Proceedings

each unit of risk. It is calculated by first subtracting the estimate return from the return
of the portfolio, then dividing by the standard deviation of the portfolio.
_
R – portfolio return
ER – estimated return for a period of time
σP – portfolio standard deviation
_
R−
ER
ShR = (13)
σ
Sortino Ratio (SR) - is similar to the Sharpe ratio, except it uses downside deviation
for the denominator instead of standard deviation, the use of which doesn't
discriminate between up and down volatility.
R − ER
SR = (14)
σ d
R – return over a period of time
σ2 – downside risk – standard deviation of negative returns
2. THE IMPLEMENTATION OF MODELS AND THE INTERPRETATION
OF RESULTS
The models will be tested empirically on a set of 137 companies listed on European
capital markets. The issuing companies that were selected for fundamental analysis are
included in the structure of some important indexes from the European capital market:
CAC40 (France), Ibex 35 (Spain), S & P MIB (Italy), DAX (Germany), FTSE 100
(UK) BUX (Hungary), PX (Czech Republic) and WIG 20 (Poland).
The perspective of analysis considered for analyzing financial descriptors is
2006-2008, and for the year 2009 we accomplished a forecast of possible returns that
an issuer can record based on the results obtained from the analysis of the financial
rates.
For an easier implementation of the model, the selected issuing companies were
grouped according to sectors in which they operate (see Annex 1-5):
� basic materials sector - 18 companies;
� industrial sector - 24 companies;
� technology, telecommunications and media sector - 19 companies;
� energy sector - 29 companies;
� consumer and health sector - 47 companies.
The used methodology implies recourse to a "Panel Generalized Method of
Movements."
There are several advantages of the GMM-SYS over other static or dynamic panel
estimation methods. Among these: static panel estimates, as the OLS models, are
subjected to the problem of dynamic panel bias (Bond, 2002); in our database, we
have 127 companies (N) analyzed over a short time span of 3 years (T) and the
literature includes several arguments for dynamic panel model being specially
designed for a situation where “T” is smaller than “N” in order to control for dynamic
panel bias (Bond 2002; Baltagi 2008); the problem of the potential endogeneity can be
easier addressed in dynamic panel models than in static and OLS models, since all
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