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). 

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.