Variables

According to the literatures, we use the generally adopted measurement, return on assets (ROA), here we divide the performance measurement into three part to capture the whole picture of bank performance, first we use three year average ROA (AVROA) form year t-2 to t to mitigate the effect of extreme value. Second we measure the volatility of ROA by counting the corresponding standard deviation (sigavroa). The last one we count the Sharpe Ratio for these years (sharpROA), which equals the AVROA divide by sigavroa., and we describe our explanatory variables in

following sections.

3.2.1 Liquidity Creation Computation

Berger & Bouwman (2005, 2008, 2009) constructed a two dimension measurement of category/maturity (cat/mat) and on/off-balance sheet activities (fat/nonfat) to compute liquidity creation. Our major variable, liquidity creation, is computed by the cat-fat combination that differs from the mat-nonfat measurement such as LT gap (Deep & Schaefer, 2004). Step 1 is to classify all activities in balance sheet and off-balance sheet as liquid, semi-liquid or illiquid and step 2 is given weights to the categories then computes it by the model they addressed.

In Step 1, we classify all the activities in the balance sheet. In the asset side, cash, due from banks, and securities can transform in financial market, thus we sort these items to liquid assets. On the contrary some items cannot be sold quickly (e.g., loan to company, hire purchase, lease, and problem loans), we classify these item as illiquid assets. Additionally, for mortgage, loans to bank and government, the liquidity is between cash and loans so we classify these items as semi-liquid assets. Similarly, we classify liabilities and off-balance activities as liquid, semi-liquid and illiquid. The original classification is shown in, besides we set up a new one due to the category restrictions of Bankscope Database as Table 1. But here is still something different from past literature, for example, the mortgages are classified in illiquidity asset by Berger and Bowuman (2005), but we sort in semi-liquid assets. We think the character of mortgage is between the liquidity and illiquidity. And we categorized all of the demand deposits and saving deposits by liquidity liabilities.

In step2, we adopt the same weight used by Berger & Bouwman (2009) but

follow the categories we prepared in Table 1. According to liquidity creation theory, they suggest that when banks transform $1 of illiquid assets into $1 of liquid liabilities,

$1 of liquidity is created, and when $1 of liquid assets transforms into $1 of illiquid liabilities, $1 of liquidity is destructed. To achieving the constraints, they assigned a weight of 1/2 for illiquid assets and liquid liabilities, it represents that both the source and the use of funds affect the liquidity creation. Where the transformation can present in formula “1/2 * $1 liquid liabilities + 1/2 * $1 illiquid assets = $1 liquid creation” as maximum created. Similarly, they applied a weight of -1/2 to liquid assets, illiquid liabilities and equity. Then the equation “-1/2 * $1 illiquid liabilities -1/2 * $1 liquid assets = $-1 liquidity creation” describes as maximum destroyed. The semi-liquid items were denoted as an intermediate weight to 0, and then gave a weight of 1/2 to illiquid financial guarantees. Then sum up the liquidity creation and liquidity destruction, the complete formula was set:

LC = 1/2 * illiquid assets + 0 * semi-liquid assets + 1/2 * liquid liabilities (2) -1/2 * liquid assets + 0 * semi-liquid liabilities -1/2 * illiquid liabilities

and equity +1/2 *illiquid financial guarantees +1/2*semi-liquid financial guarantees.

It’s obvious that the liquidity creation ties on the lending business closely. This creation may lead a premium since banks have to suffer the illiquidity of these actions, but the risk-adjusted return is unsure. More specifically, bank could obtain a premium since it suffers the liquidity risk, but it could also bare a discount because it suffers

“too much” risk.

3.2.2 Control Variables

Here are all variable definitions and we list them all in Table 2. The size factor is commonly adopted to be the natural logarithm of total asset (lnTA). Similarly, the total equity to assets ratio (TETA), is widely used in the empirical research as the key capital ratio. The liquidity is often measured by the ratio of liquid assets to total assets (L_TA), but however, the data is not available. To substitute, we use the ratio of total loan to total asset as our liquidity measurement (Athanasoglou, et al., 2006).

In order to capture the effect of the macroeconomic environment, we follow the literatures and use annual percent change of GDP (GDPC), we further add GDP annual percent change of last year (GDPCt-1) to capture the lagged effects.

For the regulation variable, we follow Barth et al. (2004) and measure the regulatory in three dimensions. The official supervisory power index (OSP) represents the power of supervisory institution; the private monitoring index (PMI) includes information on the degree to which bank regulations force banks to disclose accurate information to the public and induces private sector monitoring of banks; and the overall bank activities and ownership restrictiveness (BAR) reflects the restriction that bank encountered in the financial system, includes the regulations on security, insurance, real estate activities, and the restrictions on bank owning/controlling a financing firm. We use these three indices to proxy a relative severity among countries. But since the country indices are time-invariant variables and cannot be applied in fixed-effect model, hence we compare the fixed-effect and random-effect model and found that the fixed-effect model would be better. In replacement, we also use the interaction terms with GDPC named GDPCXOSP, GDPCXPMI, GDPCXBAR respectively as our regulation variable. Owing to the interaction term are highly correlated to each other because they are originated from GDPC and would

lead to the colinearity, hereby we only use one term in each model. So there will be four models which use gdpcxosp, gdpcxpmi, gdpxxbar, and no regulation variable in each panel.

Finally, numerous literatures argued that the position of bank would be different among bank-based and market-based countries. Bank occupies a crucial role as mobilizing savings, allocation capital, overseeing the investment decisions of corporate managers and providing risk management vehicles; but in market-based countries the securities markets share this center stage with banks (Demirgüç-Kunt &

Levine, 1999). Hence we follow them and use their structure index to proxy the financial structure. They measure the relative importance of bank or market finance by the relative size, activity, and efficiency. We classify countries with values of the Structure index above (below) the sample mean as market-based (bank-based) financial systems as Table 3. Further, financial structure (d_fs) is a dummy variable that takes the value 0 for market-based systems and 1 for bank-based systems.

3.2.3 Determinants of Liquidity Creation

According to our literature, the size and capital are the first two important factors that affect the bank liquidity creation (Berger & Bouwman, 2009). We here use the natural logarithm of total asset (lnTA) and the total equity to assets ratio (TETA), and these two factor is just discuss in prior section.

In intuition, the holding of liquidity is directly correlated to the bank liquidity creation. No matter how it influences, a higher level of bank liquidity implies that bank can operate more instruments to construct or destruct the liquidity offers in the financial system. Here we also adopt the ratio of loan to total assets (L_TA)

mentioned in prior section.

At last, we use the change in economic growth (GDPC) and lagged change in economic growth (GDPCt-1) to capture the macroeconomic changes and the lagged effect respectively. Then all the correlation coefficients and summary statistics are shown in Table 4 and Table 5.