university of macedonia - cfa institute files/greece... · itch for luxury or else… the chinese...
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University of Macedonia – Student Research Retail Sector, Fashion Industry
Athens 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 Ticker - ASE: FFGRP.AT
Current Price: €14.50 (19/02/16) Headquarters: Athens, Greece
Recommendation: BUY Target Price: €16.92 (16.69% upside)
Table 1: Market Profile
Closing Price 14,50 €
52-Week High-Low 30.00€-12.87€
Average Volume 69.289
Market Cap 970,750M
Nunber of Shares 66.948.210
Dividend Yield 0,00%
Beta 1,3
EV/REVENUE 0,92
EV/EBITDA 4,64
P/E 6,24
Sales 2015(TTM) 1,149M €
Institutional Holdings 57,40%
Insider Holding 38,60%
Investment Summary
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.
Itch for luxury or else… the Chinese advantage In the report “A multifaceted future: the jewelry industry in 2020” McKinsey foresees a strong boost on the branded jewelry share in the industry driven by “new money” and emerging-market consumers. FF Group is more than likely to significantly benefit through the Chinese market, which is packed with both kinds of those consumers. Despite the slowdown of the Chinese economy, the Chinese 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 consciousness proves to be a growth driver.
Business in Greece, really? The Greek economy has been arguably striving to survive the crisis during the recent years. Greece has been unable to borrow from the markets, Greek GDP has plummeted by more than one fourth since 2009, capital controls have been 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 undoubtedly negatively influenced all Greek businesses including FF Group, the latter has proved to be hard to succumb following the market, that is ASE General Index, with approximately 30% stronger “urge” as shown by the dual-beta analysis, when the market ascends than when it goes down, while in the first case it seems to even often outperform the market with an unlevered beta of about 1 (and a higher levered one). In any case, it is true that FF Group is a “truly” international with a well-(geographically) diversified portfolio of markets rendering capable of reaping the foreign markets’ benefits, such as those of the growing Chinese market, and mitigating country risk stemming from the Greek economy.
A glance at some fundamentals The group seems to be effectively dealing with liquidity risk posed by the financial 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, 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% percentile given the industry it operates in.
Business Description
Folli Follie company is an international lifestyle fashion brand founded in Greece in 1982. After 28 years in business, in 2010 Folli Follie S.A. merges with HELLENIC DUTY 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 segments: a) design, manufacture and distribution of jewelry, watches and accessories such as handbags, small leather goods, belts, pashminas and sunglasses, 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)
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Figure 1: Share Price Movement
FFG ASE
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Figure 2: Sales geographical segmentation (historical years)
Asia Greece Rest of Europe - Americas
Figure 4: Revenues by geographical Region
Source: FF presentation
Table 2: Governance Risk 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
Source ISS
other activities (e.g. representation of TechnoGym, surveillance and security systems). 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 group’s major source of revenue is the design, manufacture and distribution of jewelry, watches and accessories, which for the same period constituted 71.5% of the total sales of the company and even more importantly 88.79% of EBITDA. This is due to the fact that this segment had by far the highest EBITDA margin, again underlining its importance for the group. The geographic distribution of the group’s activities shows a high dependence on the Asian and the Greek market. The former accounted for 60.4% of the 2014 revenue with Greater China and Asia remaining the key growth drivers of the Group, supported by strong demographics of a strengthening middle class, while the group generated 26.1% of its revenue in the same year in Greece. At the same time, 75.67% of its non-current assets are located in Greece revealing a possibly significant exposure to the Greek Real Estate Market and economy in general.
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 Fosun International.
Wide price range: Depending on the materials used (sterling silver, stain-
less steel, precious and semi-precious stones) and in a wide price range
covering all needs, the group offers an unrivaled variety of styles.
Improved margins and a low cost base through effective use of the
available resources
Stores in strategic locations: FF Group has established a strong presence
counting more than 900 points of sale worldwide including both flagship
stores in strategic locations as well as stand-alone shops and shop-in-shop
in famous department stores.
Stock and shareholder structure 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 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 ANONYME. The main shareholders are Koutsolioutsos Family, Fosun International and Fidelity Investments, which as of 09.10.15 hold 38.6%, 13.9% and 7.4% of the share capital respectively with a significant 31.4% owned by foreign institutional investors. The remaining 8.7% is under the ownership of domestic institutional investors (4.7%), private investors (3.3%), while 0.7% is treasury shares.
Corporate Governance & Social Responsibility
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
Greece 26,10%
North America 1,4%
Asia incl.
Japan 60,4%
Europe 12,1%
Revenues By Geographical region
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2015p 2016f 2017f 2018f 2019f 2020f
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segmentation (team forecasts)
Asia Greece Rest of Europe - Americas
Figure 5: Shareholding Structure
Source: Hellenic Statistical Authority Figure 7: CCI Greece compared to EU
Source: Y Charts
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. The quality of FF Group’s Corporate Governance can be evaluated in the following facets:
Committees: Governance committees to oversee the company operations, Established Audit , election committees , Auditing Committees
Shareholder Rights: One vote per share voting policy, minority shareholder interests protected against actions of controlling shareholders through special rights, right to dividends , profit distribution policy
Code of Ethics: Code of ethics for the entire company
Corporate social responsibility FF Group has developed a holistic Corporate Social Responsibility Program, the basic axes of which are Culture and Sports, Society and Environment. The group’s actions are vigorously oriented towards environmental protection and sustainable development, while the Headquarters and the Retail stores are housed in eco-friendly buildings promoting recycling and energy saving practices. At the same time, the group has held long-lasting charity initiatives throughout Greece covering the needs of schools, non-profit institutions and NGOs. Lastly, the group embraces culture and arts as global means of communication that unite civilizations.
Industry Overview and Competitive Positioning
A. Greek economy Greek Debt Crisis: commenced in 2009 and still pertaining The Greek economy has been in the eye of the storm of the European debt crisis. Ιn 2010, when Greek state lost access to borrowing from markets, Greek government made a deal for a bailout program with its European partners and the IMF involving structural reforms and austerity measures to be taken by the Greek state under close economic supervision by its lenders. During the recent years of the financial crisis the Greek GDP has plummeted by 28.61% in the period 2009-2014. A major shock for the country’s economy came in June 2015, when capital controls were 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 been an obstacle to Greece’s returning to growth.
Negative business environment in Greece The three consecutive elections during the past year are indicative of the political 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 tax policy, capital controls and high levels of bureaucracy.
B. Jewellery 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.
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
38,60%
31,40%
13,90%
7,40%
3,30%4,70% 0,70%
Koutsolioutsos Family
Foreign Institutional Investors
Fosun International
Fidelity Investments
Private Investors
Domestic Institutional Investors
Treasury Shares
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Figure 6: Greek Unemplyment Rate
Source: McKinsey report
Source: Yahoo Finance
Source: Euromonitor Figure 11: Porter’s Five Forces Analysis
Source: Team analysis
consumption. In 2014 jewelry market sales reported 157USD billion of which mainland China and Hong Kong spent 80.7 USD billion (1USD=6.39RMB) equivalent to 51.4% of global share(increased 10.2% since last year). Gold jewelry has the largest amount of consumption, more than 50%. The consumption is affected by the price of gold (Figure 3)(consumption 2013 716.5 tons, consumption 2014 667.1 tons (-7.4%)).
Japanese market Japanese market seems to have thrived in 2014 and 2015, while weakened Yen and relaxed duty free controls have boosted tourist arrivals by 29% in 2014. The country has become a shopping tourism destination and the government’s focus on the reduction of gender inequality in executive positions is expected to contribute to an increase in the jewelry sales among women.
Competitive Positioning
The FF Group has a diversified portfolio of products to conquer the fashion market, while its operations span from the global jewelry market to the retail and wholesale sectors and department stores.
Folli Follie and Links of London Wide luxury products range and global expansion: Folli Follie and Links of London offer a wide variety of luxury goods, 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 mature luxury markets of Japan, the U.K and Greece, as well as in emerging markets, such as China, and at the same time mitigate political and country risk.
Strong positioning in “affordable luxury” sector The polarization of the fashion and jewelry market between low price and luxury 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 fashion trends faster than ever before shaping a new order of demand which is placed between the mid-market brands and high luxury brands. These brands, whose positioning is characterized by the oxymoron “accessible luxury”, have tried to provide luxury products at cost effective prices and establish a good tradeoff between quality and price. The fast-changing fashion leads the traditional customers of high-end luxury products to look for more cost effective ones. Moreover, the enhanced income of 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 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.
China’s emerging luxury products market penetration 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 Chinese jewelry market with more than 180 points of sale. The revenues in the whole of the Asian market have been steadily increasing showing the successful positioning of the group in the markets of China and Japan.
Solid cash flows from retail, wholesale & department stores The ability of these sectors to generate low risk cash flows enhance the liquidity of 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 and wholesale distribution of famous brands such as Nike, Juicy Couture, Converse, Franklin & Marshall and others not only in Greece but also in Cyprus, Bulgaria and
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Figure 8: Branded vs Unbranded Jewelry, Globaly
Unbranded
Branded
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Figure 9 : Monthly Gold Prices
price per oz
IndustryRivalry &
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Threats ofNew Entrants
BargainingPower of
Customers
8,879,06
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Figure 10: Japan Jewelry Sales Forecast (Usd Bn)
Source: FF Group
Source: FF Group Figure 14: Price Volume pyramids for Luxury Goods
Table 3: Weighted Average Cost of Capital (WACC)
2016 15.97%
2017 14.49%
2018 13.14%
2019 11.92%
2020 10.81%
Terminal 9.80%
Sources: Team analysis, FF Group's financial statements, ECB, A. Damodaran's page: http://pages.stern.nyu.edu/~adamodar/
Romania. Additionally, the liquidity offered by these sectors can be useful for serving their financial liabilities and investing opportunities.
Valuation
Target Price:16.92€ Recommendation: BUY For the valuation of FF Group a 6-year DCF and a Relative Multiples Valuation have been employed.
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 Spain), where Asia plays a significant role, their shares on sales since 2010, the condition of the 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. Furthermore, a professional outlook for the jewellery industry in the future by McKinsey & Company has been taken into consideration. A 6-year period has been selected, so that the 2015 projected cash flow is not discounted, while the forecasted cash flows for the period 2016-2020 are discounted to 01.01.16. The DCF analysis has yielded a target price of €15.51 (as of 01/01/16), which is most sensitive to the following factors:
Weighted Average Cost of Capital (WACC) The WACC has been calculated weighing the cost of debt and the cost of equity of the group using the book value of debt (bond loans, bank loans and leases) and the market value of equity respectively as weights.
Cost of equity For the estimation of the short term cost of equity CAPM is used following a bottom-up approach for beta, which is then levered, using the average 10-year Greek bond yield for the 15-year period Jan2001-Dec2015 as risk free rate and a risk premium based on current sector betas (for each of the three main activities of the Group) for each country. Cost of equity for the short term period has been assumed to 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 the risk premium (now the average for the pre-crisis Jan2001-Dec2008 period), as Greece –mainly–recovers from the financial crisis and the other five economies return to pre-crisis conditions, as well as the finding that beta is closer to the mean value of 1 in a future period, which is utilized based on Blume’s (1971) adjusted beta.
Cost of debt 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 the period 2011-2014 by the average value of the respective liabilities, ii) as the sum of the risk free rate and the typical company default spread that correspond to the group’s interest coverage ratio as of 09.09.15. Similarly, the cost of debt given by the 2nd method gradually declines, as the risk free rate is assumed to do so till it reaches its long term value.
Short term gross profit growth In order to forecast the growth of gross profit, which has been selected given the persistent value of the gross profit margin near 50%, a (geographically) weighted GDP growth has been estimated using the six above mentioned countries’ growth. The resulting weighted-GDP index is found to have a significant correlation with gross profit for the period 2010-2015 (2015 projected) and thus can be considered a well-trusted predictor of gross profit. For the period 2016-2020 the growth of gross profit is estimated using the nominal GDP growth (in US$) forecasts for the six countries by the IMF, which are converted in euros using the 2016-2017 EU Commissions forecasts for the exchange rate (projected until 2020), and the Weighted GDP CAGR to Gross profit CAGR ratio for the recent period 2013-2015.
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Figure 13 :Points of sale in China(Orange) compared to points
of sale in Japan(Red)
Change%0,00%
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2010 2011 2012 2013 2014
Change% 0,00% 7,06% 28,44% 48,37% 65,73%
Figure 12:Change% of the annual sales in Asian Markets (base year
2010)
Pyramids of price/volume: The conversion of the
price traditional pyramid price/value into a new one
to characterize the market of luxury goods.
Historical to prospective prices and volume correlation
Volume
price
Source: Goldman Sachs Research
luxury
mid
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accessible luxury
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Table 5: Basic multiples – FF comparable companies
Multiples P/E P/S EPS
Folli Follie 6.24 0.81 2.26
Weighted Average 16.68 2.31 3.18
Weighted Median 14.37 2.08 2.01
Table 6: Basic multiples – FF comparable companies (2nd part)
Multiples P/B EV/EBIT
DA EV/Rev
Folli Follie 0.64 4.64 0.92
Weighted Average 4.56 10.37 2.00
Weighted Median 3.54 8.82 1.80
Table 7: Simple Relative Valuation
Multiples Value Weights
P/E 32.2 15%
P/S 35.7 15%
Price/Book 26.26 20%
EV/EBITDA 26.21 25%
EV/Revenue 28.62 25%
Weighted value 29.14
Table 4: FF short term betas
Bottom-up Unleverd 1.036
Levered 1.3
Regression
(unlevered)
monthly obs. 01.03.2011-01.02.2016
"Common" 0.855
Downside 0.754 Upside 0.946
weekly obs. 18.02.2013-15.02.2016
"Common" 0.8
Downside 0.794 Upside 1.073
Sources: i) sector betas are from A. Damodaran’s page: http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datacurrent.html ii) FFGRP.AT and ASE General Index close prices are from Yahoo Finance and nafteboriki’s pages
Terminal growth For the terminal value, the FCFF has been assumed to grow with a weighted GDP CAGR of 2.19%, that is the estimated weighted GDP CAGR for the period 2020-2060 using the OECD’s forecasts (as of May 2014) for the countries’ GDP volume up to 2060. This is consistent with the above Weighted GDP CAGR to Gross profit CAGR ratio. Furthermore, the report “A multifaceted future: the jewelry industry in 2020” of McKinsey foresees a growing share of branded jewelry emphasizing both “new money” and emerging-market consumers as fundamental drivers of that growth, which is to have a positive influence in the group’s sales its development in China, a market with both kinds of those consumers.
Relative and Multiples Valuation 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 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, calculated for each comparable company. 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 different weight. The multiples, which are used, are P/S, P/E, P/Book Value, EV/Revenue and EV/EBITDA. We give relatively more weight to the multiples which 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 luxury sector and 30% from the fashion market and thus the retail competitors. The “recipe” of the comparable companies is based on the breakdown of the FF Group’s revenues. The simple relative valuation implies an exaggerated premium since the 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 given by the simple relative valuation. It is vital we be referred to the fact that many an analyst use a valuation method of the stocks based on the historical medians of the multiples of one company. This method is relies on the assumption that the multiples of one company will revert to the historical medians of them. The primarily 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 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 that y=x where y is the intrinsic value of the multiples of our company and x the median of the values of the relative companies while in OLS method is assumed that y=a+bx. For this method we used only the EV/Revenue and P/S multiple since only for these multiples the correlation between each of them with the historical medians is statistical significant. This method takes into account the behavior of the 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 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:
𝑊1 = ∑(𝑑𝑖 − 𝑑𝑖𝑓)/𝑑𝑖
𝑛
𝑛
𝑖=1
𝑊2 = 1 − 𝑊1 Where di = Average discount of the decade for each multiple, dif = Projected discount of the multiples for the upcoming period, n = Number of the multiples whose correlation is statistical significant.
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
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Figure 15: Weighted GDP and Gross profit indices
Weighted GDP index (2009=100), sources:IMF, EU Commission, team calculationsFF Gross profit index (2009=100)
Table 8: Linear Regression for P/S
Intercept (a) -1.8137
b coefficient 1.2371
R-squared 0.449241
Table 9: Linear Regression for EV/Revenue
Intercept (a) -0.784827
b coefficient 0.993019
R-squared 0.488385
Figure 16: Graph for median and FF’s R/S ratio
Figure 17: Graph for median and FF’s EV/Revenues ratio
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 (firms) whose cash flows are currently positive and can be estimated with some 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 cash flows align with profitability within a reasonable forecast period with which the 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 are needed to capture the “international” features of the FF Group) we have given a larger weight of 67.59% to the DCF Valuation and the remaining 32.41% to the Relative and Multiples Valuation.
Investment Risks
Political Risk The Group is likely to keep being negatively influenced by the uncertainty posed 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.”
Market risk
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 appreciation against local currencies would increase the cost of sales, whereas the increase sale prices would remain unchanged limiting gross profit. Furthermore, a probable USD revaluation in relation to the Euro would increase the Group’s operating expenses since part of its disposal expenses, and mainly royalties, is expressed in USD.
b) Risk from the conversion of financial statements expressed in foreign currency The Group is exposed to the risk from the conversion of the financial statements of foreign companies which operate in currencies other than Euro and the Group has investments in.
PR
FXR1
FXR2
FXR3PRI
CR
LR
IR
7
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ium
Hig
h
Low Medium High…
Figure 18: Investment risks assessment
Table 10: Risk Handling Measures
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
Rates (IRR)
Interest rate risk hedging tools (IRS)
US Dollar Revaluatio
n (FXR1)
Call options, forward contracts, futures in usd, swaps and other
financial market products, suppliers from different
countries1
Conversion of
Financial Statement
s (FXR2)
Foreign exchange risk setoff products, mostly forward
type agreements
Price risk – Inflation
(PRI)
Investment in short-term securities (mostly) with low price
fluctuation1
Credit Risk (CR)
Cooperation with known department 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
Table 11: Group's inventory turnover positioning in the industry
Jewelry
Apparel and
accessory stores
Department stores
(1) Avg inventory turnover
1.68 4.57 3.87
(2) Standard deviation
0.58 2.13 1.45
(3)=(2)2 Variance 0.34 4.54 2.10
(4) Group's cost of goods for 2014 (in million euros)
356.45 99.01 86.50
(5)=(4)/sum of (4) Segment weights
0.66 0.18 0.16
(6)=(1)x(5) 1.10 0.83 0.62
(7)=(3)X(5)2 0.15 0.15 0.05
Group's expected inventory turnover, sum of (6) 2.56 Standard deviation for group's inventory turnover, sum of (7) 0.59
Real group's inventory turnover 1.20 Percentile (normal distribution) 1.09%
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 with €153 million out of €328 million it acquired 1.231.233 shares of Dufry AG. In 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 appreciation of the euro against other currencies, such as the Yen, Yuan and US dollar. d) GARCH Approach Because we are in a period where economic prospects are mutable of high fluctuation in exchange rates it is urgent to try to describe the volatility in the major of them. For this reason we have employed the Garch(1,1) model, which is commonly used in finance, in order to test for heteroskedasticity and especially for Arch and Garch effects. We show that both rates which we have employed in Garch have statistically significant all of the three coefficients. This means that future price of fluctuation depends on previous volatility of residuals and itself. Additional to all, 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 because of its exposure at high currency risk.
iii) Price risk – Inflation According to the administration, the Group runs no risk from price fluctuation, since it does not own a significant securities portfolio and the prices of the products it sells do not present particular fluctuations. Thus, the international increase of inflation pressure in combination with the disturbance of the international financial system may modify consuming habits, affecting the group’s sales and profitability.
Credit Risk
FF Group cooperates with department stores and franchisees, which they have contractual obligations against the Group, hence, there is the risk of breaching these obligations on part of the other party. The Group, though, claims to operate most of its wholesale activity with known department stores and a set of selected franchisees mitigating this risk.
Liquidity risk This risk has had increased importance in the recent years of financial crisis mainly due to the shrinkage of most forms of lending. However, the current ratio and the 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 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.
Inventory risk This risk emerges from the retaining of old stock from certain companies of the Group and concerns either the incapability to sell it or being able to sell it only in prices lower than its evaluation. The inventory turnover (IT) is calculated using the average inventory of the past five quarters, so that it is adjusted for seasonality in the FF Group’s sector:
1/10/2014-30/9/2015
30/9/2014 31/12/2014 31/3/2015 30/6/2015 30/9/2015
501.1 1.2
( ) 416.235
Cost of goods soldInventory turnover
Inv Inv Inv Inv Inv
Based on data for the inventory turnover in the industries of jewelry, department stores and apparel1 and weighing using the cost of goods for each segment of the Group, we get an average IT of 2.5575 with standard deviation of 0.592 (assuming no correlation between the three industries’ ITs), which (assuming normal distribution) place the Group in the lowest 1.09% percentile. In order to manage this risk the Group offers its stock in area-markets such as: Outlet type discount department stores, discount outlets and large hotel units. The Group claims to fully cover the inventory risk through the evaluation of its stock at its net realizable value relying on the administration experience and actual market data.
Source: Yahoo Finance
Source: Yahoo Finance
Source: FF Group’s financial statements
1Gaur, Fisher and Raman (2005). An Econometric Analysis of Inventory Turnover Performance in Retail
Services. Management Science, 51(2), pp.181-194.
Financial Analysis
Outlook The beginning year (2013) does not give us the ideal perception of the financial condition of FF, since a major part of the net income derived from the disinvestment of HDF (Hellenic Duty Free). Consequently, the formed ratios for this period, which contain the net income measure, do not mirror the financial position 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 estimated CAGR is -0,11% (insubstantial).
Key financial ratios of the FF Group
2013 2014 2015p 2016f 2017f 2018f 2019f 2020f
Liquidity
Current Ratio 3.16 5.24 6.62 6.53 6.28 6.37 6.08 5.69
Cash Ratio 0.78 1.14 0.94 0.95 0.96 1.19 1.19 1.14
Quick Ratio 2.38 3.83 4.52 4.39 4.13 4.24 4.00 3.74
Profitability
Gross Profit (%) 50.38 50.27 46.75 46.40 46.04 45.69 45.35 45.00
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 (%)
178.49 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
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
Long Term Debt to Equity 0.05 0.26 0.23 0.21 0.20 0.18 0.17 0.15
Debt to Equity 0.33 0.45 0.38 0.35 0.35 0.33 0.32 0.32
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
Interest Coverage 15.47 9.29 11.65 16.40 19.43 21.65 24.20 26.31
Activity
Total Assets Turnover 0.59 0.51 0.57 0.60 0.60 0.60 0.60 0.61
Fixed Assets Turnover 3.72 3.81 4.59 4.67 4.63 4.63 5.00 4.57
Inventory Turnover 1.47 1.60 1.54 1.44 1.42 1.40 1.32 1.40
Accounts Receivable Turnover 0.76 0.76 0.70 0.66 0.63 0.62 0.59 0.58
DuPont analysis R.O.E= Profit margin (Profit/Sales)*Total Assets Turnover (Sales/Assets)*Equity Multiplier or financial leverage ratio (Assets/Equity). Decomposed dupont analysis for calculating return on equity (R.O.E)
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 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
05
101520253035
Figure 19: Silver Price($ per ounce)
0
500
1000
1500
2000
Figure 20:Gold Prices ($ per ounce)
0
20
40
60
80
100
120
140
160
180
-12,00%
-8,00%
-4,00%
0,00%
4,00%
8,00%
12,00%
16,00%
EBIT
DA
Ma
rgin
%
Figure 21:Selling Perfomance and Profitability of R&W and
Dep. Stores
RevenuesR&W
RevenuesDep.StEBITDA MR&W
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.
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.
DuPont Analysis
Profit Margin (Profit/Sales)
Total Assets Turnover
(Sales/Assets)
Equity Multiplier
(Assets/Equity)
R.O.E
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%
Appendix A: Organizational chart
FF Group‘s organizational structure follows perfectly the geographical structure. The company is organized
based on the countries (France, 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
Koutsolioutsos
Chairman,
Executive
Member
Managing Director & General
Manager Folli-Follie SA
Board of Directors at Elmec Sport SA
and Hellenic Duty Free Shops SA
Since
19-1-2011
Ketty
Koutsolioutsos
Vice Chairman,
Executive
Member
Executive Director Folli Follie SA
Executive Director Hellenic Duty Free
Shops SA.
Since
19-1-2011
George
Koutsolioutsos
Managing
Director,
Executive
Member
Vice President of Folli Follie SA
President of Seanergy Maritime
Holdings Corp
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.
Since
19-1-2011
Emmanouel
Zachariou
Deputy Managing
Director &
General Director,
Executive
Member
Chief Executive Officer of Elmec Sport
SA
Vice Chairman & Commercial
Manager of Sportsman SA
Vice Chairman & General Manager
Alouette SASU
Since
19-1-2011
George Aronis Independent,
Non-Executive
Member
Executive Director & GM-Retail
Banking Alpha Bank SA
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
National Bank of Greece SA
General Manager-Consumer Banking
ABN AMRO Bank
Non-Executive Director Hellenic Duty
Free Shops SA
Since
19-1-2011
Epaminondas
Dafermos
Independent,
Non-Executive
Managing Director AGET Heracles
Executive Director & Deputy
Since
19-1-2011
Member Managing Director at Hellenic Duty
Free Shops SA
Ilias
Koukoutas
Non-Executive
Member
General Manager Attika AE
Managing Director at North Landmark
SA
Board of Directors at Hellenic Retail
Business Association
Executive Director Elmec Sport SA
Since
19-1-2011
Ilias
Kouloukountis
Non Executive
Member Independent Director at Seanergy
Maritime Holdings Corp.
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
Since
19-1-2011
Zacharias
Mantzavinos
Non-Executive
Member
Independent Non-Executive Director
by Folli-Follie SA
Vice Chairman of Hellenic Duty Free
Shops SA
Since
19-1-2011
Irene Nioti Executive
Member
Treasurer at Folli-Follie SA
Non-Executive Director Hellenic Duty
Free Shops SA
Since
19-1-2011
Anna Marina
Xirokosta
Non-Executive
Member
Lawyer for Corporate Law Since
7-5-2013
Ioannis
Tsigkounakis
Non-Executive
Member
Board of Directors at Aspropirgos
Maritime Ltd. and Redina Maritime
Ltd.
Non-Executive Director by Hellenic
Duty Free Shops SA.
Since
7-5-2013
Jiannong Qian Non-Executive
Member
Chairman at Shanghai Fosun High
Technology (Group) Co. Ltd.
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
Since
26-5-2011
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
Resources Department of Human Resources at
Folli Follie Georgios Alavanos Chief Accountant Head-Accounts Department by Folli-
Follie SA. Elias Dimitrakopoulos Director-Internal Audit
Department Audit Department at Folli Follie
Nikos Anamouroglou Investor Relations Officer Head-Investor Relations at Elmec Sport SA
George Vlachos Chief of Strategy and Organization Development Officer
Advisory Board Member at Hellia Co.
Managing Director South East & 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, Guam
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 Macau, 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 Revenue(Turnover)
989.60 1021.42 1110.03 934.23 998.06
1218.66 1312.11 1401.64 1500.78 1615.72 1744.00
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 income
33.06 26.42 32.54 13.08 11.68
11.95 16.09 17.08 18.29 19.63 21.24
Administration expenses
-55.53 -56.62 -73.60 -59.09 -56.95
-78.47 -78.90 -83.73 -89.64 -96.24 -104.12
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 expenses
-6.55 -6.67 -16.21 -9.94 -11.82
-17.63 -15.37 -16.31 -17.47 -18.75 -20.29
Operating income (EBIT)
171.67 173.98 185.78 173.40 202.33
213.37 225.11 243.82 259.80 275.34 290.03
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 Associates
0.00 0.00 -0.09 -0.03 -0.31
-0.33 -0.35 -0.36 -0.42 -0.42 -0.42
Profit/Loss (before the tax)
124.71 121.94 130.89 397.61 192.77
207.60 225.57 252.00 260.81 278.76 290.14
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 the tax)
85.10 91.29 95.62 347.50 145.41
158.94 175.47 185.55 192.03 205.25 213.63
Growth %
0.07 0.05 2.63 -0.58
0.09 0.10 0.06 0.03 0.07 0.04
Depreciation & amortization
21.67 24.77 27.04 21.29 20.64
28.54 27.78 29.41 30.87 32.47 34.20
Profit before taxes depreciation & amortisation (EBITDA)
193.35 198.75 212.82 194.69 222.97
241.91 252.89 273.23 290.67 307.81 324.23
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 Revenue(Turnover)
100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Cost of goods -
49.63% -
49.43% -
49.83% -49.62%
-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 expenses
-5.61% -5.54% -6.63% -6.32% -5.71% -6.44% -6.01% -5.97% -5.97% -5.96% -5.97%
Selling expenses -
30.09% -
29.93% -
28.27% -25.83%
-24.28%
-
22.34% -
23.28% -
22.73% -
22.46% -
22.40% -
22.45% Other operating expenses
-0.66% -0.65% -1.46% -1.06% -1.18% -1.45% -1.17% -1.16% -1.16% -1.16% -1.16%
Operating income (EBIT)
17.35% 17.03% 16.74% 18.56% 20.27% 17.51% 17.16% 17.40% 17.31% 17.04% 16.63%
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 Associates
0.00% 0.00% -0.01% 0.00% -0.03% -0.03% -0.03% -0.03% -0.03% -0.03% -0.02%
Profit/Loss (before the tax)
12.60% 11.94% 11.79% 42.56% 19.31% 17.04% 17.19% 17.98% 17.38% 17.25% 16.64%
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 tax)
8.60% 8.94% 8.61% 37.20% 14.57% 13.04% 13.37% 13.24% 12.80% 12.70% 12.25%
Depreciation & amortization
2.19% 2.43% 2.44% 2.28% 2.07% 2.34% 2.12% 2.10% 2.06% 2.01% 1.96%
Profit before taxes depreciation &
amortisation (EBITDA)
19.54% 19.46% 19.17% 20.84% 22.34% 19.85% 19.27% 19.49% 19.37% 19.05% 18.59%
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.
*1 indicates lower governance risk, while a 10 indicates higher governance risk
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
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 Rates (IRR)
Interest rate risk hedging tools (IRS)
US Dollar Revaluation (FXR1)
Call options, forward contracts, futures in usd, swaps and other financial market
products, suppliers from different countries1
Conversion of Financial
Statements (FXR2)
Foreign exchange risk setoff products, mostly forward type
agreements
Price risk – Inflation (PRI) Investment in short-term securities
(mostly) with low price fluctuation1
Credit Risk (CR) Cooperation with known department
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)
PR
FXR1
FXR2
FXR3
PRI
CR
LR
IR
7
Pro
bab
ility
of
ince
den
ce
Low
M
ed
ium
Hig
h
Low Medium HighPossible Impact
Investment risks assessment
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
products till 2048
Improvement margins on Corporate
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
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
Ranking
0 No Threat
1 Insignificant Threat
2 Low Threat
3 Moderate Threat
4 Significant Threat
5 High Threat
0
1
2
3
4
5
IndustryRivalry &
Competition
BargainingPower OfSuppliers
Threat ofSubstitutes
Threats ofNew Entrants
BargainingPower of
Customers
Porter's 5 Forces Analysis
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
Supplier Power
-Large Economies of Scales
-Reduced Profitability
-Experience & expertise required
-Resources requirements
-Hardly accessible raw materials
-Access to distribution channels
-Product differentiation
New entry quite difficult
Threat of
New Entry
-Many competitors
-Competitors' Diversity &
Strength
-Customer Loyalty
-Fixed costs
-Exit Barriers
High Competitive Rivalry
Competitive Rivalry
Buyer
Power
Supplier Power Competitive
Rivalry
Threat of
Substitution
-Company able to
substitute
-Similar products
-Large number of suppliers
-Significant proportion of
suppliers' revenue
Low supplier power
-Several precious metals
substitutes
-Great variety
(semi)precious stones
-Same or less cost
-Fulfills customers’ needs
at the same level
Significant threat
Threat of Substitution
-Ability to substitute /
great variety
-Zero personal cost of
changing brands
-Flexible on their
needs' satisfaction
-Fully informed about
competitors prices
-Retail sales
Moderate Buyer Power
Buyer Power
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
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Greece Long-term Interest Rates
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
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
35,00%
2008 2009 2010 2011 2012 2013 2014 2015 2016
0
5
10
15
20
25
30
35
40
20… 20… 20… 20… 20… 20… 20… 20… 20…
Greece 10-Year Bond Yield
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
Points of sale by country (for country weights by region)
Region Country Points of sale
(POS) Total
Europe United Kingdom 312
341 Spain 29
Asia
China 187
281 Japan 68
Republic of Korea 26
Source: http://www.ffgroup.com/stores/
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 of Korea
3.18% 3.41% 3.24% 3.96% 5.35% 5.59% 5.96% 6.24% 6.51% 6.57% 6.57% 6.57%
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 usua lly 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 Europe
14.76% 14.01% 11.52% 11.47% 12.58% 13.50% 11.81% 11.36% 10.88% 10.50% 10.50% 10.50%
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 bottom-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%
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 Korea
901.94 1094.50 1202.46 1222.81 1305.61 1410.38 1392.95 1450.05 1545.81 1649.08 1763.36 1898.76
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 euros
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 Korea
0.94% 0.15% 0.40% 0.18% 0.45% 1.09% 0.40% 0.43% 0.44% 0.46% 0.50%
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 in euro
7.56% 0.42% 5.77% -1.32% 3.94% 16.85% 6.97% 6.12% 6.37% 6.96% 7.25%
Note: The weights used here are presented in a previous table (Sales segmentation by country from 2009 to 2020).
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 billion US Dollars)
Compound annual GDP growth rate
Weight Weighted
compound annual GDP growth rate 2020f 2060f
Greece 286.18 553.35 1.66% 18.50%
2.19%
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%
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.
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.
Appendix K: Bottom-up Beta Estimation
Bottom-up beta estimation
Sector/Company segment
Country /Region
Weight (Revenues per Market/Region 2015 forecast)
Sector beta (unlevered)
Short term bottom-up
beta (unlevered)
Short term bottom-up
beta (levered)
Terminal bottom-up beta
(levered) - Blume's (1971) adjusted beta
Jewellery
Greece 3.24% 0.813
1.0356 1.30 1.199
Europe 5.98%
China 47.20% 1.423
Japan 17.17% 0.573
Retail - Wholesale - Department stores
Greece 20.58% 0.723
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:
2 1Adjusted beta estimated beta
3 3
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): Folli Follie ASE General IndexR a b R 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:
ASE General IndexFolli FollieR a b R 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:
ASE General IndexFolli FollieR a b R 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): Folli Follie ASE General IndexR a b R 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:
ASE General IndexFolli FollieR a b R 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:
ASE General IndexFolli FollieR a b R 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
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
Risk free rate estimation for Greece
Short term risk free rate for 2016 (geometric mean of the Greek 10-year bond yield for the period 2001-2015)
2017 2018 2019 2020 Long term risk free rate (geometric mean of
the Greek 10-year bond yield for the pre-crisis, 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
Appendix N: Risk Premium Estimation
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.
Risk premium estimation
Country
Short term weight
(revenues per country 2015
forecast)
Short term risk premium by
country (current country risk premium)
Terminal weight (revenues per country - 2020
and on forecast)
Terminal risk premium by country (pro crisis 2001-2008
average)
Weighted short term
risk premium (for 2016)
Weighted terminal risk
premium
China 42.84% 6.90% 47.25% 6.29%
10.23% 6.10%
Republic of Korea
5.96% 6.74% 6.57% 6.43%
Japan 15.58% 7.05% 17.18% 6.19%
Greece 23.82% 20.90% 18.50% 6.09%
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
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)
2014 2013 2012 2011
2011-2014 total
Cost of debt before tax (average total
interest expenses over average debt for the
four-year period)
Cost of debt after tax
Interest Expenses (loans)
14.33 20.49 38.94 37.94 27.92
5.82% 4.31%
Interest Expenses (leases)
0.92 1.42 1.65 2.06 1.51
Other interests 0.40 0.54 0.46 0.02 0.36
Total interest expenses
15.65 22.45 41.05 40.01 29.79
Interest bearing debt (book value of bank and bond loans plus leases)
351.14 222.54 741.08 731.91 511.67
Notes: i) Cost of debt after tax has is cost of debt before tax multiplied by one minus the marginal tax rate:
CDBT CDAT (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 paidInterest 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 rate plus company default spread)
7.24% 6.77% 6.33% 5.93% 5.55% 5.20%
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:
CDBT CDAT (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
Before tax
Rf plus CDS approach 7.24% 6.77% 6.33% 5.93% 5.55% 5.20%
Financial statements approach
5.82%
After tax
Rf plus CDS approach 5.36% 5.01% 4.69% 4.39% 4.11% 3.85%
Financial statements approach
4.31%
Before tax Average
6.53% 6.30% 6.08% 5.87% 5.69% 5.51% 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 euros)
1,003,553,667.90 €
(4) Interest bearing debt (bond and bank loans plus leases in million euros, 30.09.2015)
343,519,992.12 €
(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
FCFF Estimation (in Million euros)
2015p 2016f 2017f 2018f 2019f 2020f
Operating income (EBIT)
213.37 225.11 243.82 259.80 275.34 290.03
EBIT*(1-t) 157.90 166.58 180.42 192.25 203.76 214.62
Less: Purchases of tangible and intangible assets
66.06 30.99 30.99 30.99 30.99 30.99
Plus: Depreciation & amortization
28.54 28.37 30.07 31.95 34.14 36.57
Less: Change in Working Capital
87.72 19.84 57.43 51.32 62.99 47.46
Free Cash Flow to the Firm (FCFF) 31/12
32.65 144.12 122.08 141.90 143.91 172.74
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.
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
Target Price Estimation
2016f 2017f 2018f 2019f 2020f Terminal Period
Free Cash Flow to the Firm (FCFF) 31/12/20XX
144.12 122,08 141,90 143,91 172,74 126,23
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€ Notes: i) Discount Ratethis year=Discount RatePrevious year*(1+WACCthis year)(-1). 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 iv) Enterprise Value on 19/02/16 =Enterprise Value01/01/16*(1+WACC2016)(50/365)
Appendix S: Multiples Analysis
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.
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.
P/E P/S
Earnings
per
share(EUR) Price/Book EV/EBITDA EV/Revenue weight
"Affordable
Luxury"
Position
Region
Activitie
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
Multiples
FF EBITDA(2015TTM) 216,200,000
Short/Long Borrowings 344,000,000
Cash and Equivalents 220,000,000
Minority Interest 28,000,000
Net Income 150,000,000.00
Number of Shares(31/12/14) 66,948,210
MRQ 1.884621296
Market Cap 937274940
Assets-Intag.Assets-Liabilities 497328000
Sales 2015(TTM) 1,149,000,000
Multiples Price Weights
P/E 32.2 15%
P/S 35.7 15%
Price/Book 26.26 20%
EV/EBITDA 26.21 25%
EV/Revenue 28.62 25%
Price 29.14
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
Riskfree rate Beta
Short term cost of debt estimation method
Financial Statements approach
Average approach
Rf+CDS approach
Geometric mean of Greek 10-year bond
yield through the period Jan 2006- Dec
2015 (monthly observations)
Bottom-up 16.86% 17.12% 17.38%
Regression (monthly obs.) 15.13% 15.39% 15.65%
Regression (weekly obs.) 14.60% 14.86% 15.12%
Geometric mean of Greek 10-year bond
yield through the period Jan 2001- Dec
2015 (monthly observations)
Bottom-up 15.84% 15.97% 16.10%
Regression (monthly obs.) 14.11% 14.24% 14.37%
Regression (weekly obs.) 13.58% 13.71% 13.85%
Geometric mean of German 10-year bond
yield through the period 2001-2015
(monthly observations)
Bottom-up 12.96% 12.73% 12.50%
Regression (monthly obs.) 11.23% 11.00% 10.77%
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
Riskfree rate Blume (1971) adjusted
Beta
Long term cost of debt estimation method
Financial Statements approach
Average approach
Rf+CDS approach
Geometric mean of Greek ten-year bond yield
through the period 2001-2008 (monthly observations)
Bottom-up 9.63% 9.80% 9.51%
Regression (monthly obs.)
9.17% 9.12% 9.06%
Regression (weekly obs.) 8.96% 8.91% 8.85%
Geometric mean of German 10-year bond
yield through the period 2001-2015 (monthly
observations)
Bottom-up 8.51% 8.28% 8.04%
Regression (monthly obs.)
7.82% 7.59% 7.36%
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).
*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.
0
20
40
60
80
100
120
140
160
180
-12,00%
-8,00%
-4,00%
0,00%
4,00%
8,00%
12,00%
16,00%
EBIT
DA
Ma
rgin
%
Selling Perfomance and Profitability of R&W and Dep. Stores
Revenues R&W
Revenues Dep. St
EBITDA M R&W
EBITDA M Dep.St
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:
Garch(1.1)= 4,98𝛦 − 07 + 0.1425𝑢𝑡−12 + 0.6378𝜎𝑡−1
2
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
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.
-0,08
-0,06
-0,04
-0,02
0
0,02
0,04
0,06
0,08
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
ACF of Residuals
-0,02
-0,01
0
0,01
0,02
0,03
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
ACF of Squared Residuals
0
100
200
300
400
500
-0.010 -0.005 0.000 0.005 0.010 0.015
Series: RESID
Sample 1/02/2013 2/04/2016
Observations 857
Mean 5.09e-06
Median -5.42e-05
Maximum 0.018328
Minimum -0.011781
Std. Dev. 0.001549
Skewness 2.387593
Kurtosis 38.78873
Jarque-Bera 46550.65
Probability 0.000000
We see that conditional variance since the
beginning of 2014 variance follows a
fickle path and has a high hit in the middle
of the third quarter which can be explained
by the devaluation of the Chinese Yuan in
August. Besides there is a Chinese crisis
that still exists and concerns the whole
world and can be shown by the conditional
variance which cannot stay steady to a low
number.
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:
Garch(1.1)= 1.27𝐸 − 07 + 0.0349𝑢𝑡−12 + 0.9611𝜎𝑡−1
2
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
.00000
.00001
.00002
.00003
.00004
.00005
I II III IV I II III IV I II III IV I
2013 2014 2015
Conditional variance
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.
Let’s assume we have the null
hypothesis of the Jarque-Bera and
P-value to be more than the level
of significance 5%. The alternate
hypothesis is that we have normal
distribution with a value bellow
5%. The P-value is 0 so we accept
the alternate hypothesis that
residuals follow the normal
distribution.
While there is a slowdown of the
high volatility from 2008 crisis in
the end of 2014 volatility seems to
boom again with great fluctuation.
This happens because the
uncertainty in the euro zone from
Greek crisis and the crisis of
Chinese Yuan which agitated the global economy.
-0,1
-0,05
0
0,05
0,1
1 3 5 7 9 1113151719212325272931333537
ACF of Residuals
-0,1
-0,05
0
0,05
0,1
1 3 5 7 9 111315171921232527293133353739
ACF of Squared Residuals
0
40
80
120
160
200
240
-0.02 -0.01 0.00 0.01 0.02 0.03
Series: RESID
Sample 1/03/2012 2/05/2016
Observations 1068
Mean -3.15e-05
Median 2.53e-05
Maximum 0.030263
Minimum -0.020900
Std. Dev. 0.005494
Skewness 0.220933
Kurtosis 5.164364
Jarque-Bera 217.1475
Probability 0.000000
.00000
.00002
.00004
.00006
.00008
.00010
I II III IV I II III IV I II III IV I II III IV I
2012 2013 2014 2015 2016
Conditional variance
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.
Simulation Statistics
Sell 0.00%
Hold 80.65%
Buy 19.35% 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)
0
200
400
600
800
1000
1200
1400
1600
13
,2
13
,6 14
14
,4
14
,8
15
,2
15
,6 16
16
,4
16
,8
17
,2
17
,6 18
18
,4
18
,8
19
,2
DCF target price Monte Carlo Simulation for varying risk free rate
Simulation Statistics
Mean 15.57
StDev 0.54
Min 13.95
Max 18.8
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:
100RSI 100
1 RS
Average of n days ' up closesRS
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:
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
Table: Technical analysis methods profitability assessment
Mean return per transaction
meanr̂
t-statistic of mean return per transaction
0 meanH :r >0
p-value (for normal distribution)
Total compounding return for 8 years
Annual compounding return (on average)
200-day SMA 10.779% 0.74348 22.86% 85.212% 8.0086%
Dual SMA (9-day and 18-day SMAs)
1.4046% 0.73805 23.02% 40.191% 4.3134%
MACD and 9-day EMA of MACD
1.1234% 0.80169 21.14% 44.403% 4.7001%
RSI 28.84% 2.3387 0.97% 9049.92% 75.864%
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.
