Akademik Veri Yönetim Sistemi
20th International Symposium on Econometrics, Operations Research and Statistics, Ankara, Türkiye, 12 Şubat 2020, ss.163
Aim
Borsa Istanbul A.Ş. (BIST) has the city indices that
allow investors to invest in a particular city. Several
studies analyze return performances of the city indices from BIST. However, efficient
results are obtained with the help of analyzes enriched with new approaches. In
this analysis we aim to examine return performances of city indices from BIST
by using Dynamic Data Envelopment Analysis which take into consideration an
intermediate input/output variable to link consecutive periods
in the time interval.
Methods/Data
Data Envelopment Analysis (DEA) is a linear
programming based technique which is used to measure the relative efficiencies
of Decision Making Units (DMUs). DMUs use same kind of inputs to produce same
kind of outputs, in other words they should be homogenous. Traditional DEA
technique consider the DMUs for a specific time or process via cross-section
data, then the relative efficiency scores are obtained for a separate time.
In this study, we focus on Dynamic DEA to measure the
relative efficiency of the return of the City Indices in BIST. In our analysis,
we try to measure the efficiency of city indices regarding their weekly returns.
The relative efficiency scores of city indices are evaluated for the time
interval between 2015-2018 in Dynamic DEA. Input of the model is considered as
absolute value of correlation coefficients of index return series and the
output of the model is mean gross return over standard deviation of returns. Additionally, to ensure the
dynamic procedure, skewness of return of city index is used as an intermediate
input/output variable to link consecutive periods in the time interval. One period’s output is used as an input for
the next time period then the intermediate input/output helps to investigate
DMUs time period instead of a specific time. With this
feature, the analysis is an example of usage a panel data.
Findings
According to the results, Antalya and Tekirdağ city
indices are not efficient among the other 10 indices. The used Dynamic DEA
model labeled a DMU as efficient if it is efficient at least one term when the
DMU is considered via traditional static DEA technique. Since, Antalya and Tekirdağ
city indices are not efficient for any time overall efficiency scores according
to Dynamic DEA is less than 1. Although the dynamic efficiency scores are equal
to 1, there is no city indices that is efficient for all the periods separately.
From this perspective static DEA and dynamic DEA can be used together. Dynamic
DEA is a powerful tool to link the consecutive time periods, on the other hand
static DEA provides to handle separate terms individually.
Results
From the perspective of our analysis, a city that is
efficient for all periods makes more sense in terms of investment, there is no
such index within the time period examined. The results are important for
researchers and investors seeking profitable investment tools and also can be
evaluated in terms of regionally in future works. Even though our analysis
based on a specific period of time and the data, the implications arising from
the results of the study are important for the companies’ policies. As a result, when static and dynamic DEA are
evaluated together, it helps to make strong decisions. They have no superior to
each other.