IMA S.p.A. : relative valuation (historical market multiples).

With regard to the valuation, it is interesting to consider also its historical market multiples.

However, it needs to mark the following considerations :

• Every time period has its own market multiples, due to the macroeconomics factors and to the economic cycle ; in this way, the comparison term loses its value ; 

• The market anticipates the events and prices the expectations ; for this reason, the past valuations had always incorporated the whole scenarios ; the valuations and future market multiples are more important ; 

• Thirdly, with regard to the litterature, the relative valuation is composed always with a peer analysis, with a focus on the expectations of the sector (see the link, https://michelecarollo.blogspot.com/2018/11/ima-spa-relative-valuation.html). 

Here, we have the historical market multiple P/S. I used this market multiple with the aim to reduce the bias : the results at the top of the income statement are less influenced by several factors. 

We can notice that the last multiples are higher than the past multiples but we must consider the previous paragraphs, of course.

Then, the market valuation tends to assign higher multiples to a growing company, over time, like IMA S.p.A.

In this way, it is advisable to consider also a median of the multiple or a weighted average (the past prices are less important than the current prices). 

The expected market multiples (the yellow columnes) are substancially lower than the mean, median and weighted average (black and dashed lines in the chart). The gap is not so wide (that confirms the previous valuation). 

IMA S.p.A. : relative valuation.

Here we have a relative valuation of IMA S.p.A. It operates in the industrial machinery and equipment, like the peers of the sample. In particular, its business is focused on the packaging industry, through the segment lines : tea, food and other ; pharmaceutical sector. 

In order to select the peers, I considered the following requirements : similar business and geographical area and similar size (revenues or market cap as the proxy of the size), having regard to the differences, like the profitability, the growth, the risk and the financial structure (in this way, I adjusted the market multiples, appropriately or I considered the difference as a discount or as a premium in the valuation ; about the financial position, the differences are very small).

I used a leading market multiple (expected results in the year 2018, 2019, 2020, data source : www.marketscreener.com). I also considered the management assumptions, expectations and business plans.

Let's look at the market multiples, in the following table.

It needs to consider that about the equity story and size GIMA TT ("recent history") seems to be a a distortion compared to the other firms. It can be interesting, regarding its similarity to IMA, however, it is more advisable to exclude it from the peers group.
Then, we can calculate the mean and the median, respectively :

EV/EBITDA (10.08 | 9.44) ; P/S (1.60 | 1,70) ; P/E (18,45 | 17)

At a glance, we can notice the discount of IMA S.p.A, apart price to earnings.

Secondly, it is more useful to link the market multiples to the fundamental variables (profitability and growth, above all). In this way, see the next table.

We can calulate the mean and median, excluding GIMA TT again and IMA, for obvious reasons.

EBITDA margin (15.72% | 18.12%) ; EBIT margin (12.55% | 15.03%) ; NI margin (8.71% | 10.40%)

CAGR EBITDA (8.94% | 7.58%) ; CAGR revenues (6.66% | 5.94%) ; CAGR NI (12,85% | 12.74%)

Substantially, IMA marginality is slighy lower compared to the sector profitability ; however, the growth is rather higher.

Finally, let's put the variables together, thanks to a regression line.

Market Multiples (Y) vs Profitability (X)

Market Multiples (Y) vs Growth (X)

In the two charts, the intercept is not considered because it is not important, in a statistical point of view (statistical significance). However, the angular coefficient is good. The R-squared is excellent, in the following order (from the lowest to the highest) : P/E regression, EV/EBITDA regression and P/S regression.

So, we can determine the fair value of IMA and its premium or discount. In other words, its intrinsic market multiple.

margins
FV (I) = P/E = 181,61*7,30% = 13,25                       
premium : +36,7%

FV (II) = EV/EBITDA = 60,513*15,65% = 9,47     
discount : -7,82%

FV (III) = P/S = 12,88*12,85% = 1,65
discount : -20,24%

growth
FV (IV) = P/E = 120,66*17,54% = 21,16                 
discount : -14,4%

FV (V) = EV/EBITDA = 100,75*12,15% = 12,24   
discount : -28,7%

FV (VI) = P/S = 23,794*7,14% = 1,7                       
discount : -22,3%

Finally, we can conclude than the discount is higher (as I said) with the growth. With the margins, the discount is smaller and we have a premium with the P/E regression. At a glance, IMA is traded at a slighty lower market price compared to its hypothetical fair value. Of course, it does not represent a safety margin, at the moment. The price still could go down for the reason that the trend is clearly bearish. However, the current market price is beginning to be interesting.

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]

Markowitz efficient frontier : backtesting.

The aim of the analysis is to build the efficient frontier that compares the average return (y) to the risk or standard deviation (x) of the top 100 Italian stocks (the market cap is the proxy of the size). Secondly, I will build one hundred portfolios (each portfolio is composed by one stock and each stock has the same weight, for simplicity), by decreasing order of best risk-reward ratio.

Thirdly, I will test the following assumption : the order of best risk-reward ratio should be the same also for the future performances (at least, substantially). 

In this way, the third step will be the backtesting that will cover a medium/long time frame, for obvious reasons and it will be a continuous updating.

The time frame of the past returns is 5 years : it means sixty returns (monthly returns), from April 30, 2013 to April 30, 2018. The source of the data is the following (historical data) : Investing.com. 

The source of the stock screener is the following : Stock Screener - Investing.com. 

About the data, I converted the monthly returns into annual returns, for greater significance. 

Average Return (annual) = Average Return (monthly) * 12

Standard Deviation (annual) = SQRT [(Standard Deviation (monthly) )^2*12]

So, I adjusted the return for the risk (Standard Deviation), through the following formula : 

Risk Adjusted Return = Average Return / Standard Deviation

Finally, I ranked the stocks, according to the abovementioned ratio (best risk-reward ratio). 

For simplicity, I built ten groups, always respecting the previous order. 

According to the portofolio theory (please consult the links : Markowitz efficient frontier ; images), the best stocks are those with the best ratio : they offers a greater return, given a risk rate or they offers a lower risk, given a return rate. Then, we have the following assumption : the expected returns and the expected risks are based on the past data. Of course, this is a limit and the aim of the analysis is also to implement a backtesting and to test the assumption. 

The following chart shows the efficient frontier. 

The following tables show the ten groups, by decreasing order of best risk-reward ratio (--> ranking).

In the future, I will test the assumption, as mentioned. The stocks with good past performance (with given risk) should outperform the others. 

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). 

The "magnitude" of the liquidity.

In the building of a trading strategy, the liquidity is a crucial variable. 

Secondly, the liquidity can be both a positive and a negative factor. 

Of course, we have two market scenarios :

1. An illiquid market ;
2. A liquid market. 

In the first one, from the negative point of view, the price of a stock hardly represents the intrinsic value : in other words, the stock price will reflect poorly the underlying drivers of the fundamental analysis. The same is also for the technical analysis. The stock price doesn't correctly respond to the graphic signals. This bias can last for a long time and sometimes the gap cannot be solved. 

In this way, the market context is artificial. 

From the other point of view, we can use that fault to our advantage. That means that it needs to follow the next steps of the market maker. If on one hand, the market maker (aka MM) easily moves the price for lack of liquidity, on the other, we must not suffer this but we must make ourselves part of it. 

In the second scenario, there is not the abovementioned bias because the market price responds quickly to the underlying factors (technical or fundamental). However, we cannot use to our advantage the previous fault. The market context is normal. 

Finally, the first scenario is difficult to manage than the second one because the illiquid market is difficult to interpret. Neverherless, if we are be able to follow the MM's moves, this will lead to higher profits for the reason that the stock price is much more influential to price changes in the face of low traded volume, due to the market structure.   

After this introduction, it is useful to indentify the two scenarios (1. and 2.) and above all the magnitude of the price influenceability. 

The following methodology shows that. The steps are : 

1. Building the daily returns (%) for a time period (in general, one year is appropriate) ;
2. Building the daily trading volumes ;
3. Pondering the first one with the second one ; in other words, we consider the ratios (the daily returns are as absolute returns ; we are interested in the amplifying effect and we are not interested in the direction of the effect). The formula is :

[Absolute Price Change %] / [Trading Volume]

The Absolute Price Change % is equal to ABS(Price Change %)

Finally, we calculate an average value of the previous ratio (=AVERAGE[Absolute Price Change %] / [Trading Volume]). Then, we multiply the average ratio for different and hypothetical volume levels. In this way, we can get an idea of the influence of volume on stock price (-> we get the hypotethical price changes % for given volumes). 

Of course, this represents the market structure and it should not be considered as a correct value a priori. There are many variables that can change the survey data and the past trend is not like the future trend : the trading book and the market environment are some factors. 

In the following chart, we have an example of illiquid market. The stock is COVER 50 S.p.A., a classic example of illiquid stock. The daily volumes are low and we can notice that it needs just a small volume to get a considerable price change (for a trading volume of 1K we have a price change % of 3.48%). 

Daily Returns, from April 10, 2017 to April 6, 2018, hidden cells
for space requirements, data source : Yahoo Finance

We can also analyse the abovementioned effect through a regression line.

In the following charts we have an example of liquid market, the stock is UNICREDIT S.p.A. 

The daily volumes are huge and in this way it needs a huge volume to get a substantial price change (indeed, for a trading volume of 1K we have a price change % of 7.85189E-07). 

For obvious reasons, the slope of the regression line is lower than the previous one. 

Daily Returns, from April 10, 2017 to April 6, 2018, hidden cells
for space requirements, data source : Yahoo Finance

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. 

The volatility.

The volatility is an important parameter in the financial markets. 

It is useful to : 

• measure the underlying risk of an asset or portfolio (higher the volatility, higher the risk);
• set the market orders, stop loss, take profit, limit orders and so on (higher the volatility, "wider" the market orders) ;
• set the trading profile (higher the volatility with pure trader strategy and lower the volatility with pure investor strategy) ;  
• measure the market mood (higher the volatility with an important event price-sensitive ; "stock market fever", see also the VIX indicator). 

As there are so many purposes, in the same way, there are so many methods of measuring and indentifying it. At first, the classical measure is the deviation standard. To calculate it, it needs to import the historical returns of an asset to an Excel spreadsheet and then applying the following formula :

=  DEV.ST.POP (historical returns).

Historical data from Investing.com

Here, we have an image that clearly explains the case. The time-period can be daily, weekly, monthly etc, in the analyst's discretion and based on the purpose of the analysis (it means also holding period and investor perspectives). The ticker is APPLE. The time-frame is daily. 

Another measure is that provided by the graph (indicators).  

In this sense, it means respectively : 

• width of the real bodies and of the shadows (candlesticks chart ; wider the bodies and the shadows, higher the volatility) ;
• width of the Bollingers Bands (wider the bands, higher the volatility) ;
• magnitude of the volumes and so market interest (higher the volumes, higher the volatility). 

As shown in the following chart, there is the silmultaneous presence of more detectors (read the notes inside the graph). 

Chart from Investing.com

Indeed, BBW and HV are useful indicators to measure the volatility. There is a peak compared to the historical series (circled ellipses). The Envelopes (EV) have the same meaning of the BB : if the stock price is above or below the envelopes, there is high volatility in both directions.

Envelopes (EV) ; chart from Investing.com

To conclude, once measured and identified the volatility, every trader/investor should set his trading strategy accordingly, pursuing own profit and loss targets, of course. 

Cement industry : Cementir Holding S.p.A. and the value map.

Here we have a classic value map that relates the market multiple P/S (price to sales) to its intrinsic profitability (EBIT/sales or EBIT margin or ROS) and then the mentioned multiple to the expected growth, from the year 2017 to the year 2019 (data source estimates : www.4-traders.com). 

The expected growth is the CAGR, aka cumulative average growth rate. 

The formula is the following : 

[(expected revenues fiscal year 2019/revenues fiscal year 2016)]^(1/3)-1

It is expressed as a percentage. 

The meaning of the relationships (P/S-ROS and P/S-CAGR) is that higher the multiple (P/S), higher the margin (ROS) and higher the multiple (P/S), higher the expected growth (CAGR) and viceversa. 

Obviously, the market generally rewards the firms with higher profitability and with higher growth, with a major market price. 

The relationships can be shown as a value map, particularly through a regression line. 

We have two equations : 

1) P/S = a + b*ROS

P/S = y ; ROS = x

2) P/S = a + b*CAGR

P/S = y ; CAGR = x

In both cases, the intercept (a) was removed for the reason of the low statistical relevance. 

The RSQ is high (0.9070 and 0.8488), which means the strenght of the model.

Graphically, we can note that the relations are clear, substantially : higher the market multiple, higher the profitability and the expected growth.

The stocks above the regression line (dashed line) are overvalued, the stocks below the regression line are undervalued. In this way, for Cementir Holding, we get a "value gap" that is equal to :

[P/S (effective)-P/S(calculated from the market model)] : [P/S(calculated from
the market model)]

The P/S (effective) is 0.72.
The P/S (calculated from the market model) is equal to (9.10%*12.54).

There are also some "outsiders" (Vulcan Materials, Eagle Materials). The profitability is very high compared to the peers. Indeed, we have a multiple P/S of about 4X.

Likewise, for the relationship P/S-CAGR : see the following chart.

The discount ("value gap") is much greater, 71.94% vs 36.91%. The "growth adjusted market multiple" is preferable to the "multiple adjusted for the profitability".

If we remove the two outsiders from the sample, the statistical models are stronger : RSQ is higher, the relations are more even significative. However, the discounts are lower : 10.36% vs 36.91% and 57.16% vs 71.94%.

Finally, Cementir Holding is cheaper than the peers. Secondly, in my opinion, the stock is much more attractive in the area 3.80-4.80 : we could benefit more so from the discount prices and from a possible graphic retracement (see the chart), event if the current prices and expectations are interesting.

Chart from Investing.com

For further information, consult the following links:
http://michelecarollo.blogspot.it/search?q=cementir ;
https://stocktwits.com/michele_finance/message/75068152 ;
https://stocktwits.com/michele_finance/message/79046330. 

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.