The SE measure ranges from 0 to ln(n), in which a larger value indicates a more diversified loan portfolio

Besides, the additional use of the SE index is to check whether our regression results are sensitive to the choice of different portfolio diversification measures. Hence, we build up the SE measure using the formula as: (3) S E i t = ? s = 1 n x s i t ? l n 1 x s i t (3)

Due to the heterogeneous banking profiles in research markets, prior works indicate slightly different ways of calculating loan portfolio diversification measures. For instance, Acharya et al. ( 2006 ) design a disaggregated sector decomposition based on each bank’s top five industrial exposures with a sixth exposure covering the sum of the remaining exposures (where some exposures could be to any of the 23 industries). Hayden et al. ( 2007 ) use the German classification system with 23 economic sectors, noting that these sectors account for the largest outstanding loans at most banks surveyed. For the Brazilian market, thanks to the standardization of financial information published by all banks, Tabak et al. ( 2011 ) efficiently compute consistent diversification measures with 21 classified economic sectors. Regarding the Chinese ) start from original loan portfolios with 13 industries, then make some adjustments to reduce to 9 industries before calculating their portfolio diversification measures.

We have to pay careful attention when setting up the formats of calculating sectoral loan portfolio diversification measures in the Vietnamese banking context. Unlike banks in many developed markets using standard sector classification systems and publishing adequate financial information, Vietnamese banks show no consensus in dealing with their sectoral loan portfolio structures. More concretely, the number of economic sectors that banks are exposed to varies among banks. Given this practice, we thus follow the seminal empirical paper of Acharya et al. ( 2006 ) to restructure Vietnamese banks’ sectoral loan portfolios. From an empirical standpoint, this study divides the loan portfolios into six sectoral exposures, including top five sectoral exposures and a sixth exposure containing the sum of all remaining exposures. Taking a step further than Acharya et al. ( 2006 ), to ensure that the random structure in this manner does not affect the robustness of our study, we also apply alternative approaches with eight and ten sectoral exposures. In sum, we create a rich set of loan portfolio diversification variables: (i) three HHI measures with HHI10 (ten exposures), HHI8 (eight exposures), and HHI6 (six exposures); (ii) three similar SE measures with SE10 (ten exposures), SE8 (eight exposures) best payday loan Oregon, and SE6 (six exposures).

For the development of portfolio diversification variables, after collecting detailed data on the sectoral loan portfolio of each bank, we arrange outstanding loan items by sector from maximum to minimum

We then take ten, eight, and six sectoral exposures, respectively, as discussed previously (the “last sectoral exposure” is the sum of all remaining outstanding loan items in the portfolio). With this approach, the set of sectoral exposures e during years. However, based on Acharya et al. ( 2006 )’s arguments and the banking practice in Vietnam, we realize that the set of sectoral exposures (ten, eight, and six sectoral exposures) does not change significantly and mainly contains several key sectors.

Therefore, we expect the portfolio diversification variables to be negatively associated with bank returns

The findings from both theoretical and experimental literature on the effects of loan portfolio diversification on bank returns have been ambiguous thus far. In the context of a nascent banking system like Vietnam, banks in general still have limited resources to exploit diversification advantages.