Proceedings

variables; (b) we are expecting the financial ratios to be highly correlated; (c)
component scores are a linear combination of the observed variables weighted by
eigenvectors and thus allows for considering the relative importance of individual
variables. Such global indicator is designed to be use for an overall assessment of the
impact exercised by the financial ratios on market values.
2. DATA AND EMPIRICAL RESULTS
2.1. Romanian capital market data
Our dataset consists in 12 companies from the first tier of the Bucharest Stock
Exchange over a time span between 2005 and 2009. These stocks have a maximal
degree of liquidity and are forming a significant fraction of the market. The variables
reflect the annual close prices, the business turnover, two liquidity ratios, the net
treasury ratio and the dividends per share. In our opinion, such financial ratios are
susceptible to capture in a synthetic manner the financial situations of the issuers as
well as the returns obtained by the investors. Consequently, these ratios are
presumably relevant for investment decisions even on an imperfect market such as the
Romanian one with slow prices adjustment mechanisms and their effects on longer
market cycles.
The data are provided by Bucharest Stock Exchange and represents the (log) last close
of the year indexes as dependent variable and a set of financial ratios computed based
on the financial statements of the issuers as explicative variables.
Table 1 reports on the main statistic characteristics of the data. The data displays nonnormal
distributions with significant fat-tails effects. The values of the dispersion as
well as the parameters of the distribution suggest the possibility of some significant
outliers in for the observation period.
Table 1. Main statistic characteristics of data (yearly values; variation, %)
Close prices Current liquidity ratioQuick ratioNet treasury ratio Dividends per share
Mean 62.65 96.37 119.06 24.44 -1.37
Median 30.00 -2.60 2.46 -0.23 0.00
Maximum 2145.22 2843.53 2430.21 1673.31 270.59
Minimum -94.05 -99.00 -99.07 -627.93 -100.00
Std. Dev. 287.44 421.74 468.11 275.25 63.07
Skewness 6.59 5.49 4.16 3.49 1.77
Kurtosis 48.35 33.97 19.23 23.63 9.00
Jarque-Bera 5482.73 2653.52 818.20 1165.79 119.37
Number of observations 60 60 60 60 60
Such potential heterogeneity requires an adequate methodology for dealing with the
induced bias in data and can be viewed as a supplementary argument for the involving
of the GMM-System approach.
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