*3.1. Data*

The sample comprises 94 active banks listed on the main stock exchange from 19 Eurozone Countries for the period between 2011 and 2016. An unbalanced panel was constructed with the 94 European banks whose information was available for at least five consecutive years. Thus, this sample was chosen for two reasons: (i) all active banks, listed on the main stock exchange from 19 Eurozone Countries, were included as they were considered the banks with the highest volume of total assets; (ii) a necessary condition was that banks must have complete information on the variables under study, for at least five consecutive years; this condition was fundamental for the use of panel data methodology and specifically the GMM system method. We emphasize that these banks correspond to about 20% of the total assets of eurozone banks in 2016. This is important to test for second-order serial correlation, as Arellano and Bond (1991); Arellano and Bover (1995) and Blundell and Bond (1998) stated. The test for second-order serial correlation was realized because the estimation method GMM is based on this assumption (Neves 2018). The data were collected from the Bankscope database (Bureau Van Dijk's company) and it was used to test the hypotheses established in the previous section. Regarding the variables used in the model (1), since there is no consensus about which variables best explain the bank profitability, the ROAA will be considered as the dependent variable, following, for instance, Trujillo-Ponce (2013). The banks with high competition and high operating costs from increasing regulation, and fewer opportunities to raise fees to o ffset these costs, include an intense balance sheet management. So, in the author's opinion, ROAA could be the best way to explain bank performance, because it is a measure which depends in a large way on the

<sup>2</sup> This author shows positive effects of size on Greek bank's performance only when the macroeconomic and financial structure variables are introduced in the model.

managemen<sup>t</sup> decisions. The explanatory variables selected in this study are related to factors that are specific to banks. These variables are controlled by managemen<sup>t</sup> and reflect the different policies and managerial decisions; consequently, they command the bank´s performance (Dietrich and Wanzenried 2014, 2011; Djalilov and Piesse 2016; Guru et al. 2002). Table 1 displays more details about the selected explanatory variables.

**Table 1.** Description of the explanatory variables. Bank-specific characteristics as determinants of bank return on average assets (ROAA).

