ESTUDIOS SOBRE SEGREGACIÓN EN COLOMBIA

The form of market discipline in capital markets is demonstrated by the ability of equity holders to evaluate the financial condition of a bank (monitoring phase) and by the responsiveness of the bank management to investors’ stock-return assessment (influencing phase) (Bliss & Flannery, 2002). Shareholder ability to assess the riskiness of publicly-traded banks is indicated by the fluctuation of the bank share prices as shareholders react to the announcements of bank financial indicators such as CAMEL ratios (Bliss & Flannery, 2002; Caner et al., 2012). Therefore, the bank risk monitoring behaviour of equity holders exists if there is a significant relationship between equity returns and risk measures obtained from the financial statements of banks (Bliss & Flannery, 2002).

The model to measure discipline by shareholders is constructed upon the growth of share price or equity return as a dependent variable. The equity returns are calculated by taking the difference between the stock return of a bank at the closing date and the return of a short-term investment alternative (Bliss & Flannery, 2002; Caner et al., 2012). In the present study, the yield of the BI certificate (SBI)38 is used as an available investment alternative for investors in Indonesia. Bank stock prices and returns fluctuate according to the risks taken by banks as signaled by the CAMEL ratios after controlling for other bank specific variables and macroeconomic conditions, amongst other factors. With an intention to make a comparison of the empirical results for each discipline agent, the monitoring model of equity holders is designed to be similar to the models for evaluating market discipline by depositors and bond holders. The following is the model used:

EQT_RT_I,T= Α₀+ Β₁ (BANK_FUNDAMENTAL)_I,T−1+ Β₂(MACRO_VARIABLE)_I,T +

Β₃BANK_SIZE_I,T+ Β₄STA_BANK_I,T+ Ε_IT

(5)

EQT_RT i,t = the bank equity return is represented as the growth of the

bank share price

BANK_FUNDAMENTALi,t = vector of bank idiosyncratic risks that represent the CAMEL

ratios which consists of CAR, NPL, OPEX, NIM, and LDR

38 _{The BI certificate (SBI) is a Rupiah-denominated security issued by BI in recognition of short-term debt and}

comprises one of the instruments used in Open Market Operations. The term of SBIs is at least 1 month and no more than 12 months. SBI may be held by banks and other parties as stipulated by BI, and are negotiable. SBI may be purchased on the primary market and traded on the secondary market under repurchase agreements (repo) or in outright purchase/sale.

110 MACRO_VARIABLE,i,t = vector of macroeconomic variables which consists of

GDP_RT, EXC_RT, and BI_RT BANK_SIZEi,t = bank total assets

STA_BANKi,t = dummy variable of government ownership; 1 for state bank,

and 0 otherwise εi,t = random error terms

In line with the regression models for depositors and debt holders, the vector of bank fundamentals for this model is included with a lag difference to account for the fact that the financial statement information is available to the public only with delay. The following sub- section explains in more detail the justification for, and measurement of, these variables.

Bank Fundamental Variables

Bank fundamentals are represented by CAMEL financial ratios, similar to the approach used to investigate discipline by depositors and bond holders in Sections 3.5.1.1 and 3.5.1.2. The use of CAMEL ratios as indicators of bank risk have previously been used in, for example, Bliss and Flannery (2002) and Caner et al. (2012). Independent variables used in the shareholder regression model are described in detail as follows (the calculation methods of the variables are provided in page 98-101):

1. CAR: The capital adequacy variable represents the level of capital to absorb losses before becoming insolvent. Therefore, this variable is expected to have a positive effect on equity returns (Beighley et al., 1975; Berger, Davies, & Flannery, 2000; Caner et al., 2012; Shome et al., 1986).

2. NPL: The non-performing loans ratio is used as an indicator for asset quality. An increase in the NPL ratio indicates a low return for the bank. Therefore, this variable is expected to have a negative effect on equity returns (Berger et al., 2000; Caner et al., 2012).

3. OPEX: The management efficiency variable is measured using the OPEX ratio. This ratio is expected to a have a negative correlation with equity returns (Caner et al., 2012) because an increase in the OPEX ratio might reduce the wealth creation ability of a bank.

111 4. ROA: The earning quality variable is closely associated with the value maximization of

shareholders and shares are priced on the basis of bank predicted future performance (Berger et al., 2000). Hence, this ratio is predicted to a have positive correlation with equity returns (Beighley et al., 1975; Berger et al., 2000).

5. NIM: The net interest margin coefficient is often employed to measure bank profitability, similar to the ROA ratio. NIM is expected to have a positive correlation with equity returns because an increase in the NIM ratio would decrease the ability of banks to generate wealth.

6. LDR: Similar to the regression model for depositors, proxy for liquidity risk is assessed by the loan to deposit ratio (LDR). Generally, banks with a large volume of liquid assets are perceived to be safer, therefore the liquidity coefficient is expected to have a positive correlation with equity returns.

7. BANK_SIZE: Bank total assets, used as a proxy for bank size, are included as a control variable to evaluate the influence of the TBTF doctrine on shareholder behaviour (Beighley et al., 1975; Berger et al., 2000).

8. STA_BANK: Previous studies of market discipline suggest that government ownership of banks might affect the extent of market discipline (Levy-Yeyati et al., 2004a; Sironi, 2003). Similar to other models, STA_BANK is included as a dummy variable in order to test the sensitivity of shareholders to the risk profile of state banks.

Macroeconomic Variables

Macroeconomics factors play significant roles in determining equity returns, as discussed in Chapter 2. The empirical evidence supports the view that equity returns could not be completely explained by the risks of an individual institution, which increases the importance of using macroeconomic indicators as control variables (Sironi, 2003). The present study incorporates three control variables: gross domestic products (GDP_RT), exchange rate (EXC_RT), and the central bank’s interest rate (BI_RT). Previous studies, particularly among emerging markets, reveal mixed results on the relationship between these variables and the yield spreads. No particular a priori direction of relationship is postulated.

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