Page 140 - Contributed Paper Session (CPS) - Volume 7

CPS2048 Md Zobaer H. et al.
SFA is become most frequently used procedure because it segregate
statistical noise from the effect of inefficiency (Kumbhakar & Lovell, 2003). In
spite of this, SFA speculates a distinct probability distribution for the efficiency
level. The DEA skips this sorts of specification error and it does not need a
prior production function for efficiency (Dong et al. 2014). In DEA, all
deviations from the frontier are measured as inefficiency and it does not allow
for random errors in the optimization which is its main drawback. Therefore, if
any noise exists, this may exaggerate the common inefficiency. Consequently,
two methods (DEA and SFA) have their advantages as well as drawbacks
(Huang & Wang 2002). Many researchers (Casu et al. (2004), Delis and
Papanikolaou (2009), Weill (2004)) found that the consistency of efficiency
derived from DEA and SFA is not significant. For this reason, this study will also
concentrate on finding the combination of the DEA and SFA efficiency scores
which will be a new experiment in literature perspective. However, Fernandes
et al. (2018) and Altunbas et al. (2007) found that there is a strong connection
between efficiency and profit risk, because inefficient financial firm tend to
take less risk by investing and hold more capital. Fernandes et al. (2018) found
that profit risk has a positive effect on the efficiency of peripheral European
domestic banks.
This study will be a new idea for the estimation of financial company’s
efficiency by using the combination of DEA and SFA in respect to developing
counties like Malaysia. The study provides a unique setting to calculate
financial efficiency matric and find the effect of efficiency on profit risk by
using regression analysis. Moreover, these findings could provide useful and
important signal in case of decision making for management.

2. Methodology
DEA-MPI
The best way to introduce DEA is via the ratio form. For each DMU
(decision making unit) needs to obtain a measure of the ratio of all outputs
over all inputs, such as = . Where, is an Mx1 vector of output weight

ℎ
for firm and is a Kx1 vector of input weight of firm. To select optimal
ℎ

weight we specify the mathematical programming problem:

127 | I S I W S C 2 0 1 9