Servizi Italia S.p.A. : plain DCF and target price.

In the following image, we have an example of DCF valuation. 

The firm is Servizi Italia S.p.A. The valuation date was August 18, 2016 and the target was 6.06 EURO (plus a range with regard to the fair value, +/-5%). The target was achieved on November 23, 2017 (in this way, the holding period was about one year and 3 months or 462 days, exactly). For this purpose, consult the following link and see the second one image : 

https://twitter.com/search?l=&q=DCF%20from%3Amichele_finance&src=typd&lang=en

The upside was about +73%, with regard to the current market price at the time of the coverage :

[6.06 - 3.50] / 3.50 = + 73.14 %

Let's look at the assumptions of the model :

1. The revenues were the proxy of the model : it means that all the variables depended directly on sales as a percentage.
2. For the estimation of the revenues of the next three years (2016-2017-2018), I assumed a CAGR of about 3% (the business of the firm is quite steady) ; I looked also at the trend of the past three years. 
3. For the other items (D&A, NFC, net income, NWC, capex), as I said previously, I assumed a percentage of the revenues : 20%, 1.5%, 5%, 0.5%, 20%. I looked also at the past trend and I considered the evolution of the sector and of the firm, in the next years (for the formula, consult the notes). 
4. For the estimation of the ERP and tax rate, I used the data source of A. Damodaran (ITA).
5. For the risk-free rate, I assumed the weighted average yield of BTP 10 years.
6. For the estimation of the beta, I used the regression between FTSE-mib and Servizi Italia with a time frame of 5 years and with monthly returns (I added a spread of 0.10 to consider the additional risk, due to the reduced liquidity of the stock). 
7. For the cost of debt and for the financial structure, see the notes.
8. For the growth rate (g), I assumed a conservative rate of 1% (see the litterature). 
9. With regard to the calculation of the EV and of the fair value, see the following formula and consult the notes:

EV=[FCF16/(1+wacc)^1]+[FCF17/(1+wacc)^2]+[FCF18/(1+wacc)^3]+[FCF18*(1+g)/(wacc-g)]*[1/(1+wacc)^3]

The passive management.

The passive management is a style of investing associated with mutual and exchange-traded funds (ETF) where an investor aims to mirror a market index. 

We can build the passive strategy through the following steps : 

1. Choice of a panel of funds ;
2. Ranking of the funds ; 
3. Choice of the funds in the panel with the best ranking.

The panel is chosen in accordance with particular requirements (filter by class, macrocategory, assets, country, risk, currencies and so on ; it depends on the investor's preferences : see the following link ANIMA sgr products). 

For example, the aim of the analysis is to rank the funds of the system "Anima Italia" ; the fund ISIN codes are respectively : IT0001040051, IT0005158784, IT0004896541. 

The asset allocation is composed by equities, largely ; the currency is EUR ; the equity country is Italy, substantially : for further information, please consult the portfolio breakdown and the fund profile. 

The site provides a rating and a benchmark for each fund ; however, we can build our benchmark and our rating. For the benchmark, we choose the FTSE-mib index because it can be a good comparative parameter, given the structure of the funds. For the ranking, we use the classic portfolio performance indicators. In this way, we import the NAVs on a excel sheet and then we calculate the daily returns. 

The time frame is from February 22, 2016 to date. I converted the daily returns into annual returns, for greater significance. 

Historical data (hidden cells for space requirements) : data source ANIMA sgr.

The same for the FTSE-mib index, aka benchmark. 

Historical data : data source Investing.com. 

In the following chart, we can see the performance of each fund compared to the benchmark performance. 

Now, we can calculate the performance indicators :

• The Sharp's Measure : the ratio uses standard deviation to measure a fund's risk-adjusted returns ; it quantifies a fund's return in excess of our proxy for a risk-free investment. It is equals to : 

(R - Rf) / Std Dev

R = average return of the fund ; Std Dev = standard deviation of the fund

Rf = risk-free rate (I assume the average return of the BTP 10Y ITA)

• The Treynor's Measure : the ratio is equal to the previous one ; however, the risk is adjusted for the beta. The index is equal to : 

(R - Rf) / Beta

Beta = beta of the fund

• The Jensen's Alpha : the index is a risk-adjusted measure that compares the average return of a fund to the estimated return of the Capital Asset Pricing Model (CAPM). The formula is equal to : 

R - [Rf  +  Beta*(Rm - Rf)]

Rm = average return of the benchmark (or market index)

• The M Squared Measure : it is a risk-adjusted measure ; it explains the surplus return of the fund compared to the risk-free investment, considering that the variability of the fund is equal to the variability of the benchmark. The formula is :

(Sharp's Measure)*(Std Devm) + Rf

Std Devm = standard deviation of the benchmark (or market index)

• The T Squared Measure : the structure is the same compared to the previous one ; the difference is the risk, systematic risk or beta ; substantially, it calcualtes the surplus return compared to the risk-free rate, under the assumption that the systematic risk of the fund is equal to the systematic risk of the market. The formula is :

[(1 / Beta)*(R - Rf) - (Rm - Rf)]

• The Sortino Index : rather than considering premiums regarding the risk-free asset, the index explains the surplus return with a minimum accettable return ; then, about the risk, it considers a minimum accettable risk, aka down side risk (the variability not appreciated by the investor ; we calculate a semi-standard deviation, only the negative deviations from the mean). The ratio is equal to : 

(R - Minimum Return) / Down Side Risk

For semplicity, we consider the minimum return equal to the risk-free return

Finally, the higher the ratios, the better fund past performance (we must note that the future performance is not linked to the past performance ; however, it is a good beginning). 

In this way, we can calculate the ratios and rank the three funds (see the following table). 

The ranking is :

1) Fund ISIN code IT0004896541 (the best) ;

2) Fund ISIN code IT0001040051 ;

3) Fund ISIN code IT0005158784 (the worst). 

UCG S.p.A. : chart framework.

From a lateral trend (July-until the end of the year 2017), the stock began an uptrend until about mid-year (2017). More recently, UCG has a lateral trend again. This trend has just formed : in this way, it needs to monitor the next trading sessions, to better understand the subsequent events. 

The first lateral trend is indentified by the two horizontal lines : the support at about 9 EUR and the resistance at about 12 EUR. The uptrend is identified by the two bullish TLs, respectively, below and above the price chart. The previous resistance (12 EUR) becomes support and then, we have another resistance at about 15 EUR. The last one price level is important also for the raison that, between 14 and 15 EUR, we can notice a gap up in the chart. This gap up is a valid level in order to set bearish or bullish trading strategies (see the yellow stripe in the following chart). 

Finally, we can notice the abovementioned lateral trend : it is indentified in the same way like the previous one by the horizontal lines (supports and resistances). The support and resistance levels are light blue circles.  

The trend is also shown by the slope of the EMA 40 (red line) : the sequence is flat-upward sloping-flat. Another aspect is that the EMA can be an indicator of the volatility of the asset : wider deviation  of the price chart from the EMA, higher volality and viceversa. This arrangement is useful to set purchase or sale orders. 

Chart from Investing.com

If we extend the timeframe, we get the same conclusions : with a weekly chart, the sequence is lateral trend-uptrend-lateral trend. Now, the RSI is not overbought : the allert levels are 30 and 70. Previously, there was a "RSI exhaust",  an oversold, in the first half of the year 2016. 

Chart from Investing.com

Lastly, it's interesting to see the FIBO retracements (see the chart below). I set the 100% and the 0% FIBO level, respectively at the bottom (8.54 EUR) and at the top (18.36 EUR). The yellow circles indentify the previous price levels (supports and resistances). This is another confirmation. 

Chart from Investing.com

This is the substantial chart framework. However, we must not to forget that other factors influence the stock market. UCG belongs to the banking sector. In this way, primarily, it means that its price will be directly correlated to the macroaggregate events and to the italian economy and italian politics. The beta of the stock is about 1.93 : we have an high leverage compared to the FTSE-mib. 

Data source : FT.com

The comparative chart UCG-FTSE-mib is very explanatory.

Chart from Investing.com

Brunello Cucinelli S.p.A. : comments with regard to the 1rst half 2017 results.

We have a double-digit growth in both revenues and in profit. The perfomance is better in the international markets than in domestic market. However, overall, the results are good. 

Net sales benefit from the forex : at current exchange rates, net sales rose by 10.7% ; at costant exchange rates, net sales rose by 9.7%. 

The brand has a great perception among the customers and among the different distribution channels ("digital" and "physical"; retail, wholesale multibrand and wholesale monobrand).

About the net financial position, we have a decrease due to the good cash generation from operating activities (+26,519 vs +4,339) and due to the positive changes of the net working capital (for further info consult the consolidated statements of cash flows).

It was used cash flow in investing activities (-22,749 vs -16,849) to support the brand, to safeguard its prestige and its exclusivity.

The following table shows the income statement data : there is an improvement of the items (double-digit growth, like I said previously) and, at the same time, an amelioration in the margins. 

The management explains that this good policy will be proposed also in the future, thanks to the investment plan 2017-2019. To read the full press release, please see the link (Brunello Cucinelli investor relations) :

http://investor.brunellocucinelli.com/yep-content/media/BC_1H_17_Consuntivo_ENG.pdf

At the release of the data, the market reaction was good : the volumes increased and there was a white candle. However, just after, the candles presented some shadows (Doji Star Bearish pattern). 

It means a weakness in the bullish wave, a resistance in the maximums and a pressure from the sellers. The BB shows an high volatility that may be absorbed, subsequently. See the chart.

Chart from Investing.com

The stock is not at all cheap. 

If we project the growth of the first half of the year, applied to the full year 2016, we get :

Full Year 2017 Revenues  = 457,029 * (1 + 10.97%) = 507,165.1

Full Year 2017 Net Income = 37,119 * (1 + 23.88%) = 45,983.02

If we project the margin of the first half of the year, we get :

Full Year 2017 Net Income = 507,165.1 * 8.12% = 41,181.8

At the current prices, we have a market cap of  1,731 EUR M. It means about 40X the expected earnings and about 3.40X the expected revenues.

There is a premium compared to the peers (considered also the smaller size of the firm). 

This premium can be explained by : 

• The high brand perception ;
• The rarity and the craftsmanship of the product ;
• The quality of the management and of the business ;
• M&A opportunity, having regard to the small size ;
• The higher defensiveness of the stock (low beta 0.29, Brunello Cucinelli overview from Investing.com, vs the industry average). On the long run, it is a safety for the price stability. 

Event-study strategy.

An event-study is a statistic method applied to the financial markets that has the purpose to measure the relevance/irrelevance of an event on the stock prices ; in other words, we value if an event is or is not price-sensitive, "over the normal returns", calculated by a market model through the following regression : 

R(stock) = α + β*R(benchmark)

R are the returns, expressed as a percentage, respectively of a single bond (the subject of the analysis) and of the benchmark (a market index).

α is the intercept.

β is the slope. 

An Event-study strategy has the aim to :

• value the relevance of an event (like the earnings releases, like a particular press release, like a macro-event and so on) ;
• value the impact and the statistical significance of the event ;
• measure the market moves of the insiders and the market mood, at the time of the event ;
• by combining the previous aspects, it means to make profitable a trade just buying or just selling the stocks (it depends on the specific situation) a moment before the event (some days, a week or a month before).  

The success of the strategy is not absolute but it is very true as the event is significative and repeatable. As known, the market is unpredictable and facing the same news, we could have a different market reaction. 

The following case shows the strategy. We speak about the full year 2016 results of Moncler S.p.A. The press release is dated 28, February 2017.

(To read the complete document, see the link https://monclergroup-dunebuggysrl.netdna-ssl.com/wp content/uploads/2016/07/Moncler_Press_Release_FY_2016.pdf). 

As the theory says, it needs to create a "window" in three parts :

1. Estimation window, useful to build the regression (usually, it is a period of 252 days) ;
2. Event window, the window of the event (it is a period of 6 days, 2 days before the event, the day of the event, and 3 days after the event, to cover any delays or news leaks) ; 
3. Post-event window, useful to value the impact of the event in the following days.

In this way, we can build the regression : we take the historical returns of the stock (Moncler, Y) and of the market index (FTSE-mib, X). The regression is shown in the following image. 

The parameters of the regression are the following (see the table).

For the returns (change %), I used the historical data from Investing.com and I put them on a spreadsheet. 

The estimation window is the period from March 3, 2016 to February 23, 2017 (in the image, there are some hidden cells, for space requirements).

The event window is the period from February 24, 2017 to March 3, 2017. 

The post-event window is the period from March 6, 2017 to March 31, 2017.

With the regression, we measure the abnormal returns (AR). 

The formula of the abnormal returns is :

(Effective return of the stock - Estimated return from the market model).

The estimated return is equal to [α + β*(effective return of the market index)].

The AR test t is : AR / STD.ERR.(YX).

To understand if the AR is significative and so if the event is price-sensitive, it needs to measure the statistic significance of the AR : is the absolute value of the AR test t is greater (YES) or less (NO) than 1.96 ? 

Abnormal returns (AR) in the event window

We can conclude that the earnings release impacted positively on the stock prices and that the event was significative. Just buying before the event, betting on the publication of good data, it would have been profitable. The following image shows the candlesticks chart of the event. 

Chart from Investing.com

As I previously said, the good success is basically based on the repetition of the event and the importance of the event itself. It means also that there is the same market reaction in every occasion (it is not so obvious). 

With Moncler, there are many aspects that confirm this, see the following link :

https://charts.stocktwits.com/production/original_67508754.?1479577951. 

Geox S.p.A. : the historical correlation with the FTSE-mib is significative.

If we look at the historical chart of Geox (red) compared with the FTSE-mib (blue), we can notice that the similitude  is clear : some exceptions apart (like the section of the circled graph, yellow ellipse), the direction of the two assets is substantially the same. To make the comparison more effective, the chart is divided into vertical lines that represent the date ranges with equal period (150 bars) : the most important thing is to value the same direction of the two graphs, inside the date range, in order to have the correlation ; otherwise, the two assets would be unrelated.

In this way, the last performance of Geox is driven by the trend of the FTSE-mib, most likely. 

Indeed, the positive newsflow is absent and the fundamentals are weak (see the last news) : they don't justify all this ; the perfomance is related to the macro context. 

Chart from Investing.com

The statistical analysis confirms that, too. With sixty returns (monthly time frame, from 2012 to 2017, five years), we can build the regression analysis. 

Historical data from Investing.com

The results show a beta of about 0.93 in the classic market model, as the following equation : 

GEOX (y) = a + beta*FTSE-mib (x)

If the beta is 1, it is how to hold the index ; if we have lower values, the asset (GEOX) dampens the index movements ; otherwise, with higher values, the stock amplifies the FTSE-mib movements ("leverage effect"). The correlation with the FTSE is evident. We have two outputs for the reason that in the first result, the intercept (a) is sparely significant (see the Stat t or the significance value). We discarded that and finally, we got an equation in the following form :

GEOX (y) = beta*FTSE-mib (x)

RSQ (0.3098) could be low ; it says that 30.98% of the Geox returns (variability) is explained by FTSE-mib returns. However, the CAPM literature shows that for the stocks, on average, the percentage is equal to 30-40% ; for the rest, specific factors affect stock performance. 

At the same time, we have the scatterplot (see the following chart). 

To conclude, another statistical indicator is the Pearson correlation coefficient : with the previous returns (see the second table), we got a value of 0.55 ; it means that there is a positive correlation between the two assets, Geox and FTSE-mib. Secondly, if we analyze the signs of the returns, we can see that in 65% of cases (39/60) the sign is the same (+,+ or -,-) and in 35% of cases (21/60) the sign is different (+,- or -,+). The relation is positive and significative.