Apart from bank-level factors, we also allow for the macroeconomic variables in explaining bank returns
The Lerner index ranges from 0 to 1, in which higher values indicate larger market power. The Lerner index displays the extent to which a bank gains the market power to set its price above the marginal cost.
Considering the two opposing theories of “competition-fragility” and “competition-stability”, we realize that ong banks. However, in the Vietnamese banking market, a bank with larger market power tends to gain the advantages in competitive capacity, business resources, expertise, and experience. Hence, assuming that these advantages might support banks with greater market power in loan portfolio diversification, we expect that the adverse impact of loan portfolio diversification on bank returns should be more pronounced at banks with less market power.
3.2.4. Control variables
In the model specification of bank returns, we control for bank capital (the ratio of bank equity to total assets), because more capitalized banks may grant loans cautiously and borrow less, thus decreasing their costs and increasing their profits (Tan, 2016 ). We also introduce the ratio of liquid assets to total assets to capture bank liquidity positions and the natural logarithm of total assets to measure bank size. We expect bank liquidity to be negatively correlated with bank returns, as more liquid assets regularly produce lower returns than less liquid assets (Dermine, 1986 ). As for bank size, larger banks may have a higher yield, since they could take greater advantage of economies of scale (Carter McNulty, 2005 ; Hughes Mester, 1998 ).
The business cycle, captured by the growth rate of gross domestic product (GDP) and the inflation, captured by the annual inflation rate, are considered as key determinants of bank performance. During the economic upturn, the demand for financial services tends to increase, thus improving bank returns (Athanasoglou et al., 2008 ; Dietrich Wanzenried, 2014 ). Bank performance can also be affected by inflation under a mechanism in which inflation plays a critical role in shaping interest rates. For example, a higher inflation rate might lead to higher interest rates on loans, which potentially enhance bank profits (Pervana et al., 2015 ).
3.3. Model specification and econometric method
To empirically analyze the impact of loan portfolio diversification on bank return s , we approach the dynamic panel model proposed by Acharya et al. ( 2006 ) and Tabak et al. ( 2011 ), in which we expand with interaction terms to explore the conditioning roles of business models and market power. Hence, the model is specified as follows: (7) R e t u r n i , t = ? 0 + ? 1 ? R e t u r n i , t ? 1 + ? 2 ? D i v e r s i f i c a t i o n i , t ? 1 + ? 3 ? D i v e r s i f i c a t i o n i , t ? 1 ? M o d e r a t o r i , t ? 1 + ? 4 ? Z i , t ? 1 + ? 5 ? X t ? 1 + ? i , t (7)
where i captures bank dimension and t denotes time dimension. R e t u r n is the dependent variable, measuring bank returns. D i v e r s i f i c a t i o n indicates loan portfolio diversification variables, illustrated by either the HHI or the SE indicators. Z is a vector of bank-level control factors, X comprises of macroeconomic control variables, and ? is the error term. M o d e r a t o r represents bank business models and market power, separately. The regression coefficient on the D i v e r s i f i c a t i o n ? M o d e r a t o r interaction term exhibits the differential impact of loan portfolio diversification on bank profitability across heterogeneous bank groups. Opposite signs between the regression coefficients of the standalone D i v e r s i f i c a t i o n variable and the interaction term imply a weakening role of business models or market power, while similar signs support the strengthening effect of these factors.