Stage Two: Quantifying the Effect of the Operating Environment

There are two regression equations from equation (12), one for each input as below.

xsi1

= f1(Ei1

, 1, ui1

) (20)

xs_i² = f₂(E_i², 2, u_i²) . (21)

The dependent variables (xs_i¹and xs_i²) are total radial movement plus slack movement based on the first stage DEA results. Here, Ei1

and Ei2

are the vectors of environmental variables for ISF i that may affect the utilization of input.

The four independent variables are VOL for annual brokerage volume in an ISF, which is deeply influenced by Taiwan’s trading volume, DUR for the duration or the years of service in the securities market, ASV for the ISF’s asset value and one dummy variable FHC to show if this ISF is a subsidiary of an FHC. The purpose of the FHC dummy is to investigate whether the FHC would benefit from its ISF subsidiary or not.

A (positive) negative coefficient on these environmental variables suggests that the environment is (un)favorable for a DMU, since it is associated with (greater) less excess use of inputs.

This regression result indicates that the duration of establishment (DUR) has a significantly negative coefficient in two equations, suggesting that it is a favorable environmental variable. It shows that the ISFs with a longer duration are able to draw the customers’ attention, build up customer loyalty, and create a lot of wealth from the brokerage revenue. Experienced ISFs are able to make less discounted

expenditures and utilize branch resources.

The FHC subsidiary variable (FHC) has a significantly positive coefficient in two equations. This suggests that an ISF under FHC is in an unfavorable operating environment. The empirical result in the first stage has shown that it is most of the non-efficient ISFs that are able to join FHCs. Fourteen law-induced FHCs were established through persuasion from Taiwan’s regulatory authority. This might reveal that politics are possibly intertwined with economic activities in Taiwan.

Consequently, the purpose of forming an FHC is not to leverage the synergy among subsidiaries and to improve their efficiency score, but instead an FHC is turned into a negative factor from its securities subsidiary. This result is consistent with the empirical finding for Malaysian banks in 2006. Chong et al. (2006) indicated that the forced merger mechanism destroys shareholders’ value. Contrary to the findings on voluntary mergers in the United States and Europe, Malaysian acquiring banks that had merged the other target banks have a significantly negative cumulated abnormal return under the forced merger scheme. Moreover, FHC’s securities subsidiaries diversify their dedication on the brokerage business in Taiwan due to on-going mergers from FHCs and the cross-selling of banking products. Plus, Taiwanese regulatory authority limited banking branches to not sell securities products to customers directly due to the firewall issue and protecting small-scale securities firms from banks competition. It is another major reason corrupting the one-stop shopping synergy. It also prompts the ISFs under FHCs not to be able to leverage the banking resources and furthermore decrease branches.

The annual sales amount has an insignificant coefficient on two equations in model I of Table 4-3. It shows that ISFs could increase their share of the brokerage

asset value of each firm has also an insignificant coefficient on two equations in model I of Table 4-3. More assets is not proven to be favorable or unfavorable to the securities firms.

The coefficients of annual sales volume variable (VOL) and asset value (ASV) are insignificant and are hence omitted from the equation of slack prediction. Those environmental variables with significant coefficients such as DUR and FHC are included for slack prediction.

TABLE 4-3. Tobit Regression Results

Model I Model II

function -89.3665 -56.1899 -90.2002 -56.2851

Note: 1. Numbers in the parentheses are standard errors;

2. **, *, and^†indicate significance at the 1%, 5%, and 10% levels, respectively;

3. The sample size is 54.

The parameter estimates present in model II of Table 4-3 and the following Tobit regression models (22) and (23) shown below are used to adjust the original input data according to equation (13).

xŝ¹ = 5.35987 – 0.31405 DUR +1.8928 FHC (22)

xŝ² = 1.18623 – 0.090371 DUR +0.0643657 FHC (23)

Table 4-4 summarizes the predicted slacks and maximum predicated slacks for all inputs based on equation (14). The adjusted data control influences the external operating environment. In 2002, one year before most FHCs’ establishment, the result that Taiwan, Sinopac and Grand Cathay securities firms under FHCs contributed to the maximum predicted slack reveals an unfavourable external environment under FHCs. In 2003 and 2004, the maximum predicted slacks are from Fuhwa and Mega securities firms, which own the least favourable external environment including the shortest duration in the securities industry and a subsidiary of an FHC. The maximum predicted slacks are contributed by Fuhwa securities firms in 2004 and Mega securities firms in 2005, respectively which are all securities subsidiaries in FHC. This predicted slack result is also consistent with the result of the parameter estimates above.

TABLE 4-4. Predicted Slacks and Maximum Predicted Slacks

4. Yuanta Core Pacific 41 0 -7.516 -2.519

5. Capital 14 0 0.963 -0.079

6. President 14 0 0.963 -0.079

7. Polaris 14 0 0.963 -0.079

8. MasterLink 13 0 1.277 0.011

9. SinoPac^f 14 1 2.856 0.565

10. Grand Cathay^f 14 1 2.856 0.565

11. Jih Sun^f 41 1 -5.623 -1.875

2002

12. Taiwan International 14 0 0.963 -0.079

Maximum predicted slack [Max^k{xŝik}] 2.856 0.565

1. Fubon^f 20 1 0.972 0.022

2. Taiwan^f 15 1 2.542 0.474

3. KGI 15 0 0.649 -0.169

4. Yuanta Core Pacific 42 0 -7.830 -2.609

5. Capital 15 0 0.649 -0.169

6. President 15 0 0.649 -0.169

7. Polaris 15 0 0.649 -0.169

8. MasterLink 14 0 0.963 -0.079

9. SinoPac^f 15 1 2.542 0.474

10. Grand Cathay^f 15 1 2.542 0.474

11. Jih Sun^f 42 1 -5.937 -1.966

12. Taiwan International 15 0 0.649 -0.169

13. Fuhwa^f 7 1 5.054 1.197

2003

14. Mega^f 14 1 2.856 0.565

Maximum predicted slack [Max^k{xŝik}] 5.504 1.197

1. Fubon^f 21 1 0.658 -0.068

2. Taiwan^f 16 1 2.228 0.384

3. KGI 16 0 0.335 -0.260

4. Yuanta Core Pacific 43 0 -8.144 -2.700

5. Capital 16 0 0.335 -0.260

6. President 16 0 0.335 -0.260

7. Polaris 16 0 0.335 -0.260

8. MasterLink 15 0 0.649 -0.169

9. SinoPac^f 16 1 2.228 0.384

10. Grand Cathay^f 16 1 2.228 0.384

11. Jih Sun^f 43 1 -6.251 -2.056

12. Taiwan International 16 0 0.335 -0.260

13. Fuhwa^f 8 1 4.740 1.107

2004

14. Mega^f 15 1 2.542 0.474

Maximum predicted slack [Max^k{xŝik}] 4.740 1.107

1. Fubon^f 22 1 1.914 0.294

2. Taiwan^f 17 1 1.914 0.294

3. KGI 17 0 -8.458 -2.790

4. Yuanta Core Pacific 44 0 0.021 -0.350

5. Capital 17 0 0.021 -0.350

6. President 17 0 0.021 -0.350

7. Polaris 17 0 0.335 -0.260

8. MasterLink 16 0 0.021 -0.350

9. SinoPac^f 17 1 1.914 0.294

10. Grand Cathay^f 17 1 -6.566 -2.146

11. Jih Sun^f 44 1 1.914 0.294

12. Taiwan International 17 0 2.533 0.373

13. Fuhwa^f 9 1 2.228 0.384

2005

14. Mega^f 16 1 6.939 1.740

Maximum predicted slack [Max^k{xŝik}] 6.939 1.740