This document provides an overview of a long-short equity strategy focused on exploiting pricing inefficiencies related to the quality factor. The strategy aims for returns between 0.5-1 times the market with half the volatility through a combination of long positions in a quality value portfolio and short positions in equity futures to remain beta neutral. Since inception the strategy has achieved an average return of 3% annually with 6% volatility. Stock selection involves filtering for tangible book value growth, high Piotroski scores, and ranking based on free cash flow yield and return on invested capital to identify high quality, undervalued companies.

1. FLEXIBLE EQUITY EURO
LONG-SHORT PRESENTATION
Stock picking and portfolio construction
March 2018

2. OVERVIEW
2
Executive Summary 3
Investment Strategy 4
Investment Process - Stock Picking & Portfolio Construction For The Long Portfolio 5-13
Why We Like This Strategy - Some Evidence 14
Performance 15
Bibliography and Glossary 16-17

3. EXECUTIVE
SUMMARY
▪ Alpha strategy that aims to exploit pricing inefficiencies from the
underappreciation of the quality factor*
▪ No material correlation to major market risk factors
▪ Targeting Sharpe ratio between 0.5 and 1 (mid single digit performance
over a market cycle with volatility half that of equity indices)
▪ Since inception** achieved an average return of 3% per annum with 6%
volatility

4. INVESTMENT STRATEGY
Strategy
Gross exposure
Net exposure (beta adjusted)
Investment Universe
Long-short equity
(LONG a quality value portfolio, SHORT equity futures)
Maximum gross exposure designed for the strategy is 200%
Generally in the range 150-180%
-10% > -- 0 -- < 10%
All caps - Mid cap biased
4
Correlations with major indexes Very low ~ 0
Market (Beta) Neutral
by design
A model-based approach to alpha generation through systematic stock selection

5. INVESTMENT PROCESS
➢All European stocks with at least 50 mln.€ market cap & 5Y of financial statements -->
2,400 available
➢Check liquidity: at least 100.000 € avg. traded value --> 1,300 stocks available
➢Financials excluded --> 1,200 available
INVESTABLE UNIVERSE
➢At least one ‘time-window’ (5 years, 6 years, …, 26 years) of 5% c.a.g.r. in tangible book value per
share --> 1,000 stocks available
1ST FILTER:
TANGIBLE BOOK VALUE PER SHARE GROWTH
➢At least ‘5’ F (Piotroski) - score --> 750 stocks available
2ND FILTER:
F-SCORE (PIOTROSKI)
➢Applying a weight to each measure (F- score, Roic, Fcfy) generates a rank
➢We focus on the 1st quintile --> 150 stocks availableRANKING
➢Selecting the best 50-70 stocks
➢Minimizing industry concentration
➢Determining weights (help via risk-parity)
➢Check liquidityBULD A DIVERSIFIED PORTFOLIO
STOCKPICKING
PORTFOLIO CONSTRUCTION
5
STOCK PICKING & PORTFOLIO CONSTRUCTION FOR THE LONG PORTFOLIO

6. STOCK PICKING (i)
i. Filter based on a minimum threshold of 5% growth in tangible book value per share
✓ ‘Tangible book value per share’ = (Total equity – intangibles)/ Shares outstanding
✓ We begin by selecting stocks of companies that have built ‘tangible book value per share’ growth (counting
dividends) in at least one of these ‘time-window’): 5 years, 6 years, …, 26 years
Synthesis of the universe showed
in the 1st filter
0%
5%
10%
15%
20%
25%
last5years
last6years
last7years
last8years
last9years
last10years
last11years
last12years
last13years
last14years
last15years
last16years
last17years
last18years
last19years
last20years
last21years
last22years
last23years
last24years
last25years
last26years
NESN VX: c.a.g.r. in tangible book value per share
n Ticker Name Sub-Industry Name MktCap_€ count if
1 NESN VX Nestle Sa-Reg Packaged Foods & Meats 221 bln. € 22
2 ROG VX Roche Holding Ag Pharmaceuticals 209 bln. € 22
3 FP FP Total Sa Integrated Oil & Gas 114 bln. € 22
… … … … … …
2,398 ABI BB Anheuser-Busch Inbev Brewers 204 bln. € 0
2,399 AIR FP Airbus Se Aerospace & Defense 54 bln. € 0
2,400 SHP LN Shire Plc Biotechnology 44 bln. € 0
ABI BB Intang. Hist.MktC. Nav Tbv/Sh. DVD.P.Sh. Int./Nav
2001 3,259 13,097 4,818 2.3 0.2 67.6%
2002 3,791 9,711 4,694 1.3 0.2 80.8%
2003 3,972 9,133 4,720 1.1 0.2 84.2%
2004 7,705 16,439 8,319 0.7 0.2 92.6%
2005 11,648 22,356 11,471 -0.2 0.3 101.5%
2006 13,570 30,563 12,262 -1.3 0.5 110.7%
2007 15,084 35,057 13,625 -1.5 1.5 110.7%
2008 52,950 26,568 16,084 -23.0 0.2 329.2%
2009 52,536 58,389 21,156 -19.6 0.3 248.3%
2010 56,754 68,702 26,380 -18.9 0.6 215.1%
2011 57,963 75,975 28,929 -18.1 1.1 200.4%
2012 57,693 105,630 31,184 -16.5 1.3 185.0%
2013 71,989 124,222 36,525 -22.1 1.6 197.1%
2014 83,207 150,950 41,299 -26.1 2.3 201.5%
2015 87,188 183,983 38,779 -30.1 3.5 224.8%
2016 171,709 204,145 67,639 -51.5 3.4 253.9%
Example of a stock that passes
this test (NESN, on the left) and
one that fails (ABI, on the right)
6

7. STOCK PICKING (ii)
ii. Filter based on ‘F-score’, designed by prof. Piotroski, whose value is derived from accounting data
✓ Piotroski (2002) built a score with the objective to reduce the typical high drawdown and higher volatility of
the cheapest (in terms of price to book) quintile (the classical deep value strategy)
✓ Financial stocks are not eligible
✓ We, from our backtest and our live track record, can say that this score is useful to filter the stocks that we
like: quality stocks and/or companies whose fundamentals are improving. We’ll begin from a score of 5.
Piotroski’s F-score (see the definitions on the Glossary page) assign 1 (if the
signal is GOOD) or 0 (if the signal is BAD) in each of the financial indicators
selected.
There are three groups of accounting based financial indicators:
4 based on ‘profitability’ (Roa >0, Roa1>Roa0, Cfo >0, Cfo> Net income)
3 based on ‘financial health’ (‘Lt debt’ reducing, ‘current ratio’ increasing,
‘shares outstanding’ not increasing)
2 based on ‘operating efficiency’ (‘gross margin’ increasing, ‘delta-turnover’
increasing)
Synthesis of the universe showed in the 2nd filter
7
0.5%
2.5%
5.4%
16.5%
24.8% 24.5%
17.1%
7.0%
1.6%
0%
5%
10%
15%
20%
25%
30%
1 2 3 4 5 6 7 8 9
F-score (Piotroski)
distribution of F-score @ March '17
We Want To Hold At Least A ‘5’ Piotroski Score Stocks

8. STOCK PICKING (iii)
iii. Ranking. Now we are looking for the most ‘value’ inside a ‘higher quality’ group of stocks
✓ Inspired by the J.Greenblatt’s ‘magic formula’ approach, we rank stocks based on (high!) ‘free cash flow yield
as a measure of value and then, as a measure of profitability, we use ‘roic’ that pays a critical role in order to
select the highest 'quality' possible stock.
✓ Quality for us means stocks of companies that are safe (low leverage: low ‘net debt/ebit’, high interest
coverage: ‘ebit/interest expense’), well managed, so with a clear path to maximizing the spread between the
return on invested capital and average cost of capital, and, possibly, growing.
Free cash flow yield = (Total cash available for distribution to owners and
creditors after funding all worthwhile investment activities) / (Market cap
[or, better, Enterprise value ]);
Return on invested capital (Roic) = (EBIT * (1 - Tax rate)) / (Interest-
bearing debt + Equity)
For a more complete calculation of Roic we refer to what is reported (as a
good example) by Starbucks on theirs 10-k:
ROIC = [adjusted net operating profit after taxes (adjusted for implied
interest expense on operating leases)] / (average invested capital)
where
invested capital is calculated on a five-point average and includes
shareholders equity, short- and long-term debt, all other long-term
liabilities, and capitalized operating leases, less cash, cash equivalents and
short- and long-term investments.
8
Synthesis of the universe showed in ‘Ranking’
Ticker Short Name GICS SubInd NameMarket Cap Prim Exch NmPiot Azs D/MKTCAPP/B FCFY-EV5y-Roics.gr.
CEY LN EquityCentamin Plc Gold 2,390,702,080 London 9 21.1 1.5 12.9 13.1 20.7
VBK GR EquityVerbio Vereini Oil & Gas Refini 878,133,248 Xetra 8 5.7 1 2.8 7.4 43.6 2.5
NESTE FH EquityNeste Oyj Oil & Gas Refini 9,237,948,416 Helsinki 8 4.5 16.8 2.3 8.8 9.9 18.7
FAE SMEquityFaes Farma Pharmaceuticals 952,047,488 Soc.Bol SIBE 8 12.4 0.4 3 4.7 14.6 14
AMS SMEquityAmadeus It Group Data Processing 21,010,444,288 Soc.Bol SIBE 8 7 12.2 7.1 6.1 15.0 13.9
STO3 GR EquitySto Se & Co.-Pfd Construction Mat 699,276,224 Xetra 8 4.5 8.7 1.7 8.6 11.6 3
PPB ID EquityPaddy Power Betf Casinos & Gaming 9,071,918,080 Dublin 7 17.5 2.5 0.8 2.3 37.1 37
PNDORA DC EquityPandora A/S Apparel, Accesso 13,991,121,920 Copenhagen 7 14.2 3.1 13.6 5.8 43.4 18.6
GSF NO EquityGrieg Seafood As Packaged Foods & 982,860,224 Oslo 8 4.9 22.6 2.6 7.0 7.7 69.6
RRS LN EquityRandgold Res Ltd Gold 7,944,398,336 London 8 15.1 2.2 4.5 9.5 25.7
SOW GR EquitySoftware Ag Systems Software 3,025,542,144 Xetra 9 6 10.5 2.2 7.2 10.0 1.6
ATCOA SS EquityAtlas Copco-A Industrial Machi 40,301,686,784 Stockholm 8 6.6 6.8 6.8 4.6 20.9 12.3
TOMNO EquityTomra Systems As Environmental & 1,612,484,736 Oslo 8 6.9 5.5 3.3 7.6 12.8 0.7
BESI NA EquityBe Semiconductor Semiconductor Eq 1,582,787,584 EN Amsterdam7 6.1 9.1 4.3 7.1 14.1 17.6
CWC GR EquityCewe Stiftung & Diversified Supp 620,293,888 Xetra 8 6.2 2.1 3.1 6.7 12.6 2.1
MYCR SS EquityMycronic Ab Electronic Equip 1,032,701,184 Stockholm 6 8.6 6.4 17.1 18.0 57.1
FORN SW EquityForbo Holdin-Reg Home Furnishings 2,695,272,960 SIX-SW 8 6.3 4.1 5.1 15.7 4.7
MHG NO EquityMarine Harvest Packaged Foods & 7,373,577,216 Oslo 8 4.4 14.2 3.3 5.9 10.3 19.7
MMT FP EquityM6-Metropole Tel Broadcasting 2,686,389,504 EN Paris 7 4.6 0.1 4.1 11.0 21.7 1.4

9. OUR EXPERIENCE: 1st EXAMPLE OF A NICE STOCK SELECTION
▪ Kingspan Group PLC has been selected since the beginning of our European equity strategy
▪ From the end of November ‘14 to the end of February ‘17 the stock has grown 133% return vs 5% for the Euro
Stoxx
▪ We selected it because, at that time, it showed a high Piotroski score (8), a relatively high free cash flow yield
(5%), a very healthy and improving trend in return on invested capital
9
KSP ID Roic Fcfy
2009 S2 5.8% 13.9%
2010 S1 6.3% 9.4%
2010 S2 6.6% 2.8%
2011 S1 7.3% 3.8%
2011 S2 7.8% 7.0%
2012 S1 8.3% 5.6%
2012 S2 8.1% 6.6%
2013 S1 7.9% 5.0%
2013 S2 8.8% 3.3%
2014 S1 9.6% 4.4%
2014 S2 9.9% 3.9%
2015 S1 12.0% 4.6%
2015 S2 13.6% 6.1%
2016 S1 14.3% 7.0%
2016 S2 14.0% 3.7%

10. OUR EXPERIENCE: 2nd EXAMPLE OF A NICE STOCK SELECTION
▪ We initiated a position in Actelion after the summer of ’16. We selected it because, at that time, it showed a
high Piotroski score (8), a high free cash flow yield (8%), a very high return on invested capital and a very
healthy balance sheet.
▪ On 01/26/2017 Johnson & Johnson announced the acquisition of Actelion for $30bn in cash
5 5
6 6
4 4
5
8
7
4
6
5 5
4
6 6
5 5 5 5
6
8
7 7
0
1
2
3
4
5
6
7
8
9
0%
2%
4%
6%
8%
10%
12%
2005 S1 2008 S2 2012 S1 2015 S2
PiotroskiScore
FreeCashFlowYield
Historical Piotroski score and freecash flow yield
Piotr.
Fcfy
10
ATLN VX Equity Roic Fcfy
2009 S2 21.1% 4.4%
2010 S1 23.3% 4.8%
2010 S2 23.7% 3.1%
2011 S1 -7.3% 5.4%
2011 S2 -5.0% 8.4%
2012 S1 20.2% 9.9%
2012 S2 21.1% 11.0%
2013 S1 21.4% 9.4%
2013 S2 28.7% 6.8%
2014 S1 39.4% 0.6%
2014 S2 33.8% 1.0%
2015 S1 24.8% 4.2%
2015 S2 30.4% 4.4%
2016 S1 38.0% 4.6%
2016 S2 50.1% 3.0%

11. OUR EXPERIENCE: EXAMPLE OF A BAD STOCK SELECTION
▪ IG Group Holdings PLC: we initiated a position in September ’16. Primarily a financial spread betting company,
on 8th December lost 38% on the news of regulatory tightening on binary products.
▪ We selected it because, at that time, it showed a high Piotroski score (7), a high free cash flow yield (6%) and
an average roic of 25% … but we know this is not enough (in the short term). We continue to like it because it
has a good balance sheet with 680 mln. GBP in equity and no debt
11
IGG LN Equity Roic Fcfy
2010 S1 25.4% 16.8%
2010 S2 23.9% 6.2%
2011 S1 -8.1% 2.8%
2011 S2 -5.3% 4.8%
2012 S1 35.3% 10.0%
2012 S2 37.3% 11.6%
2013 S1 30.4% -0.1%
2013 S2 32.6% 0.8%
2014 S1 34.1% 5.8%
2014 S2 30.1% 5.9%
2015 S1 28.6% 9.5%
2015 S2 23.9% 5.8%
2016 S1 22.8% 3.4%
2016 S2 28.4% 6.1%
2017 S1 28.0% 6.5%
WE AIM TO BE RIGHT ON AVERAGE …
SO, IT MAY HAPPEN, SOMETIMES, THAT SOME PORTFOLIO’S COMPANIES MAY BE SUBJECT TO SHORT-TERM HICCUPS

12. PORTFOLIO CONSTRUCTION
Number of stocks
How we’ll assign the weights
Sector - Industry representation
Portfolio turnover***
Between 50-80
Risk-parity as a guide but special attention to liquidity
Max single stock weight --> 6%. Max Gics Industry Group weight: 20%
Not ALL the stocks part of the 1st quintile of the ‘ranked’ universe are invested: when there
are too many stocks in the same industry group (max 15-20%), we’ll select the best one and
skip to the next ranked stock of another industry.
Not invested in BANKS (since F-score score works only for an ‘industrial’ type of company)
but inside the Financials sector there may be space for stocks of ‘Asset Management’, ‘Multi-
Sector Holdings’ and ‘Exchanges’ companies.
2017: 33% 2016: 102% 2015: 150% 2014: 11%
12*** Accounting data, particularly in Europe, are updated mostly every six months and between reporting and data publication by the providers there is a delay that varies between a few days for the major traded stocks to several weeks for
stocks with less turnover. So our experience is for an average 4-5 portfolio rebalancing a year, mainly on March and on September.

13. PORTFOLIO CONSTRUCTION
13** Accounting data, particularly in Europe, are updated in many cases every six months. We use Bloomberg and between financials published by the companies and data publication there is a delay of a few days for the major
traded stocks to weeks for less owned stocks. So our experience is for an average 4-5 portfolio rebalancing a year, mainly on March and on September
AT THE END OF THE DAY, OUR (LONG) PORTFOLIO WILL SHOW HIGHER QUALITY AND, MORE OFTEN THAN NOT, LOWER VOLATILITY THAN THE
INDEX BUT SURELY WE’LL STAY PATIENT BECAUSE …
0
20
40
60
80
100
Historical GICS sector allocation for the long porfolio
› Consumer Discretionary › Consumer Staples
› Energy › Financials
› Health Care › Industrials
› Information Technology › Materials
› TelecommunicationServices › Utilities -20
0
20
40
Historical GICS sector allocation for the long-short porfolio
› Consumer Discretionary › Materials
› Health Care › Utilities
› Energy › TelecommunicationServices
› Information Technology › Consumer Staples
› Industrials › Financials

14. WHY WE LIKE THIS STRATEGY - SOME EVIDENCE
W.Buffett has built a great performance by buying low volatility and high quality stocks …
“(…) Berkshire Hathaway has realized a Sharpe ratio of 0.76, higher than any other stock or mutual fund with a history of
more than 30 years, and Berkshire has a significant alpha to traditional risk factors. However, we find that the alpha becomes insignificant
when controlling for exposures to Betting-Against-Beta and Quality-Minus-Junk factors. Further, we estimate that Buffett's leverage is
about 1.6-to-1 on average. Buffett's returns appear to be neither luck nor magic, but, rather, reward for the use of leverage combined with
a focus on cheap, safe, quality stocks (...)” ‘Buffett's Alpha’ [A.Frazzini - 2013]
… and recent studies show that being different and low turnover has high probability of success
“(…) Among high Active Share portfolios - whose holdings differ substantially from their benchmark - only those with patient
investment strategies (with holding durations of over 2 years) on average outperform, over 2% per year. Funds trading frequently generally
underperform, including those with high Active Share (...)”
‘Patient Capital Outperformance: The Investment Skill of High Active Share Managers Who Trade Infrequently’ [M.Cremers - 2015]
Furthermore, this was the answer Ben Graham gave to a FAJ interview: “(…) What general approach to portfolio
formation do you advocate?
“Essentially, a highly simplified one that applies a single criteria or perhaps two criteria to the price to assure that full value is
present and that relies for its results on the performance of the portfolio as a whole--i.e., on the group results--rather than on the
expectations for individual issues.”
14

15. PERFORMANCE - STATISTICS
15
Year to Date YEAR Gen Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-1.51 2018 -1.51 0.01 -- -- -- -- -- -- -- -- -- --
7.8 2017 1.02 2.16 -0.39 2.44 0.45 -0.9 -1.21 -0.37 0.68 0.74 -0.29 3.31
0.29 2016 -1.4 1.9 1.62 -0.64 1.24 -1.2 1.57 0.14 0.98 -2.33 0.54 -2.09
13.63 2015 1.54 0.78 -0.49 1.87 2.06 0.18 -0.33 2.87 1.11 -0.09 0.99 2.27
3.19 2014 -- -- -- -- -- -- -- -- -- -- -- 3.19
YTD 6 m. 1Y 2Y (ann.) 3Y (ann.) s.inc. (a.)
-1.51 2.9 2.88 2.81 5.73 7.05
-1.6 1.21 6.93 11.98 2.02 5.23
-0.11 -0.04 0.55 -0.24 0.41 0.87
Performance Analysis (%)
Port
EURO STOXX 50
Peers**
1 Y 2 Y 3 Y
› Risk
St. Deviation (Ann.) 5.26 5.86 6.32
› Risk/Return
Sharpe Ratio 0.64 0.56 0.98
Portfolio Statistics (%)
-20
-15
-10
-5
0
5
10
15
20
25
30
11/14 03/15 07/15 11/15 03/16 07/16 11/16 03/17 07/17 11/17
Cumulative performance
Port SX5E Index of market neutral funds
-0.3
-0.2
-0.1
0.1
0.2
05/15 11/15 05/16 11/16 05/17 11/17
BETA
Rolling 6m. Beta vs SX5E
Beta
-1
-0.5
0
0.5
1
1.5
2
2.5
11/15 03/16 07/16 11/16 03/17 07/17 11/17
Sharpe
Sharpe-1y(w)

16. PERFORMANCE – STATISTICS (monthly factsheet)
16

17. BIBLIOGRAPHY
Financial Analysts Journal, 1976 'A Conversation with Benjamin
Graham' www.cfapubs.org/toc/faj/1976/32/6
J.D. Piotroski, 2002, Value Investing: The Use of Historical Financial
Statement Information to Separate Winners from Loser
J. Greenblatt, 2010, The Little Book That Still Beats the Market
A.Frazzini et al., 2011, ‘Betting Against Beta’. “We present a model
with leverage and margin constraints that vary across investors
and time. We find evidence consistent with each of the model’s
five central predictions: (1) Since constrained investors bid up high-
beta assets, high beta is associated with low alpha (...) (2) A
betting-against-beta (BAB) factor, which is long leveraged low
beta assets and short high-beta assets, produces significant
positive risk-adjusted returns; (3) When funding constraints
tighten, the return of the BAB factor is low; (4) Increased funding
liquidity risk compresses betas toward one; (5) More constrained
investors hold riskier assets (…)”.
A.Frazzini et al., 2013, ‘Buffett's Alpha’
C.S.Asness et al., 2014
W. R. Gray, 2015, Simple Methods to Improve the Piotroski F-Score
M.Cremers, 2015, ‘Patient Capital Outperformance: The
Investment Skill of High Active Share Managers Who Trade
Infrequently’
17

18. GLOSSARY
Below we show the 9 measures used to calculate the F- Piotroski score.
Financial performance signals: Profitability
ROA --> net income before extraordinary items scaled by beginning of the year total
assets (if ROA > 0, 1, 0)
CFO --> cash flow from operations scaled by beginning of the year total assets (if CFO
> 0, 1, 0)
F_ROA --> ROA this year - ROA previous year (if F_ROA > 0, 1, 0)
F_ ACCRUAL --> (if CFO > ROA, 1, 0)
Earnings contain a lot of non cash earnings which is called accruals. Sloan (1996)
shows that when profits are greater than cash flow from operations is a bad signal
about future profitability and returns.
Financial performance signals: Leverage, liquidity, and source of funds
t_Lev --> change in LT debt to Tot.Assets (if LT-d/T.A. increase, 1, 0)
t_Liq --> change in current ratio (if curr.ratio increase, 1, 0)
t_Sh.O. --> change shares outstand (if shares outst. increase, 0, 1)
Financial performance signals: Operating efficiency
t-G. --> if % gross margin t > % gross margin t-1, 1, 0
t-A. --> if asset turnover t > asset turnover t - 1, 1, 0
18
BREIM EquityInc(L.)_b Cfo Tot.Ass. ROA CFO F_ROA F_CFO Roa Cfro t_R Acc Long Cur.R.Shares t_Le t_Li t_Sh Sales, GPCDt_A. t_G. Piot A.Tu Gm
2006 S2 20 82 774 5.9% 11.4% -1.2% 5.5% 1 1 0 1 31 1.2 66.8 1 0 0.5 392 0 0 4.5 1.1 9.9%
2007 S1 26 10 842 6.0% 11.8% -0.3% 5.8% 1 1 0 1 27 1.2 66.8 1 1 0.5 457 0 0 5.5 1.1 9.4%
2007 S2 35 79 898 7.6% 10.9% 1.3% 3.3% 1 1 1 1 38 1.1 66.4 0 0 1 455 0 1 6 1.2 9.7%
2008 S1 30 -11 1,034 7.5% 7.8% 0.3% 0.3% 1 1 1 1 146 1.1 65.5 0 0 1 568 0 0 5 1.2 9.3%
2008 S2 6 67 955 3.7% 5.9% -3.1% 2.1% 1 1 0 1 108 1.1 65.3 1 1 1 493 0 0 6 1.2 7.0%
2009 S1 -1 74 874 0.5% 14.2% -3.4% 13.7% 1 1 0 1 191 1.1 65.3 0 0 1 404 0 0 4 0.9 3.7%
2009 S2 11 64 854 1.0% 15.0% -0.2% 14.0% 1 1 0 1 122 1.0 65.3 1 0 0.5 422 0 0 4.5 0.9 2.7%
2010 S1 18 35 958 3.4% 11.5% 2.0% 8.1% 1 1 1 1 177 1.1 65.3 0 1 0.5 532 0 1 6.5 1.1 4.7%
2010 S2 13 64 967 3.5% 10.9% 0.6% 7.4% 1 1 1 1 225 1.3 65.3 0 1 0.5 544 0 1 6.5 1.3 5.2%
2011 S1 25 60 1,052 4.0% 12.8% 0.4% 8.8% 1 1 1 1 256 1.3 65.3 0 1 0.5 633 0 1 6.5 1.2 5.7%
2011 S2 18 61 1,133 4.3% 12.0% 0.3% 7.7% 1 1 1 1 255 1.1 65.0 1 0 1 622 0 1 7 1.3 5.8%
2012 S1 35 49 1,255 4.9% 10.1% 0.6% 5.2% 1 1 1 1 268 1.1 65.0 1 0 0.5 703 0 1 6.5 1.3 6.0%
2012 S2 42 96 1,220 6.5% 12.2% 1.4% 5.7% 1 1 1 1 270 1.1 65.0 1 1 0.5 686 0 1 7.5 1.2 6.4%
2013 S1 43 48 1,316 6.9% 11.6% 0.6% 4.8% 1 1 1 1 305 1.1 65.0 0 1 0.5 763 0 1 6.5 1.2 6.6%
2013 S2 46 119 1,299 7.0% 13.2% 0.2% 6.1% 1 1 1 1 259 1.1 65.0 1 0 0.5 803 0 1 6.5 1.3 7.8%
2014 S1 64 87 1,471 8.4% 15.8% 1.2% 7.3% 1 1 1 1 305 1.2 65.0 0 1 0.5 902 0 1 6.5 1.3 9.2%
2014 S2 65 125 1,502 9.3% 15.3% 1.0% 6.0% 1 1 1 1 277 1.3 65.0 1 1 0.5 902 0 1 7.5 1.4 9.5%
2015 S1 91 114 1,567 10.4% 16.1% 1.1% 5.6% 1 1 1 1 235 1.2 65.0 1 0 0.5 1,039 0 1 6.5 1.3 10.2%
2015 S2 95 199 1,585 12.1% 20.4% 1.6% 8.2% 1 1 1 1 215 1.3 65.0 1 1 0.5 1,034 0 1 7.5 1.4 11.7%
2016 S1 127 145 1,842 14.1% 21.8% 2.0% 7.7% 1 1 1 1 232 1.1 65.0 0 0 0.5 1,147 1 1 6.5 1.4 13.4%
Here an example of Brembo’s historical
Piotroski score