Samsung Quant Portfolio Quantitative September Recommendations Issue

August 29, 2014

 QUANTITATIVE RESEARCH Samsung Quant Portfolio Quantitative September recommendations Issue

August review

 We have employed a quant-based pair-trading model as our main strategy in our Samsung Quant Portfolio since June, replacing our dynamic model to reflect a changing market environment. For September, we update the long/short strategy for the industrial goods (other) sector.

Long/short pair recommendations by industry For September, we maintain three long/short pairs, change one constituent in each of eight pairs, and present five brand-new pairs—all based on one-month changes in share prices and fundamentals. The brand-new pairs are as follows (denoted by sector):  Chemicals: OCI/Kolon Industries—while industry conditions remain unfavorable, the former shows better operating profit momentum (-1% vs -12%) and TP upside (57%

Dongyoung Kim, CFA and 26%) than the latter. Analyst [email protected]  Construction: Daewoo Engineering & Construction/GS Engineering & Construction— 822 2020 7839 the returned portion of the securities lending balance (SLB) has dropped for the former and surged for the latter.

Hyein Ok  Industrial goods (other): Hyundai Glovis/Asiana Airlines—the logistics firm is enjoying Research Associate [email protected] solid earnings momentum, while momentum at the airline is slowing. 822 2020 7795  Retail: CJ O Shopping/Shinsegae—the former has a better SLB and TP upside.  Consumer discretionary (other): Hotel Shilla/Grand Korea Leisure—we selected this pair for their respective earnings momentum.

Samsung Securities (Korea) www.samsungpop.com

August 29, 2014 Quantitative Issue

 CONTENTS

Model portfolio – macro dynamic p2 model Auxiliary portfolio — p13 value/momentum model Appendix p20

Model portfolio: Quant-based pair trading model We have employed a quant-based pair-trading model as our main strategy in our Samsung Quant Portfolio since June, replacing our dynamic model to reflect a changing market environment. The new model uses different quant strategies for different industries and selects just one pair of stock from each industry, an approach we believe boosts returns. For more information, see our June 5 report “Quant-based pair trading: Changing model amid boom in long/short funds”.

1. Long/short pair recommendations, by industry The industrial goods (other) sector comprises all the industrial goods stocks that are not assigned to the construction or shipbuilding & machinery industries—eg, transportation companies, holding companies, trading firms, and commercial service providers. In this sector, we removed Samsung SDI from the quant universe from July and Hyundai Merchant Marine (HMM) from September. Reflecting changes to stocks in the universe over the past three months, we backtested our strategies for the sector, and found that quant strategies are not effective for shipping companies, noting that: 1) shipping share prices are near bottom now and highly volatile as a result of years-long industry recession; and 2) HMM shares are influenced more by North Korea-related issues than by quant factors—revealing in the relative ineffectiveness of a quant-based approach. Thus, we eliminate shipping firms from the Samsung Quant Universe—reasonable considering institutional investors now account for less than 10% of the trading volume of such stocks. The table below shows the stocks still in our industrial goods (other) list.

Industrial goods (other): Stock list Code Company Code Company Code Company A096770 LG Corporation A011170 CJ Korea Express A047050 Korean Air A003600 Hyundai Glovis A010060 LS Corporation A012750 iMarketKorea A010950 Daewoo International A009830 SK Networks A000150 LG International A078930 S-1 A003550 Hanssem A000120 Asiana Airlines A051910 Doosan Corporation A086280 LS Industrial Systems Source: Samsung Securities For our September portfolio, we backtested operating profit 3-week momentum and EPS 3-week momentum, because: 1) earnings momentum can be measured over shorter periods of time than one month, as it can affect share prices for quite some time; and 2) checking earnings momentum over shorter time frames is necessary, as information is priced in quickly. Backtesting results reveal that these two quant factors outperformed our previously recommended ones.

August 29, 2014 Quantitative Issue

Strategy combination update: Industrial goods (other)

Industrial goods (other): Stock list Code Stock Code Stock Code Stock A096770 LG Corporation A011170 CJ Korea Express A047050 Korean Air A003600 Hyundai Glovis A010060 LS Corporation A012750 iMarketKorea A010950 Daewoo International A009830 SK Networks A000150 LG International A078930 S-1 A003550 Hanssem A000120 Asiana Airlines A051910 Doosan Corporation A086280 LS Industrial Systems Source: Samsung Securities

Industrial goods (other): Performance Factor Information coefficient (%) EPS 3-week momentum 7.6 EPS momentum 7.5 Operating profit 3-week momentum 6.8 Earnings revision rate 6.7 Operating profit momentum 5.9 P/E 4.0 TP upside 2.7 Institutional net buying 1.9 P/B 1.5 P/S (2.9) Returned portion of stock lending balance (3.6) Note: Backtested from Dec 2006 to Jun 2014 Source: Samsung Securities

Industrial goods (other): Final strategy combinations and backtesting results Results Strategy combination EPS 3-week momentum + Operating profit momentum + ERR Information coefficient (%) 9.6 Long/short return (annualized simple average) (%) 29.4 Long/short standard deviation (%) 30.5 Long/short Sharpe ratio 1.0 Maximum drawdown (%) 15.0 Note: Based on strategy of going long on top-performing quintile and shorting bottom quintile Source: Samsung Securities

Industrial goods (other): Cumulative return from final strategy combination

(Indexed, Jan 2007 = 100) 350

300

250

200

150

100

0 2007 2008 2009 2010 2011 2012 2013 2014

Long/short return

Source: Samsung Securities

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EPS 3-week momentum recorded the highest information coefficient (IC) score in the industrial goods (other) sector. We note that EPS momentum outperformed operating profit momentum because: 1) net profit of the sector constituents is fairly volatile due to their high foreign-currency-denominated portion of assets and liabilities; and 2) a comparison of operating profits is meaningless, as the firms belong to different sub- sectors—which also explains the low scores for the valuation and TP upside factors. In this sector, EPS 3-week momentum, operating profit momentum, and earnings revision rate performed well and we recommend using a combination of all three. Our September recommendations for quant-based pair trading follow.

Quant-based pair trading: Different quant strategies used for different sectors Sector Strategy combination Energy P/E + TP upside Chemicals P/B + Operating profit momentum + TP upside Steel/Non-ferrous metals Operating profit momentum + Institutional net buying Construction Returned portion of stock lending balance + TP upside Shipbuilding & machinery* P/B + Returned portion of stock lending balance EPS 3-week momentum + Operating profit momentum + Earnings Industrial goods (other)* revision rate Autos P/E + Earnings revision rate + TP upside Retail Returned portion of stock lending balance + TP upside Consumer discretionary (other)* EPS momentum + Operating profit 3-week momentum + TP upside Consumer staples P/E + EPS growth Health care EPS momentum + Earnings revision rate Banking/card TP upside Securities brokers P/E + P/B + Institutional net buying Insurance P/S + TP upside IT EPS growth + Institutional net buying Telecom services P/B + TP upside Utilities EPS momentum + Operating profit momentum Note: * Strategy combination has changed from June portfolio Source: Samsung Securities

Quant strategies applied to model (factor list) Name Indicator Description Category P/E Fwd P/E (x) Low 12-month forward P/E Valuation P/B Fwd P/B (x) Low 12-month forward P/B Valuation P/S FY1 P/S (x) Low 12-month forward P/S Valuation EPS growth FY2 EPS growth (%) High EPS y-y growth rate Growth Earnings EPS momentum FY1 EPS 1-month change (%) High FY1 EPS 1-month change momentum Earnings Operating profit momentum FY1 operating profit 1-month change (%) High FY1 operating profit 1-month change momentum Earnings Earnings revision rate FY1 1-month earnings revision rate (%) High FY1 1-month earnings revision rate (ERR)* momentum High 1-month institutional net buying relative to market cap Institutional net buying 1-month institutional portion of net buying (%) Supply/demand (Institutional net buying/market cap) Returned portion of SLB 1-month stock lending returned (%) High 1-month SLB returned (change in SLB /listed shares) Supply/demand High upside to target price TP upside TP upside (%) Sentiment (based on consensus target price from past month) Note: * ERR= (number of increases - number of decreases)/number of total estimates Source: Samsung Securities

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The following are our recommendations based on one-month changes in share prices and fundamentals.

Current short-term long/short pairs, by industry Long Short Energy SK Innovation P/E : 11.8x TP upside: 39.8% S-Oil P/E : 15.1x TP upside: 26.9% Chemicals OCI P/B: 1.2x Operating profit TP upside: Kolon Industries P/B: 0.8x Operating profit TP upside: momentum: 56.5% momentum: 26.5% (1.2%) (12.0%) Steel/non- Hyundai Hysco Operating profit Institutional net Korea Zinc Operating profit Institutional net ferrous metals momentum: buying: 2.9% momentum: buying: (0.1%) 13.0% (0.8%) Construction Daewoo E&C Returned portion TP upside: 30.0% GS E&C Returned portion TP upside: 14.2% of SLB: (1.2%) of SLB: 9.5% Shipbuilding & Doosan Infracore P/B: 0.8x Returned portion Hyundai Rotem P/B: 0.9x Returned portion of machinery of SLB: (1.2%) SLB: 0.8% Industrial goods Hyundai Glovis EPS 3-week Operating profit ERR: 91.7% Asiana Airlines EPS 3-week Operating profit ERR: (54.5%) (other) momentum: momentum: momentum: momentum: 1.9% (0.6%) (90.9%) (17.9%) Autos Hyundai Mobis P/E : 7.3x ERR: 12.0% TP upside: Hyundai Wia P/E : 11.8x ERR: (5.0%) TP upside: 25.9% 14.3% Retail CJ O Shopping Returned portion TP upside: 25.7% Shinsegae Returned portion TP upside: 8.5% of SLB: (0.3%) of SLB: 1.8% Consumer Hotel Shilla EPS momentum: Operating profit TP upside: Grand Korea EPS momentum: Operating profit 3- TP upside: discretionary 12.0% 3-week 25.5% Leisure (10.2%) week momentum: 14.5% (other) momentum: 5.2% (9.3%) Consumer Dongwon P/E : 14.1x EPS growth: Orion P/E : 29.9x EPS growth: 15.5% staples Industries 36.8% Healthcare Medytox EPS momentum: ERR: 25.0% Hanmi EPS momentum: ERR: (90.9%) 18.8% Pharmaceutical (20.9%) Banking/card DGB Financial TP upside: Samsung Card TP upside: (1.4%) Group 17.6% Securities Woori Investment P/E : 19.2x P/B: 0.7x Institutional net Daewoo P/E : 24.5x P/B: 0.9x Institutional net brokers & Securities buying: 0.2% Securities buying: 0.1% IT LG Display EPS growth: Institutional net NCsoft EPS growth: Institutional net 28.2% buying: 0.6% 12.6% buying: (2.7%) Telecom KT P/B: 0.7x TP upside: 13.4% SK Telecom P/B: 1.3x TP upside: 11.8% services Utilities Kepco EPS momentum: Operating profit Korea District EPS momentum: Operating profit 10.7% momentum: 8.4% Heating (22.9%) momentum: (11.4%) Note: Long/short pair based on 1-3 month investment horizon; recommendations based on consensus data and quantitative analysis, and therefore may differ from those of sector-based analysts; insurance sector is excluded due to M&A/restructuring issue and absence of adequate pair Source: Samsung Securities

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Sector long/short pairs for September Sector Long Short Strategy combination Change Energy SK Innovation S-Oil P/E + TP upside Long chg Chemicals OCI Kolon Industries P/B + Operating profit momentum + TP upside New pair Steel/non-ferrous metals Hyundai Hysco Korea Zinc Operating profit momentum + Institutional net buying Long chg Construction Daewoo E&C GS E&C Returned portion of stock lending balance + TP upside New pair Shipbuilding & machinery Doosan Infracore Hyundai Rotem P/B + Returned portion of stock lending balance Long chg EPS 3-week momentum + Operating profit momentum Industrial goods (other) Hyundai Glovis Asiana Airlines New pair + Earnings revision rate Autos Hyundai Mobis Hyundai Wia P/E + Earnings revision rate + TP upside Short chg Retail CJ O Shopping Shinsegae Returned portion of stock lending balance + TP upside New pair EPS momentum + Operating profit 3-week momentum Consumer discretionary (other) Hotel Shilla Grand Korea Leisure New pair + TP upside Consumer staples Dongwon Industries Orion P/E + EPS growth Healthcare Medy-Tox Hanmi Pharmaceutical EPS momentum + Earnings revision rate Long chg Bank/card DGB Financial Group Samsung Card TP upside Securities brokers Woori Investment & Securities Daewoo Securities P/E + P/B + Institutional net buying IT LG Display NCsoft EPS growth + Institutional net buying Long chg Telecom services KT SK Telecom P/B + TP upside Long chg Utilities Kepco Korea District Heating EPS momentum + Operating profit momentum Long chg Note: Insurance sector excluded due to M&A/restructuring issues and absence of adequate pair; recommendations based on consensus data and quantitative analysis, and may differ from those of sector-based analysts Source: Samsung Securities For September, we maintain three long/short pairs, change one constituent in each of eight pairs, and present five brand-new pairs—all based on one-month changes in share prices and fundamentals. The brand-new pairs are as follows (denoted by sector):  Chemicals: OCI/Kolon Industries—while industry conditions remain unfavorable, the former shows better operating profit momentum (-1% vs -12%) and TP upside (57% and 26%) than the latter.  Construction: Daewoo Engineering & Construction/GS Engineering & Construction— the returned portion of the securities lending balance (SLB) has dropped for the former and surged for the latter.  Industrial goods (other): Hyundai Glovis/Asiana Airlines—the logistics firm is enjoying solid earnings momentum, while momentum at the airline is slowing.  Retail: CJ O Shopping/Shinsegae—the former has a better SLB and TP upside.  Consumer discretionary (other): Hotel Shilla/Grand Korea Leisure—we selected this pair for their respective earnings momentum.

August 29, 2014 Quantitative Issue

2. Review of August performances, by industry

August pair performances, by sector Long Short Long/short Stock Performance (%) Stock Performance (%) performance (%) Consumer staples Dongwon Industries 18.5 Orion (4.0) 22.5 Consumer discretionary (other) Youngone Corporation 26.7 Cheil Worldwide 8.8 17.9 Energy SK Holdings (5.3) S-Oil (14.5) 9.2 Telecom services LG Uplus 21.8 SK Telecom 14.5 7.3 Utilities Kogas (0.4) Korea District Heating (7.4) 7.1 Construction Daelim Industrial (4.4) Samsung Engineering (11.4) 7.0 Retail Lotte Himart 13.7 Lotte Shopping 7.5 6.3 Shipbuilding & Machinery Hyundai Heavy Industries (18.1) Hyundai Rotem (17.6) (0.5) Bank/card DGB Financial Group 8.4 Samsung Card 11.0 (2.6) Steel/non-ferrous metals Hyundai Steel (2.7) Korea Zinc 0.1 (2.8) Healthcare Green Cross (2.5) Hanmi Pharmaceutical 1.0 (3.4) IT Samsung Electro-Mechanics (13.5) NCsoft (8.9) (4.6) Chemicals Hanwha Chemical (11.5) LG Chem (5.8) (5.7) Autos Hyundai Mobis (2.7) Halla Visteon Climate Control 3.0 (5.8) Securities brokers Woori Investment & Securities 10.1 Daewoo Securities 17.2 (7.1) Industrials (other) Daewoo International (2.4) Hyundai Merchant Marine 24.8 (27.1) Average 1.1 Note: Sorted by long/short performance; As of Aug 28 Source: Samsung Securities Our quant-based pair-trading model generated average long/short returns of 1.1% in 16 sectors in August. The consumer staples pair—Dongwon Industries (long)/Orion (short)—returned 22.5%, with the former advancing 18.5% and the latter retreating 4%. In the consumer discretionary (other) sector, the Youngone Corporation/Cheil Worldwide pair performed well, posting a 17.9% return.

August 29, 2014 Quantitative Issue

3. Quant-based pair-trading model Our quant portfolio follows a two-step procedure: 1) weight each industry as it is in the index—ie, market-neutral; and 2) rank stocks in an industry based on long/short pair scoring, and then choose a weight for each stock based on this scoring. Step 1: Determining market-neutral weights for industries We weight each of 17 sectors (based on WiseFn industry classification standards) according to its index weighting.

WiseFn sector (WICS): Quant sector mapping WICS sector code WICS sector Quant sector G110 KSE Energy Energy G1151010 KSE Chemicals Chemicals G1151030 KSE Containers & Packaging Chemicals G1151040 KSE Nonferrous Metals Steel/non-ferrous metals G1151050 KSE Steel Steel/non-ferrous metals G1151060 KSE Paper & Forest Products Steel/non-ferrous metals G1201010 KSE Aerospace & Defense Shipbuilding & Machinery G1201020 KSE Building Products Construction G1201025 KSE Construction Materials Construction G1201030 KSE Construction & Engineering Construction G1201035 KSE Furnishing Industrial goods (others) G1201040 KSE Electrical Equipment Industrial goods (others) G1201050 KSE Industrial Conglomerates Industrial goods (others) G1201060 KSE Machinery Shipbuilding & Machinery G1201065 KSE Shipbuilding Shipbuilding & Machinery G1201070 KSE Trading Companies & Distributors Industrial goods (others) G12020 KSE Commercial Services & Supplies Industrial goods (others) G12030 KSE Transportation Industrial goods (others) G12510 KSE Automobiles & Components Autos G12520 KSE Consumer Durables & Apparel Consumer discretionary (others) G12530 KSE Consumer Services Consumer discretionary (others) G12540 KSE Media Consumer discretionary (others) G12550 KSE Retailing Retail G12560 KSE Education Services Consumer discretionary (others) G130 KSE Consumer Staples Consumer staples G135 KSE Health Care Healthcare G14010 KSE Banks Bank/card G14020 KSE Securities Securities brokers G14030 KSE Diversified Financials Bank/card G14040 KSE Insurance Insurance G14050 KSE Real Estate Bank/card G14055 KSE Other Financial Services Bank/card G145 KSE Information Technology IT G150 KSE Telecommunication Services Telecom services G155 KSE Utilities Utilities Source: WiseFn, Samsung Securities

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As our model is designed to seek only the effects of stock picking (not industry allocation), we use the weightings below to determine the final weightings in our portfolio.

Kospi weightings, by sector Sector Market cap (KRWb) Weight (%) Energy 27,200 2.3 Chemicals 58,149 4.9 Steel/non-ferrous metals 63,011 5.3 Construction 53,047 4.4 Shipbuilding & machinery 42,912 3.6 Industrials (other) 65,068 5.4 Autos 140,560 11.8 Retail 32,386 2.7 Consumer discretionary (other) 74,136 6.2 Consumer staples 66,942 5.6 Healthcare 17,007 1.4 Bank/card 89,691 7.5 Securities brokers 23,760 2.0 Insurance 56,884 4.8 IT 304,489 25.4 Telecom services 35,972 3.0 Utilities 43,655 3.7 Note: As of Aug 27 Source: Samsung Securities Step 2: Assigning weights to stocks in an industry Our model next determines the stocks to go long or short in each of the 17 industries and ranks them based on long/short pair scoring. We overweight the top-two stocks (the former being the target of a long position), and underweight the bottom two. On the other hand, it is hard to set a negative weight (short position), so we adopt a heuristic approach.

Assigning stock weightings in each industry 1. Conduct long/short pair scoring. 2. Overweight the most attractive long candidate 1.5-2% relative to its Kospi weighting. 3. Overweight the second most attractive long candidate 1-1.5%. 4. Underweight the two best short candidates by a respective 1.5-2% and 1-1.5%. 5. Adjust the combined weight of the stocks to the same as the industry weight.

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Our September quant portfolio derived from a quant-based pair-trading model follows.

September model portfolio Sector Weight Stock composition (%) Code Stock Kospi weight (%) Final weight (%) Energy 2.3 A096770 SK Innovation 0.7 2.3 Chemicals 4.9 A010060 OCI 0.3 2.2 A009830 Hanwha Chemical 0.2 1.0 A004800 Hyosung 0.2 1.7 Steel/non-ferrous metals 5.3 A005490 Posco 2.4 1.8 A010520 Hyundai Hysco 0.2 2.0 A001430 Seah Besteel 0.1 1.5 Construction 4.4 A000720 Hyundai E&C 0.6 1.9 A047040 Daewoo E&C 0.3 2.5 Shipbuilding & Machinery 3.6 A010620 Hyundai Mipo Dockyard 0.2 1.5 A042670 Doosan Infracore 0.2 2.1 Industrials (other) 5.4 A003550 LG Corporation 1.0 1.5 A086280 Hyundai Glovis 1.0 2.5 A000120 CJ Korea Express 0.3 1.4 Autos 11.8 A005380 Hyundai Motor 4.2 4.2 A012330 Hyundai Mobis 2.3 4.0 A000270 Kia Motors 2.0 3.6 Retail 2.7 A035760 CJ O Shopping 0.0 1.5 A028150 GS Home Shopping 0.0 1.2 Consumer discretionary 6.2 (other) A090430 Amorepacific 1.1 2.7 A008770 Hotel Shilla 0.4 2.5 A130960 CJ E&M 0.0 1.0 Consumer staples 5.6 A097950 CJ CheilJedang 0.4 2.0 A005440 Hyundai Green Food 0.2 1.5 A006040 Dongwon Industries 0.1 2.1 Healthcare 1.4 A086900 Medytox 0.0 1.4 Note: Recommendations based on consensus data and quantitative analysis, may differ from those of sector- based analysts, and are sorted with Kosdaq stocks having no Kospi weight Source: Samsung Securities

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September model portfolio (continued) Sector Weight Stock composition (%) Code Name Kospi weight (%) Final weight (%) Bank/card 7.5 A105560 KB Financial Group 1.4 2.7 A086790 Hana Financial Group 1.0 2.6 A139130 DGB Financial Group 0.2 2.2 Securities brokers 2 A005940 Woori Investment & Securities 0.2 2.0 Insurance 4.8 A032830 Samsung Life Insurance 1.8 1.6 A000810 Samsung Fire & Marine 1.1 1.0 A002550 LIG Insurance 0.2 2.2 IT 25.4 A005930 Samsung Electronics 15.2 13.5 A000660 SK Hynix 2.7 1.5 A035420 Naver 2.2 3.9 A066570 LG Electronics 1.0 2.0 A034220 LG Display 1.1 3.0 A009150 Samsung Electro-Mechanics 0.3 1.5 Telecom services 3 A017670 SK Telecom 1.9 1.5 A030200 KT 0.8 1.5 Utilities 3.7 A015760 Kepco 2.3 3.7 Total 100.0 42 stocks 100.0 Note: Recommendations based on consensus data and quantitative analysis, may differ from those of sector- based analysts, and are sorted with Kosdaq stocks having no Kospi weight Source: Samsung Securities

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August portfolio review

August performance

Performance (daily cumulative, %, %pts) 1.5 1.0 0.5 0.0 (0.5) (1.0) (1.5) (2.0) (2.5) Jul 29 Aug 4 Aug 8 Aug 14 Aug 21 Aug 27

Relative performance Quant Model portfolio Kospi

Note: As of Aug 28 Source: Samsung Securities Our quant-based pair-trading model recorded an absolute return of 0.1% in August, underperforming the Kospi’s 0.7% return. While the long/short pairs performed well, our quant model portfolio recorded lackluster results due to disappointing picks in the industrial goods (other) sector—the portfolio did not include transportation stocks, which performed strongly last month on expectations of earnings turning around.

August 29, 2014 Quantitative Issue

 CONTENTS

Model portfolio – macro dynamic p2 model Auxiliary portfolio — p13 value/momentum model Appendix p20

Auxiliary portfolio: Macro dynamic model We shall continue to offer our macro dynamic model—which was the basic strategy of our previous quant portfolio—as auxiliary data.

Dynamic model Our dynamic model recognizes previous periods with similar macroeconomic conditions and identifies factors that performed well during such periods. The model then generates a portfolio by choosing factors and weights. (See our Jul 24, 2013 report for more details.)

Dynamic model

Phase recognition: Factor Identify historic identification: Portfolio creation: periods with Identify factors Choose factors similar macro that performed and weights -economic well in identified conditions period(s)

Macro phase Factor recognition model model

Performance measurement

Source: Samsung Securities

August 29, 2014 Quantitative Issue

Step 1: Identifying macroeconomic phases Our dynamic quant model first calculates correlation-adjusted distances to identify similarities in different time periods to find those with macroeconomic conditions similar to the present environment.

Correlation-adjusted distance between each point and Aug 28

(pts) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 08 13 04 06 09 r 11 y y g p g p Jul 07 Jul Jul 12 Jul Oct 03 Apr 06 Apr A Oct 08 Oct 13 Jan 05 Jun 05 Jan 10 Jun 10 Mar 04 Mar Feb 07 Mar 09 Mar 14 Mar Feb 12 Au Au Dec 02 Dec Sep 11 Nov 05 Nov Se 07 Dec 10 Nov Dec 12 Dec May 03 May Ma Ma Correlation-adjusted distance

Note: The shorter the correlation-adjusted distance, the more similarity between points Source: Samsung Securities

Periods most similar with the present (as of Aug 28) Date Macro correlation-adjusted distance Apr 2012 1.4 Oct 2012 1.4 Feb 2013 1.4 Mar 2013 1.4 Apr 2013 1.3 Aug 2013 1.3 Sep 2013 1.3 Oct 2013 1.1 Nov 2013 1.2 Dec 2013 1.2 Mar 2014 1.4 Apr 2014 1.4 Source: Samsung Securities The table above shows periods most similar with the present, with 12 periods identified by a regime-switching model as of end-August.

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Step 2: Formulating factor strategy Our dynamic quant model next identifies factors that performed well in historic periods with macroeconomic conditions on par with the current environment and applies higher weightings to such factors. We utilize a pool of over 100 factors to select our monthly strategy, calculating the information coefficient (IC) from historic periods identified as being similar to the present, average the ICs, and weight the factors according to the average IC.

Strategy universe Factor Description V10Q: FQ0 P/E Low FQ0 P/E V16T: Time-adjusted forward P/E Low 12-month-forward P/E (time adjusted) V20Q: FQ0 P/B Low FQ0 P/B V16R: Relative P/E Low 12-month-forward P/E relative to sector average B61: FY1 P/E one-month decline FY1 forward P/E one-month decline E12: FY2 EPS growth High FY2 EPS growth rate (y-y) C14: FY1/FY2 EPS one-month change Combining high FY1 EPS one-month change and high FY2 EPS one-month change C98: One-month/three-month FY1 earnings revision rate Combining high one-month FY1 ERR and high three-month FY1 ERR C61: FQ1 EPS one-month change High FQ1 EPS one-month change High composite index D19: Supply/demand composite index = [(change in institutional holding of stock + change in foreigner holdings on industry level) / 2] P19: Price composite index High composite index = [(price change of industry x 2) – (price change of stock)] S13: Target-price upside (one-month consensus) High upside to target price (one-month consensus) Note: FY1 = current year; FQ1 = current quarter, FQ0 = latest quarter with earnings released; time-adjusted forward P/E = adjusted P/E to compensate for weakness of existing low P/E strategy; EPS 1 - month change = (current EPS estimate/estimated EPS of 1-month prior) -1; earnings revision ratio [ERR] = (number of upward EPS revisions – number of downward EPS revisions) / total number of EPS forecasts Source: Samsung Securities

Factors utilized in September strategy IC V10Q V16T V20Q V16R B61 E12 C14 C98 C61D19 P19 S13 Apr 2012 0.05 0.09 (0.12) 0.13 0.17 0.00 (0.02) (0.04) (0.02) (0.07) 0.11 0.07 Oct 2012 0.12 0.09 (0.08) 0.02 0.23 0.05 0.04 0.09 0.02 (0.06) 0.16 0.17 Feb 2013 (0.06) (0.14) (0.20) (0.18) (0.13) 0.10 0.03 (0.01) (0.00) 0.06 0.18 0.12 Mar 2013 (0.28) (0.25) (0.28) (0.34) (0.19) 0.01 0.21 0.26 0.18 0.25 0.06 (0.34) Apr 2013 0.16 0.28 0.15 0.08 0.30 (0.04) (0.11) (0.05) (0.13) (0.22) (0.01) 0.36 Aug 2013 (0.02) 0.01 0.16 0.11 0.12 (0.05) 0.06 (0.04) 0.10 (0.08) 0.12 0.22 Sep 2013 0.23 0.28 0.08 0.10 0.02 0.06 0.04 (0.00) 0.09 0.13 (0.11) 0.06 Oct 2013 0.18 0.16 (0.22) 0.15 (0.03) (0.00) 0.09 0.21 (0.11) (0.05) (0.10) 0.13 Nov 2013 (0.00) 0.05 0.11 0.03 0.03 (0.07) 0.06 (0.03) (0.06) 0.04 0.01 (0.10) Dec 2013 (0.35) (0.31) (0.12) (0.15) (0.05) (0.09) (0.03) (0.09) (0.10) 0.00 (0.16) (0.01) Mar 2014 (0.06) (0.02) (0.04) 0.01 0.09 0.05 0.20 0.08 0.10 (0.02) 0.04 (0.05) Apr 2014 0.15 (0.06) (0.14) 0.02 0.05 (0.31) 0.15 0.20 0.08 0.22 (0.01) (0.27) IC average 0.01 0.02 (0.06) (0.00) 0.05 (0.03) 0.06 0.05 0.01 0.02 0.02 0.03 Portion (%) 5.0 5.0 5.0 5.0 15.6 5.0 18.1 14.5 5.0 5.0 7.1 9.7 Source: Samsung Securities

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Step 3: Select top picks The followings are the top picks derived from our macro dynamic model for September.

Macro dynamic model: Data used to select top picks Code Name Sector Market Valuation Growth Earnings momentum (%) Supply/demand (%) cap FQ0 Time- FQ0 Relative P/E FY2 FY1 FY2 FY1 FY1 FQ1 Supply- Price Target- (KRWt) P/E adjusted P/B P/E 1-mo EPS EPS EPS 1M 3M EPS demand comp price

(x) P/E (x) (%) chg growth 1-mo 1-mo ERR ERR 1-mo comp index upside (x) (%) (%) chg chg chg index A004020 Hyundai Steel Materials 9.0 7.8 10.2 0.7 (17.8)(5.1) 8.5 1.7 1.8 12.5 26.3 0.0 (0.8) 1.9 17.4 A010520 Hyundai Hysco Materials 1.9 2.7 9.6 2.3 (21.5) (12.9) (11.4) 12.4 18.8 78.6 61.1 17.6 3.0 3.9 24.4 A028050 Samsung Engineering Industrials 2.5 (6.5) 12.4 2.0 (0.4) (13.8) 50.0 (7.2) (0.4) 0.0 (21.4) 31.0 (0.9) 10.7 49.0 A000150 Doosan Corporation Industrials 2.5 84.0 16.1 1.4 10.5 (6.2) 32.8 (3.8) 1.5 0.0 12.5(0.9) (0.1) 17.9 39.3 A042670 Doosan Infracore Industrials 2.4 30.0 13.7 0.8 (14.7) (13.7) 37.3 4.5 (0.3) 66.7 43.8 (1.0) (3.9) (7.5) 41.2 Samsung Heavy A010140 Industrials 6.2 124.2 15.0 1.0 (9.6) (10.9) 50.0 7.6 (1.0) 47.1 52.6 5.3 (1.2) (21.0) 32.5 Industries Consumer A000270 Kia Motors 24.2 6.5 6.4 1.1 (31.5) (2.3) 5.8 0.2 (0.0) 3.8 (3.6) 0.3 0.6 (1.8) 21.6 Discretionary Consumer A012330 Hyundai Mobis 28.0 7.8 7.3 1.3 (22.0) (7.4) 8.0 (0.0) (0.2) 12.0 3.4 (0.1) (0.6) 1.4 25.9 Discretionary Consumer A005380 Hyundai Motor 50.2 7.8 7.5 1.2 (19.7) (7.6) 6.1 (0.0) (0.0) (33.3) (40.0) (0.3) (1.0) 3.3 30.1 Discretionary Consumer A008770 Hotel Shilla 4.7 112.1 30.7 6.9 228.7 (7.7) 50.0 4.4 13.7 71.4 50.0 1.8 (1.7) 9.8 25.5 Discretionary Consumer A035760 CJ O Shopping 2.2 16.2 16.7 2.8 79.0 (12.3) 11.3 2.4 2.9 (13.3) (11.8) 0.5 (0.8) 23.0 25.7 Discretionary Consumer A033780 KT&G 13.3 24.1 15.3 2.4 (26.5) (4.0) 9.1 0.2 0.4 50.0 (21.1) 0.0 0.1 10.7 15.4 Staples A105560 KB Financial Group Financials 16.2 13.3 10.1 0.6 (14.0) 3.3 11.4 0.4 0.2 5.3 (4.8) 1.1 0.0 8.9 13.4 A055550 Shinhan Financial Group Financials 24.6 12.6 11.2 0.9 (4.7) 0.2 7.5 0.8 0.4 20.0 38.1 1.4 (0.1) 13.6 9.4 A086790 Hana Financial Group Financials 12.5 14.9 9.8 0.6 (14.3) 1.6 15.9 0.7 0.0 10.0 9.5 1.1 (0.5) 13.8 16.2 A032830 Samsung Life Insurance Financials 21.9 17.7 19.2 1.0 62.5 (5.5)(12.2) 22.8 3.9 50.0 28.6 6.6 0.3 13.0 13.4 A000810 Samsung Fire & Marine Financials 13.6 18.0 15.0 1.5 28.2(2.9) 12.7 2.9 4.1 18.2 7.1 10.2 0.2 11.4 11.3 A003690 Korean Re Financials 1.4 10.2 8.2 1.0 (29.1)2.4 12.5 5.0 (0.1) 33.3 16.7 4.2 (0.8) 8.1 20.4 A088350 Hanwha Life Financials 6.1 13.7 12.3 0.8 4.4 2.2 9.9 2.3 (1.4) 25.0 0.0 15.1 (0.3) 13.7 18.4 A037620 Mirae Asset Securities Financials 2.1 18.1 12.1 0.9 1.8 (1.4) 9.4 5.8 4.1 71.4 70.0 (2.9) 0.1 16.6 12.1 Korea Investment A071050 Financials 2.8 20.9 15.7 1.1 33.4 (2.3) 5.1 16.2 6.7 0.0 0.0 10.2 0.5 10.9 12.9 Holdings A078340 Com2us IT 1.3 57.9 14.5 10.4 58.1 (16.4) 28.5 62.5 57.6 86.7 88.2 65.4 0.4 (28.2)32.0 A000660 SK Hynix IT 32.8 10.0 8.5 2.2 (11.0) (2.2) 10.6 0.4 1.6 (17.6) (25.9) (1.4) (0.1) (1.3) 33.9 A066570 LG Electronics IT 12.2 29.0 11.8 1.1 17.1 (5.7) 35.0 1.4 0.1 50.0 67.9 3.5 0.5 (1.4)27.8 A015760 Kepco Utilities 26.9 12.1 9.5 0.5 (6.7) (8.8) 18.7 9.2 5.5 42.1 42.1 4.1 0.0 5.4 23.1 Note: Recommendations based on consensus and quantitative analyses, which may differ from the recommendations of covering analysts; in order of sector and code; the level of holdings companies’ FY0 P/E, time-adjusted P/E, FY0 P/B, relative P/E received a 30% premium; EPS growth indicator limited to 50% Source: FnGuide, Samsung Securities

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Step 4: Finalizing portfolio Our quant portfolio adds a construction process between stock picking and finalization to systematically create the product while maintaining an efficient risk-return profile. We utilize a 50:50 methodology, with the first 50% given to large caps to manage risk, while the remainder is chosen based on a quant strategy, as detailed below.

Portfolio construction: 50:50 methodology

1. Half of our portfolio is initially determined by market cap. Starting with the largest cap (SEC) and working downward until the combined market cap reaches 50% of the KSE, each stock is given the same weighting in our portfolio as its index weighting—eg, 17.5% for SEC and 6% for HMC. 2. All other stocks are given a temporary weighting of zero.

3. The portfolio weightings for the bottom 25 stocks based on quantitative strategies (among large caps with an initial weighting of above zero) are decreased by 2%pts and the void in the portfolio is filled with the next largest stocks.

4. The portfolio weightings for 25 stocks—chosen using quantitative methods from the top 150 KSE stocks (by market cap)—are then increased by 2%pts, equating to the other 50% of our portfolio.

5. Adding the initial weightings and changes gives our final portfolio weightings (totaling 100%). 6. This methodology offers half the excess return (above that of the Kospi) and half the tracking error found in an equally weighted Value/Momentum+ Portfolio—ie, an annualized relative return of 13.5% and tracking error of 4.5%—but the same IR (3.0).

In other words, a 50:50 methodology limits exposure to ultra-large caps, while offering a similar IR to that provided by existing quant strategy.

See our report, Portfolio construction, new supply-demand factor, non-linear combination with quantitative strategy (Apr 27, 2012) for more details.

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The following is our final portfolio derived from macro dynamic model-based stock recommendations and a 50:50 methodology.

March quant model portfolio (macro dynamic model) Code Name Sector Kospi weight Final weight Difference (%, A) (%, B) (%pts, B - A) A005930 Samsung Electronics IT 15.2 15.2 0.0 A005380 Hyundai Motor Consumer Discretionary 4.2 6.2 2.0 A000660 SK Hynix IT 2.7 4.7 2.0 A012330 Hyundai Mobis Consumer Discretionary 2.3 4.3 2.0 A015760 Kepco Utilities 2.3 4.3 2.0 A055550 Shinhan Financial Group Financials 2.1 4.1 2.0 A000270 Kia Motors Consumer Discretionary 2.0 4.0 2.0 A032830 Samsung Life Insurance Financials 1.8 3.8 2.0 A105560 KB Financial Group Financials 1.4 3.4 2.0 A000810 Samsung Fire & Marine Financials 1.1 3.1 2.0 A033780 KT&G Consumer Staples 1.1 3.1 2.0 A066570 LG Electronics IT 1.0 3.0 2.0 A086790 Hana Financial Group Financials 1.0 3.0 2.0 A005490 Posco Materials 2.4 2.4 0.0 A035420 Naver IT 2.2 2.2 0.0 A004020 Hyundai Steel Materials 0.8 2.0 1.2 A010140 Samsung Heavy Industries Industrials 0.5 2.0 1.5 A088350 Hanwha Life Financials 0.5 2.0 1.5 A008770 Hotel Shilla Consumer Discretionary 0.4 2.0 1.6 A000150 Doosan Corporation Industrials 0.2 2.0 1.8 A010520 Hyundai Hysco Materials 0.2 2.0 1.8 A028050 Samsung Engineering Industrials 0.2 2.0 1.8 A037620 Mirae Asset Securities Financials 0.2 2.0 1.8 A042670 Doosan Infracore Industrials 0.2 2.0 1.8 A071050 Korea Investment Holdings Financials 0.2 2.0 1.8 A003690 Korean Re Financials 0.1 2.0 1.9 A035760 CJ O Shopping Consumer Discretionary 0.0 2.0 2.0 A078340 Com2us IT 0.0 2.0 2.0 A017670 SK Telecom Telecom Services 1.9 1.9 0.0 A051910 LG Chem Materials 1.5 1.5 0.0 A034220 LG Display IT 1.1 1.1 0.0 A090430 Amorepacific Consumer Staples 1.1 1.1 0.0 A003550 LG Corporation Industrials 1.0 1.0 0.0 A086280 Hyundai Glovis Industrials 1.0 0.6 (0.4) Total 100.0 Note: Recommendations based on consensus and quantitative analyses, which may differ from the recommendations of covering analysts, and it is sorted with Kosdaq stocks having no Kospi weight Source: Samsung Securities

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August review Our dynamic model gained 0.8% in absolute terms during the month, outperforming the Kospi (which gained 0.7%). Prices of some of our top picks—LG Uplus and Paradise— jumped during the month, increasing the portfolio’s performance. Our dynamic model has outperformed the Kospi in each of the past three months.

August performance of macro dynamic model

Performance (daily cumulative, %pts) Performance (daily cumulative, %) 0.4 1.5 0.3 1.0 0.2 0.5 0.1 0.0 0.0

(0.1) (0.5) (0.2) (1.0) (0.3) (1.5) (0.4) (0.5) (2.0) Jul 29 Aug 4 Aug 8 Aug 14 Aug 21 Aug 27 Relative performance (LHS) Quant model portfolio (RHS) Kospi (RHS)

Note: As of Aug 28 Source: Samsung Securities

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Appendix

Universe for pair trading (152 stocks) Stock Code Name Stock Code Name Stock Code Name Energy A096770 SK Innovation Industrial goods A122900 iMarketKorea Healthcare A006280 Green Cross A003600 SK Holdings (others) A001120 LG International A085660 Chabiotech A010950 S-Oil A020560 Asiana Airlines A128940 Hanmi Pharmaceutical A078930 GS Holdings Autos A005380 Hyundai Motor A096530 Seegene Chemicals A051910 LG Chem A012330 Hyundai Mobis A086900 Medytox A011170 Lotte Chemical A000270 Kia Motors Bank/card A055550 Shinhan Financial Group A010060 OCI A161390 Hankook Tire A105560 KB Financial Group A009830 Hanwha Chemical A018880 Halla Visteon Climate Control A086790 Hana Financial Group Korea Kumho A011780 A011210 Hyundai Wia A053000 Woori Finance Holdings Petrochemical A004800 Hyosung A060980 Mando A024110 Industrial Bank of Korea A000880 Hanwha Corporation A000240 Hankook Tire Worldwide A029780 Samsung Card A120110 Kolon Industries A073240 Kumho Tire A138930 BS Financial Group A003240 Taekwang Industrial A002350 Nexen Tire A139130 DGB Financial Group A006120 SK Chemicals A003620 Ssangyong Motor Securities A016360 Samsung Securities A011790 SKC A900140 Kolao Holdings brokers A006800 Daewoo Securities Korea Investment Steel/ A005490 Posco Retail A023530 Lotte Shopping A071050 Holdings non-ferrous A004020 Hyundai Steel A139480 E-mart A037620 Mirae Asset Securities Woori Investment metals A010130 Korea Zinc A069960 Hyundai Department Store A005940 & Securities A000670 Youngpoong A035760 CJ O Shopping A003450 Hyundai Securities A010520 Hyundai Hysco A004170 Shinsegae Insurance A032830 Samsung Life Insurance A001430 Seah Besteel A007070 GS Retail A000810 Samsung Fire & Marine Construction A000830 Samsung C&T A057050 Hyundai Home Shopping A088350 Hanwha Life A000720 Hyundai E&C A071840 Lotte Himart A005830 Dongbu Insurance A002380 KCC A028150 GS Home Shopping A001450 Hyundai Marine & Fire A047040 Daewoo E&C A027410 BGF Retail A002550 LIG Insurance A028050 Samsung Engineering Consumer A090430 Amorepacific A000060 Meritz Fire & Marine A000210 Daelim Industrial Discretionary A021240 Coway A003690 Korean Re A006360 GS E&C (others) A035250 Kangwon Land IT A005930 Samsung Electronics A012630 Hyundai Development A002790 AmoreG A000660 SK Hynix A108670 LG Hausys A034230 Paradise A035420 Naver A022100 Posco ICT A008770 Hotel Shilla A066570 LG Electronics Shipbuilding A009540 Hyundai Heavy Industries A030000 Cheil Worldwide A034220 LG Display & Machinery A010140 Samsung Heavy Industries A114090 Grand Korea Leisure A034730 SK C&C A042660 DSME A130960 CJ E&M A006400 Samsung SDI Samsung Electro- A034020 DHIC A111770 Youngone Corporation A009150 Mechanics A010620 Hyundai Mipo Dockyard A037560 CJ HelloVision A036570 NCsoft A047810 Korea Aerospace Industries A053210 KT Skylife A011070 LG Innotek A012450 Samsung Techwin Consumer A033780 KT&G A046890 Seoul Semiconductor A042670 Doosan Infracore Staples A051900 LG H&H A181710 NHN Entertainment A064350 Hyundai Rotem A001800 Orion Telecom A017670 SK Telecom Industrial A003550 LG Corporation A097950 CJ CheilJedang Services A030200 KT goods A086280 Hyundai Glovis A001040 CJ Corporation A032640 LG Uplus (others) A047050 Daewoo International A004990 Lotte Confectionery A033630 SK Broadband A012750 S-1 A005300 Lotte Chilsung Utilities A015760 Kepco A000150 Doosan Corporation A004370 Nong Shim A036460 Kogas A000120 CJ Korea Express A005440 Hyundai Green Food A051600 Kepco KPS A006260 LS Corporation A000080 Hite Jinro A052690 Kepco E&C A001740 SK Networks A026960 Dongsuh A071320 Korea District Heating A009240 Hanssem A007310 Ottogi A010120 LS Industrial Systems A001680 Daesang A003490 Korean Air A006040 Dongwon Industries A011200 Hyundai Merchant Marine Healthcare A000100 Yuhan Note: Cheil Industries excluded due to the merger with Samsung SDI Source: Samsung Securities

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Portfolio simulation methodology  Stocks Our investment universe comprises the top-150 KSE stocks by market cap at a specified time each month. Delisted or merged stocks are also included in the universe to remove survivorship bias.  Portfolio selection Stocks are given scores based on each factor. The top 20% are allocated to the 1st quintile portfolio, the next 20% to the 2nd quintile portfolio, and so on. Cumulative returns for the five quintile groups are calculated to determine each factor’s influence on share price and its usefulness as an investment indicator.  Simulation period From Jan 2003 to the last day of the previous month.  Bull and bear markets A bull market is defined as one in which absolute monthly returns of the Kospi are positive for at least three months, and a bear market as one in which such returns are negative for at least three months. Bull markets occurred in 2001, 2003, late-2004 through 2007, and from 2009 to date, while bear markets were seen in 2002, early 2004, and 2008.  Portfolio returns Based on an equally-weighted portfolio indexed to Jan 2003.  Portfolio rebalancing For back testing, our portfolio is rebalanced on the last trading day of the month, and remains unchanged until the end of the following month.  Consensus data Based on FnGuide. For certain factors that cover short periods or involve other data-processing difficulties we use Thomson Reuters I/B/E/S data—eg, comparisons with average valuations, PBRs, and PCRs.  Performance indicators Performance indicators for long-only and long/short strategies differ. For long-only strategies, we check relative return and information ratio (IR). For long-short strategies, we check long-short returns and information coefficient (IC).

[Terms used]  Relative return: Return of portfolio relative to benchmark [R-Rb].  Tracking error: Standard deviation of relative return σ (R-Rb).  Information ratio (IR): Relative return/relative risk. Excess return of portfolio over benchmark divided by tracking error. Portfolios which continue outperforming the benchmark should have high IRs.  Information coefficient (IC): Correlation coefficient between portfolio’s estimated and actual returns—a high IC means investment success. In real life, the Spearman rank correlation between a stock’s factor and yield rankings is used.  Hit rate (%): Portion of 1st quintile outperforming the benchmark.  Maximum drawdown (%): Loss from peak to next trough; investors’ potential maximum loss.  Fwd: Estimates for the next 12 months—eg, for May, forecasts for the remaining 7 months of the year are added to those for 5 months of the following year.  FY0: Forecast year 0, the last year for which earnings have been released—eg, 2010 if we are in May 2011.  FY1: Forecast year 1, the next year for which earnings will be released—eg, 2011 if we are in May 2011.  FY2: Forecast year 2, the next-but-one year for which earnings will be released—eg, 2012 if we are in May 2011.  FQ1: Forecast quarter 1, the next quarter for which earnings will be released—eg, 2Q11 if we are in May 2011.  FQ2: Forecast quarter 2, the next-but-one quarter for which earnings will be released—eg, 3Q11 if we are in May 2011.

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Samsung Factor List

Strategy code Strategy name Description V10 FY0 P/E Low FY0 P/E V10Q FQ0 P/E Low FQ0 P/E V11 FY1 P/E Low FY1 P/E V12 FY2 P/E FY2 P/E V13 FY3 P/E Low FY3 P/E V15 Trailing P/E Low 12-month trailing P/E V16 Forward P/E Low 12-month-forward P/E V16R Relative P/E Low 12-month-forward P/E relative to sector average V16S Forward P/E (1-month consensus) Low 12-month-forward P/E (one-month consensus basis) V16T Time-adjusted forward P/E Low 12-month-forward P/E (time adjusted) V20 FY0 P/B Low FY0 P/B V20Q FQ0 P/B Low FQ0 P/B V21 FY1 P/B (since 2005) Low FY1 P/B V21B FY1 P/B (IBES) Low FY1 P/B V22 FY2 P/B (since 2005) Low FY2 P/B V22B FY2 P/B (IBES) Low FY2 P/B V30 FY0 PCR Low FY0 PCR V31B FY1 PCR (IBES) Low FY1 PCR V41 FY1 dividend yield (since 2005) High FY1 dividend yield V41B FY1 dividend yield (IBES) High FY1 dividend yield V50 FY0 EV/EBITDA Low FY0 EV/EBITDA V51 FY1 EV/EBITDA (since 2005) Low FY1 EV/EBITDA V61 FY1 PEG Low FY1 PEG V62 FY2 PEG Low FY2 PEG V71 FY1 P/S Low FY1 P/S R16 P/E relative to sector P/E Low 12-month-forward P/E relative to sector’s P/E B16 Forward P/E relative to historic (2-year) P/E Low 12-month-forward P/E relative to history (two years) B21 FY1 P/B relative to historic (2-year) P/B Low FY1 P/B relative to history (two years) B61 FY1 P/E one-month decline FY1 forward P/E one month decline B62 (FY1 EPS one-month chg) – (price 1-month change) High (FY1 EPS 1 month change - price change) B63 FY1 earnings yield one-month change High growth rate of FY1 earnings yield (one month) E10 FY0 EPS growth High FY0 EPS growth rate (y-y) E11 FY1 EPS growth High FY1 EPS growth rate (y-y) E12 FY2 EPS growth High FY2 EPS growth rate (y-y) E13 FY3 EPS growth High FY3 EPS growth rate (y-y) E14 FY2 EPS 2Y CAGR High FY2 EPS 2Y CAGR (y-y) E16 Forward EPS growth High 12-month-forward EPS growth rate (y-y) E21 FY1 sales growth High FY1 sales growth rate (y-y) E31 FY1 Operating profit growth High FY1 Operating profit growth rate (y-y) E60 FQ0 EPS growth (y-y) High FQ0 EPS growth rate (y-y) E61 FQ1 EPS growth (y-y) High FQ1 EPS growth rate (y-y) E62 FQ2 EPS growth (y-y) High FQ2 EPS growth rate (y-y) E65 FQ0 EPS growth (q-q) High FQ0 EPS growth rate (q-q) E81 FQ1 Operating profit growth (y-y) High FQ1 Operating profit growth rate (y-y) E82 FQ2 Operating profit growth (y-y) High FQ2 Operating profit growth rate (y-y) C11 FY1 EPS one-month change High FY1 EPS one-month change C12 FY2 EPS one-month change High FY2 EPS one-month change C14 FY1/FY2 EPS one-month change Combining high FY1 EPS 1-month change and high FY2 EPS 1-month change C16 FY1 EPS three-month change High FY1 EPS three-month change C21 FY1 sales one-month change High FY1 Sales one-month change C31 FY1 Operating profit one-month change High FY1 Operating profit one-month change C41 FY1 ROE one-month change (since 2005) High FY1 ROE one-month change since 2005 C61 FQ1 EPS one-month change High FQ1 EPS one-month change C81 FQ1 Operating profit one-month change High FQ1 Operating profit one-month change C91 FY1 earnings revision rate (1-month) High FY1 earnings revision rate (one month) C96 FY1 earnings revision rate (3-month) High FY1 earnings revision rate (three months) (Continued on next page)

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Strategy code Strategy name Description C97 FY2 earnings revision rate (3-month) High FY2 earnings revision rate (three months) C98 1-mo/3-mo FY1 earnings revision rate Combining high one-month FY1 ERR and high three-month FY1 ERR CC2 Earnings Momentum Composite Combining high FY1/FY2 EPS one-month change and high one-month/three-month FY1 ERR D10 Foreign ownership one-month change High growth rate of foreign ownership (one month) D11 1-mo cumulative net buying by foreign investors High foreign cumulative net buying relative to market cap (one month) D12 1-mo cumulative net buying by institutions High institutional cumulative net buying relative to market cap (one month) D13 1-mo cumulative net buying by retail investors High retail cumulative net buying relative to market cap (one month) D16 4-week institutional net buying momentum High linear trend of four-week net buying by institutions D17 1-mo cumulative gross buying by institutions High institutional cumulative gross buying High composite index = [(change in institutional holding of stock + change in foreign investor D19 Supply/demand composite index holdings on industry level)/2] D31 Stock lending portion (since 2005) Low stock lending portion since 2005 D32 Stock lending return portion (since 2005) High pay back stock lending (one month) D33 Short selling volume (1-month, since mid-2008) Low short selling volume compared to market cap (one month) P11 1-month price reversion Low rate of return (one month) P12 3-month price momentum High rate of return (three months) P13 6-month price momentum High rate of return (six months) P16 Price momentum (13months ago to 1month ago) High rate of return (thirteen months prior to one month prior) P17 1-month price reversion relative to sector Low rate of return relative to sector P19 Price composite index High composite index = [(price change of industry x 2) – (price change of stock)] P21 Market cap Large market cap P22 Stock price High stock price P23 Low beta (daily, 1yr) Low beta stock P24 Low volatility (weekly, 2yr) Low volatility stock Q11 FY1 ROE (since 2005) High FY1 ROE since 2005 Q11B FY1 ROE (IBES) High FY1 ROE (IBES) Q21 FY1 ROA (since 2005) High FY1 ROA since 2005 Q21B FY1 ROA (IBES) High FY1 ROA (IBES) Q31 FY1 Operating margin High FY1 Operating margin Q40 Accrual Low accrual [ (NI t - OCF t) / B t] Q51 FY1 EPS coefficient of variation Low FY1 EPS coefficient of variation S11 Recommendation score High recommendation score S12 Target price upside High upside to target price S13 Target price upside (1-month consensus) High upside to target price (one-month consensus target price basis) S61 Recommendation 1-month change High recommendation score’s one-month change S62 Target price 1-month change High target price 1-month change MM1 P/E & Earning Momentum Composite (sector tilted) Combining FY2 P/E and FY1 EPS one-month change by sector tilted weight MM2 Compact Model Compact model MM6 Value/Momentum+ Model Value/momentum + model Z11 (Supposition) Real FY1 P/E (Supposition) low P/E assuming advanced knowledge of FY1 EPS Z12 (Supposition) Real FY1 P/B (Supposition) low P/B assuming advanced knowledge of FY1 BPS Z21 (Supposition) Real FY1 EPS growth (Supposition) high growth rate of FY1 EPS assuming advanced knowledge of FY1 EPS (Supposition) FY1 EPS 1-month change one month (Supposition) high FY1 EPS one-month change assuming advanced knowledge of FY1 EPS Z23 later over the next month (Supposition) Change in foreign ownership (Supposition) high growth rate of foreign ownership (one month) assuming advanced Z40 (1-month) knowledge of foreign ownership over the next month (Supposition) next 1-month cumulative net buying (Supposition) high foreign cumulative net buying assuming advanced knowledge of foreign Z41 by foreign investors net buying over the next month (Supposition) next 1-month cumulative net buying (Supposition) high institute cumulative net buying assuming advanced knowledge of Z42 by institutions institutional net buying over the next month (Supposition) next 1-month cumulative net buying (Supposition) high retail cumulative net buying assuming advanced knowledge of retail net Z43 by retail investors buying over the next month Source: Samsung Securities

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 Compliance Notice - This report is independently based on quantitative analysis, so it may differ from those by sector-based analysts. - This report has been prepared using data based on past performances, which are not an indicator of future performance. - As of Aug 28, 2014, Samsung Securities shared group affiliation with Hotel Shilla, Samsung Card and S-1. - During the three months prior to Aug 28, 2014, Samsung Securities had not participated in any securities issuance (including DRs, CBs, and IPOs) by companies covered in this report. - As of Aug 28, 2014, Samsung Securities' holdings of shares and debt instruments convertible into shares of Hotel Shilla, S-1 would, if such debt instruments were converted, exceed 1% of the outstanding shares. - As of Aug 28, 2014, the covering analyst(s) did not own any shares, or debt instruments convertible into shares, of any company covered in this report. - This material has not been distributed to institutional investors or other third parties prior to its publication. - This report has been prepared without any undue external influence or interference, and accurately reflects the views of the analyst(s) covering the company or companies herein. - All material presented in this report, unless specifically indicated otherwise, is under copyright to Samsung Securities. - Neither the material nor its content (including copies) may be altered in any form, or by any means transmitted, copied, or distributed to another party, without prior express written permission from Samsung Securities. - This memorandum is based upon information available to the public. While we have taken all reasonable care to ensure its reliability, we do not guarantee its accuracy or completeness. This memorandum is not intended to be an offer, or a solicitation of any offer, to buy or sell the securities mentioned herein. Samsung Securities shall not be liable whatsoever for any loss, direct or consequential, arising from the use of this memorandum or its contents. Statements made regarding affiliates of Samsung Securities are also based upon publicly available information and do not necessarily represent the views of management at such affiliates.

August 29, 2014 Quantitative Issue

Global Disclosures & Disclaimers General This research report is for information purposes only. It is not and should not be construed as an offer or solicitation of an offer to purchase or sell any securities or other financial instruments or to participate in any trading strategy. This report does not provide individually tailored investment advice. This report does not take into account individual client circumstances, objectives, or needs and is not intended as recommendations of particular securities, financial instruments or strategies to any particular client. The securities and other financial instruments discussed in this report may not be suitable for all investors. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein and investors should seek the advice of a financial adviser. This report may not be altered, reproduced, distributed, transmitted or published in whole or in part for any purpose. 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