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 management efficiency : cost-to-income of the italian banks.

The cost to income ratio is equal to the operating costs divided by the intermediation margin. 

In other words, using the bank balance sheets items, it is equal to the following expression :

COST/INCOME RATIO : operating costs ("item 230") / total banking income ("item 120")

The meaning is : the lower the value expressed by the indicator, the greater the efficiency of the bank. Usually, the index is expressed as a percentage. Otherwise, the operating expenses are a percentage of the operating income. The ratio is also useful to value the productivity and the profitability for the banks. 

Here we have a sample : it is the peer group of the main banks, in Italy. In order to have a fair comparison, I used the adjusted results and I used the same criteria with regard to the same period, last year (we speak about the nine months ended on 30th September 2017 and about the nine months ended on 30th September 2016). For further info, please consult the notes below the table (1-7) and visit the section investor relations. 

At a glance, we can notice that : 

• The efficiency is substantially the same compared to last year ; indeed, we can see a slight improvement, in general.
• We have two banks that represent the outliers of the peer group : CVAL and CRG ; the ratios had a strong worsening, compared to the nine months of the year 2016. 
• In the same way, we have other outliers (by the positive side), FBK and BGN. However, the two banks have a similar cost/income ratio, substantially, compared to last year. Reasoning by p.p. : by the negative side, about + 60 p.p. ; by the positive side, about from -2 to -7 p.p. (2017 vs 2016). 
• Following the considerations of the previous points, it is more useful to focus on the median than on the mean. See the following table. 

Finally, the bar chart is much more explanatory. The two period have two colors, respectively, the light blue for the year 2017 and the red for the year 2016. The median is highlighted by a horizontal line.

With the aim of ranking the abovementioned banks, we have :

1) BGN ; 2) FBK ; 3) ISP ; 4) IFIS; 5) BPSO ; 6) MED ; 7) UCG ; 8) BMPS ; 9) BPER ; 10) UBI ; 11) CREDEM ; 12) BANCO BPM ; 13) CVAL ; 14) CRG.

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

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.