already inexpensive stocks result in stronger performance rebounds, whilst crowded buys on already expensive stocks usually leads to larger underperformance in the medium- to long-term.

Sell-side sentiment matters Institutional sells are usually accompanied by sell-side downgrades, whilst institutional buys are associated with sell-side upgrades. A change in the direction of sell-side sentiment indicates a future performance reversal. Crowded institutional sells with improving ratings and earnings estimates tend to subsequently outperform. Similarly, crowded institutional buys with deteriorating ratings and earnings estimates usually underperform.

Screen for stocks with strong upside and downside potential We have built an alpha model based on the above findings. For stocks with strong upside potential, we screen for non-expensive companies previously heavily sold by institutional investors where their ratings and earnings estimates are starting to improve. For stocks with strong downside potential, we screen for expensive names previously overbought by institutional investors where their ratings and earnings estimates are starting to be revised down. This strategy has demonstrated a strong and consistent performance over the back-test period, and we will provide readers with model updates on a monthly basis going forwards.

Figure 1: Relative performance - MSCI AC World Figure 2: Long short performance - MSCI AC World

Source: MSCI, IBES, FactSet, UBS Quantitative Research Source: MSCI, IBES, FactSet, UBS Quantitative Research

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Equities

Global

Quantitative

Shanle Wu, PhD Analyst

[email protected] +852-2971 7513

Paul Winter Analyst

[email protected] +61-2-9324 2080

David Jessop Analyst


Josh Holcroft Analyst

[email protected] +852-2971 7705

Nick Baltas, PhD Analyst


Oliver Antrobus, CFA Analyst


Sebastian Lancetti, CFA Analyst


Claire Jones, CFA Analyst


Luke Brown Analyst


Pieter Stoltz Analyst


Quantitative Monographs 15 July 2015

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Summary As the story goes, Joe Kennedy exited the stock market before the 1929 crash after a shoe-shine boy offered him stock tips. He saw it as a sign of over-crowding. Almost nine decades later, crowded trades again were the precursor of the financial crisis. The widespread adoption of similar strategies among institutional investors led to a quant meltdown in August 2007, which marked the start of the global financial crisis. In recent years, the growing popularity of smart beta strategies, essentially based on the idea of investment factors, may bring the risk of crowded trades to another level.

In this report, we study institutional investors' security ownership data provided by FactSet. Using this data, we build a signal to identify stocks that have been overbought or oversold by institutional investors. Further, we study the price impact of institutional crowded trades and find that:

• Overbought and oversold names exhibit significant performance reversals. After the initial outperformance (for bought names) and underperformance (for sold names), performance reverses in the next 12 months during our testing period. This phenomenon has become more pronounced over recent history.

• Inexpensive stocks heavily sold by institutional investors rebound strongly. Those with improving sell-side sentiment tend to outperform by a greater margin.

• Expensive stocks heavily bought by institutional investors tend to underperform in the medium- to long-term. Those with deteriorating sell-side sentiment underperform by a greater margin.

• We have designed a strategy based on the above findings. The back-test results suggest the performance is strong and consistent for both the long and short sides.

Figure 3: Strategy performance summary – MSCI AC World

Long: Crowded sold & Low PE

& Improving sentiment

Short: Crowded bought & High

PE & Worsening sentiment Long – Short

Annualized relative returns 7.1% -5.3% 12.3%

Risk-adjusted returns 1.52 -1.28 1.61

Hit Rate 51% 46%

Note: Long/Short in this quantitative modelling context describes the back-test/performance results of stocks screened based on the model's strategy/methodology. Source: MSCI, FactSet, UBS Quantitative Research

Crowded trades are a key market risk we cannot afford to ignore

We studied the institutional crowded trades to avoid risk and generate alpha


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Gauging the institutional crowds

Data

This study is based on the FactSet ownership database, which collects institutional investors' ownership data globally and contains both terminated and active global equity securities. The data starts from 1999 and currently covers about 37,800 funds globally. In this report, we focus on the MSCI AC World universe.

Figure 4: Number of funds reported each month

Source: FactSet, UBS Quantitative Research

Measures of crowded trades Here we define three measures to gauge the level of crowdedness. Measure 1 is based purely on the number of funds that bought or sold in the previous period, whilst measures 2 and 3 take into account the actual amount of trades.

Measure 1. The proportion of institutional investors buying a stock relative to the total number of institutional investors trading the stock over the same period. A fund is considered as buying or selling a stock if the number of shares it holds in the stock increased or decreased over one month. Stocks with a proportion greater than 50% are classified as being bought and those with a proportion less than 50% are classified as being sold1.

Measure 2. Changes in the proportion of shares outstanding held by institutional investors.

Measure 3. The rolling 12-month Z-score of Measure 2.

When we calculate month over month changes for all three measures, we only consider the funds that exist in both months. By doing so, we remove the impact of changing numbers of funds in the market as well as changing coverage of the database itself. Changing coverage of the database adds 'noise' to our analysis. Newly established funds need to build-up their positions while funds that are closing down will liquidate all their positions. Neither case is related to the crowded trades we want to capture.

1 Measure 1 is a simplified version of the Lakonishok, Shleifer and Vishny metric of herding. For more detail, please see: “The Impact of Institutional Trading on Stock Prices”, Josef Lakonishok, Andrei Shleifer, and Robert W. Vishny, 1992.

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Figure 5: Average relative returns at month 0 Figure 6: Cumulative relative performance – Measure 1

Source: MSCI, FactSet, UBS Quantitative Research Source: MSCI, FactSet, UBS Quantitative Research

Figure 7: Cumulative relative performance – Measure 2 Figure 8: Cumulative relative performance – Measure 3


Figure 9: % changes in volume at month 0 Figure 10: Ratings revisions at month 0

Source: MSCI, FactSet, UBS Quantitative Research Source: MSCI, IBES, FactSet, UBS Quantitative Research

Each month we formed five portfolios based on the three measures mentioned above. We calculated the equal-weighted portfolio returns (relative to equally-weighted MSCI AC World benchmark) for the month when the portfolios were formed (month 0 denoted as M0), the subsequent month (month 1 denoted as M1), and the month after that (month 2 denoted as M2), etc. We then

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accumulated the returns up to 12 months after the portfolios were formed to gauge the longer-term performance of the portfolios. We found that:

• All three measures are positively correlated with the stock returns in the month at which the portfolios were formed. More crowdedly bought names have a better performance while more crowdedly sold names have a worse performance (see Figure 5).

• Measure 1 exhibits the clearest and strongest trend of performance reversal after the initial phase. For the most crowdedly sold basket (Q1) especially, after the initial underperformance (in M0 and M1), it rebounded strongly in the following months (see Figure 6, Figure 7 and Figure 8).

• The level of crowded trades gauged by both measures 1 and 3 are positively correlated with the changes in trading volumes. The most crowdedly sold and bought baskets (Q1 and Q5) have the highest level of increasing trading volumes (see Figure 9).

• All measures are positively correlated with the sell-side revisions and the relationship is strongest with Measure 1 (see Figure 10).

As shown above, Measure 1 captures all the desired characteristics of crowded trades and therefore may be the better measure of crowdedness than measures 2 and 3. This is consistent with some of the academic research. Sias, Starks and Titman (2006) argued that a count of funds trading a stock may represent a stronger signal than the size of their trades. As a result, in the following section, we will focus on Measure 1.

Further study of Measure 1

Asymmetric on buy and sell

We take a closer look at the subsequent performance within the bought and sold names, respectively. We note that price reversals are strong and effective for stocks previously sold by institutional investors. The most heavily sold basket (Q1) tends to rebound most strongly in the following 12 months, followed by Q2, Q3 and so on (see Figure 11). On the other hand, stocks previously bought by institutional investors do not necessarily underperform in the following year. In fact, the most crowded bought names (Q5) have slightly outperformed the other previously bought names (see Figure 12).

Measure 1 captures the desired characteristics of crowded trades and will be our focus in this report

Performance reversals are strong within crowdedly sold baskets and less so with crowdedly bought baskets


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Figure 11: Stocks sold by institutional investors Figure 12: Stocks bought by institutional investors


Performance reversal has become more pronounced in recent years

We back-tested the performance of crowded bought names (top quintile) and crowded sold names (bottom quintile) with a three-month lag2 and six-month holding period3 in the MSCI AC World universe. Portfolios are rebalanced monthly and returns are calculated using equal weights. We highlight a number of interesting observations from the results (see Figure 13 and Figure 14):

• There was an extremely strong reversal for the crowded sold names during March to June 2009. That could be explained by the 'junk rally', which drove the equity market rebound during the same period. Inexpensive names, previous underperformers led the rally; the majority of which could also have been previously heavily sold by institutional investors.

• Performance reversals have become more significant and consistent in recent years for both crowded bought and sold names. This may be due to the growing numbers of institutional investors in the market, which in turn pushed up the level of crowdedness and increased its impact.

2 According to FactSet, about 94% of funds have embargo days less or equal to 90 days. Applying a three-month lag makes sure the data for the majority of funds are available if the strategy goes live.

3 As shown above, performance reversal could still be effective 12 months after the transactions. We want to capture this longer-term effect. At the same time, we do not want to use information that is too old. The six-month holding period we use here is not the optimal result from back-testing. Rather we think it is a balance between these two points addressed above.

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We built the base case strategy using Measure 1 and back-tested its performance through time


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Figure 13: Performance of crowded trades Figure 14: Long short performance

Source: MSCI, Factset, UBS Quantitative Research Source: MSCI, Factset, UBS Quantitative Research

This forms our base-case strategy. In the following section, we aim to improve the performance and consistency of the strategy.

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How to harvest alpha using crowded trades? The question we really need to answer here is: what type of crowded trades will lead to a performance reversal? In other words, what are the differentiating factors among crowdedly-traded stocks?

Valuation is key

We conditioned the performance on the level of forward 12-month PE. Crowded sold names with low PE rebound the strongest, whilst crowded bought names with high PE experienced the largest underperformance (see Figure 15, Figure 16 and Figure 17).

Figure 15: High PE Figure 16: Mid PE Figure 17: Low PE

Source: MSCI, IBES, FactSet, UBS Quantitative Research



We then added a PE screen onto our base-case strategy, ie, we screened low PE names in the crowded sold basket and high PE names in the crowded bought basket. The back-test result improves substantially. The main drawback is the period during the global financial crisis from late 2007 to the beginning of 2009. During that period, inexpensive names significantly underperformed expensive ones. The valuation overlay had an impact on the performance of the strategy (see Figure 18, Figure 19 and Figure 20).

Figure 18: Performance of crowded trades, conditioned on valuation

Figure 19: Long short performance


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Figure 20: Performance summary

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Crowded sell Crowded buy Crowded sell Crowded buy Crowded sell Crowded buy Long - Short

Annualised rel. return -0.3% -3.6% 2.1% -0.7% 5.3% 3.8% 8.9%

Risk-adj return -0.06 -0.78 0.67 -0.22 0.79 0.68 0.85

Hit rate4 48% 47% 50% 48% 50% 50%

Note: Long/Short in this quantitative modelling context describes the back-test/performance results of stocks screened based on the model's strategy/methodology. Source: MSCI, IBES, FactSet, UBS Quantitative Research

Sentiment changes can trigger further reversals

In the previous section we demonstrated the positive relationship between institutional trades and sell-side revisions5 in the month the trades occur (see Figure 10). Now we examine the relationship between performance and sell-side revisions in the 12 months prior to and after the month when the crowded sell and crowded buy baskets are formed.

Figure 21: Returns vs. Ratings revision – crowded sell Figure 22: Returns vs. EPS revision – crowded sell


4 Monthly hit rate is calculated as the percentage of stocks in the portfolio that outperform the benchmark. We then take the mean of the monthly hit rate to get the overall measure. 5 Ratings and earnings revision are calculated as (number of upgrades – number of downgrades)/ (number of upgrades + number of downgrades).

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Figure 23: Returns vs. Ratings revision – crowded buy Figure 24: Returns vs. EPS revision – crowded buy


As expected, we note that performance tends to move with sell-side ratings and earnings revisions. For crowded sell baskets prior to month 0, performance deteriorates as both ratings and earnings continue to be revised down. All three reach a trough point at month 0. During the 12-month period following month 0, performance rebounds and both ratings and earnings are revised up gradually (see Figure 21 and Figure 22); and vice versa for the crowded bought basket (see Figure 23 and Figure 24).

Given the close relationship shown above, the intuitive way to improve the performance of the strategy is to incorporate the most recent rating and earnings revisions. For the crowded sold names, if the sell-side sentiment starts to improve, we could expect a stronger reversal. Similarly for the crowded bought names, if the sell-side sentiment starts to deteriorate the performance could fall further.

Figure 25: Performance of crowded trades, conditioned on valuation and sell-side sentiment

Figure 26: Long short performance


We screen for stocks with either positive rating or EPS revisions within non-expensive crowded sold names, as well as stocks with negative rating or EPS revisions within expensive crowded bought names. The performance of the refined strategy is significantly improved. Adding sell-side sentiment further differentiates the beneficiaries from the non-beneficiaries in the crowded traded baskets (see Figure 25, Figure 26 and Figure 27).

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Performance is closely related to sell-side revisions

Adding sell-side revisions further enhanced the strategy performance


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Figure 27: Performance summary

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Risk-adj return -0.22 0.09 0.26 1.52 -1.28 -0.35 -0.39 1.13 1.61

Hit Rate6 48% 49% 49% 51% 46% 48% 48% 51%

Note: Long/Short in this quantitative modelling context describes the back-test/performance results of stocks screened based on the model's strategy/methodology. Source: MSCI, IBES, FactSet, UBS Quantitative Research

By comparing the strategy using only PE and sentiment, we note the crowded trade signal enhances the performance for both the long and short sides (see Figure 28, Figure 29 and Figure 30).

Figure 28: Performance comparison Figure 29: Performance comparison – Long short


Figure 30: Summary – performance comparison

Low PE & Improving sentiment High PE & Worsening sentiment Long - Short

Without crowded trades measure 4.64% -2.44% 7.08%

1.42 -0.67 1.11

Overlay with crowded trades measure 7.07% -5.27% 12.34%

1.52 -1.28 1.61 Note: Long/Short in this quantitative modelling context describes the back-test/performance results of stocks screened based on the model's strategy/methodology. Source: MSCI, IBES, FactSet, UBS Quantitative Research

Is this a small-cap effect? To check whether the performance is mainly driven by small caps, we divided the universe into Large, Mid and Small cap baskets and back-tested the same strategy within each size band.

The performance does improve as we move down the size bands. Nevertheless the strategy has been quite effective for large caps (see Figures 31 to 37).

6 Monthly hit rate is calculated as the percentage of stocks in the portfolio that outperform the benchmark. We then take the mean of monthly hit rate to get the overall measure.

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Figure 31: Performance summary for each size band – MSCI AC World

Annualized relative returns Risk-adj returns Hit rates

Long Short Long - Short Long Short Long - Short Long Short

Large 4.6% -2.6% 7.2% 1.03 -0.56 0.96 50% 46%

Mid 7.6% -3.2% 10.8% 1.51 -0.59 1.29 52% 47%

Small 9.0% -8.1% 17.1% 1.07 -0.98 1.46 51% 46% Note: Long/Short in this quantitative modelling context describes the back-test/performance results of stocks screened based on the model's strategy/methodology. Source: MSCI, IBES, FactSet, UBS Quantitative Research

Figure 32: Large Figure 33: Mid Figure 34: Small




Figure 35: Large – Long short Figure 36: Mid – Long short Figure 37: Small – Long short




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Conclusion Crowded trades have affected the equity market for decades, and should be avoided. However, we believe we have shown that they can also be used to generate alpha.

In this report, we studied crowded trades by institutional investors using the FactSet security ownership database.

• We found that after the initial phrase, crowded trades tend to lead to performance reversals

Inexpensive stocks crowdedly sold by institutional investors rebound in the coming months, and the rebound is stronger for those with rating or EPS upgrades.

Expensive stocks crowdedly bought by institutional investors underperform, especially those with subsequent ratings or EPS downgrades.

• Based on these findings, we formulated a strategy to screen out the potential outperformers and underperformers (see the next few pages for our screens).


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Potential outperformers

Figure 38: Crowded sold names with reasonable PE and improving revisions

Revision

BB ticker Sedol Name Country Sector % Buy PE Ratings EPS

US

LYB.UN B3SPXZ3 LYONDELLBASELL INDU-CL A US Materials 33% 11.0 NA 100%

RNR.UN 2728429 RENAISSANCERE HOLDINGS LTD US Financials 34% 11.4 NA 100%

CMCSK.UW 2089687 COMCAST CORP-SPECIAL CL A US Consumer Discretionary 34% 16.7 NA 50%

ALB.UN 2046853 ALBEMARLE CORP US Materials 37% 14.5 NA 60%

DOW.UN 2278719 DOW CHEMICAL CO/THE US Materials 40% 15.7 NA 100%

DISCK.UW B3D7KG4 DISCOVERY COMMUNICATIONS-C US Consumer Discretionary 40% 15.8 NA 14%

CMCSA.UW 2044545 COMCAST CORP-CLASS A US Consumer Discretionary 41% 16.8 NA 50%

TWX.UN B63QTN2 TIME WARNER INC US Consumer Discretionary 43% 17.1 NA 60%

NOC.UN 2648806 NORTHROP GRUMMAN CORP US Industrials 43% 16.5 100% 100%

CSC.UN 2215200 COMPUTER SCIENCES CORP US Information Technology 43% 13.3 100% 50%

ATVI.UW 2575818 ACTIVISION BLIZZARD INC US Information Technology 44% 18.1 NA 100%

JAZZ.UW B4Q5ZN4 JAZZ PHARMACEUTICALS PLC US Health Care 44% 16.9 NA 100%

EBAY.UW 2293819 EBAY INC US Information Technology 45% 18.1 100% 100%

TMO.UN 2886907 THERMO FISHER SCIENTIFIC INC US Health Care 45% 17.8 NA 50%

GME.UN B0LLFT5 GAMESTOP CORP-CLASS A US Consumer Discretionary 45% 9.1 NA 47%

SNI.UN B39QT24 SCRIPPS NETWORKS INTER-CL A US Consumer Discretionary 45% 15.4 NA 33%

Europe

SAB.SQ B1X8QN2 BANCO DE SABADELL SA ES Financials 21% 12.7 NA 33%

DAI.GY 5529027 DAIMLER AG-REGISTERED SHARES DE Consumer Discretionary 28% 10.5 100% 71%

DBK.GY 5750355 DEUTSCHE BANK AG-REGISTERED DE Financials 32% 9.3 0% 50%

ML.FP 4588364 MICHELIN (CGDE) FR Consumer Discretionary 42% 10.2 NA 100%

SAN.FP 5671735 SANOFI FR Health Care 42% 14.1 0% 33%

DG.FP B1XH026 VINCI SA FR Industrials 47% 12.7 0% 33%

HO.FP 4162791 THALES SA FR Industrials 49% 12.0 NA 33%

NDA.SS 5380031 NORDEA BANK AB SE Financials 49% 10.6 100% 7%

ISP.IM 4076836 INTESA SANPAOLO IT Financials 49% 14.0 100% 83%

FP.FP B15C557 TOTAL SA FR Energy 50% 14.2 0% 100%

Asia ex-Japan

051915.KP 6346924 LG CHEM LTD-PREFERENCE KR Materials 35% 8.2 NA 14%

138930.KP B3S98W7 BNK FINANCIAL GROUP INC KR Financials 35% 7.3 NA 67%

6239.TT 6599676 POWERTECH TECHNOLOGY INC TW Information Technology 36% 11.4 NA 100%

003600.KP 6988371 SK HOLDINGS CO LTD KR Industrials 36% 6.2 NA 33%

088350.KP B62B9W7 HANWHA LIFE INSURANCE CO LTD KR Financials 37% 10.8 100% 100%

000720.KP 6450988 HYUNDAI ENGINEERING & CONST KR Industrials 37% 8.7 100% 100%

030200.KP 6505316 KT CORP KR Telecommunication Services 38% 11.6 NA 50%

018880.KP B00LR01 HALLA VISTEON CLIMATE CONTRO KR Consumer Discretionary 38% 11.4 100% 100%

078930.KP B01RJV3 GS HOLDINGS KR Energy 39% 11.0 100% 100%

386.HK 6291819 CHINA PETROLEUM & CHEMICAL-H CN Energy 43% 13.2 100% 100%

Japan

7832.JT B0JDQD4 BANDAI NAMCO HOLDINGS INC JP Consumer Discretionary 39% 12.7 NA 100%

8053.JT 6858946 SUMITOMO CORP JP Industrials 46% 6.1 NA 33%

7261.JT 6900308 MAZDA MOTOR CORP JP Consumer Discretionary 48% 7.7 NA 33%

9433.JT 6248990 KDDI CORP JP Telecommunication Services 49% 12.8 NA 45%

Australia

DUE.AT B01WT63 DUET GROUP AU Energy 31% 15.7 NA 100%

CSR.AT 6238645 CSR LTD AU Industrials 37% 6.5 100% 60%

QAN.AT 6710347 QANTAS AIRWAYS LTD AU Industrials 47% 8.2 NA 100%

BPT.AT 6088204 BEACH ENERGY LTD AU Energy 48% 18.2 100% 100% Source: MSCI, FactSet, IBES, UBS Quantitative Research


15

Potential underperformers

Figure 39: Crowded bought names with high PE and worsening revisions

Revision

BB ticker Sedol Name Country Sector % Buy PE Ratings EPS

US

TSLA.UW B616C79 TESLA MOTORS INC US Consumer Discretionary 65% 116.2 NA -33%

TWTR.UN BFLR866 TWITTER INC US Information Technology 64% 102.7 -100% -100%

N.UN B2B0FZ2 NETSUITE INC US Information Technology 63% 254.1 NA -100%

RAX.UN 2591524 RACKSPACE HOSTING INC US Information Technology 62% 51.1 NA -100%

Z.UW BVYJBR3 ZILLOW GROUP INC - CL A US Information Technology 59% 78.1 NA -100%

HLT.UN BH3XFX2 HILTON WORLDWIDE HOLDINGS IN US Consumer Discretionary 58% 33.8 NA -100%

ISRG.UW 2871301 INTUITIVE SURGICAL INC US Health Care 58% 28.1 NA -100%

LVLT.UN B5LL299 LEVEL 3 COMMUNICATIONS INC US Telecommunication Services 58% 30.2 NA -100%

ARG.UN 2011561 AIRGAS INC US Materials 57% 19.2 -100% -100%

YHOO.UW 2986539 YAHOO! INC US Information Technology 56% 49.1 -100% -100%

SPN.UN 2806109 SUPERIOR ENERGY SERVICES INC US Energy 56% 88.5 NA -71%

BMRN.UW 2437071 BIOMARIN PHARMACEUTICAL INC US Health Care 55% -61.5 NA -100%

FAST.UW 2332262 FASTENAL CO US Industrials 55% 21.5 NA -100%

EGN.UN 2012672 ENERGEN CORP US Energy 55% 62.4 NA -13%

WFT.UN BLNN369 WEATHERFORD INTERNATIONAL PL US Energy 55% 46.1 -100% NA

Europe

WDH.DC 5961544 WILLIAM DEMANT HOLDING DK Health Care 75% 17.8 NA -100%

MAN.GY 5563520 MAN SE DE Industrials 73% 48.3 NA -100%

TEN.IM 7538515 TENARIS SA IT Energy 71% 15.3 NA -100%

SUN.SE 4854719 SULZER AG-REG CH Industrials 70% 19.4 NA -100%

VK.FP B197DR6 VALLOUREC SA FR Industrials 69% 24.0 NA -85%

RRS.LN B01C3S3 RANDGOLD RESOURCES LTD GB Materials 69% 24.6 NA -100%

GFS.LN B01FLG6 G4S PLC GB Industrials 69% 16.7 NA -50%

TKA.GY 5636927 THYSSENKRUPP AG DE Materials 69% 15.9 NA -50%

VOD.LN BH4HKS3 VODAFONE GROUP PLC GB Telecommunication Services 68% 33.0 -20% -43%

Asia ex-Japan

2353.TT 6005850 ACER INC TW Information Technology 60% 25.4 -100% -50%

AGI.PM 6147105 ALLIANCE GLOBAL GROUP INC PH Industrials 61% 14.7 NA -50%

MAHB.MK 6188193 MALAYSIA AIRPORTS HLDGS BHD MY Industrials 74% 37.8 NA -100%

2498.TT 6510536 HTC CORP TW Information Technology 60% 42.9 -100% -100%

MBT.PM 6514442 METROPOLITAN BANK & TRUST PH Financials 75% 14.6 -100% -100%

LPPF.IJ 6665878 MATAHARI DEPARTMENT STORE TB ID Consumer Discretionary 66% 26.0 NA -100%

83.HK 6810429 SINO LAND CO HK Financials 61% 14.5 NA -20%

SIA.SP 6811734 SINGAPORE AIRLINES LTD SG Industrials 64% 15.9 NA -33%

2615.TT 6932334 WAN HAI LINES LTD TW Industrials 65% 14.2 -100% -33%

ST.SP B02PY22 SINGAPORE TELECOMMUNICATIONS SG Telecommunication Services 58% 16.4 0% -67%

SM.PM B068DB9 SM INVESTMENTS CORP PH Industrials 58% 21.4 NA -100%

1919.HK B0B8Z18 CHINA COSCO HOLDINGS-H CN Industrials 64% 26.6 NA -100%

EXCL.IJ B0LD0W9 XL AXIATA TBK PT ID Telecommunication Services 68% 26.6 -100% -100%

MNCN.IJ B1Z5HY9 MEDIA NUSANTARA CITRA TBK PT ID Consumer Discretionary 63% 16.3 NA -100%

MAXIS.MK B5387L5 MAXIS BHD MY Telecommunication Services 58% 24.1 -100% -100%

HPHT.SP B56ZM74 HUTCHISON PORT HOLDINGS TR-U SG Industrials 57% 25.0 NA -100%

ASII.IJ B800MQ5 ASTRA INTERNATIONAL TBK PT ID Consumer Discretionary 58% 14.8 NA -80%

SCMA.IJ B8HWJY1 SURYA CITRA MEDIA PT TBK ID Consumer Discretionary 64% 24.8 NA -100%

Japan

4613.JT 6483746 KANSAI PAINT CO LTD JP Materials 70% 21.3 NA -33%

9062.JT 6642127 NIPPON EXPRESS CO LTD JP Industrials 70% 17.2 NA -25%

6098.JT BQRRZ00 RECRUIT HOLDINGS CO LTD JP Industrials 66% 25.3 NA -67%

5012.JT 6366007 TONENGENERAL SEKIYU KK JP Energy 66% 17.3 NA -43%

5214.JT 6642666 NIPPON ELECTRIC GLASS CO LTD JP Information Technology 65% 39.2 NA -33%

4519.JT 6196408 CHUGAI PHARMACEUTICAL CO LTD JP Health Care 65% 30.9 NA -33%


16

4324.JT 6416281 DENTSU INC JP Consumer Discretionary 65% 26.9 -100% NA

4506.JT 6250865 SUMITOMO DAINIPPON PHARMA CO JP Health Care 65% 25.8 NA -33%

Australia

OZL.AT 6397825 OZ MINERALS LTD AU Materials 81% 27.5 NA -50%

ALQ.AT B86SZR5 ALS LTD AU Industrials 81% 47.8 -33% -83% Source: MSCI, FactSet, IBES, UBS Quantitative Research


17

Appendix

Performance for different regions/countries

For US, Europe and Australia, overall performance has been mainly be driven by the long side. For US and Europe the short side performance improved significantly in the past few years. On the other hand, For Japan, the short side has been more significant than the long side. For Asia ex-Japan, the performance for both long and short sides has been strong (see Figures 40 to 51).

Figure 40: MSCI World (DM) Figure 41: MSCI US Figure 42: MSCI Europe




Figure 43: MSCI Asia ex-JP Figure 44: MSCI Japan Figure 45: Australia – ASX200




Figure 46: MSCI World (DM) – Long short

Figure 47: MSCI US – Long short Figure 48: MSCI Europe – Long short




-70%

-20%

30%

80%

01 03 05 07 09 11 13Long Short

-20%

0%

20%

40%

60%

01 03 05 07 09 11 13Long Short

-60%

-30%

0%

30%

60%

01 03 05 07 09 11 13Long Short

-120%

-70%

-20%

30%

80%

130%

01 03 05 07 09 11 13Long Short

-60%

-40%

-20%

0%

20%

40%

01 03 05 07 09 11 13Long Short

-50%

0%

50%

100%

150%

01 03 05 07 09 11 13

Long Short

0%

20%

40%

60%

80%

100%

120%

01 03 05 07 09 11 13

-10%

0%

10%

20%

30%

40%

50%

01 03 05 07 09 11 130%

20%

40%

60%

80%

100%

120%

01 03 05 07 09 11 13


18

Figure 49: MSCI Asia ex-JP – Long short

Figure 50: MSCI Japan – Long short Figure 51: Australia (ASX200) – Long short




0%

40%

80%

120%

160%

200%

01 03 05 07 09 11 130%

20%

40%

60%

80%

01 03 05 07 09 11 130%

30%

60%

90%

120%

150%

180%

01 03 05 07 09 11 13


19

UBS Equity Quantitative Research publications

Monographs and Keys Academic Research Monitor

Title Date

A closer look at the trend factor Jun-15

Understanding Size Investing Jun-15

Safe dividends in times of financial repression Jun-15

Cross-Asset Seasonality Mar-15

Extending our quality model to financials Mar-15

Where are the crowded trades? Jan-15

Stock Selection using Machine Learning Jan-15

Investing in Growth Jan-15

Harvesting Cross-Asset Value Dec-14

How to avoid 'Torpedoes' Nov-14

US Quantitative Conference Highlights Nov-14

What happens when volatility normalises? Oct-14

Watch out for macro risk: the impact on China H- and A-share markets Oct-14

Three key questions on low volatility Oct-14

Trading around M&A announcements in the US Sep-14

Investing in Value Sep-14

How to avoid 'torpedoes' in Asia Jul-14

Correlation, De-correlation and Risk-Parity Jun-14

Risk-Parity versus Mean-Variance May-14

Timing the US earnings yield style May-14

Investing in Quality Apr-14

How Asian stocks trade after earnings surprises Mar-14

From forecasts to portfolios Jan-14

Three reasons why high-risk underperformed Dec-13

Trend following meets risk parity Dec-13

Topic Date

Quality and Size Investing May-15

European Quantitative Conference 2015 Highlights Apr-15

Smart Beta, Factors and Style Investing Feb-15

Momentum-Investing Jan-15

Investment Strategies & Textual Analysis Signals Dec-14

Commodity Risk & Institutional Investing Habits Nov-14

Index Membership, Investor (in)attention to News & Spurious Correlations Sep-14

Forecasting the Equity Risk Premium Aug-14

Implied Cost of Capital & Shorting Premium Jun-14

European Quantitative Conference 2014 Highlights May-14

Trend Following Mar-14

Factor investing & Quality Feb-14

Quality & Gross Profitability Jan-14

Minimum variance: valuation, concentration and exchange rates Dec-13

Liquidity & back test overfitting Oct-13

News and its effect on asset prices Sep-13

Asset pricing & skewness Aug-13

Timing momentum & risk parity Jul-13

PAS User Guides

Introduction to the UBS Portfolio Analysis System Jan-15

Advanced Analysis Oct-12

Installation May-14

Long-Short Analysis Jan-15

Optimisation with PAS Jun-10

PAS Macros Nov-11

Quick Reference Guide Jun-10

Portfolio Analysis Jun-10

Reports Apr-14

Risk Models Nov-11

Risk Parity Feb-13

Risk Parity and Composite Assets Jan-15

UBS Hybrid Risk Model Dec-10

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20

Statement of Risk

Our quantitative models rely on reported financial statement information, consensus earnings forecasts and stock prices. Errors in these numbers are sometimes impossible to prevent (as when an item is misstated by a company). Also, the models employ historical data to estimate the efficacy of stock selection strategies and the relationships among strategies, which may change in the future. Additionally, unusual company-specific events could overwhelm the systematic influence of the strategies used to rank and score stocks.


21

Required Disclosures

This report has been prepared by UBS Securities Asia Limited, an affiliate of UBS AG. UBS AG, its subsidiaries, branches and affiliates are referred to herein as UBS.

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UBS Investment Research: Global Equity Rating Definitions

12-Month Rating Definition Coverage1 IB Services2

Buy FSR is > 6% above the MRA. 45% 36%

Neutral FSR is between -6% and 6% of the MRA. 42% 32%

Sell FSR is > 6% below the MRA. 13% 20%

Short-Term Rating Definition Coverage3 IB Services4

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Sell Stock price expected to fall within three months from the time the rating was assigned because of a specific catalyst or event. less than 1% less than 1%

Source: UBS. Rating allocations are as of 30 June 2015. 1:Percentage of companies under coverage globally within the 12-month rating category. 2:Percentage of companies within the 12-month rating category for which investment banking (IB) services were provided within the past 12 months. 3:Percentage of companies under coverage globally within the Short-Term rating category. 4:Percentage of companies within the Short-Term rating category for which investment banking (IB) services were provided within the past 12 months.

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22

UBS AG Hong Kong Branch: Shanle Wu, PhD; Josh Holcroft. UBS Securities Australia Ltd: Paul Winter; Oliver Antrobus, CFA; Luke Brown; Pieter Stoltz. UBS Limited: David Jessop; Nick Baltas, PhD; Claire Jones, CFA. UBS Securities LLC: Sebastian Lancetti, CFA.

Unless otherwise indicated, please refer to the Valuation and Risk sections within the body of this report.


23

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Page 1Institutional crowded trades result in mid- to long-term performance reversalsValuation plays a key role in differentiating winners and losersSell-side sentiment mattersScreen for stocks with strong upside and downside potentialGraphics Title: Figure 1: Relative performance - MSCI AC WorldGraphics Title: Figure 2: Long short performance - MSCI AC WorldDisclosure

Page 2SummaryTable Title: Figure 3: Strategy performance summary – MSCI AC World

Page 3Gauging the institutional crowdsDataGraphics Title: Figure 4: Number of funds reported each monthMeasures of crowded trades

Page 4Measures of crowded trades (CONT)Graphics Title: Figure 5: Average relative returns at month 0Graphics Title: Figure 6: Cumulative relative performance – Measure 1Graphics Title: Figure 7: Cumulative relative performance – Measure 2Graphics Title: Figure 8: Cumulative relative performance – Measure 3Graphics Title: Figure 9: % changes in volume at month 0Graphics Title: Figure 10: Ratings revisions at month 0

Page 5Measures of crowded trades (CONT)Further study of Measure 1Asymmetric on buy and sell

Page 6Graphics Title: Figure 11: Stocks sold by institutional investorsGraphics Title: Figure 12: Stocks bought by institutional investorsPerformance reversal has become more pronounced in recent years

Page 7Performance reversal has become more pronounced in recent years (CONT)Graphics Title: Figure 13: Performance of crowded tradesGraphics Title: Figure 14: Long short performance

Page 8How to harvest alpha using crowded trades?Valuation is keyGraphics Title: Figure 15: High PEGraphics Title: Figure 16: Mid PEGraphics Title: Figure 17: Low PEGraphics Title: Figure 18: Performance of crowded trades, conditioned on valuationGraphics Title: Figure 19: Long short performance

Page 9Table Title: Figure 20: Performance summarySentiment changes can trigger further reversalsGraphics Title: Figure 21: Returns vs. Ratings revision – RecommendationGraphics Title: Figure 22: Returns vs. EPS revision – Recommendation

Page 10Graphics Title: Figure 23: Returns vs. Ratings revision – RecommendationGraphics Title: Figure 24: Returns vs. EPS revision – RecommendationSentiment changes can trigger further reversals (CONT)Graphics Title: Figure 25: Performance of crowded trades, conditioned on valuation and sell-side sentimentGraphics Title: Figure 26: Long short performance

Page 11Table Title: Figure 27: Performance summarySentiment changes can trigger further reversals (CONT)Graphics Title: Figure 28: Performance comparisonGraphics Title: Figure 29: Performance comparison – Long shortTable Title: Figure 30: Summary – performance comparisonIs this a small-cap effect?

Page 12Table Title: Figure 31: Performance summary for each size band – MSCI AC WorldGraphics Title: Figure 32: LargeGraphics Title: Figure 33: MidGraphics Title: Figure 34: SmallGraphics Title: Figure 35: Large – Long shortGraphics Title: Figure 36: Mid – Long shortGraphics Title: Figure 37: Small – Long short

Page 13Conclusion

Page 14Potential outperformersTable Title: Figure 38: Crowded sold names with reasonable PE and improving revisions

Page 15Potential underperformersTable Title: Figure 39: Crowded bought names with high PE and worsening revisions

Page 16Table Title: Figure 39: Crowded bought names with high PE and worsening revisions (CONT)

Page 17AppendixPerformance for different regions/countriesGraphics Title: Figure 40: MSCI World (DM)Graphics Title: Figure 41: MSCI USGraphics Title: Figure 42: MSCI EuropeGraphics Title: Figure 43: MSCI Asia ex-JPGraphics Title: Figure 44: MSCI JapanGraphics Title: Figure 45: Australia – ASX200Graphics Title: Figure 46: MSCI World (DM) – Long shortGraphics Title: Figure 47: MSCI US – Long shortGraphics Title: Figure 48: MSCI Europe – Long short

Page 18Graphics Title: Figure 49: MSCI Asia ex-JP – Long shortGraphics Title: Figure 50: MSCI Japan – Long shortGraphics Title: Figure 51: Australia (ASX200) – Long short

Page 19Table Title: UBS Equity Quantitative Research publications

Page 20Statement of Risk

Page 21DisclosureTable Title: Report Key

Page 22Disclosure (CONT)



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