University of Macedonia – Student Research Retail Sector, Fashion Industry Stock Exchange (ASE) FF Group This report is published for educational purposes only by students competing in the CFA Institute Research Challenge. Date: 21/02/2016 Current Price: €14.50 (19/02/16) Recommendation: BUY Ticker - ASE: FFGRP.AT Headquarters: Athens, Target Price: €16.92 (16.69% upside)

Investment Summary Figure 1: Share Price Movement The DCF together with the relative-multiples approach have yielded a target price of 16.92€ as of 19/02/2016 leading to a buy recommendation. 40 2000 30 1600 Itch for luxury or else… the Chinese advantage 1200 20 In the report “A multifaceted future: the jewelry industry in 2020” McKinsey 800 foresees a strong boost on the branded jewelry share in the industry driven by “new 10 400 money” and emerging-market consumers. FF Group is more than likely to 0 0 significantly benefit through the Chinese market, which is packed with both kinds of those consumers. Despite the slowdown of the Chinese economy, the Chinese FFG ASE luxury market has still largely favorable characteristics. The constant enhancement of the urban population together with the strengthened middle class has been continuously broadening the group’s target group, whose high fashion Table 1: Market Profile consciousness proves to be a growth driver. Closing Price 14,50 € 30.00€- Business in Greece, really? 52-Week High-Low 12.87€ The Greek economy has been arguably striving to survive the crisis during the recent Average Volume 69.289 years. Greece has been unable to borrow from the markets, Greek GDP has Market Cap 970,750M plummeted by more than one fourth since 2009, capital controls have been Nunber of Shares 66.948.210 imposed, political instability has been evident, tax policy is tight -and tightening- and Greek stock market has been faced with major shocks. Even though such issues have Dividend Yield 0,00% undoubtedly negatively influenced all Greek businesses including FF Group, the Beta 1,3 latter has proved to be hard to succumb following the market, that is ASE General EV/REVENUE 0,92 Index, with approximately 30% stronger “urge” as shown by the dual-beta analysis, EV/EBITDA 4,64 when the market ascends than when it goes down, while in the first case it seems P/E 6,24 to even often outperform the market with an unlevered beta of about 1 (and a Sales 2015(TTM) 1,149M € higher levered one). In any case, it is true that FF Group is a “truly” international Institutional Holdings 57,40% with a well-(geographically) diversified portfolio of markets rendering capable of reaping the foreign markets’ benefits, such as those of the growing Chinese market, Insider Holding 38,60% and mitigating country risk stemming from the Greek economy.

A glance at some fundamentals Figure 2: Sales geographical The group seems to be effectively dealing with liquidity risk posed by the financial segmentation (historical years) crisis which has led to shrinking lending ability for most firm, on the grounds that its current ratio and quick ratio were 6.86 and 4.57 respectively as of 30/9/2015, 1200 indicating high liquidity. Nonetheless, in terms of operating performance, the group had at the same time an inventory turnover of 1.2 placing it in the lowest 1.09% 1000 percentile given the industry it operates in.

800 Business Description 600

400 Folli Follie company is an international lifestyle fashion brand founded in Greece in in in million 1982. After 28 years in business, in 2010 Folli Follie S.A. merges with HELLENIC DUTY 200 FREE SHOPS S.A. and Elmec Sport S.A. to create the Folli Follie Group of Companies, which operates in more than 28 countries. FF Group operates in four main 0 segments: a) design, manufacture and distribution of jewelry, watches and 2009 2010 2011 2012 2013 2014 accessories such as handbags, small leather goods, belts, pashminas and sunglasses, Asia Greece Rest of Europe - Americas b) operation of five department stores through its subsidiary “Attica Department Stores S.A.” and two discount department stores c) retail sale of footwear,

accessories, apparel, perfumes and wholesale of clothing, shoes and accessories, d) other activities (e.g. representation of TechnoGym, surveillance and security Figure 3: Sales geographical systems). segmentation (team forecasts) The total revenue of the FF Group for the period 1.10.14-31.9.15 was 1147.5 million € generating an EBITDA of 237.3 million € and a net profit of 150 million €. The 2000 group’s major source of revenue is the design, manufacture and distribution of 1800 jewelry, watches and accessories, which for the same period constituted 71.5% of 1600 the total sales of the company and even more importantly 88.79% of EBITDA. This is 1400 due to the fact that this segment had by far the highest EBITDA margin, again underlining its importance for the group. 1200 The geographic distribution of the group’s activities shows a high dependence on 1000 the Asian and the Greek market. The former accounted for 60.4% of the 2014 800 revenue with Greater and Asia remaining the key growth drivers of the Group, in euros in million 600 supported by strong demographics of a strengthening middle class, while the group 400 generated 26.1% of its revenue in the same year in Greece. At the same time, 200 75.67% of its non-current assets are located in Greece revealing a possibly significant 0 exposure to the Greek Real Estate Market and economy in general. 2015p 2016f 2017f 2018f 2019f 2020f Asia Greece Rest of Europe - Americas Strategy The Group’s strategy is based on the following pillars:  Expansion of brands and diversification into new markets: FF Group follows an expansion strategy for own brands Folli Follie and Links of London in Asia supported by the strategic partner . Figure 4: Revenues by geographical  Wide price range: Depending on the materials used (sterling silver, stain- Region less steel, precious and semi-precious stones) and in a wide price range covering all needs, the group offers an unrivaled variety of styles. Revenues By Geographical  Improved margins and a low cost base through effective use of the region available resources Europe  Stores in strategic locations: FF Group has established a strong presence 12,1% Asia counting more than 900 points of sale worldwide including both flagship Greece incl. stores in strategic locations as well as stand-alone shops and shop-in-shop 26,10% in famous department stores. 60,4% North Americ Stock and shareholder structure a 1,4% The group’s share capital amounts to € 20,084,463 divided into 66,948,210 common nominal shares with nominal value € 0.30 each and paid in full. All shares are listed for trade at the Athens Stock Exchange in the category of Big Capitalization. Folli Source: FF presentation Follie company was first listed in 1997 and its shares continued being traded until December 30th, 2010, when they were suspended from trading. On January 7th, 2011 the FF Group shares started to trade on the Athens Stock Exchange after the merger. After the curve out of travel retail sector, the trading shares are that of the company FOLLI-FOLLIE COMMERCIAL MANUFACTURING AND TECHNICAL SOCIETE Table 2: Governance Risk ANONYME. Criteria Risk The main shareholders are Koutsolioutsos Family, Fosun International and Fidelity Board Extremely High 10 Investments, which as of 09.10.15 hold 38.6%, 13.9% and 7.4% of the share capital Structure respectively with a significant 31.4% owned by foreign institutional investors. The Shareholder Medium 6 remaining 8.7% is under the ownership of domestic institutional investors (4.7%), Rights private investors (3.3%), while 0.7% is treasury shares. Compensation Medium 5 Audit & Risk Extremely High 10 Oversight FFG Rating HIGH Governance Corporate Governance & Social Responsibility Risk 9 Source ISS Contribution to Greek economy The Retail sector constitutes (alongside with the wholesale sector) almost 20% of the total GVA and 18% of the total workforce in Greece. Thus, jewelry and apparel industries as a part of the retail sector have a leading role in the Greek economy. At the same time, group’s 2014 revenue accounted for 0.12% of the Greek GDP.

Corporate governance In Folli Follie Group, the Code of Corporate Governance has three basic principles; the adoption of optimum corporate governance practices to be implemented by the company, the improvement of information sharing with private and institutional shareholders and the company’s obligation to effectively comply with the requirements of the respective laws. Figure 5: Shareholding Structure The quality of FF Group’s Corporate Governance can be evaluated in the following 4,70% facets: 3,30% 0,70%  Committees: Governance committees to oversee the company operations, 7,40% Established Audit , election committees , Auditing Committees 38,60%  Shareholder Rights: One vote per share voting policy, minority shareholder 13,90% interests protected against actions of controlling shareholders through 31,40% special rights, right to dividends , profit distribution policy  Code of Ethics: Code of ethics for the entire company Corporate social responsibility Koutsolioutsos Family FF Group has developed a holistic Corporate Social Responsibility Program, the basic Foreign Institutional Investors axes of which are Culture and Sports, Society and Environment. The group’s actions Fosun International are vigorously oriented towards environmental protection and sustainable development, while the Headquarters and the Retail stores are housed in eco- Fidelity Investments friendly buildings promoting recycling and energy saving practices. At the same Private Investors time, the group has held long-lasting charity initiatives throughout Greece covering Domestic Institutional Investors the needs of schools, non-profit institutions and NGOs. Lastly, the group embraces Treasury Shares culture and arts as global means of communication that unite civilizations.

Industry Overview and Competitive Positioning

A. Greek economy Figure 6: Greek Unemplyment Greek Debt Crisis: commenced in 2009 and still pertaining Rate The Greek economy has been in the eye of the storm of the European debt crisis. Ιn 26,5 2010, when Greek state lost access to borrowing from markets, Greek government 26 made a deal for a bailout program with its European partners and the IMF involving 25,5 structural reforms and austerity measures to be taken by the Greek state under 25 close economic supervision by its lenders. During the recent years of the financial 24,5 crisis the Greek GDP has plummeted by 28.61% in the period 2009-2014. A major 24 shock for the country’s economy came in June 2015, when capital controls were 23,5 imposed restricting withdrawals from banks and capital outflows from the country.

This restriction on the capital movements –which is still in force– seems to have

July

May June

April been an obstacle to Greece’s returning to growth.

March

August

January

October February

November Negative business environment in Greece September The three consecutive elections during the past year are indicative of the political Source: Hellenic Statistical Authority risk in Greece, as Greek governments have traditionally found it hard to secure a

safe majority in the parliament in the recent years, while recent coalition

governments have proved to be fragile. Furthermore, tax policy has been both tight

and changing, while a 100% advance tax payment has been imposed on firms. In the

light of this evidence, strategic planning can be difficult for firms due to the unstable Figure 7: CCI Greece compared to EU tax policy, capital controls and high levels of bureaucracy.

B. industry Based on McKinsey&Company “the jewelry market seems poised for a glittering future. The industry is very vulnerable to the prices of the raw materials used, most of which is negotiable in the stock market meaning that unexpected changes in their price could affect firm’s revenues. A positive point made by the same consulting firm is the increase of the share of branded jewelry in the market until 2020 by at least 10 points percent (Figure 2), while it also predicts a revenue growth of 5-6% each year reaching €250 billion in 2020.

Source: Y Charts Chinese market

Jewelry in China raised at 593.4 billion CNY and 5% at value terms in 2015. The raise of the middle class which is spectacular combined with its need to enhance their personal appearance will lead to a new increase in the revenues of the jewelry industry. The mainland of China is a major jewelry consumer. In 2013 Chinese mainland sales hit 75.8 USD billion (1USD=6.2RMB) equivalent to 41.2% of global consumption. In 2014 jewelry market sales reported 157USD billion of which mainland China and spent 80.7 USD billion (1USD=6.39RMB) equivalent Figure 8: Branded vs Unbranded to 51.4% of global share(increased 10.2% since last year). Gold jewelry has the Jewelry, Globaly largest amount of consumption, more than 50%. The consumption is affected by the 120 price of gold (Figure 3)(consumption 2013 716.5 tons, consumption 2014 667.1 tons 100 (-7.4%)).

80 60-70 Japanese market 60 81 81 80 Unbranded Japanese market seems to have thrived in 2014 and 2015, while weakened Yen and 40 relaxed duty free controls have boosted tourist arrivals by 29% in 2014. The country 20 30-40 has become a shopping tourism destination and the government’s focus on the 19 19 20 Branded 0 reduction of gender inequality in executive positions is expected to contribute to an 2007 2009 2011 2020 increase in the jewelry sales among women. Source: McKinsey report Competitive Positioning Figure 9 : Monthly Gold Prices The FF Group has a diversified portfolio of products to conquer the fashion market, 2000 while its operations span from the global jewelry market to the retail and wholesale 1500 sectors and department stores.

1000 Folli Follie and Links of London 500 Wide luxury products range and global expansion: Folli Follie and Links of London 0 offer a wide variety of , which include jewelry, watches (hard luxury), apparel and accessories (soft luxury), a product mix that can be thought to contribute to its income stability. The global expansion and the enhanced brand

name of the two companies has helped the group secure a considerable share in the

2013-01 2013-05 2013-09 2014-01 2014-05 2014-09 2015-01 2015-05 2015-09 mature luxury markets of Japan, the U.K and Greece, as well as in emerging markets, price per oz such as China, and at the same time mitigate political and country risk. Source: Yahoo Finance Strong positioning in “affordable luxury” sector Figure 10: Japan Jewelry Sales The polarization of the fashion and jewelry market between low price and luxury Forecast (Usd Bn) products has left space for a new rapidly developing sector. The mass market domain has grown in the last year (e.g. Inditex) and developed the ability to create 10,5 fashion trends faster than ever before shaping a new order of demand which is 10 placed between the mid-market brands and high luxury brands. These brands, whose positioning is characterized by the oxymoron “accessible luxury”, have tried 9,5 to provide luxury products at cost effective prices and establish a good tradeoff between quality and price. 10,16 The fast-changing fashion leads the traditional customers of high-end luxury 9 9,84 9,55 products to look for more cost effective ones. Moreover, the enhanced income of 9,26 9,06 8,5 8,87 the middle class globally drives many mass market consumers to more accessible luxury products. These two factors are expected to lead to the reinforcement of the 8 accessible/affordable luxury segment, since the key driver of the world’s middle class population is up to reach 3.2 billion in 2020 according to OECD.

Source: Euromonitor China’s emerging luxury products market penetration Figure 11: Porter’s Five Forces Analysis The Chinese emerging market is one of the most promising and profitable ones, while its vast population and fast growth rate could be a key factor for the success of the group, which has managed to capture and maintain a strong position in the Industry Rivalry & Chinese jewelry market with more than 180 points of sale. The revenues in the Competition whole of the Asian market have been steadily increasing showing the successful positioning of the group in the markets of China and Japan. Bargaining Bargaining Power of Power Of Customers Suppliers Solid cash flows from retail, wholesale & department stores The ability of these sectors to generate low risk cash flows enhance the liquidity of Threats of Threat of New Entrants Substitutes the group and can be thought of as a competitive advantage compared to other luxury brands. This competitive advantage is also supported by the strong and low- risk cash flow and high earnings potential thanks to the prosperous exclusive retail Source: Team analysis and wholesale distribution of famous brands such as Nike, Juicy Couture, Converse, Franklin & Marshall and others not only in Greece but also in , and . Additionally, the liquidity offered by these sectors can be useful for serving Figure 12:Change% of the annual their financial liabilities and investing opportunities. sales in Asian Markets (base year 2010) Valuation 80,00% 60,00% Target Price:16.92€ Recommendation: BUY

40,00% For the valuation of FF Group a 6-year DCF and a Relative Multiples Valuation have 20,00% been employed. 0,00% Change% DCF model The discounted cash flow analysis has been mainly based on some fundamental factors, such as the GDP growth forecasts for the countries the Group generates most of its profit (i.e. China, Greece, Japan, UK, Republic of Korea and ), where 2010 2011 2012 2013 2014 Asia plays a significant role, their shares on sales since 2010, the condition of the Change% 0,00% 7,06% 28,44% 48,37% 65,73% stock market for each country and each of the three main activities of the Group, its competitive positioning as well as the Group’s financial statements’ historical data. Source: FF Group Furthermore, a professional outlook for the jewellery industry in the future by McKinsey & Company has been taken into consideration. Figure 13 :Points of sale in A 6-year period has been selected, so that the 2015 projected cash flow is not China(Orange) compared to points discounted, while the forecasted cash flows for the period 2016-2020 are of sale in Japan(Red) discounted to 01.01.16. The DCF analysis has yielded a target price of €15.51 (as of 300 01/01/16), which is most sensitive to the following factors: 200

100 Weighted Average Cost of Capital (WACC) The WACC has been calculated weighing the cost of debt and the cost of equity of 0 the group using the book value of debt (bond loans, bank loans and leases) and the 2010 2011 2012 2013 2014 2015 market value of equity respectively as weights. Source: FF Group Cost of equity Figure 14: Price Volume pyramids for For the estimation of the short term cost of equity CAPM is used following a bottom- Luxury Goods up approach for beta, which is then levered, using the average 10-year Greek bond Pyramids of price/volume: The conversion of the yield for the 15-year period Jan2001-Dec2015 as risk free rate and a risk premium price traditional pyramid price/value into a new one to characterize the market of luxury goods. based on current sector betas (for each of the three main activities of the Group) for Historical to prospective prices and volume correlation each country. Cost of equity for the short term period has been assumed to luxury gradually decline from 2016 till 2020, when it reaches its long term value. This gradual decline has been employed to capture the decline of the risk free rate and mid the risk premium (now the average for the pre-crisis Jan2001-Dec2008 period), as mass Greece –mainly–recovers from the financial crisis and the other five economies

Volume return to pre-crisis conditions, as well as the finding that beta is closer to the mean price value of 1 in a future period, which is utilized based on Blume’s (1971) adjusted beta. Cost of debt luxury Short term cost of debt has been calculated as the average of the two following methods: i) dividing the average bond, bank loans and leases interest expenses for accessible luxury the period 2011-2014 by the average value of the respective liabilities, ii) as the sum

mid of the risk free rate and the typical company default spread that correspond to the

mid group’s interest coverage ratio as of 09.09.15. Similarly, the cost of debt given by mass nd the 2 method gradually declines, as the risk free rate is assumed to do so till it reaches its long term value. Source: Goldman Sachs Research

Short term gross profit growth Table 3: Weighted Average Cost of Capital In order to forecast the growth of gross profit, which has been selected given the (WACC) persistent value of the gross profit margin near 50%, a (geographically) weighted 2016 15.97% GDP growth has been estimated using the six above mentioned countries’ growth. 2017 14.49% The resulting weighted-GDP index is found to have a significant correlation with 2018 13.14% gross profit for the period 2010-2015 (2015 projected) and thus can be considered 2019 11.92% a well-trusted predictor of gross profit. For the period 2016-2020 the growth of gross 2020 10.81% Terminal 9.80% profit is estimated using the nominal GDP growth (in US$) forecasts for the six Sources: Team analysis, FF Group's financial countries by the IMF, which are converted in euros using the 2016-2017 EU statements, ECB, A. Damodaran's page: Commissions forecasts for the exchange rate (projected until 2020), and the http://pages.stern.nyu.edu/~adamodar/ Weighted GDP CAGR to Gross profit CAGR ratio for the recent period 2013-2015.

Terminal growth Table 4: FF short term betas For the terminal value, the FCFF has been assumed to grow with a weighted GDP Unleverd 1.036 CAGR of 2.19%, that is the estimated weighted GDP CAGR for the period 2020-2060 Bottom-up Levered 1.3 using the OECD’s forecasts (as of May 2014) for the countries’ GDP volume up to monthly obs. "Common" 0.855 2060. This is consistent with the above Weighted GDP CAGR to Gross profit CAGR Regres 01.03.2011- Downside 0.754 ratio. Furthermore, the report “A multifaceted future: the jewelry industry in 2020” sion 01.02.2016 Upside 0.946 of McKinsey foresees a growing share of branded jewelry emphasizing both “new (unlev weekly obs. "Common" 0.8 money” and emerging-market consumers as fundamental drivers of that growth, ered) 18.02.2013- Downside 0.794 which is to have a positive influence in the group’s sales its development in China, a 15.02.2016 Upside 1.073 Sources: i) sector betas are from A. Damodaran’s page: market with both kinds of those consumers. http://pages.stern.nyu.edu/~adamodar/New_Home_P age/datacurrent.html Relative and Multiples Valuation ii) FFGRP.AT and ASE General Index close prices are from Yahoo Finance and nafteboriki’s pages With the relative valuation, we try to gage the “fair” value of an asset based on the price given to the similar assets in the market. The crucial steps are: i) Find the Figure 15: Weighted GDP and appropriate comparable companies and the estimated price of them. ii) Convert the market prices to a common variable. For that reason, a range of multiples is used, 150 Gross profit indices calculated for each comparable company. 120 To define the intrinsic value of FF Group, we enhanced our evaluation by forming a simple relative valuation using 5 different multiples giving to each of them a 90 different weight. The multiples, which are used, are P/S, P/E, P/Book Value, 60 EV/Revenue and EV/EBITDA. We give relatively more weight to the multiples which 30 contain EV, since these multiples capture the financial leverage of the companies. The comparable companies used for this method are 70% from the jewelry and 0 luxury sector and 30% from the fashion market and thus the retail competitors. The 2009 2010 2011 2012 2013 2014 2015 “recipe” of the comparable companies is based on the breakdown of the FF Group’s Weighted GDP index (2009=100), sources: IMF, EU Commission, team calculations revenues. The simple relative valuation implies an exaggerated premium since the FF Gross profit index (2009=100) estimated stock price is 29.14€. However, if we come to the attention of the historical multiples of FF Group during the decade we will consider that the multiples of FF all over the decade are well down of those of the comparable companies. Because of that we cannot presume that FF Group’s share price will reach the price Table 5: Basic multiples – FF comparable given by the simple relative valuation. It is vital we be referred to the fact that many companies an analyst use a valuation method of the stocks based on the historical medians of Multiples P/E P/S EPS the multiples of one company. This method is relies on the assumption that the Folli Follie 6.24 0.81 2.26 multiples of one company will revert to the historical medians of them. The primarily Weighted Average 16.68 2.31 3.18 used multiple for this method is P/S since, sales usually display more steady percentage of change. This method is appropriate to capture the traditional Weighted Median 14.37 2.08 2.01 discount of a stock. In our occasion and prompted by the existence of these not

frequently used method, we have implemented an OLS model to capture the

“traditional” discount of FF multiples. In the simple relative valuation we assume Table 6: Basic multiples – FF comparable that y=x where y is the intrinsic value of the multiples of our company and x the companies (2nd part) EV/EBIT median of the values of the relative companies while in OLS method is assumed that Multiples P/B DA EV/Rev y=a+bx. For this method we used only the EV/Revenue and P/S multiple since only Folli Follie 0.64 4.64 0.92 for these multiples the correlation between each of them with the historical Weighted Average 4.56 10.37 2.00 medians is statistical significant. This method takes into account the behavior of the Weighted Median 3.54 8.82 1.80 investors of FF Group during the decade and the movement of the comparable companies’ multiples. The intrinsic value given from this method is 13.71€. The final intrinsic value of the two methods is 20.04€. The weights of the simple relative Table 7: Simple Relative Valuation method are w1=0.41 and for the OLS method w2=0.59. The formula which is used to measure the weight of the two methods is: Multiples Value Weights 푛 (푑푖 − 푑푖푓)/푑푖 P/E 32.2 15% 푊 = ∑ 1 푛 P/S 35.7 15% 푖=1 푊2 = 1 − 푊1 Price/Book 26.26 20% Where di = Average discount of the decade for each multiple, dif = Projected EV/EBITDA 26.21 25% discount of the multiples for the upcoming period, n = Number of the multiples EV/Revenue 28.62 25% whose correlation is statistical significant. Weighted value 29.14

Valuation Methods Weighting

According to the American Society of Appraisers (ASA) Business Valuation Standards, “in assessing the relative importance of indications of value determined under each method […] the appraiser should consider” (among others) “the quality and reliability of data underlying the indication of value”. In his book Investment Valuation Damodaran (2002) explains that DCF Valuation “is easiest to use for assets Table 8: Linear Regression for P/S (firms) whose cash flows are currently positive and can be estimated with some Intercept (a) -1.8137 reliability for future periods”, while in Equity asset Valuation Pinto et al. (2015) underline that analysts tend to use free cash flow methods of valuation, when free b coefficient 1.2371 cash flows align with profitability within a reasonable forecast period with which the R-squared 0.449241 analyst is comfortable. What is more, we have assumed the reliability of the FCFF forecasts to be assessed through Pearson’s coefficient of correlation (0.6795) between the weighted GDP and the Gross profit indices. Thus, given also that foreign companies have been used in the Relative and Multiples Valuation (which of course Table 9: Linear Regression for are needed to capture the “international” features of the FF Group) we have given EV/Revenue a larger weight of 67.59% to the DCF Valuation and the remaining 32.41% to the Intercept (a) -0.784827 Relative and Multiples Valuation. b coefficient 0.993019 R-squared 0.488385 Investment Risks Political Risk Figure 16: Graph for median and FF’s The Group is likely to keep being negatively influenced by the uncertainty posed R/S ratio during the Greek financial crisis. As of April 2015 Greece has a score of 66 in Political Risk Index (PRI) issued by the PRS Group placing it last among the west European countries, which have a mean of 80. Of course, since that time there have been serious developments. On 12/08/15 and after agreement between Greece and its creditors, PRS Group underlines: ‘Crisis averted, but risks remain’. From then on, given the smoothing of the negotiation process, the implementation of measures proposed by creditors and the Greek banks’ recapitalization foreshadowing the end of capital controls –predicted to take place in the first half of 2016 by L. Katseli, head of the Hellenic Bank Association– the situation may have been progressing. Further, in April 2015 East Asia and the Pacific countries had a mean PRI score of 79. China specifically scored 70, while the IMF has stressed the government’s insufficient initiative for economic reform and the importance of the transition to a more open and market-based economy. At the same time, D. Dollar, senior fellow in the John L. Thornton China Center of the Brookings Institution, has emphasized that “on average, autocratic nations grow faster than democratic ones up to around where China is now, but successful cases democratize at around this level of income per capita.”

Figure 17: Graph for median and FF’s Market risk EV/Revenues ratio i) Interest Rate Risk This risk derives from bond loans, short-term bank loans and leasing contracts of the Group, which are denominated at a floating rate linked to EURIBOR. The total liabilities for bond loans, bank loans and leases as of 30/09/2015 amounted to €343,519,992.12, so, the Group is exposed to a considerable interest rate fluctuation risk. However, the major source of financing is equity, which at that time was €1.483.532.268.62.

ii) Foreign Exchange Risk a) Reduced gross profit margin due to strengthening of the US dollar The risk stems from the group purchasing most of its products in USD prices while sells them in the local currency of each market in prices which are determined several months before their receipt and repayment. Therefore, any possible dollar Figure 18: Investment risks assessment appreciation against local currencies would increase the cost of sales, whereas the increase sale prices would remain unchanged limiting gross profit. Furthermore, a High FXR2 probable USD revaluation in relation to the would increase the Group’s 7 operating expenses since part of its disposal expenses, and mainly royalties, is PR expressed in USD. FXR3CR b) Risk from the conversion of financial statements expressed in foreign PRI FXR1 LR currency

Probability of incedence Probability IR The Group is exposed to the risk from the conversion of the financial statements of Low Medium Low Medium foreign companies which operate in currencies other than Euro and the Group has High… investments in.

c) Revaluation of the euro against other currencies Despite the sale of Hellenic Duty Free Shops to the Swiss Dufry AG €328 million, the Group maintains a strong presence in the travel retail sector on the grounds that Table 10: Risk Handling Measures with €153 million out of €328 million it acquired 1.231.233 shares of Dufry AG. In Risk Mitigating Factors 2014 it further increased its number of shares by 377.200 paying €53 million. Through this strategic partnership with Dufry AG the Group is exposed to the risk of Diversification of its operation in appreciation of the euro against other currencies, such as the Yen, Yuan and US Political both emerging and developped Risk (PR) markets (or in a wider dollar. geographical area)1 d) GARCH Approach Because we are in a period where economic prospects are mutable of high Rise in fluctuation in exchange rates it is urgent to try to describe the volatility in the major Interest Interest rate risk hedging tools Rates (IRS) of them. For this reason we have employed the Garch(1,1) model, which is (IRR) commonly used in finance, in order to test for heteroskedasticity and especially for Call options, forward contracts, Arch and Garch effects. We show that both rates which we have employed in Garch US Dollar futures in usd, swaps and other have statistically significant all of the three coefficients. This means that future price Revaluatio financial market products, of fluctuation depends on previous volatility of residuals and itself. Additional to all, n (FXR1) suppliers from different countries1 this long term volatility stands at 0.15% for USD/CNY and 0.56% for USD/EUR. In a period of high volatility this means that firm may face some extra costs on hedging Conversio n of Foreign exchange risk because of its exposure at high currency risk. Financial setoff products, mostly forward iii) Price risk – Inflation Statement type agreements According to the administration, the Group runs no risk from price fluctuation, since s (FXR2) it does not own a significant securities portfolio and the prices of the products it sells Price risk Investment in short-term – Inflation securities (mostly) with low price do not present particular fluctuations. Thus, the international increase of inflation (PRI) fluctuation1 pressure in combination with the disturbance of the international financial system may modify consuming habits, affecting the group’s sales and profitability. Cooperation with known Credit Risk department stores and use of (CR) credit insurance contracts Credit Risk FF Group cooperates with department stores and franchisees, which they have Preparation of statements of contractual obligations against the Group, hence, there is the risk of breaching these expected future cash flows, obligations on part of the other party. The Group, though, claims to operate most of Liquidity disposal of older stock through Risk (LR) discount outlets, high unused its wholesale activity with known department stores and a set of selected credit limits in short-term bank franchisees mitigating this risk. loans Liquidity risk Evaluation of its older stock at its This risk has had increased importance in the recent years of financial crisis mainly Inventory net realizable value, specialized Risk (IR) due to the shrinkage of most forms of lending. However, the current ratio and the disposal area-markets quick ratio for the Group on 30/9/2015 were 6.86 and 4.57 respectively, which reveals that it maintains high liquidity. This liquidity is supported by the capital Source : Team Analysis structure of the Group, the disposal of older stock through discount outlets, the retail nature of most of its sales and its funding flexibility thanks to high unused

credit limits in short-term bank loans. Table 11: Group's inventory turnover positioning in the industry Inventory risk Apparel and Department This risk emerges from the retaining of old stock from certain companies of the Jewelry accessory stores stores Group and concerns either the incapability to sell it or being able to sell it only in (1) Avg inventory 1.68 4.57 3.87 prices lower than its evaluation. The inventory turnover (IT) is calculated using the turnover (2) Standard average inventory of the past five quarters, so that it is adjusted for seasonality in 0.58 2.13 1.45 deviation the FF Group’s sector: (3)=(2)2 Variance 0.34 4.54 2.10 Cost of goods sold 501.1 Inventory turnover 1/10/2014-30/9/2015   1.2 (4) Group's cost ()Inv30/9/2014 Inv 31/12/2014  Inv 31/3/2015  Inv 30/6/2015  Inv 30/9/2015 416.23 of goods for 2014 356.45 99.01 86.50 5 (in million euros) Based on data for the inventory turnover in the industries of jewelry, department (5)=(4)/sum of (4) 0.66 0.18 0.16 1 Segment weights stores and apparel and weighing using the cost of goods for each segment of the (6)=(1)x(5) 1.10 0.83 0.62 Group, we get an average IT of 2.5575 with standard deviation of 0.592 (assuming (7)=(3)X(5)2 0.15 0.15 0.05 no correlation between the three industries’ ITs), which (assuming normal Group's expected inventory turnover, sum of (6) 2.56 distribution) place the Group in the lowest 1.09% percentile. In order to manage this Standard deviation for group's 0.59 risk the Group offers its stock in area-markets such as: Outlet type discount inventory turnover, sum of (7) department stores, discount outlets and large hotel units. The Group claims to fully Real group's inventory turnover 1.20 cover the inventory risk through the evaluation of its stock at its net realizable value Percentile (normal distribution) 1.09% relying on the administration experience and actual market data.

1Gaur, Fisher and Raman (2005). An Econometric Analysis of Inventory Turnover Performance in Retail Services. Management Science, 51(2), pp.181-194.

Figure 19: Silver Price ($ per ounce) Financial Analysis 35 30 Outlook 25 The beginning year (2013) does not give us the ideal perception of the financial 20 condition of FF, since a major part of the net income derived from the 15 disinvestment of HDF (Hellenic Duty Free). Consequently, the formed ratios for this 10 5 period, which contain the net income measure, do not mirror the financial position 0 of FF. However, according to the results of 2014 and the financial projection for the upcoming years, FF shows increased liquidity and stable profitability (R.O.E display small decline in the future). The radical restructure of the group, which befell with the disposal of HDF, did not have negative impact on FF’s financial condition. R.O.E’s proposed CAGR for the period 2014-2020 is -1,41% and R.O.A’s Source: Yahoo Finance estimated CAGR is -0,11% (insubstantial).

Key financial ratios of the FF Group 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Figure 20:Gold Prices Liquidity ($ per ounce) Current Ratio 3.16 5.24 6.62 6.53 6.28 6.37 6.08 5.69 2000 Cash Ratio 0.78 1.14 0.94 0.95 0.96 1.19 1.19 1.14 1500 Quick Ratio 2.38 3.83 4.52 4.39 4.13 4.24 4.00 3.74 1000 Profitability 500 Gross Profit (%) 50.38 50.27 46.75 46.40 46.04 45.69 45.35 45.00 0 EBITDA margin (%) 20.84 22.34 19.85 19.27 19.49 19.37 19.05 18.59 Net Income margin (NI) (%) 37.20 14.57 13.04 13.37 13.24 12.80 12.70 12.25

Net Income Matgin/EBITDA 178.4 (%) 9 65.22 65.70 69.39 67.91 66.07 66.68 65.89 Operating Income Margin (%) 18.56 20.27 17.51 17.16 17.40 17.31 17.04 16.63 Source: Yahoo Finance Return on Assets (R.O.A) (%) 22.11 7.39 7.50 7.98 7.94 7.64 7.60 7.43

Return on Equity (R.O.E) (%) 29.36 10.69 10.31 10.79 10.70 10.18 10.07 9.81 Financial Leverage Long Term Debt to Assets 0.04 0.18 0.17 0.15 0.15 0.14 0.13 0.12 Figure 21:Selling Perfomance and Profitability of R&W and Long Term Debt to Equity 0.05 0.26 0.23 0.21 0.20 0.18 0.17 0.15 Dep. Stores Debt to Equity 0.33 0.45 0.38 0.35 0.35 0.33 0.32 0.32 16,00% 180 Capitalization Ratio 0.05 0.20 0.19 0.17 0.16 0.15 0.14 0.13 Financial Leverage 1.15 1.08 1.07 1.05 1.04 1.04 1.03 1.03 160 12,00% Reve Interest Coverage 15.47 9.29 11.65 16.40 19.43 21.65 24.20 26.31 140 nues Activity 8,00% R&W Total Assets Turnover 0.59 0.51 0.57 0.60 0.60 0.60 0.60 0.61 120 Reve Fixed Assets Turnover 3.72 3.81 4.59 4.67 4.63 4.63 5.00 4.57 4,00% 100 nues Inventory Turnover 1.47 1.60 1.54 1.44 1.42 1.40 1.32 1.40 Dep. Accounts Receivable St 0,00% 80 EBIT Turnover 0.76 0.76 0.70 0.66 0.63 0.62 0.59 0.58 DA M EBITDA EBITDA Margin% 60 DuPont analysis -4,00% R&W R.O.E= Profit margin (Profit/Sales)*Total Assets Turnover (Sales/Assets)*Equity 40 Multiplier or financial leverage ratio (Assets/Equity). -8,00% 20 Decomposed dupont analysis for calculating return on equity (R.O.E)

-12,00% 0 Taking into account the period from 2014 to 2015, R.O.E presents a slight decline according to our projections. For the mid-term period of 2016-2017, R.O.E’s value is Source: FF Group’s financial statements enhanced while for the years of the longer term period 2019 and 2020 the R.O.E is decreased, taking values <10% for the long term period. The profit margin shows a moderate decline in annual base. Taking into account the period from 2014 to 2015, R.O.E presents a slight decline according to our projections. Taking into account the period from 2014 to 2015, R.O.E presents a slight decline according to our projection

For the mid-term period of 2016-2017, R.O.E’s value is enhanced while for the years of the longer term period 2019 and 2020 the R.O.E is decreased, taking values <10% for the long term period. The profit margin shows a moderate decline in annual base. The total assets turnover is increased about 10% percentage points from 2014 to 2020. The main reason of this phenomenon is that 2014 was a year of reformation of its capital structure. The following years, total assets turnover value will be augmented and approach those of 2011 and 2012 (59,25% and 61,13% respectively), since FF could take advantage of its established mass distribution system and economy of scales. The financial leverage, explained with the Equity Multiplier (Assets/Equity), is enhanced in 2014 but, according to our estimation, it will follow a downward course the following years. Equity Total Assets DuPont Profit Margin Multiplier Turnover (Profit/Sales) (Assets/Equit R.O.E Analysis (Sales/Assets) y) 2013 37.20% 59.44% 1.3282 29.36% 2014 14.57% 50.69% 1.4468 10.69% 2015E 13.04% 57.48% 1.3754 10.31% 2016E 13.37% 59.65% 1.3528 10.79%

2017E 13.24% 59.96% 1.3479 10.70% 2018E 12.80% 59.75% 1.3316 10.18% 2019E 12.70% 59.87% 1.3242 10.07% 2020E 12.25% 60.67% 1.3203 9.81% Suppressed silver and gold prices during the last years

During the last 3 years the prices of silver and gold has shown a substantial decline. Silver prices, which is the main raw material used by the FF Group (the vast majority of jewelries consist of silver which is gold-plated or overlaid with others metals), fell down from almost 30 dollar per ounce to 14 dollar per ounce. The similar down-trend is observed for Gold prices for the same period. The relatively low metal prices comprise an opportunity for jewelry sector to increase the gross margin for this activity, especially for Links which aims at a higher-end segment. However, may an analyst insist that the instability of global economy stemmed from the slowing growth of the emerging markets, will lead to strengthened demand for the safe investment in gold. According to this scenario, the high-correlated silver prices to gold movements will be going up, putting pressure at FF Groups profitability margin. Despite the negative consuming ambience the department stores and retail & wholesale sectors showed steady growth.

The difficult financial situation in Greece has little impact on these two activities. The negative outcomes of 2011 and 2012 have been overcome and consecutive years have been sealed with profitability and growth. The successful positioning of these two segments led to lion share in the Greek fashion market. The pessimism, which inundated Greek consumers, over the years of the financial crisis (CCI= -61,1 Dec. 2015) profoundly affect the Greek fashion market, but the products distributed by FF’s department stores and the Retail & Wholesale segment has maintained their position at Greek consuming preferences. We have to say that despite the economic turmoil in Greece, FF managed to redeem EBITDA margins approximately 8% for department stores and 10%for Retail & Wholesale, while the revenues for R&WH has an estimated CAGR=17,05% and for Dep. St. CAGR=11,17%1.

Technical analysis approach Using a 200-day SMA, a dual SMA (9- and 18-day), MACD and RSI to generate buy signals for FF’s stock during the period 02/02/2008-02/02/2016), we notice that RSI has yielded by far the largest and the only statistically significant returns at the 0.05 level. Second comes the 200-day SMA, while the dual SMA and MACD have produced lower returns (similar for the two methods). Of course, there are two sides to every coin; we see that the returns of the dual SMA and MACD have considerably lower standard deviation of 15.25% and 12.14% respectively, while for the 200-day SMA the standard deviation is 68% and for the RSI an immense 91.47%. Bearing that in mind, we conclude that the seemingly everlasting trade-off between return and risk is still evident in our case. Even though RSI has performed significantly better (and the 200-day SMA has also yielded better returns), that comes with a risk a potential speculator has to keep in mind; they need to be aware that using the RSI may yield very high returns in the long run, but also serious losses in the meantime meaning that -among others- they should be ready to deal with liquidity issues.

1 For the CAGR’s Calculations are used the trailing revenues of the final Quarter of 2015 as ending value and the estimated revenues 9months revenues of 2012 plus those of the final Quarter of 2011 as the beginning value. Appendix A: Organizational chart

FF Group‘s organizational structure follows perfectly the geographical structure. The company is organized based on the countries (, Spain, UK, Romania, etc) and states where it operates, or groups of companies with many subsidiaries around the world (Links of London). However the vertical hierarchical structure is obvious, so every group of companies or subsidiaries companies from different countries, are accountable to the mother company. Also through the organizational chart we derive data on the percentage of Mother Company’s participation to the rest of the firms. Generally this type of structure is common in companies with strong presence in many geographical areas, especially when company’s direct contact to the local markets and climates is necessary and adopts the principle “Think global, act local”.

Appendix B: FF Group Key Executives

Board of Members Director Position Affiliates/Other work Tenure

Dimitris Chairman,  Managing Director & General Since Koutsolioutsos Executive Manager Folli-Follie SA 19-1-2011 Member  Board of Directors at Elmec Sport SA and Hellenic Duty Free Shops SA Ketty Vice Chairman,  Executive Director Folli Follie SA Since Koutsolioutsos Executive  Executive Director Hellenic Duty Free 19-1-2011 Member Shops SA.

George Managing  Vice President of Folli Follie SA Since Koutsolioutsos Director,  President of Seanergy Maritime 19-1-2011 Executive Holdings Corp Member  Chairman of Hellenic Duty Free Shops S.A  Chairman of Elmec Sport SA  Chairman of the Board of Folli Follie SA  Co-Chairman of Seanergy Maritime Holdings Corp.  Director Of Folli Follie Japan Ltd, Folli Follie Group And Dufry AG  Director if Hellenic Duty Free Shops S.A.

Emmanouel Deputy Managing  Chief Executive Officer of Elmec Sport Since Zachariou Director & SA 19-1-2011 General Director,  Vice Chairman & Commercial

Executive Manager of Sportsman SA Member  Vice Chairman & General Manager Alouette SASU George Aronis Independent,  Executive Director & GM-Retail Since Non-Executive Banking SA 19-1-2011 Member  Chairman Alpha Insurance Agents SA

 Board of Directors at Alpha Bank SA, ALBA Graduate Business School, The Hellenic Ombudsman for Banking- Investment Services, Alpha Life KK  Vice Chairman Alpha Asset Management A.E.D.A.K  General Manager-Retail Banking SA  General Manager-Consumer Banking ABN AMRO Bank  Non-Executive Director Hellenic Duty Free Shops SA Epaminondas Independent,  Managing Director AGET Heracles Since Dafermos Non-Executive  Executive Director & Deputy 19-1-2011

Member Managing Director at Hellenic Duty Free Shops SA Ilias Non-Executive  General Manager Attika AE Since Koukoutas Member  Managing Director at North Landmark 19-1-2011 SA

 Board of Directors at Hellenic Retail Business Association  Executive Director Elmec Sport SA Ilias Non Executive  Independent Director at Seanergy Since Kouloukountis Member Maritime Holdings Corp. 19-1-2011  Chief Executive Officer & Director at Equity Shipping Co. Ltd.  Board of Directors at Seanergy Maritime Holdings Corp.  Board Of Directors at Hellenic Duty Free Shops SA  Board of Directors at Equity Shipping Co. Ltd.  Chief Executive Officer & Director by Naval Engineering Dynamics Ltd  Chief Executive Officer & Director by Off Shore Consultants, Inc.  Board Of Directors at Kassian Maritime Shipping Agency Ltd., Kassos Maritime Enterprises Ltd. , Point Clear Navigation Agency Ltd Zacharias Non-Executive  Independent Non-Executive Director Since Mantzavinos Member by Folli-Follie SA 19-1-2011  Vice Chairman of Hellenic Duty Free Shops SA Irene Nioti Executive  Treasurer at Folli-Follie SA Since Member  Non-Executive Director Hellenic Duty 19-1-2011 Free Shops SA

Anna Marina Non-Executive  Lawyer for Corporate Law Since Xirokosta Member 7-5-2013

Ioannis Non-Executive  Board of Directors at Aspropirgos Since Tsigkounakis Member Maritime Ltd. and Redina Maritime 7-5-2013 Ltd.  Non-Executive Director by Hellenic Duty Free Shops SA. Jiannong Qian Non-Executive  Chairman at Shanghai Fosun High Since Member Technology (Group) Co. Ltd. 26-5-2011  Board of Directors at Club

Méditerranée SA and Osborne Group  Chief Executive Officer of China Nepstar Chain Drugstore Ltd.  Vice President China of OBI Bau & Heimwerkermärkte GmbH & Co. Franchise Center KG  Board of Directors at Shanghai Yuyuan

Tourist Mart Co. Ltd.  Senior Manager of Food Purchase Department at Metro AG  Senior Manager of Weixing Company Group

Corporate Officers

Officer Position Affiliates Fragiskos Gratsonis Chief Financial Officer  Senior Banker at Emporiki Bank, Credit Agricole Group  Senior Relationship Manager at BNP Paribas  Senior Manager at Emporiki Investment Bank  Relationship Manager at Citibank Nana Vlahou Director-Human  Department of Human Resources at Resources Folli Follie Georgios Alavanos Chief Accountant  Head-Accounts Department by Folli- Follie SA. Elias Dimitrakopoulos Director-Internal Audit  Audit Department at Folli Follie Department Nikos Anamouroglou Investor Relations Officer  Head-Investor Relations at Elmec Sport SA George Vlachos Chief of Strategy and  Advisory Board Member at Hellia Co. Organization  Managing Director South East & Development Officer Eastern Europe/Middle East & Africa  General Manager at Mattel Greece, Cyprus, Bulgaria  Regional Sales Director at Mattel Greece, Cyprus & European Sales Executive Board  Sales manager at Matte Greece  Key Account Manager at Matte Greece  Area Sales Supervisor at Danone  Hotel Manager at Palmyra Hotel

FFG Committees, Source: Compan Website, Capital Audit Committee Title Zacharias Mantzavinos Chairman Epaminondas Dafermos Member George Aronis Member Internal Audit Department Title Mr. Dimitrakopoulos Head of Department Mr. Makris Member Mrs. Antonia Stavropoulou Member

Appendix C: Folli Follie History

Dates Events 1982 Foundation of Folli Follie in Greece. Opening of the first store 1994 Launch of the Folli Follie women’s watch collection 1995 Japanese market entry and shop openings in New York, Hawaii, 1996 Launch of the Folli Follie women’s accessories collection 1997 Company listing at the Athens Stock Exchange 1998 Entry in key Asian markets. Launch of the Folli Follie accessories collection 1999 Folli Follie subsidiaries in France and UK 2000 Acquisition of 40% of the Japanese distribution operation 2002 Entering the Spanish and Chinese markets and the development of Travel Retail business 2003 Purchase of 20% of Hellenic Duty Free Shops (HDFS) 2006 HDFS acquisition of Links of London (July). Obtainment of the Chinese retail license (November). Launch of Folli Follie Baby (December). 2007 Entering , Opening of the first Links of London store in Athens. Acquisition of Elmec Sport via HDFS 2008 Acquisition of Folli Follie’s affiliate in Japan 2010 Merger of the companies Folli Follie S.A., HELLENIC DUTY FREE SHOPS S.A. and Elmec Sport S.A. Creation of Folli Follie Group. 2011 Fosun International acquires a stake of 9,5% in FF Group 2012 The FF Group gains the exclusive distribution and representation of PROCTER & GAMBLE PRESTIGE perfumes in Greece 2013 Sale of the 51% stake of the travel retail business to Dufry AG. In December, the Group announced the sale of the remaining 49% of the travel retail business to Dufry AG and enters as a strategic investor to Dufry AG 2014 FF Group announces exclusive wholesale and retail distribution rights for the Juicy Couture brand in all Continental Europe, UK, Ireland and Cyprus

Source: company Website

Appendix D: Historical and forecasted financial statements Balance Sheet In Million € Historical Forecasted 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Assets Non current assets Tangible assets 232.03 233.19 240.10 175.30 185.78 190.45 205.51 224.87 242.60 267.72 292.11 Intagible assets 105.46 103.73 99.60 11.94 11.69 41.48 49.75 66.67 87.18 103.87 121.73 Investment property 74.85 73.80 72.86 76.05 76.04 75.21 75.28 77.89 81.35 85.22 89.38 Goodwill 252.83 252.82 252.77 91.87 94.54 94.69 105.72 121.94 102.22 107.01 112.17 Investment availiable for sale 0.49 0.40 0.62 153.75 207.16 185.69 177.89 149.85 138.15 127.74 118.27 Deffered tax claims 12.54 12.54 22.63 3.48 0.00 ------Other long term assets 27.27 37.07 31.31 34.87 29.85 40.79 44.21 49.53 56.02 63.54 72.16 Total non-current assets 705.47 713.55 719.89 547.26 605.06 628.31 658.36 690.76 707.52 755.10 805.81

Current assets Inventories 296.95 339.17 377.61 254.82 366.56 474.28 504.91 562.89 602.29 663.06 711.30 Trade receivables 335.07 399.45 445.54 390.40 533.81 531.85 538.67 555.62 567.97 585.75 602.99 Derivatives 0.29 0.06 0 0.02 0.37 0.26 0.25 0.26 0.27 0.29 0.31 Other financial assets at fair value 0.38 0.07 0.04 0.03 0.15 23.48 18.90 16.73 14.46 12.97 11.40 Other current assets 110.85 136.11 146.28 127.66 165.97 250.19 253.45 260.77 280.88 301.72 329.82 Cash and cash equivalents 133.76 135.50 126.48 251.59 297.03 211.78 225.06 250.67 338.50 380.00 412.69 Total current assets 877.30 1010.36 1095.95 1024.52 1363.89 1491.84 1541.24 1646.96 1804.38 1943.80 2068.51 Total Assets 1582.77 1723.91 1815.84 1571.78 1968.95 2120.15 2199.60 2337.71 2511.90 2698.90 2874.32 Growth % 0.09 0.05 -0.13 0.25 0.08 0.04 0.06 0.07 0.07 0.06

Equity Share Capital 18.18 20.08 20.09 20.08 20.08 20.08 20.08 20.08 20.08 20.08 20.08 Share Premium account 62.53 145.21 145.21 145.21 95.00 81.73 83.37 89.85 98.19 107.99 116.84 Other Reserves -12.92 -22.92 -13.42 47.74 291.69 259.44 264.49 267.57 289.03 301.28 318.10 Other Equity -124.14 -95.72 -114.56 -130.65 -38.01 36.22 43.95 55.68 70.84 79.98 111.70 Retained earnigs 585.53 674.73 768.22 1077.76 965.30 1113.91 1180.64 1262.33 1362.88 1474.11 1542.24 Minority Interest 15.28 18.37 20.41 23.29 26.80 30.08 33.45 38.84 45.29 54.73 67.98 Total Equity 544.46 739.75 825.95 1183.43 1360.86 1541.45 1625.97 1734.35 1886.32 2038.17 2176.95 Growth % 0.36 0.12 0.43 0.15 0.13 0.05 0.07 0.09 0.08 0.07

Liabilities Long Term Liabilities Long Term Borrowings 649.43 314.66 428.83 35.90 304.34 312.45 298.78 301.36 302.41 301.24 295.30 Deferred Tax Liabilities 20.84 30.92 36.11 12.76 19.01 20.23 22.09 22.28 22.36 22.28 21.84 Provisions/Other long term Liabilities 48.12 49.94 40.42 15.75 24.90 20.81 16.85 17.30 17.36 17.30 16.95 Total Long Term Liabilities 718.39 395.52 505.36 64.41 348.25 353.49 337.72 340.94 342.13 340.82 334.10

Short Term Liabilities Short Term Borrowings 136.62 417.25 312.24 186.64 46.79 35.73 52.05 73.59 90.81 124.81 168.80 Trade and Other Payables 136.25 154.02 152.29 120.25 181.87 168.56 162.98 166.82 169.57 171.11 169.91 Current income tax 13.06 8.55 15.04 11.46 26.83 16.51 16.46 17.35 18.17 18.89 19.33 Current tax liabilities 6.67 8.74 4.90 5.57 5.00 4.41 4.41 4.66 4.90 5.11 5.25 Derivatives- Dividends payable 0.32 0.08 0.06 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total Short Term Liabilities 292.92 588.64 484.53 323.94 260.49 225.20 235.90 262.42 283.45 319.92 363.28 Total Equity and Liabilities 1555.77 1723.91 1815.84 1571.78 1969.60 2120.15 2199.60 2337.71 2511.90 2698.90 2874.32 Growth % 0.11 0.05 -0.13 0.25 0.08 0.04 0.06 0.07 0.07 0.06 Note: Projections for 2015 were made using the 9-month data for the FF Group.

Common-Size Balance Sheet Historical Forecasted 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Assets Non current assets Tangible assets 14.66% 13.53% 13.22% 11.15% 9.44% 8.98% 9.34% 9.62% 9.66% 9.92% 10.16% Intagible assets 6.66% 6.02% 5.49% 0.76% 0.59% 1.96% 2.26% 2.85% 3.47% 3.85% 4.24% Investment property 4.73% 4.28% 4.01% 4.84% 3.86% 3.55% 3.42% 3.33% 3.24% 3.16% 3.11% Goodwill 15.97% 14.67% 13.92% 5.84% 4.80% 4.47% 4.81% 5.22% 4.07% 3.97% 3.90% Investment availiable for sale 0.03% 0.02% 0.03% 9.78% 10.52% 8.76% 8.09% 6.41% 5.50% 4.73% 4.11% Deffered tax claims 0.79% 0.73% 1.25% 0.22% 0.00% ------Other long term assets 1.72% 2.15% 1.72% 2.22% 1.52% 1.92% 2.01% 2.12% 2.23% 2.35% 2.51% Total non-current assets 44.57% 41.39% 39.65% 34.82% 30.73% 29.64% 29.93% 29.55% 28.17% 27.98% 28.03%

Current assets Inventories 18.76% 19.67% 20.80% 16.21% 18.62% 22.37% 22.95% 24.08% 23.98% 24.57% 24.75% Trade receivables 21.17% 23.17% 24.54% 24.84% 27.11% 25.09% 24.49% 23.77% 22.61% 21.70% 20.98% Derivatives 0.02% 0.00% 0.00% 0.00% 0.02% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% Other financial assets at fair value 0.02% 0.00% 0.00% 0.00% 0.01% 1.11% 0.86% 0.72% 0.58% 0.48% 0.40% Other current assets 7.00% 7.90% 8.06% 8.12% 8.43% 11.80% 11.52% 11.16% 11.18% 11.18% 11.47% Cash and cash equivalents 8.45% 7.86% 6.97% 16.01% 15.09% 9.99% 10.23% 10.72% 13.48% 14.08% 14.36% Total current assets 55.43% 58.61% 60.35% 65.18% 69.27% 70.36% 70.07% 70.45% 71.83% 72.02% 71.97% Total Assets 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Equity Share Capital 1.17% 1.16% 1.11% 1.28% 1.02% 0.95% 0.91% 0.86% 0.80% 0.74% 0.70% Share Premium account 4.02% 8.42% 8.00% 9.24% 4.82% 3.85% 3.79% 3.84% 3.91% 4.00% 4.07% Other Reserves -0.83% -1.33% -0.74% 3.04% 14.81% 12.24% 12.02% 11.45% 11.51% 11.16% 11.07% Other Equity -7.98% -5.55% -6.31% -8.31% -1.93% 1.71% 2.00% 2.38% 2.82% 2.96% 3.89% Retained earnigs 37.64% 39.14% 42.31% 68.57% 49.01% 52.54% 53.68% 54.00% 54.26% 54.62% 53.66% Minority Interest 0.98% 1.07% 1.12% 1.48% 1.36% 1.42% 1.52% 1.66% 1.80% 2.03% 2.37%

Total Equity 35.00% 42.91% 45.49% 75.29% 69.09% 72.71% 73.92% 74.19% 75.10% 75.52% 75.74%

Liabilities Long Term Liabilities Long Term Borrowings 41.74% 18.25% 23.62% 2.28% 15.45% 14.74% 13.58% 12.89% 12.04% 11.16% 10.27% Deferred Tax Liabilities 1.34% 1.79% 1.99% 0.81% 0.97% 0.95% 1.00% 0.95% 0.89% 0.83% 0.76% Provisions/Other long term Liabilities 3.09% 2.90% 2.23% 1.00% 1.26% 0.98% 0.77% 0.74% 0.69% 0.64% 0.59% Total Long Term Liabilities 46.18% 22.94% 27.83% 4.10% 17.68% 16.67% 15.35% 14.58% 13.62% 12.63% 11.62%

Short Term Liabilities Short Term Borrowings 8.78% 24.20% 17.20% 11.87% 2.38% 1.69% 2.37% 3.15% 3.62% 4.62% 5.87% Trade and Other Payables 8.76% 8.93% 8.39% 7.65% 9.23% 7.95% 7.41% 7.14% 6.75% 6.34% 5.91% Current income tax 0.84% 0.50% 0.83% 0.73% 1.36% 0.78% 0.75% 0.74% 0.72% 0.70% 0.67% Current tax liabilities 0.43% 0.51% 0.27% 0.35% 0.25% 0.21% 0.20% 0.20% 0.20% 0.19% 0.18% Derivatives- Dividends payable 0.02% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Total Short Term Liabilities 18.83% 34.15% 26.68% 20.61% 13.23% 10.62% 10.72% 11.23% 11.28% 11.85% 12.64% Total Equity and Liabilities 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Statement of comprehensive income In Million € Historical Forecasted 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f

Total 989.60 1021.42 1110.03 934.23 998.06 1218.66 1312.11 1401.64 1500.78 1615.72 1744.00 Revenue(Turnover) Cost of goods -491.10 -504.90 -553.17 -463.55 -496.30 -648.91 -703.34 -756.28 -815.03 -883.07 -959.20

Gross profit 498.51 516.52 556.86 470.68 501.72 569.75 608.77 645.36 685.75 732.65 784.80

Growth % 0.04 0.08 -0.15 0.07 0.14 0.07 0.06 0.06 0.07 0.07

Other operating 33.06 26.42 32.54 13.08 11.68 11.95 16.09 17.08 18.29 19.63 21.24 income Administration -55.53 -56.62 -73.60 -59.09 -56.95 -78.47 -78.90 -83.73 -89.64 -96.24 -104.12 expenses Selling expenses -297.81 -305.68 -313.81 -241.34 -242.30 -272.23 -305.49 -318.58 -337.13 -361.95 -391.60

Other operating -6.55 -6.67 -16.21 -9.94 -11.82 -17.63 -15.37 -16.31 -17.47 -18.75 -20.29 expenses Operating income 171.67 173.98 185.78 173.40 202.33 213.37 225.11 243.82 259.80 275.34 290.03 (EBIT) Growth % 0.01 0.07 -0.07 0.17 0.05 0.06 0.08 0.07 0.06 0.05

Financial income 23.58 15.57 3.91 563.42 26.42 15.55 17.28 23.24 15.07 16.89 13.02

Financial expenses -70.54 -67.61 -58.70 -339.18 -35.66 -20.99 -16.47 -14.70 -13.64 -13.05 -12.49

Investments in 0.00 0.00 -0.09 -0.03 -0.31 -0.33 -0.35 -0.36 -0.42 -0.42 -0.42 Associates Profit/Loss (before 124.71 121.94 130.89 397.61 192.77 207.60 225.57 252.00 260.81 278.76 290.14 the tax) Growth % -0.02 0.07 2.04 -0.52 0.08 0.09 0.12 0.03 0.07 0.04

Income tax -39.61 -30.65 -35.27 -50.11 -47.36 -48.66 -59.48 -66.45 -68.78 -73.51 -76.51

Profit/Loss (after 85.10 91.29 95.62 347.50 145.41 158.94 175.47 185.55 192.03 205.25 213.63 the tax) Growth % 0.07 0.05 2.63 -0.58 0.09 0.10 0.06 0.03 0.07 0.04

Depreciation & 21.67 24.77 27.04 21.29 20.64 28.54 27.78 29.41 30.87 32.47 34.20 amortization Profit before taxes depreciation & 193.35 198.75 212.82 194.69 222.97 241.91 252.89 273.23 290.67 307.81 324.23 amortisation (EBITDA) Growth % 0.03 0.07 -0.09 0.15 0.08 0.05 0.08 0.06 0.06 0.05

Note: Projections for 2015 were made using the 9-month data for the FF Group

Common-Size Statement of comprehensive income Historical Forecasted 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Revenue(Turnover) ------49.62% Cost of goods 49.63% 49.43% 49.83% 49.73% 53.25% 53.60% 53.96% 54.31% 54.65% 55.00% Gross profit 50.37% 50.57% 50.17% 50.38% 50.27% 46.75% 46.40% 46.04% 45.69% 45.35% 45.00%

Other operating income 3.34% 2.59% 2.93% 1.40% 1.17% 0.98% 1.23% 1.22% 1.22% 1.21% 1.22% Administration -5.61% -5.54% -6.63% -6.32% -5.71% -6.44% -6.01% -5.97% -5.97% -5.96% -5.97% expenses ------25.83% Selling expenses 30.09% 29.93% 28.27% 24.28% 22.34% 23.28% 22.73% 22.46% 22.40% 22.45% Other operating -0.66% -0.65% -1.46% -1.06% -1.18% -1.45% -1.17% -1.16% -1.16% -1.16% -1.16% expenses Operating income 17.35% 17.03% 16.74% 18.56% 20.27% 17.51% 17.16% 17.40% 17.31% 17.04% 16.63% (EBIT)

Financial income 2.38% 1.52% 0.35% 60.31% 2.65% 1.28% 1.32% 1.66% 1.00% 1.05% 0.75% Financial expenses -7.13% -6.62% -5.29% -36.31% -3.57% -1.72% -1.26% -1.05% -0.91% -0.81% -0.72% Investments in 0.00% 0.00% -0.01% 0.00% -0.03% -0.03% -0.03% -0.03% -0.03% -0.03% -0.02% Associates Profit/Loss (before the 12.60% 11.94% 11.79% 42.56% 19.31% 17.04% 17.19% 17.98% 17.38% 17.25% 16.64% tax)

Income tax -4.00% -3.00% -3.18% -5.36% -4.74% -3.99% -4.53% -4.74% -4.58% -4.55% -4.39% Profit/Loss (after the 8.60% 8.94% 8.61% 37.20% 14.57% 13.04% 13.37% 13.24% 12.80% 12.70% 12.25% tax)

Depreciation & 2.19% 2.43% 2.44% 2.28% 2.07% 2.34% 2.12% 2.10% 2.06% 2.01% 1.96% amortization

Profit before taxes 19.54% 19.46% 19.17% 20.84% 22.34% 19.85% 19.27% 19.49% 19.37% 19.05% 18.59% depreciation & amortisation (EBITDA)

Appendix E: Corporate Governance

In order to analyze the level of FFG‘s corporate governance and risk, the Institutional Shareholder Service Rating Methodology was used. Below we are rating the corporate governance:

Board of Directors: High - The structure of the Board of Directors is rather the most significant governance risk for the investors. The members are experienced professionals with long-lasting board membership, difficulty manipulated. In terms of independence, unawareness about whether the members of the board are acting in the interest of shareholders or company management creates noticeable risk, although the ideal number of members.

Transparency & Information Sharing: Low - Company’s Management provides quarterly financial reports, financial highlights, result presentations and interim reports. Stakeholders can also be updated through the annual reports of the company and its subsidiaries. Investors can be also informed about acquisition targets and potential expansions. The strong investor relations site enables investors acquire information since 2002.

Executive Management: Low - FFG management team has guided FFG through many difficulties, like the global economic crisis and especially in Greece, liquidity and financial issues and the enforcement of bail out programs on Greece, into continuous profitability and sales increase over the last 20 years. Giving development first priority, management creates value for the shareholders.

Takeover Defense: Medium - FFG requires super majority vote from the shareholders in order to proceed to any acquisition. Thus the risk of takeover from a competitor is abated.

Furthermore, we quote Institutional Shareholder Services (ISS) Rating, which comes in agreement with our assessment. Criteria Risk Board Structure Extremely High 10 Shareholder Rights Medium 6 Compensation Medium 5

Audit & Risk Oversight Extremely High 10

FFG Rating HIGH Governance Risk 9

*1 indicates lower governance risk, while a 10 indicates higher governance risk

Appendix F: Investment Risks Risk Handling Risk Mitigating Factors

Political Risk (PR) Diversification of its operation in both emerging and developped markets (or in a wider geographical area)1 Rise in Interest Interest rate risk hedging tools (IRS) Rates (IRR)

Call options, forward contracts, futures in US Dollar usd, swaps and other financial market Revaluation (FXR1) products, suppliers from different countries1

Conversion of Foreign exchange risk Financial setoff products, mostly forward type Statements (FXR2) agreements

Price risk – Inflation (PRI) Investment in short-term securities (mostly) with low price fluctuation1 Cooperation with known department Credit Risk (CR) stores and use of credit insurance contracts

Liquidity Risk (LR) Preparation of statements of expected future cash flows, disposal of older stock through discount outlets, high unused credit limits in short-term bank loans

Inventory Risk (IR) Evaluation of its older stock at its net realizable value, specialized disposal area-markets Source : Team Analysis

Note: The mitigation of the revaluation of the euro (FXR3) concerns the management of Dufry, and therefore it is not presented here

1 These confrontation factors are proposed by the team (whereas the rest are already used by FF Group)

Investment risks assessment High

FXR2

7

PR

Probability of incedence CR

FXR3 Low Medium Low PRI FXR1 LR

IR Low Medium High Possible Impact Notes: 1. The probability and impact assessment of the risks is comparative (e.g. high probability means higher than the other risk’s probability, not 80% or 90% probability) 2. Bubble sizes are according to the product of probability and impact (i.e. the expected impact)

Appendix G: SWOT Analysis

Strengths Weaknesses

 Brand awareness in Europe & Asia  Increased Competition

 Strong existing distribution and sales networks  Low Investment in R&D

 Leading travel retailer in Greece  Very High penetration in Greek market already  Strong presence in Chinese market  Weak online presence

 Right for selling duty free and duty paid  Improvement margins on Corporate products till 2048 Governance

 Attractive mix product: jewelry, watches > 90 % of total sales

Opportunities Threats

 Improved Online market / e-retailing  Intense competition

 Emerging markets / global markets  Weak economy

 New Products  Exchange rate volatility

 Growth Rates , Profitability  Volatile costs

 New Acquisitions  Increasing competition in China from local players  Financial capacity

 Unfavorable Greek tax system

The SWOT analysis is used to analyze the internal and external environment of FF Group and comprehend better its market position. The factors of each category will be ranked, based on their likelihood, importance and level of strength, weakness, opportunity or threat respectively (1 less important or likelihood, 3 most important or greater likelihood). With the aid of this further analysis it is easier to emphasize on the most important factors and ponder the overall position of FF Group.

Appendix H: Porter’s Five Forces Analysis

Porter's 5 Forces Analysis Industry Rivalry & Ranking Competition 5 4 0 No Threat 3 Bargaining Bargaining 1 Insignificant Threat Power of 2 Power Of Customers 1 Suppliers 2 Low Threat 0 3 Moderate Threat 4 Significant Threat Threats of Threat of 5 High Threat New Entrants Substitutes

Intensity of Competitive Rivalry: Usually the most powerful of the 5 forces of competition and so it is in jewelry industry too. It is characterized from the competitive pressures which are generated from the companies’ will to achieve a greater competitive advantage in the market. FF Group competes in many different geographic areas against powerful competitors. There is a multitude of reason that contributes to this intense rivalry’s creation. A significant factor is the existence of many competitors in the same industry, which provides customers with many different options and subsequently leads to the increase of the competition between the interested companies. Apart from the strength of the competitors, leading role has the competitors’ products’ diversity and range. On the other hand customer loyalty is ambiguous: if there is high customer loyalty companies have to be more and more effective and competitive either in order to satisfy them and preserve its market share either to attract consumers of other brands, while if there is low customer loyalty companies have to strongly compete each other to convince the customers acquire their goods instead of competitors’. High fixed costs constitute a great proportion of the total cost, so companies try to utilize their production capacity. Lastly jewelry industry has some exit barriers. Entry in this sector requires high initial investment, so companies keep competing each other and being in the sector even if the capital returns are not the expected ones. Bargaining Power of Customers: This force is moderate to significant. Because of the form of the sales, which are mostly retail sales the bargaining power of the customers is not high enough because they do not get large quantities so they can affect the price greatly. However, due to the financial crisis all over the years they can negotiate and earn a better price or a discount. Also customers have the ability to satisfy their needs through many different brands and a great variety of products. The technology development and the fact that they are continuously updated about the demand and the prices give them great flexibility. Last but not least, buyers have no personal cost if they choose to switch to other brands. Bargaining Power of Suppliers: This force is relatively low. Suppliers do not have strong bargaining power, because there are many substitutes for the basic products and the company can make the procurement from another supplier. FF Group has a big number of suppliers, the products are mostly similar, consequently are easily substituted. Furthermore, FF Group possesses many distribution networks, sales networks and branches and composes a large proportion of its suppliers’ revenue. Threat of Substitutes: In this specific industry the threat of substitutes is moderate to high. There are many metals or precious stones that can fulfill customers’ needs or many substitutes of metals or semiprecious stones which are a more economical solution and can relatively easily replace each other. Critical factor for the substitution of the basic product from an alternative metal or stone is that they cover the customers’ needs almost at the same level, even totally. Also they cost less and are more competitive in the market and this comes as an aftereffect of the fact that customers in jewelry market take price under serious consideration.

Threat of New Entry: Many critical factors make entry to the industry difficult. Big companies enjoy large economies of scales, preventing in that way new competitors enter the market or forcing them make a very risky investment or accept noticeably reduced profitability. Also jewelry industry requires a great initial capital investment, firstly because it deals with products of great value and secondly because of the excessive cost of raw materials and the difficult access to them. However, except for the resources requirements, the industry demands access to new technology, experience and expertise. The entry is getting more and more difficult due to the distribution networks that new competitors have to ensure. The few wholesale or retail sales channels in combination with the fact that the big companies have secured their networks put new potential competitors in predicament. FF Group is a company with its own distribution network and marketing of its products, so the threat of new entries is significantly low

.

Threat of new entry Competitive Rivalry -Large Economies of Scales -Many competitors -Reduced Profitability -Competitors' Diversity &

-Experience & expertise required Strength Threat of -Customer Loyalty -Resources requirements -Hardly accessible raw materials New Entry -Fixed costs -Access to distribution channels -Exit Barriers -Product differentiation High Competitive Rivalry

New entry quite difficult

Competitive Buyer Supplier Power Rivalry Power

Supplier Power -Company able to Buyer Power substitute -Ability to substitute / -Similar products great variety -Large number of suppliers -Zero personal cost of -Significant proportion of changing brands suppliers' revenue -Flexible on their

needs' satisfaction Low supplier power -Fully informed about Threat of Threat of Substitution competitors prices Substitution -Retail sales -Several precious metals Moderate Buyer Power substitutes -Great variety (semi)precious stones -Same or less cost -Fulfills customers’ needs at the same level

Significant threat

Appendix I: Macroeconomic Factors

Greece Features & Forecasts 2012 2013 2014 2015 2016 2017 GDP(YoY%) -7.3 -3.2 0.7 0 -0.7 2.7 Private Consumption (YoY%) -8.0 -2.3 0.5 0.5 -0.7 1.8 Public Consumption(YoY%) -6.0 -6.5 -2.6 -0.2 -1.0 -0.9 Unemployment(%) 24.5 27.5 26.5 25.1 24.0 22.8 Inflation(%) 1.5 -0.9 -1.4 -1.1 0.5 0.8 Gross Public Debt(% of GDP) 159.4 177.0 178.6 179.0 185.0 181.8 Source: europa.eu

Unemployment Rate 30,00%

25,00%

20,00%

15,00%

10,00%

5,00%

0,00% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Greece Long-term Interest Rates 35,00%

30,00%

25,00%

20,00%

15,00%

10,00%

5,00%

0,00% 2008 2009 2010 2011 2012 2013 2014 2015 2016

Greece Long-Term Interest rates JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 2008 4.40% 4.36% 4.42% 4.54% 4.74% 5.17% 5.15% 4.87% 4.88% 4.93% 5.09% 5.08% 2009 5.60% 5.70% 5.87% 5.50% 5.22% 5.33% 4.89% 4.52% 4.56% 4.57% 4.84% 5.49% 2010 6.02% 6.46% 6.24% 7.83% 7.97% 9.10% 10.34% 10.70% 11.34% 9.57% 11.52% 12.01% 2011 11.73% 11.40% 12.44% 13.86% 15.94% 16.69% 16.15% 15.90% 17.78% 18.04% 17.92% 21.14% 2012 25.91% 29.24% 19.07% 21.48% 26.90% 27.82% 25.82% 24.34% 20.91% 17.96% 17.20% 13.33% 2013 11.10% 10.95% 11.38% 11.58% 9.07% 10.70% 10.53% 10.10% 10.15% 8.74% 8.41% 8.66% 2014 8.18% 7.70% 6.90% 6.20% 6.38% 5.93% 6.10% 6.09% 5.89% 7.26% 8.10% 8.42% 2015 9.48% 9.72% 10.52% 12.00% 10.95% 11.43% - 10.26% 8.54% 7.81% 7.41% 8.21% Source: europa.eu , bloomberg

Greece 10-Year Bond Yield 40

35

30

25

20

15

10

5

0 20… 20… 20… 20… 20… 20… 20… 20… 20… Appendix J: Economic Value Added (EVA)

Economic Value Added Opportunity Cost=WACC*Capital, ROI=NOI/Capital ,Total Ca[ital=Total IBD + Total Equity , EVA=NOI – Opportunity cost 2016 2017 2018 2019 2020 NOI 225.11€ 243.82€ 259.8€ 275.34€ 290.03€ WACC 15.97% 14.49% 13.14% 11.92% 10.81% Total Capital 1293.74 1479.18 1638.61 1782.19 1909.64 Opportunity Cost 206.61 214.33 215.33 212.44 206.43 EVA 18.50€ 29.49€ 44.48€ 62.90€ 83.59€ ROI 17.40% 16.48% 15.85% 15.45% 15.19% *In million €

With the aid of NOI, we calculated the Economic Value Added and Return on Investment figures for FFG. As it is obvious in the figure, EVA is constantly growing from 2016 to 2020, as a result of the NOI increase and the WACC decrease simultaneously. The changes in the above indicators (NOI, WACC) reflect the overall expected improvement in the global economy the following years. Consequently, the calculation discloses the fact that FFG will be able to create value over the opportunity cost of their capital.

Appendix K: Historical and Forecasted Revenue Geographical Segmentation

Sales segmentation by country from 2009 to 2020 - 6 main countries (historical data and team forecasts) Country 2009 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Greece 50.85% 49.17% 53.46% 45.77% 29.60% 26.08% 23.82% 21.22% 18.81% 18.50% 18.50% 18.50% UK 13.50% 12.82% 10.54% 10.49% 11.51% 12.35% 10.81% 10.39% 9.95% 9.61% 9.61% 9.61% Spain 1.26% 1.19% 0.98% 0.98% 1.07% 1.15% 1.00% 0.97% 0.93% 0.89% 0.89% 0.89% China 22.89% 24.50% 23.31% 28.46% 38.48% 40.21% 42.84% 44.87% 46.79% 47.25% 47.25% 47.25% Republic 3.18% 3.41% 3.24% 3.96% 5.35% 5.59% 5.96% 6.24% 6.51% 6.57% 6.57% 6.57% of Korea Japan 8.32% 8.91% 8.47% 10.35% 13.99% 14.62% 15.58% 16.32% 17.01% 17.18% 17.18% 17.18% Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Notes: i) UK has by far the largest number of Points of Sale (312) among the other European countries and second comes Spain with 29. Therefore, these two countries have been selected as representative of the whole of Europe. Similarly, China (187 POS), Japan (68 POS) and Republic of Korea (26 POS) are the countries in which the FF Group mainly operates in Asia. ii) United States of America were omitted and their proportion in Group’s sales was included in that of Europe (this is also usually the way that the Group’s financial statements are presented (see for example the Annual Report of 2014)).

In this table the weight of Greece is the country's proportion in Group’s sales found in the recent financial statements for the historical years and forecasted from 2015 to 2020. For the rest of the countries we calculated their weights using a combination of the continent's proportion in Group's sales and the number of Points of Sale. So for example, the weight of China for 2015p is (64,36%)*187/(187+26+68) where 64,36% is the projected proportion of Asia for 2015p, 187 are the POS in China and (187+26+68) are the total POS in the three Asian countries (under the assumption that the POS remain the same for the whole time period 2009-2020 examined). The predictions here are mainly based on sales segmentation for the historical years (2009-2014) and on the Groups points of sale (POS) as of today.

Sales segmentation by main geographical area from 2009 to 2020 (historical data and team forecasts) Region 2009 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Greece 50.85% 49.17% 53.46% 45.77% 29.60% 26.08% 23.82% 21.22% 18.81% 18.50% 18.50% 18.50% Rest of 14.76% 14.01% 11.52% 11.47% 12.58% 13.50% 11.81% 11.36% 10.88% 10.50% 10.50% 10.50% Europe Asia 34.39% 36.82% 35.02% 42.76% 57.82% 60.42% 64.37% 67.42% 70.31% 71.00% 71.00% 71.00% Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Asian countries segmentation excluding Republic of Korea from 2009 to 2020 (historical data and team forecasts) - used for bot tom-up beta estimation Country 2009 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f China 25.22% 27.00% 25.68% 31.36% 42.40% 44.31% 47.20% 49.44% 51.56% 52.07% 52.07% 52.07% Japan 9.17% 9.82% 9.34% 11.40% 15.42% 16.11% 17.17% 17.98% 18.75% 18.93% 18.93% 18.93% Total 34.39% 36.82% 35.02% 42.76% 57.82% 60.42% 64.37% 67.42% 70.31% 71.00% 71.00% 71.00%

Points of sale by country (for country weights by region) Points of sale Region Country Total (POS) 312 Europe 341 Spain 29 China 187 Asia Japan 68 281 Republic of Korea 26 Source: http://www.ffgroup.com/stores/

Appendix L: Forecasting Gross Profit GDP at purchaser's prices, in Billion US dollars 2009 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Greece 330.69 300.16 289.07 249.66 242.31 237.97 192.98 192.52 201.34 211.90 222.36 234.58 UK 2310.67 2407.35 2593.45 2623.83 2678.38 2950.04 2864.90 3054.84 3232.28 3425.54 3616.82 3851.98 Spain 1502.88 1434.26 1495.97 1356.48 1393.48 1406.54 1221.39 1265.12 1318.83 1372.18 1427.74 1497.67 China 5059.72 6039.55 7492.53 8461.51 9490.85 10356.51 11384.76 12253.98 13173.59 14272.35 15620.71 17100.06

Republic of 901.94 1094.50 1202.46 1222.81 1305.61 1410.38 1392.95 1450.05 1545.81 1649.08 1763.36 1898.76 Korea Japan 5035.14 5498.72 5908.99 5957.25 4919.59 4602.37 4116.24 4170.64 4342.16 4446.33 4590.91 4746.88 Source: IMF World Economic Outlook (WEO), October 2015.

GDP at purchaser's prices, in Βillion euro s 2009 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Exchange rate 1.394782 1.325717 1.391955 1.284789 1.328118 1.328501 1.109513 1.084580 1.084560 1.084560 1.084560 1.084560 Greece 237.09 226.41 207.67 194.32 182.45 179.13 173.93 177.51 185.64 195.38 205.02 216.29 UK 1656.65 1815.89 1863.17 2042.23 2016.67 2220.58 2582.12 2816.61 2980.27 3158.46 3334.83 3551.65 Spain 1077.50 1081.88 1074.73 1055.80 1049.21 1058.74 1100.83 1166.46 1216.00 1265.20 1316.42 1380.90 China 3627.61 4555.69 5382.74 6585.92 7146.09 7795.64 10261.04 11298.36 12146.48 13159.58 14402.81 15766.82 Republic of Korea 646.65 825.59 863.86 951.76 983.05 1061.63 1255.46 1336.97 1425.29 1520.51 1625.88 1750.72 Japan 68 3609.98 4147.73 4245.10 4636.75 3704.18 3464.33 3709.95 3845.40 4003.61 4099.66 4232.97 4376.78 Note: The source for the euro-US dollar exchange rate until 2017f is "AMECO: the annual macro-economic database of the European Commission's Directorate General for Economic and Financial Affairs (DG ECFIN)" as of 04/02/2016. For the time period 2018-2020 we consider it to remain constant at the value of the European Commissions's last prediction (2017f), that is 1.08456.

Weighted GDP growth at purchaser's prices, in euro 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Greece -2.21% -4.43% -2.94% -1.81% -0.47% -0.69% 0.44% 0.86% 0.97% 0.91% 1.02% UK 1.23% 0.27% 1.01% -0.14% 1.25% 1.76% 0.94% 0.58% 0.57% 0.54% 0.62% Spain 0.00% -0.01% -0.02% -0.01% 0.01% 0.04% 0.06% 0.04% 0.04% 0.04% 0.04% China 6.27% 4.23% 6.36% 3.27% 3.65% 13.55% 4.54% 3.51% 3.94% 4.46% 4.47%

Republic of 0.94% 0.15% 0.40% 0.18% 0.45% 1.09% 0.40% 0.43% 0.44% 0.46% 0.50% Korea Japan 1.33% 0.20% 0.95% -2.81% -0.95% 1.10% 0.60% 0.70% 0.41% 0.56% 0.58% Weighted GDP growth 7.56% 0.42% 5.77% -1.32% 3.94% 16.85% 6.97% 6.12% 6.37% 6.96% 7.25% in euro Note: The weights used here are presented in a previous table (Sales segmentation by country from 2009 to 2020).

Gross Profit Forecast 2009 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Weighted GDP (2009=100) 100 107.56 108.02 114.25 112.73 117.17 136.91 146.46 155.42 165.33 176.84 189.66 Weighted GDP Growth

in euro 7.56% 0.42% 5.77% -1.32% 3.94% 16.85% 6.97% 6.12% 6.37% 6.96% 7.25%

Gross Profit (2009=100) 100 101.07 104.72 112.90 95.42 101.72 115.51 2009-2015 Pearson Coefficient between Weighted GDP (2009=100) and Gross Profit (2009=100)= 0.68 2013 - 2015 Weighted GDP (2009=100) CAGR= 10.20% 2013 - 2015 Gross Profit CAGR= 10.02% CAGR ratio[Gross Profit CAGR/Weighted GDP CAGR]= 98.21% Forecasted Gross Profit Growth in euro[Weighted GDP Growth in euro*CAGR Ratio]1 13.56% 6.85% 6.01% 6.26% 6.84% 7.12% Forecasted Gross Profit1 (in Millions €) 569.75 608.77 645.36 685.75 732.65 784.80 1 Except for 2015 were the 9-month data for the FF Group were used.

In order to forecast the Group’s Gross Profit we have computed the correlation between the Weighted GDP (2009=100) and the Gross Profit (2009=100) for the time period 2009-2015 using the Pearson coefficient. Taking into consideration that there is a relatively strong positive correlation (ρ=0.68) we have calculated the Compounded Annual Growth Rate (CAGR) for the two above mentioned Indices from 2013 to 2015 so as to divide them and find what we call the “CAGR Ratio”. The latter measure is the one we use to predict the Gross Profit Growth in euro multiplying it by the Weighted GDP Growth in euro.

Appendix J: Terminal Growth Rate Terminal compound annual GDP growth rate from 2020 to 2060 - 6 main countries GDP volume (in Compound Weighted billion US Dollars) annual GDP Weight compound annual 2020f 2060f growth rate GDP growth rate Greece 286.18 553.35 1.66% 18.50% United Kingdom 2559.59 5945.95 1.21% 17.18% Spain 1363.05 2627.15 1.85% 6.57% China 17709.69 53827.70 1.65% 0.89% 2.19% Japan 4318.23 6995.52 2.13% 9.61% Korea 1988.40 4143.14 2.82% 47.25% Total 100.00% GDP volume forecasts source: OECD Dataset: Economic Outlook No 95 - May 2014 - Long-term baseline projections

In estimating the terminal growth rate for the group, we have assumed it to be equal to the weighted compound annual GDP growth rate of the six countries, which is consistent with ratio of the group’s gross profit CAGR to the weighted GDP growth rate as shown earlier in our analysis, which is close to one (98.21%), that is, the two growth rates are found to be close to unity. The weighted compound annual GDP growth rate has been estimated using the OECD’s forecasts for the countries’ GDP volume in US Dollars from 2020 until 2060. Of course, it is crucial that it be underlined that in contrast to the short term period, where the euro-dollar exchange rate has been taken into consideration, here the exchange rate effect has not been taken into consideration in estimating GDP growth, since the course of the exchange rate from 2020 to 2060 is really hard to predict and may be of no use or serious credibility.

Appendix K: Bottom-up Beta Estimation

Bottom-up beta estimation

Terminal Weight Short term Short term bottom-up beta Sector/Company Country (Revenues per Sector beta bottom-up bottom-up (levered) - segment /Region Market/Region (unlevered) beta beta Blume's (1971) 2015 forecast) (unlevered) (levered) adjusted beta

Greece 3.24% 0.813 Europe 5.98% Jewellery China 47.20% 1.423 Japan 17.17% 0.573 1.0356 1.30 1.199 Retail - Wholesale - Greece 20.58% 0.723 Department stores Europe 5.83% Total 100%

Notes: i) unlevered sector betas come from A. Damodaran’s page: http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datacurrent.html ii) for the jewelry segment the mean of the retail (general), retail (special lines) and apparel sector betas has been used to better capture the jewelry sector, while for retail-wholesale-departments stores segment the mean of the retail (distributors), retail (general) and retail (special lines) sector betas has been used to grasp the diverse activities of the group regarding sales of various products both in retail and wholesale iii) for Greece Europe’s sector betas have been used iv) Republic of Korea has not been taken into consideration due to lack of sector beta data for the country, China and Japan have been used to account for Asia

In estimating the group’s beta, we have followed a bottom-up approach using unlevered sector betas by geographical region/country as calculated in A. Damodaran’s analysis and weighing those using our forecast for the revenues segmentation. What is more, Blume (1971) has found beta to have a tendency to approach the value of one, so for the terminal period, the beta has been adjusted using Blume’s (1971) method: 21 Adjusted betaestimated beta 33

Appendix L: Regression Betas – Dual Betas Estimation

Regression betas estimation – common, downside and upside (monthly observations for the period 01.03.2011-01.02.2016)

Sharpe’s Single-Index model (SIM): RabRFolli FollieASE General Index Multiple R 0.611785669 R Squared 0.374281704 Adjusted R Squared 0.363493458 Standard Error 0.115221847 Observations 60 Coefficients Standard Error t Stat P-value

Intercept (a) 0.018813943 0.014972824 1.256539368 0.213957805 (Common) Beta 0.854933631 0.145147156 5.89011629 2.06711×10-7

Single-Index model (SIM) for negative market returns:  RabRFolli Follie  ASE General Index Multiple R 0.487465 R Squared 0.237622 Adjusted R Squared 0.21221 Standard Error 0.095878 Observations 32 Coefficients Standard Error t Stat P-value

Intercept (a  ) 0.008994 0.027074 0.332209 0.742041 Downside beta (b ) 0.75366 0.246465 3.057872 0.004657

Single-Index model (SIM) for non-negative market returns:  RabRFolli Follie  ASE General Index Multiple R 0.399701 R Squared 0.159761 Adjusted R Squared 0.127444 Standard Error 0.137523 Observations 28 Coefficients Standard Error t Stat P-value

Intercept (a  ) 0.013494 0.040397 0.334037 0.741031 Upside beta (b) 0.946133 0.425531 2.223414 0.035088

Regression betas estimation – common, downside and upside (weekly observations for the period 18.02.2013-15.02.2016)

Sharpe’s Single-Index model (SIM): RabRFolli FollieASE General Index Multiple R 0.670819 R Squared 0.449999 Adjusted R Squared 0.44645 Standard Error 0.049319 Observations 157 Coefficients Standard Error t Stat P-value

Intercept (a) 0.005179 0.003942 1.313824 0.190847 (Common) Beta 0.799536 0.070998 11.26133 7.15×10-22

Single-Index model (SIM) for negative market returns:    RFolli Follie  a  b  RASE General Index Multiple R 0.556357 R Squared 0.309534 Adjusted R Squared 0.300903 Standard Error 0.046326 Observations 82 Coefficients Standard Error t Stat P-value

Intercept (a  ) 0.00795 0.007704 1.032027 0.305169 Downside beta (b ) 0.793812 0.132553 5.988631 5.73×10-8

Single-Index model (SIM) for non-negative market returns:  RabRFolli Follie  ASE General Index Multiple R 0.560752 R Squared 0.314443 Adjusted R Squared 0.304921 Standard Error 0.052424 Observations 74 Coefficients Standard Error t Stat P-value

Intercept (a  ) -0.00947 0.009875 -0.95926 0.340636 Upside beta (b ) 1.072844 0.18669 5.746657 2.04×10-7

Appendix M: Risk Free Rate Estimation

Risk free rate estimation for Greece

Short term risk free rate for 2016 Long term risk free rate (geometric mean of (geometric mean of the Greek 10-year 2017 2018 2019 2020 the Greek 10-year bond yield for the pre-crisis, bond yield for the period 2001-2015) post Eurozone entry period 2001-2008)

6.49% 6.02% 5.58% 5.18% 4.80% 4.45% Notes: i) the risk free rate has been assumed to decrease with an annual rate of -7.28%, as the Greek economy recovers to reach its terminal value of 4.45% ii) Source for bond yields: ECB, Long-term interest rate for convergence purposes - 10 years maturity, denominated in Euro

Given the large volatility of the Greek 10-year bond yield in the recent years, the (geometric) mean of the Greek 10-year bond yield for the period 2001-2015 has been used as the risk free rate for 2016. As it has been underlined by Ernst & Young, “the valuer needs to consider whether current spot yields are a reliable indicator or not, given the levels of volatility and the falling rates seen since the start of 2014. Additionally, consideration should be given to whether the current negative real yields are supportable beyond the short term. Mechanically applying the spot government bond yield as the risk-free rate in the CAPM context is an issue, where it can be argued that the spot yield is not deemed to be a good proxy for the risk-free rate.” Given this large fluctuation, they suggest that “where Government bond yields are not deemed to be the best proxy for the risk-free rate […] using an average Government bond yield over a period as a proxy for the risk-free rate” can be a way to go1.

For the terminal period, when Greece is expected to have returned to a more stable situation, we expect the risk free rate to have largely decreased approaching pre-crisis levels, so we have used again the geometric mean of the Greek 10-year bond yield, but for the period 2001-2008. Lastly, we have assumed the risk free rate to decrease during the 5-year-period, as Greek economy returns to growth, to reach its estimated terminal value of 4.45%.

1 Ernst & Young, “Estimating risk-free rates for valuations”. Complete file can be found at: http://www.ey.com/Publication/vwLUAssets/EY-estimating-risk-free-rates-for-valuations/$FILE/EY-estimating-risk- free-rates-for-valuations.pdf

Appendix N: Risk Premium Estimation

Risk premium estimation

Short term Short term risk Terminal weight Terminal risk Weighted weight premium by Weighted (revenues per premium by country short term Country (revenues per country (current terminal risk country - 2020 (pro crisis 2001-2008 risk premium country 2015 country risk premium and on forecast) average) (for 2016) forecast) premium)

China 42.84% 6.90% 47.25% 6.29% Republic 5.96% 6.74% 6.57% 6.43% of Korea Japan 15.58% 7.05% 17.18% 6.19% Greece 23.82% 20.90% 18.50% 6.09% 10.23% 6.10% UK 10.81% 6.59% 9.61% 4.89% Spain 1.00% 8.84% 0.89% 4.98% Total 100.00% 100.00%

Risk premia source: A. Damodaran’s page: http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html

Similar to the risk free rate, risk premia have been assumed to fall in the terminal period, as the world economy (and the specific six countries’ economies in particular) stabilizes and risks that have been posed by the financial crisis are mitigated. The larger reduction in the risk premium is of course expected to happen in Greece. What is more, as you will see later in the cost of equity estimation, the weighted short term risk premium (for 2016) has been assumed to gradually decrease, till it reaches its long term value.

Appendix O: Cost of Equity assessment

Cost of equity estimation 2016 2017 2018 2019 2020 Terminal

Cost of Equity 19.56% 17.64% 15.91% 14.35% 12.94% 11.67% Notes: i) 2016 and terminal cost of equity are estimated using the CAPM and the above estimated betas, risk free rates and risk premia ii) cost of equity assumed to decrease with an annual rate of -9.81% during the short term period (as economy -especially the Greek one- recovers and bond yields and risk premia fall) to reach each estimated terminal value of 11.67%

Appendix P: Cost of Debt assessment

Cost of debt estimation - financial statements approach (amounts in millions of Euros) Cost of debt before tax (average total 2011-2014 Cost of debt 2014 2013 2012 2011 interest expenses over total after tax average debt for the four-year period) Interest Expenses 14.33 20.49 38.94 37.94 27.92 (loans) Interest Expenses 0.92 1.42 1.65 2.06 1.51 (leases) Other interests 0.40 0.54 0.46 0.02 0.36 Total interest 15.65 22.45 41.05 40.01 5.82% 4.31% expenses 29.79 Interest bearing debt (book value 351.14 222.54 741.08 731.91 of bank and bond 511.67 loans plus leases) Notes: i) Cost of debt after tax has is cost of debt before tax multiplied by one minus the marginal tax rate: CDBTCDAT(1- t) ii) The tax rate used is 26%, which is based on the group’s financial statements.

In this method, we estimate the cost of debt as the ratio of interest expenses to debt, which is based on the basic formula for interest rate: int erest paid Interest rate paid  borrowed funds

Since the interest expenses are not evenly distributed in regards to the borrowed funds in all financial years, the 4- year averages for the period 2011-2014 have been used.

Cost of debt estimation – Rf plus CDS approach 2016 2017 2018 2019 2020 Terminal

Risk free rate (Rf) 6.49% 6.02% 5.58% 5.18% 4.80% 4.45% Company default spread (CDS) 0.75% Cost of debt before tax (risk free 7.24% 6.77% 6.33% 5.93% 5.55% 5.20% rate plus company default spread) Cost of debt after tax 5.36% 5.01% 4.69% 4.39% 4.11% 3.85% Notes: i) Cost of debt after tax has is cost of debt before tax multiplied by one minus the marginal tax rate: CDBTCDAT(1-t) ii) The tax rate used is 26%, which is based on the group’s financial statements.

Cost of debt estimation - average of two approaches Approach 2016 2017 2018 2019 2020 Terminal

Rf plus CDS approach 7.24% 6.77% 6.33% 5.93% 5.55% 5.20% Before tax Financial statements 5.82% approach Rf plus CDS approach 5.36% 5.01% 4.69% 4.39% 4.11% 3.85% After tax Financial statements 4.31% approach Before tax 6.53% 6.30% 6.08% 5.87% 5.69% 5.51% Average After tax 4.83% 4.66% 4.50% 4.35% 4.21% 4.08%

Appendix Q: WACC estimation

Short term and terminal WACC estimation (1) Number of Shares on 15/02/16 66,948,210 (2) FF Stock Price on 15/02/16 14.99 € (3)=(1)X(2) Market Value of Equity (in million 1,003,553,667.90 € euros) (4) Interest bearing debt (bond and bank loans 343,519,992.12 € plus leases in million euros, 30.09.2015) (5)=(3)/[(3)+(4)] Cost of equity weight 74.50% (6)=(4)/[(3)+(4)] Cost of debt weight 25.50% Short term cost of equity (for 2016) 19.78% Short term cost of debt after tax (for 2016) 4.83% Short term WACC (for 2016) 15.97% Terminal cost of equity 11.76% Terminal cost of debt after tax 4.08% Terminal WACC 9.80%

Weighted Average Cost of Capital (WACC) for every period 2016 2017 2018 2019 2020 Terminal 15.97% 14.49% 13.14% 11.92% 10.81% 9.80% Note: WACC has been assumed to decrease with an annual rate of -9.3%, as the world economy –and the economies of interest to the group particularly– recovers and risks posed by the financial crisis are mitigated, till it reaches its terminal value of 9.8%

Appendix R: Discounted Cash Flow (DCF) Analysis

Change in Working Capital Calculation (in Million euros) 2010 2011 2012 2013 2014 2015p 2016f 2017f 2018f 2019f 2020f Total current assets 877.30 1010.36 1095.95 1024.52 1363.89 1491.84 1541.24 1646.96 1804.38 1943.80 2068.51 Less: Cash and cash equivalents 133.76 135.50 126.48 251.59 297.03 211.78 225.06 250.67 338.50 380.00 412.69 Less: Total Short Term Liabilities 292.92 588.64 484.53 323.94 260.49 225.20 235.90 262.42 283.45 319.92 363.28 Plus: Trade and Other Payables 136.25 154.02 152.29 120.25 181.87 168.56 162.98 166.82 169.57 171.11 169.91 Working Capital 586.87 440.24 637.23 569.24 988.24 1223.42 1243.26 1300.68 1352.00 1414.99 1462.45 Change in Working Capital - -146.63 196.99 -67.99 -119.80 87.72 19.84 57.43 51.32 62.99 47.46

FCFF Estimation (in Million euros) 2015p 2016f 2017f 2018f 2019f 2020f Operating income 213.37 225.11 243.82 259.80 275.34 290.03 (EBIT) EBIT*(1-t) 157.90 166.58 180.42 192.25 203.76 214.62 Less: Purchases of tangible and 66.06 30.99 30.99 30.99 30.99 30.99 intangible assets Plus: Depreciation 28.54 28.37 30.07 31.95 34.14 36.57 & amortization Less: Change in 87.72 19.84 57.43 51.32 62.99 47.46 Working Capital Free Cash Flow to the Firm (FCFF) 32.65 144.12 122.08 141.90 143.91 172.74 31/12 Note: Although we estimate the FCFF for 2015p we do not use it to estimate the enterprise value as it is a future FCFF on 19/2/2016.

Target Price Estimation 2016f 2017f 2018f 2019f 2020f Terminal Period Free Cash Flow to the Firm (FCFF) 144.12 122,08 141,90 143,91 172,74 126,23 31/12/20XX WACC 15.97% 14.49% 13.14% 11.92% 10.81% 9.80% Discount Ratei 86.23% 75.32% 66.57% 59.48% 53.68% 53.68% Present Value of FCFF 124.27 91.95 94.47 85.61 92.73 Cumulative Present Value of FCFFs 124.27 216.22 310.68 396.29 489.02 Long term growth 2.19% Terminal Valueii 1,657.79 Present Value of Terminal Value, 01/01/2016 889.94 Enterprise Value on 01/01/16iii 1,378.95 Less: Net Debt on 01/01/16 366.92 Equity Value on 01/01/16 1,012.04 Enterprise Value on 19/02/16iv 1,407.23 Less: Net Debt on 19/02/16 374.44 Equity Value on 19/02/16 1,032.79 Number of shares 66,948,210.00 Target Price on 19/02/2016 15.43€ (-1) Notes: i) Discount Ratethis year=Discount RatePrevious year*(1+WACCthis year) . ii) Terminal Value =FCFF in Terminal Period/(Terminal WACC - Terminal growth) iii) Enterprise Value on 01/01/16 =Cumulative Present Value2020f + Present Value of the Terminal Value (50/365) iv) Enterprise Value on 19/02/16 =Enterprise Value01/01/16*(1+WACC2016)

Appendix S: Multiples Analysis

Multiples Earnings "Affordable Region per Luxury" Activitie P/E P/S share(EUR) Price/Book EV/EBITDA EV/Revenue weight Position s Chow Tai Fook 10.5 0.72 0.05 1.17 8.1 0.92 0.05  Pandora 32.37 7.12 3.67 19.38 19.54 7.28 0.05  Michael Kors 8.68 1.61 3.92 3.65 4.75 1.33 0.05  Tse Sui Len 14.37 0.11 0.05 0.44 6.86 0.29 0.1   Coach 18.35 2.13 1.65 3.66 9.4 1.96 0.1   Chow Sang Sang 6.39 0.43 0.23 0.89 7.88 0.54 0.1   Burberry 13.84 1.93 1.06 3.53 8.63 1.8 0.05  Signet Jewelers 20.7 1.57 5.65 3.55 13.09 1.75 0.05  Tiffany 17.42 2.07 3.57 3.07 9.01 2.1 0.05  Swatch 12.8 2.03 21.09 2 7.42 1.81 0.05  Louis vuitton 12.07 2.1 11.41 3.03 10.34 2.17 0.05  Inditex 32.89 4.64 0.91 8.52 21.6 4.38 0.1 H&M 21.41 2.66 1.4 8.89 13.3 2.58 0.1 Gap 9.19 3.54 2.36 3.54 4.17 0.62 0.1 Folli Follie 6.24 0.81 2.26 0.64 4.64 0.92 Weighted Average 16.68 2.31 3.18 4.56 10.37 2.00 Weighted Median 14.37 2.08 2.01 3.54 8.82 1.80

In order to achieve the best combination of multiples to create an integrated perception about company's price we reached a price threw a combination of Enterprise Value and Equity Multiples. We used 5 multiples in order to capture all the dimensions of the firm. More specifically P/E, P/S, EV/EBITDA, Price/Book, EV/Revenue multiples applied to our valuation. Our firm runs its businesses in three regions and thirty countries. EV multiples are less affected by accounting differences and measure the levered value of the company. Additional to these EBITDA is the most common measure of performance and value that overcomes the problem of accounting differences. Besides Price/Book value is an appropriate multiple for our company since she uses her tangible assets to generate its value. So we conclude to a weighted approach for the price of our company with the follow weights to our multiples.

FF EBITDA(2015TTM) 216,200,000

Short/Long Borrowings 344,000,000 Cash and Equivalents 220,000,000 Minority Interest 28,000,000 Multiples Price Weights Net Income 150,000,000.00 P/E 32.2 15% Number of Shares(31/12/14) 66,948,210 P/S 35.7 15% MRQ 1.884621296 Price/Book 26.26 20% Market Cap 937274940 EV/EBITDA 26.21 25% Assets-Intag.Assets-Liabilities 497328000 EV/Revenue 28.62 25% Sales 2015(TTM) 1,149,000,000 Price 29.14

The final price of the multiple valuation will be calculated by the combination of the main method of valuation and the OLS approach. Firm's financial statements claim a higher price which can be found threw multiple valuation. Although, we think that the political and economic factor which affect Greek Stock Exchange and are responsible for the high volatility and low company's market price cannot depict in the simple Multiple approach. For this reason why try to find the weights of the two multiples that describe the perfect affiliation among the pre-crisis and in-crisis period.

We determine that the correlated values are statistical significant at the level of 0.05. The measures of the statistical significance, which are used, are t-statistics and p-value.

At the level of 5% statistical significant are:

• The correlation between median P/S of the relatives companies and FF group's P/S

• The correlation between median EV/Revenues of the relatives companies and FF group's EV/Revenues

(Statistical significance p-value< 0.05 and t-statistics>2 or <-2)

We formed the diagrams of P/S and EV/Revenue because, historically, the margin between those of ff group and those of the medians of the similar companies seem to be stable.

The respective diagrams of the other multiples do no show equivalent stability of margin between ff and median multiples despite the fact that they also advocate the historical underestimation of FF intrinsic value. We can observe that the medians of the multiples are above of ff group's multiples. The approximately nonflunctuating gap can be perceived since: The linear trend lines of the two variables are almost pararrel, that's a slight indicator of the relative stable difference between the medians of the multiples (generated from the relative companies) and FF Group's multiples. Also, the statistical significant correlation coefficient shows a moderately strong positive connection of the two variables The correlation is estimated at 0.67 for the P/S multiples and at 0.699 for EV/Revenue multiples That figures witnesses that the values of the two variables move to the same direction in most occasions and the statistical significance shows that the probability of the variables preserving this relationship is big. In these occasions, the fact that coefficients’ estimated values are above 0,5 implies relatively strong positive correlation These two parameters enhance the belief that the variance between the historical medians of the similar companies and the ff group's respective multiples is approximately fixed, and ff group's multiples maintain low figures, traditionally, Compared to those of the relative companies, over the last decade.

As we can see from the graphs, the series of the ff group's multiples is down of those of the median's multiples. Only at the beginning of the time series of the variables the margin between them is narrowed, in relation to the followings level of margin. This may reveal that the economic instability in Greece during the last 8 years or generally the immaturity of the Greek stock market or the low credibility of the whole of the Greek financial sector did not allow the FF Group's multiples to approach the median's one of the similar companies. Additionally, there are many factors which may be responsible for the lack of correspondence between ff's group and market's multiples, but we claim that political and country risks are the most crucial. Consequently, we can assume that these multiples and mainly the gap between them will follow this standardized movement if the conditions not only in Greek economy but also in Greek financial market do not change. These elements prompt as to implement OLS models for the medians of the multiples and the ff group's multiples. The regression models configured only for the P/S and EV/Revenues multiples since only the correlation of them with the medians of the sector demonstrates statistical significance .We define the factor of the medians of the multiples as the independent variable and the factor of ff's group multiple as the dependent one.

The outcome of the implementation of the regression models is two equation which has the form Y=a+bX a: constant coefficient b: slope of the linear equation

For P/S: For EV/Revenues:

The R-squared, as we can see, takes prices > 0.4 .This indicates that these models offer a relative adequate "explanation" of the variance and the standard deviation.

Values for R-squared from 0.4 to 0.6 are usually acceptable for the simple linear regression. The value of R-squared, generated from the simple regression between ff group's multiples and the medians of them, for the other multiples

P/E= 0.222713 P/Book= 0.285706 EV/EBITDA = 0.001206

<0,3 for these 3 multiples and implies that the regression models do not give as sufficient credibility.

The data, which has been used to construct OLS models, is from 2005 to the end of the 2015. The time series of the data created from the historical prices of the medians of the multiples, formed by the relative companies, and the historical ff group's respective multiples.

Calculation of the implied P/S for FF, using as independent variable the median P/S of similar companies, for the end of 2015.

Median P/S (2015) = 2.050

FF Group P/S = 0.7223

Calculation of the implied EV/Revenue for FF, using as independent variable the median EV/Revenues of similar companies, for the end of 2015.

Median EV/Revenues (2015) = 1.805

FF Group EV/Revenues = 1.0076

Intrinsic Value with P/S = 12.397€ Intrinsic Value with EV/Revenue = 15.023

Blended Multiples Sum Intrinsic Value: Weighted 0,5 each: 13.710€

Our OLS approach is based on that the market sentiment is perhaps one of the main reasons why the stock of FF is negotiated at a high discount, according to simple multiples valuation. Market sentiment refers to the psychology of the market participants and is matter of concern for the relatively new field of behavioral finance. In this occasion the pessimism, which inundated Greek stock market, led to negative market sentiment and because of that the multiples P/S and EV/REV of FF group was suppressed. To determine market sentiment, we need to take into account that is often subjective, biased and obstinate. For example, it seems to be solid the judgment that ff group’s stock has strong future growth prospects. However, if we examine the patterns of the past stock’s price we will see that and the potential increase, proposed by the simple multiples valuation, is not so probable. The last decade, as we mentioned above, the ff group’s share price was negotiated in a relative stable discount. We capture this “traditional” discount with the implementation of the OLS approach and we showed that the correlation between the multiples is adequately significant. Nevertheless we cannot wholly ignore the outcome of the simple multiple valuation method since the estimations of the forecast Greek GDP and of the forecast Business Confidence Index for Greece shows a relative switch of the Greek market sentiment. This switch could restrict the gap between the median of the multiples of the similar companies and ff group’s multiples but it does not seem so radical to eliminate this gap, at least for the short-mid term period.

Business Confidence Index for Greece:

Source: Trading Economics

Source: International Monetary Fund

Because of that we choose to give some gravity to the simple relative valuation. The weights chosen for each method are:

 w1 (Normal relative valuation)=41% or 0.41

 w2 (Formed OLS method)=59% or 0.59

For instance, in the end of 2014 and in the beginning of 2015 the discount estimated by P/S multiple reached 26.71% while the average of the decade was 60.20% and for EV/Revenue multiple the discount reached 25.81% in the same period while the average of the decade was 35.59%. The positive market sentiment created this period, since Greek economy showed positive GDP change and positive prospects of growth after 6 years of crisis, led to relative narrowed margin. Nevertheless, the resurgence of the economic crisis in Greece and the enforcement of the capital controls did not allow the breakdown of the relative stable relationship between the multiples. Because of that we think that the slowing down recession and the estimated growth of Greek Economy for the upcoming years and the increase of Business Confidence index will reduce the discount, and the multiples of FF group will approach those of the companies of the same sector. Specifically, the forecasted percentage down of the median’s multiple of which the multiples of FF will move to is assumed to be approximately the same of that in 2014. We have presumed that because the situation in Greek economy is similar to this of the end of 2014, when the projections showed a potential growth of Greek GDP and business confidence in the upcoming year. We have estimated the gravities of the two methods using this formula.

W1= (((60.20-26.71)/60.20+(35.59-25.81)/35.59))/2 %

W2= (1- W1) %

The final intrinsic value, which calculated by the combination of these two methods and their based in multiples valuation, is 20.04€.

Appendix T: WACC Sensitivity Analysis Short term WACC (for 2016) sensitivity analysis Short term cost of debt estimation method Financial Riskfree rate Beta Rf+CDS Statements Average approach approach approach Geometric mean of Bottom-up 16.86% 17.12% 17.38% Greek 10-year bond yield through the Regression (monthly obs.) 15.13% 15.39% 15.65% period Jan 2006- Dec 2015 (monthly Regression (weekly obs.) 14.60% 14.86% 15.12% observations)

Geometric mean of Bottom-up 15.84% 15.97% 16.10% Greek 10-year bond yield through the period Jan 2001- Dec Regression (monthly obs.) 14.11% 14.24% 14.37% 2015 (monthly observations) Regression (weekly obs.) 13.58% 13.71% 13.85%

Geometric mean of Bottom-up 12.96% 12.73% 12.50% German 10-year bond yield through the Regression (monthly obs.) 11.23% 11.00% 10.77% period 2001-2015 (monthly observations) Regression (weekly obs.) 10.70% 10.47% 10.24% Source for bond yields: ECB, Long-term interest rate for convergence purposes - 10 years maturity, denominated in Euro.

Long term WACC sensitivity analysis Long term cost of debt estimation method Blume (1971) adjusted Financial Riskfree rate Average Rf+CDS Beta Statements approach approach approach

Geometric mean of Greek Bottom-up 9.63% 9.80% 9.51% ten-year bond yield through the period 2001- Regression (monthly 9.17% 9.12% 9.06% 2008 (monthly obs.) observations) Regression (weekly obs.) 8.96% 8.91% 8.85%

Geometric mean of Bottom-up 8.51% 8.28% 8.04% German 10-year bond Regression (monthly yield through the period 7.82% 7.59% 7.36% obs.) 2001-2015 (monthly observations) Regression (weekly obs.) 7.61% 7.38% 7.15%

Source for bond yields: ECB, Long-term interest rate for convergence purposes - 10 years maturity, denominated in Euro.

Appendix U: Department Stores, Retail & Wholesale sectors performance compared to CCI

In contrary to the economic situation in Greece, FF managed to redeem EBITDA margins approximately 8% for department stores and 10%for Retail & Wholesale, while the revenues for R&WH has an estimated CAGR=17,05% and for Dep. St. CAGR=11,17% .

Consumer Confidence Index (CCI): Aims for measuring the consumers’ level of confidence and depicts the degree of optimism on the economic landscape that is perceived by measuring consumers’ spending and saving. CCI is adjusted monthly, taking into account consumers’ opinion, and is 40% formed by the current opinions about the economy while the future conditions comprising the left over 60%. In Greece, CCI mirrors the turbulent domestic economy, especially when is compared to the respective indicator for the sum of E.U.

We tried to define the correlation between the performance of the Retail & Wholesale and the Department Stores segments with the Consumer Confidence Index of Greece, since 100% of department stores segment and the 61% of the Retail & Wholesale taking place in Greece. The initial used observations are the Quarter EBITDA margins originated by these activities and the average CCI of Greece for respective three-month period. At a statistical significance level of 5% the correlation coefficients are not acceptable, although the p-value for the compared variables takes prices borderline lower of the admissible ones (<0,08). The positive definition of the two coefficients is a slight indicator (not the most reliable one) that the profitability of these sectors is positive correlated with Greece general CCI. Despite the fact that the CCI takes very low values compared to those E.U and the improvement of its condition may lead to supplementary profits for these sectors, the FF group has shown substantial growth of revenues and EBITDA after 2012,(was the last year in which Retail& Wholesale sector declare loses).

Selling Perfomance and Profitability of R&W and Dep. Stores

16,00% 180

160 12,00% 140 8,00% 120 Revenues R&W

4,00% 100 Revenues Dep. St EBITDA M R&W 0,00% 80 EBITDA M Dep.St

EBITDA EBITDA Margin% 60 -4,00% 40 -8,00% 20 -12,00% 0

*For the CAGR’s Calculations are used the trailing revenues of the final Quarter of 2015 as ending value and the estimated revenues 9months revenues of 2012 plus those of the final Quarter of 2011 as the beginning value.

Appendix V: GARCH (1,1) modeling of the exchange rates

Through this process we tried to forecast volatility in exchange rates. More specifically we are looking for Garch effects time series where a non-constant variance and variance at one time depends on the variance at previous time periods.  For the USD/CNY exchange rate we fit a Garch(1.1) , which is commonly used in finance, obtaining the following:

2 2 Garch(1.1)= 4,98훦 − 07 + 0.1425푢푡−1 + 0.6378휎푡−1 All the constants are statistically significant because they a P-value less than 5%.  Long-term volatility : 0.15% ACF Plots of the Residuals and Squared Residuals ACF of Residuals 0,08 ACF of Squared Residuals 0,03 0,06 0,04 0,02 0,02 0,01 0 -0,02 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 0 -0,04 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 -0,01 -0,06 -0,02 -0,08 From the ACF charts above we have the exception of three values, one in the first plot(12th lag) and two in the second plot(6th and 24th lag) and beyond that all the autocorrelation values are within two standard deviations (±2s) of the sample autocorrelation. For these reason we adopt the assumption of independent errors. Normality of the Residuals In order to check the residuals Normal distribution we plotted the histogram. It is known that if the errors follow a normal distribution , the residuals also follow an approximately normal distribution.

500 Series: RESID Sample 1/02/2013 2/04/2016 400 Observations 857

Mean 5.09e-06 300 Median -5.42e-05 Maximum 0.018328 Minimum -0.011781 200 Std. Dev. 0.001549 Skewness 2.387593 Kurtosis 38.78873 100 Jarque-Bera 46550.65 Probability 0.000000 0 -0.010 -0.005 0.000 0.005 0.010 0.015

.00005 We see that conditional variance since the beginning of 2014 variance follows a .00004 fickle path and has a high hit in the middle of the third quarter which can be explained .00003 by the devaluation of the Chinese Yuan in August. Besides there is a Chinese crisis .00002 that still exists and concerns the whole world and can be shown by the conditional variance which cannot stay steady to a low .00001 number.

.00000 I II III IV I II III IV I II III IV I 2013 2014 2015

Conditional variance

Based on our estimated model we see that the volatility of the USD/CNY exchange rate depends on its previous variance and residuals for one lag since these to constants of the model are statistically significant and garch effects exist in this time series. We are in a period of increased volatility for the Chinese currency which unravels and despite the attempts of the government , it seems to be continued for a long time. For these reason company’s earnings in these market may be more mutable in the next period because of fluctuation of the exchange rate which reflects the route of the economy where investors set their supervision.  For the USD/EUR exchange rate we fit a Garch(1.1) , which is commonly used in finance, obtaining the following:

2 2 Garch(1.1)= 1.27퐸 − 07 + 0.0349푢푡−1 + 0.9611휎푡−1 All the constants are statistically significant because they a P-value less than 5%.  Long-term volatility : 0.56% ACF Plots of the Residuals and Squared Residuals

ACF of Residuals ACF of Squared Residuals 0,1 0,1

0,05 0,05

0 0 1 3 5 7 9 1113151719212325272931333537 1 3 5 7 9 111315171921232527293133353739 -0,05 -0,05

-0,1 -0,1

From the ACF charts above we have the exception of four values, two in the first plot(11th and 15th lag) and two in the second plot(25th and 30th lag) and beyond that all the autocorrelation values are within two standard deviations (±2s) of the sample autocorrelation. For these reason we adopt the assumption of independent errors. Checking Normality of the Residuals In order to check the residuals Normal distribution we plotted the histogram. It is known that if the errors follow a normal distribution , the residuals also follow an approximately normal distribution.

240 Series: RESID Sample 1/03/2012 2/05/2016 200 Observations 1068

160 Mean -3.15e-05 Median 2.53e-05 Maximum 0.030263 120 Minimum -0.020900 Std. Dev. 0.005494 80 Skewness 0.220933 Kurtosis 5.164364

40 Jarque-Bera 217.1475 Probability 0.000000 0 -0.02 -0.01 0.00 0.01 0.02 0.03 .00010 Let’s assume we have the null hypothesis of the Jarque-Bera and

.00008 P-value to be more than the level of significance 5%. The alternate hypothesis is that we have normal .00006 distribution with a value bellow 5%. The P-value is 0 so we accept the alternate hypothesis that .00004 residuals follow the normal distribution. .00002 While there is a slowdown of the high volatility from 2008 crisis in .00000 the end of 2014 volatility seems to I II III IV I II III IV I II III IV I II III IV I boom again with great fluctuation. 2012 2013 2014 2015 2016 This happens because the

Conditional variance uncertainty in the euro zone from Greek crisis and the crisis of Chinese Yuan which agitated the global economy.

The positive value and the significance of the coefficients of residuals and σ2 means that the present and the future value of volatility is affected by the previous terms with one lag. One month implied volatility in EUR/USD has spiked higher while in the same time is more fluctuated because of the low price of oil and the insulation that ECB tries to achieve for the monetary union. A possible rise in Euro would seem to be a function of market positioning and risk aversion. Moreover a fundamental balance of payments and fiscal positioning shows that is justified. However the level and the time that volatility will remain at this levels may test the strength of Euro.

Appendix W: Monte Carlo Analysis for DCF approach with varying risk free rate

Since risk free rate can be hard to estimate and even maybe an issue of dispute in modern Greece of crisis, we have employed a Monte Carlo Simulation in order to analyze the effect of a varying risk free rate on the DCF model’s target price. This methodology simulates a range of possible outcomes given a normally distributed risk free rate with a 6.5% mean and 5.4% standard deviation. This has been found using monthly observations for the Greek 10-year bond yield in the period 01/01/2001 to 31/12/2015 (source: ECB, Long-term interest rate for convergence purposes - 10 years maturity, denominated in Euro). We run 10000 simulations to conclude how risk free rate can influence the target price derived from the DCF model. The mean target price of the simulation is 15.57 and issues a hold recommendation. Degrees of freedom 179. DCF target price Monte Carlo Simulation for varying risk free rate 1600

1400

1200

1000

800

600

400

200

0

18 14 16

15,6 13,6 14,4 14,8 15,2 16,4 16,8 17,2 17,6 18,4 18,8 19,2 13,2

Simulation Statistics Simulation Statistics Mean 15.57 Sell 0.00% StDev 0.54 Hold 80.65% Min 13.95 Buy 19.35% Max 18.8 Note: “hold” recommendation is given for a range of ±10% of the FF stock price as of 19/02/2016, while buy for target prices higher than 15.95 (i.e. 1.1 times the stock price as of 19/02/2016)

Appendix X: Technical Analysis Approach *The theoretical background of the technical analysis methods discussed below is mainly based on Robert D. Edwards, John Magee W.H.S. Bassetti’s (2013) “Technical analysis of Stock Trends” and John J. Murphy’s (1999) “Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications”. Long term simple moving average for 200 days (200-day SMA)

Moving Averages can be classified as Simple Moving Averages, Weighted or Exponential Moving Averages, and Linear Moving Averages. Among Simple Moving Averages the most common are the 50-day and the 200-day Moving Averages, while shorter-term Moving Averages are more sensitive. To construct a Simple Moving Average you add the value of the stock of n days and divide by n (i.e. the sum of the last 200 closes divided by 200 for a 200-SMA)

When using a moving average to generate signals, one can follow the general rules below:

1. When the closing price moves above the moving average, it triggers a buy signal.

2. Long positions are retained as long as the price trend remains above the Moving Average Line.

3. When the closing price moves below the moving average, a sell signal is generated.

4. Short positions are held as long as the price trend remains below the Moving Average.

The 200-day SMA for the stock of Folli Follie along with its stock price for the period (02/02/2008 to 02/02/2016) are presented below:

Dual Moving Averages – The Double Crossover Method

Moving Averages can often become more efficient, when multiple Moving Averages are plotted. One combination is to plot a 9-day Moving Average and an 18-day Moving Average on the same chart. A buy signal is given when the 9-day Moving Average crosses above the 18-day Moving Average. A sell signal is given when the 9-day Moving Average crosses below the 18-day Moving Average. Of course, an even higher number of Moving Averages can be used; the use of three or four is not uncommon. Studies have shown that the use of two moving averages tends to be the most effective. Like many other indicators, averages can be unreliable or difficult to make use of in fast markets. They contain data not within the current range of volatility; the data never reach the present. The averages do call attention to the current movement in relation to the past.

The dual moving average (that is the 9- and 18-day SMAs) for the stock of Folli Follie along with its stock price for the period (02/02/2008 to 02/02/2016) are presented below:

Moving Average Convergence Divergence (MACD)

MACD was developed by Gerald Appel. It is an oscillator that combines some oscillator principles with a dual moving average approach and is calculated as the difference between two exponential moving averages (commonly the 12- and 26-day EMAs) by subtracting the long-term moving average from the short-term one.

In order to generate signals with MACD, a slower line is also used, commonly a 9-day EMA of the MACD. Buy signals are generated when the faster line (MACD line) crosses the slower line (9-day EMA) from below. Sell signals come from the opposite, that is, when the faster line crosses the slower line from above. One should beware of mechanically trading every MACD crossover, as such a practice could result in considerable losses.

The MACD (calculated using the 12- and 26-day EMAs) for the stock of Folli Follie along with the 9-day EMA of the MACD for the period (02/02/2008 to 02/02/2016) are presented below:

Relative Strength Index (RSI)

The RSI was introduced by J. Welles Wilder, Jr. in his book “New Concepts in Technical Trading Systems” in 1978. As Wilder underlines, two major problems in constructing a momentum line using price differences are:

i. The erratic movement often caused by sharp changes in the value of the security, which poses the need for some smoothing that minimizes these distortions.

ii. There needs to be a constant range for comparison purposes.

The RSI formula is calculated as follows providing the necessary smoothing and creating a constant vertical range between 0 and 100: 100 RSI100 1RS Average of n days' up closes RS  Average of n days' down closes Wilder originally used a 14-day period (n=14), while shorter or longer periods can also be used. Of course, one needs to be aware that the shorter the time period the more sensitive the oscillator and the wider its amplitude are. Using RSI to generate signals

As mentioned above, the RSI can take values from a range between 0 and 100. Values above 70 are commonly considered to indicate an overbought stock, while values below 30 an oversold condition2. Traders often use those levels to generate buy and sell signals. When the RSI fells below 30, a crossing back above this level is considered by many traders as a sign that the trend in RSI has turned up. Similarly, in an overbought condition, a crossing back under the 70 level is often taken as a sell signal.

The RSI for the stock of Folli Follie for the period (02/02/2008 to 02/02/2016) is presented below:

Profitability of the four technical analysis methods on the Folli Follie stock

2 Instead of 70 and 30, the levels 80 and 20 are sometimes used.

At this point, we evaluate the performance of each of the four technical analysis methods, which is presented in the table below:

Table: Technical analysis methods profitability assessment Mean t-statistic H :0mean r > 0 Total Annual return per of mean p-value (for compounding compounding transaction return per normal return for 8 return (on rˆm e a n  transaction distribution) years average) 200-day SMA 10.779% 0.74348 22.86% 85.212% 8.0086% Dual SMA (9-day 1.4046% 0.73805 23.02% 40.191% 4.3134% and 18-day SMAs) MACD and 9-day 1.1234% 0.80169 21.14% 44.403% 4.7001% EMA of MACD RSI 28.84% 2.3387 0.97% 9049.92% 75.864%

One can notice that the Relative Strength Index (RSI) has yielded by far the largest returns, while its mean return per transaction has the highest statistical significance and at the same time it is the only one statistically significant at the 0.05 level . Notable is the fact that in the beginning of the 8-year period (mainly) the RSI has led to some serious losses (e.g. it has generated pairs of buying at approximately €23.16 and selling at €12.23 and similarly buying at €23.13 and selling at €12.45). Nevertheless, later in the 8-year period it has often performed outstandingly well, generating for example pairs of buying at €6.9 and selling at €22.79 or even buying at €4.38 and selling at €24.3.

Second comes the 200-day SMA, which although is the simplest one, has performed better than the more sophisticated ones dual SMA and MACD with an annual compounding return of approximately 8%. The best signals of the 200-day SMA are by far the ones, where it has indicated a long position at €6.61 and then selling at €27.42.

Lastly, the dual SMA and MACD have produced lower returns (similar for the two methods). During the 8-year period rarely have they produced very high returns, or at least high enough

to be comparable to some outstanding ones generated by the RSI (mainly) and 200-day SMA in the time period examined. Of course, there are two sides to every coin; the dual SMA and MACD do not seem to have generated serious losses either, which has

sometimes been the case with the RSI (but not that much with the 200-day DMA).

On the whole, we see that the returns of the dual SMA and MACD have considerably lower variance. That is, their returns have a standard deviation of 15.25% and 12.14% respectively, while for the 200-day SMA the standard deviation is 68% and for the RSI an immense 91.47%. Bearing that in mind, what we conclude -at least regarding the stock of Folli Follie in the 8-year period we have examined- is that the seemingly everlasting trade-off between return and risk is still evident in our case. Even though the RSI has performed significantly better (and the 200-day SMA has also yielded better returns), that comes with a risk a potential speculator has to keep in mind; they need to be aware that using the RSI may yield very high returns in the long run, but also serious losses in the meantime meaning that -among others- they should be ready to be faced with liquidity issues and deal with them.