The Journal of Asian Finance, Economics and Business

Korea Distribution Science Association (한국유통과학회)
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Approaching the Negative Super-SBM Model to Partner Selection of Vietnamese Securities Companies

  • Received : 2020.11.20
  • Accepted : 2021.02.03
  • Published : 2021.03.30
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Abstract

The purpose of the study is to determine the efficiency, position, and partner selection of securities companies via the negative super-SBM model used in data envelopment analysis (DEA). This model utilizes a variety of inputs, including current assets, non-current assets, fixed assets, liabilities, owner's equity and charter capital, and outputs including net revenue, gross profit, operating profit, and net profit after tax collected from the financial reports (Vietstock, 2020) of 32 securities companies, operating during the period from 2016 to 2019, negative data are collected as well. Empirical results determined both efficient and inefficient terms, and then further determined the position of each securities firm under consideration of every term. The overall score arrived at discovered a large performance change realizing a maximum score able to reach 20.791. In the next stage, alliancing inefficient companies was carried out based on the 2019 scores to seek out optimal partners for the inefficient companies. The tested result indicated that AAS was the best partner selection when its partners received a good result after alliancing, as with FTS (11.04469). The partner selection is deemed as a solution helpful to inefficient securities companies in order to improve their future efficiency scores.

Keywords

1. Introduction

The securities industry around the world is growing fast in both developed and developing countries. According to statistics (October, 2020), revenues in the security segment from 2017 to 2019 increased from US$5, 726m to US$9, 956.8m. In addition, the scale and scope of larger companies have moved from private firm development to that of cooperation, and they have contributed to enhancement of economic development. A good example of such positive business development is located in Southeast Asia. In Vietnam, over the past twenty years, the securities industry has formed, boosted and developed sharply in order to achieve a high rate of economic growth. In recent years, the securities market in Vietnam has achieved a development rate contributing 20% to the total investment digest in 2019. The development of a local securities industry equips a nation to restructure the economy and to maintain stable economic development (Pham, Sriratanaviriyakul, & Nkhoma 2013). Thus, the securities industry remains an important foundation for continued and future economic development considered as a central finance mechanism to ensure adequate financing for growing enterprises and to boost domestic investment and wealth creation (Steil, 2001). Securities activities are involved in managing and controlling investment banking, market creation, trading, portfolio management and corporate strategic growth (Bayyurt & Akin, 2014). To attract a variety of domestic and international investors, securities firms always promote better strategies, such as technical innovation involving the development of communication channels between investors and consumers (Iwamura & Jog, 1991). The operational efficiency of securities firms is measurable via the negative-SBM model in DEA.

In the financial statement of securities firms located in Vietnam, financial indices often present negative values, so the negative super-SBM model (Lin, Yang, & Huang, 2019) is useful to analyze operating processes because of its function of dealing with the negative data. Moreover, this model can compute super efficiency (Lin & Liu, 2019) with the separate performance and the maximum point approach that can help to rank-order the different positions of each securities firm according to every term. In this research, actual data possessing both positive and negative values gathered from Vietstock (2020). Therefore, the purpose of this study is to measure the performance of securities companies in Vietnam from 2016 to 2019 and then seek out the future partner selection for the inefficient securities firms based on 2019’s empirical results. Firstly, the efficiency scores and positioning of each securities company were determined by means of the negative super-SBM model since this model can solve the presence of negative values. Secondly, an alliance was created between inefficient securities firms in 2019 along with other securities firms in order to select the best future partner bid. As a result, the inefficient securities firms had the potential to improve their future operational performance by choosing a right partner and strategic direction.

The paper is organized as follows: Section 1 introduces the purpose of this research. Section 2 reviews the literature about past studies of securities market, negative super- SBM model and partner selection. Section 3 provides the mathematical equations offered by the negative super- SBM model and describes the raw data resultant from such equations. Section 4 delivers the empirical results and discusses primary findings. Finally, Section 5 summarizes the main results and notes limitations and directions for future research.

2. Literature Review

Securities are always attractive to regular investors because securities indices change consistently and rapidly. Further, scientific research methodology has been utilized to study the securities industry through various methods and problem scenarios. For example, Gao, O’Sullivan, and Sherman (2017) tested the performance persistence of Chinese equity securities investment funds from May 2003 to May 2014 by means of persistence test methods. Gustafson (2018) gave evidence of the public securities market’s accessibility creating a slight reduction in criminal bank hold-ups. Fisher, Gissler, and Verani, (2019) studied securities lending practices to show how they impacted over- the-counter market liquidity in U.S. insurance companies. Chanto and Fioriti (2019) investigated bidding on securities and discovered three fundamental characteristics, which included the losing bidder, payment of bidder and implementation decision in the winning bidder’s decision. Faias and Guedes (2020) described the diffusion process of a financial innovation via the CAT bond market, and then, the analytic results indicated that investors could improve their estimates by observing the performance of successive vintages of the security. Silvers (2020) indicated that the securities market had expanded and grew around the world, in such a way that cross-border securities always required efforts to have suitable securities regulations in place in the capital markets of participating countries. Therefore, the securities industry has attracted a broad range of research including the present study of evaluative performance of securities companies located in Vietnam.

According to Alexander (2009), efficiency is a practical tool, an intellectual construct, a comparative mean, and a vision. In common terms, efficiency is a measurement tool for an organization when it utilizes its resources to produce goods and services, whereas, its resources are called “inputs”; and, its goods and service are called “outputs”. DEA is a statistical tool that may determine the efficiency of an organization by calculating the ratio between outputs and inputs. The improvement of ineffective DMU’s in the traditional DEA model is represented as a proportional reduction in the inputs/outputs. Tone (2001) proposed the SBM model with a non-radial aspect that addressed slack directly. However, this model only gives a limited score represented as “1” along with the efficient DMU’s. Tone (2002) continued to recommend the super-SBM model with unlimited scores to be an efficient DNMU. In addition, Tone (2004) proposed an undesirable model with new non-parametric DEA scheme to compute measurable performance in the presence of undesirable outputs based on the demonstrated principles already found in the SBM model.

Consequently, there are many different models in DEA such as Slack-based measure, Super slack-based measure, and so on. These models have already been applied to the various aspects of enterprise. Feng, Zeng, and Ming, (2018) indicated the green innovation efficiency (GIE) of China’s manufacturing industry from 2009 to 2015 through the use of a super-SBM model. An investigation of bank efficiency in China and Taiwan from 2008 to 2017 determined the overall technical efficiency of banks after China and Taiwan signed the ECFA cooperation agreement (Liao, 2020). Naushad, Faridi, and Faisal (2020) computed the managerial efficiency of 30 insurance companies in Saudi Arabia by means of the DEA approach. An application of the Malmquist productivity index investigated empirical evidence of productivity in life insurance institutions in Malaysia by Masud, Rana, Mia, and Saifullah (2020). An evaluation of the land-use efficiency of 17 cities in Shangdong, China from 2006 to 2018 was conducted by means of a super-SBM model (Pang & Wang, 2020). Previous research approached the DEA method to measure the performance of a variety of areas. In this study, due to the presence of negative values, the negative super-SBM model has been selected for computing the performance of securities firms in Vietnam.

The negative super-SBM model is an analytical statistics model in DEA, which can conduct the efficiency score of a Decision-Making Unit (DMU) with the presence of negative input and/or output values (Khoveyni, Eslami, & Yang, 2017; Tone, Chang, & Wu, 2020). A return to scale is defined by variable return to scale technology (Allahyar & Malkhalifeh, 2015). Moreover, the negative super-SBM model owns the super efficiency. In a progressive comparison, there are many DMU’s using similar terms, the super efficiency model conducts separate scores for both inefficient cases and efficient cases (Wang, Day, Nguyen, & Luu, 2018). Hence, it is a good tool to evaluate the performance and to distinguish the rank of each DMU. Thus, the efficiency scores overcome a limited efficiency score of “1”. Many previous studies analyzed the presence characteristics of negative data in DEA, such as a semi-oriented radial measurement (Emrouznejad, Anouze, & Thanassoulis, 2010), a variant of radial measurement (Cheng, Zervopoulos, & Qian, 2013), congestion approaches (Mehdiloozad, Joe, & Sahoo, 2018), etc. Such a model was applied in measuring the performance of supply chains (Chiang, 2020), global airlines (Cui & Jin, 2020), the banking industry (Tavana, Izadikhah, Caprio, & Saen, 2018), and so on.

In this study, the negative super-SBM model in DEA was used for conducting the performance of Vietnamese securities companies from 2016 to 2019. In addition, from empirical results taken from the negative super-SBM model and the principle of partner selection, a good partner of inefficient DMU based on the efficiency score in 2019 was identified. Several previous studies utilized the partner selection strategy to determine the best partner who could cooperate and form a future alliance. Determination of a right partner is a crucial task in alliancing operations and development orientation (Nielsen, 2003). This is so much so that the right partner selection has been given an important position in the alliance process (Duisters, Duysters, & Man, 2001). Examples include the global aerospace and defense industry (Wang, Nguyen, Le, & Hsueh, 2018) and the Vietnamese construction industry (Nguyen, 2020), which revealed the importance of valuable performance indicators in any operational alliance. Inefficient firms located their partners who could assist them to extend their scores from inefficient to efficient.

3. Materials and Methodology

3.1. Data Collection

With the objective research of partner selection for securities companies in Vietnam, past operating progress of selected companies was evaluated and then given a decision related to partner selection. Therefore, the study chose to list Vietnamese securities companies found in Dunn & Bradstreet (2020) (see Table 1).

Table 1: Name of Securities Companies in Vietnam

OTGHEU_2021_v8n3_527_t0001.png 이미지

Source: Dum & Bradsheet (2020).

Analyzing the effect of operating progress required having full and exact information of financial reports so that all of the actual values of input variables and output variables. Thirty-two securities companies, operating during the period from 2016 to 2019, were collected upon their posting on the financial report, Vietstock (2020). Based on the principle of the negative-SBM model in DEA, the quantity of input variables, as well as the quantity of output variables, showed that they cannot overcome the total DMU’s. Hence, six input factors were chosen, including current assets (CA), non-current assets (NA), fixed assets (FA), liabilities (LS), owner’s equity (OE), and charter capital (CP); and, four output factors, including net revenue (NR), gross profit (GP), operating profit (OP), and net profit after tax (NP), were also selected.

Input factors:

CA: All securities company assets that are sold, consumed, used, and exhausted in one year’s time through standard business operations.

NA: All securities company assets that are invested on a long-term basis.

FA: All securities company assets that comprise land, machinery, equipment, buildings and other durables.

LS: All of the money a securities company owes to outside parties.

OE: All of the money a securities company must pay-off in the event of liquidation.

CP: All of the capital holdings of a securities company that are invested by the owner into the company within a specified period.

Output factors:

NR: All of the money that the securities company receives from its securities trading activities in which there has been no deduction of service charges, interest, and taxes.

GP: All of the profitability of a securities company after deducting service fees.

OP: All of the profitability of a securities company before deduction of interest and taxes.

NP: All of the profitability of a securities company after deduction of interest and taxes.

From the actual data posted to Vietstock (2020), there are three output variables including GP, OP and NP that appeared to contain negative values. Thus, the negative super-SBM model in DEA with the function of dealing with the presence of negative data was particularly equipped to calculate the efficiency score of securities companies

3.2. Super Slack-Based Measurement Model

DEA is a useful statistical tool used in operational research and economics for the estimation of a production frontier useful to evaluate the performance of DMU’s. It utilizes the nonparametric method of benchmarking to measure the efficiency of operations research. According to the common principle of DEA, the efficiency of a DMU is to be determined by the given ratio between outputs and inputs. Let the inputs as x, output as y and the production possibility as p:

\(p=(x, y)\)       (1)

s− and s+ are considered as input excess and output shortfall, respectively, the expression for a certain DMU = (x0, y0) is determined by:

\(x_{0}=x \lambda+s^{-}\)       (2)

\(y_{0}=y \lambda+s^{+}\)       (3)

The index p is calculated by:

\(p=\frac{1-\frac{1}{m} \sum_{h}^{m} s_{h}^{-} / x_{h 0}}{1+\frac{1}{s} \sum_{k}^{s} s_{k}^{+} / y_{k 0}}\)       (4)

The efficiency score of DMU = (x0, y0) is formulated by the following fractional program SBM in λ, s−, s+ as follows:

\(\operatorname{Min}_{p}=\frac{1-\frac{1}{m} \sum_{h}^{m} s_{h}^{-} / x_{h 0}}{1+\frac{1}{s} \sum_{k}^{s} s_{k}^{+} / y_{k 0}}\)       (5)

The score of each DMU is computed between [0 ~ ∞], it will occur two cases as follows:

If p* < 1, the DMU does not have efficiency.

If p* ≥ 1, the DMU has efficiency.

4. Results and Discussions

4.1. Data Analysis

In order to compute the efficiency score and then to determine the alliance partner selection for securities firms in Vietnam, input and output variables have been selected and then summarized (see Table A1). The maximum value of CA, NA, FA, LS, OE, CC, NR, GP, OP and NP during the period of 2016–2019 attained as 22, 290, 867; 4, 753, 248; 179, 210; 17, 643, 055; 9, 401, 060; 5, 100, 637; 3, 672, 838; 2, 063, 970; 1, 567, 030 and 1, 302, 937, respectively. The minimum value of CA, NA, FA, LS, OE, CC, NR, GP, OP and NP during the period of 2016–2019 was 134, 806; 2, 336; 21; 984; 102, 019; 96, 000; 1, 780; 1, 602; 72; 10, respectively. These values revealed that all input and output values were suitable for inclusion into the super-SBM model in DEA.

However, these values must be tested with Pearson’s correlation coefficient to ensure the appropriate relationship between input and inputs; output and output; and, input and output exist before their application into DEA. The relationship between two variables is always isotonic (Wang, Nguyen, Le, & Hsueh, 2018). The correlation coefficients are ranged from −1 to +1, it has a perfect linear relationship as near to ±1, a strong correlation as near to ±0.5 and ±0.8, a medium correlation as near to ±0.3 and ±0.49, and a low correlation when lower than ±0.29. All variables demonstrating unqualified Pearson’s correlation must be removed. The data of thirty-two securities companies were checked with the Pearson’s correlation coefficient before they were used for conducting the efficiency score by means of the negative super-SBM model. In this research, the Pearson correlations between variables ranged from 0.46146 to 1, thus, they were determined to have a good linear relationship. All raw data were suitable to approach for the super-SBM model in DEA.

4.2. Efficiency and Position before Alliance

The use of super efficiency provides the separate scores of each DMU in every year observed. In this research, a determination of the efficiency scores of securities companies in Vietnam during the time-period of 2016–2019 was conducted (see Table 2).

Table 2: Efficiency of Securities Companies before Alliance

OTGHEU_2021_v8n3_527_t0002.png 이미지

As seen in Table 2, the performance of most of securities companies fluctuated sharply with the exception of SSI, which obtained efficiency and owned a stable score for the entire term of “1”. The scores of APG, FTS, HCM, VCI, APS, SHS, CSI and DSC in every term achieved the efficiency level, but they had a large amount of variation, ranging from 1.0871 to 14.3305. Whereas, the score of FTS increased sharply and received the highest score as 14.3305 in 2019, it became the best securities firm in 2019. In contrast, CTS, VDS, IVS, AAS and PHS were the worst companies since they did not attain the efficiency level standard during the whole time because their scores were always under “1”. The score of these companies always had a variation slightly and at the lowest value, with IVS determined to be the worst company having the lowest scores ranging from 0.2096 to 0.4461 during 2016–2019. The remaining securities companies were determined to exist with both efficient and inefficient scores, whereas, EVS and HBS had a large fluctuation from 0.4756 to 20.793 and from 0.5164 to 17.0089, respectively. The empirical results indicated that the efficiency scores of these securities firms were divided into three groups: an efficient group; an inefficient group; and a mixed efficient and inefficient group.

Based on the available efficiency scores, the study defined the position of securities companies in every term (see Table 3).

Table 3: Position of Securities Companies before Alliance

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Correspondingly, the principle of super efficiency and the conducted score presented in Table 3 for each securities company in every year describes a separate position from the first to the thirtieth ranking. The position of a given securities companies always changed in each year. For instance, HBS ranged in the first, second, nineteenth and twelfth positions, and EVS ranked in the twelfth, first, twentieth and twenty-third position over 2016–2019, respectively. FTS was the only company having made a big effort to improve their efficiency score and rank up from the fourth and third positions in 2016 and 2017, respectively, to reach the first position during the period of 2018–2019. IVS was considered as the worst performing company after this company ranked twenty-ninth in 2016 and 2019, thirty-first in 2017 and thirty-second in 2018.

In consideration of the above analysis, the securities companies altered sharply not only their efficiency scores but also their position according to each term. Respective the normal way to realize super efficiency, inefficiency could actually improve the overall score by reducing input excess and increasing output shortfall. From these analysis results of past data, future operational performance can be further extended. Hence, the reduction of input factors and the extension of output factors could promote performance operations. Besides, another method such as the alliance selection method is applicable to improve the efficiency score. The scores in 2019 determined the relative market standing of inefficient securities companies in need of future score improvement. The particular results of this study are offered in Section 4.3.

4.3. Efficiency and Position after Alliance

To provide suggestions for improvement to inefficient securities in the future, the research used the alliance method to explore potential partnerships based on historical financial reports and efficiency scores from 2019. All inefficient securities companies were utilized to make an alliance and then to calculate novel performance as shown in Tables A2 and A3. Based on the rule of negative super-SBM models, each company post-alliancing determined their new scores. The allied securities companies also reached a desired efficiency levels when their scores were higher than “1” and considered as non-performing when they obtained performance scores lower than “1”. Examination results indicated that the total partners of AAS, TCI, CTS, EVS, IVS, PHS, PSI, TVB, VDS and VFS reached efficiency levels as 13, 13, 6, 6, 8, 4, 6, 4, 5 and 3, respectively. All inefficient companies determined their partners were able to help them to improve operational processes and achieve an upgrade to reach an efficient score. In contrast, the alliance partners and inefficient securities companies should not make an alliance when their scores were lower than “1”.

The findings revealed that AAS and TCI had the same numbers and their total efficient partners were more than the other securities companies. The efficiency score of TCI ranged from 1 to 2.11842; and, the efficiency score of AAS was from 1 to 11.04469. Therefore, AAS possessed a higher score than TCI, so it was seen to be the best partner selection available.

As seen in Table 4, the optimal alliance partners have an efficiency score including FTS, VCI, HMC, VND, APG, ORS, VIX, ART, MBS, BVS, BMS, BSI and SSI. The variance of their scores was 10.51017; 2.01812; 1.25584; 0.83979; 0.77179; 0.60265; 0.56914; 0.56897; 0.54885; 0.54629; 0.517; 0.51337; and 0.46548, respectively. The variance of their position was 41; 36; 29; 24; 23; 20; 19; 18; 16; 15; 11; 10; and 8, respectively. All of them had a positive variance. The alliance with others securities companies was comprised of SHS, TVS, TCI and CTS which could potentially improve the score; however, the score was still lower than “1”. Thus, these companies improved their score and position, and it was recommended that they should not form an alliance.

Table 4: Good Alliance Partner of ASS

OTGHEU_2021_v8n3_527_t0004.png 이미지

Moreover, Table 5 indicated those securities firms constituting so-called “bad partner” selections. The securities companies possessed a score lower than the target score of 0.53452 (see Table 5). These partners had scores from 0.22995 to 0.47587, and their position ranged according to the final positions. The variance of scores for these companies with ASS target was from −0.30457 to −0.05865. The variance position of EVS, VDS, HAC, DSC, PHS, CSI, PSI, SBS, APS, HBS, TVB, VFS, WSS and IVS was −4; −5; −7; −8; −10; −11; −12; −14; −16; −17; −18; −19; −20; and −21, respectively. Consequently, it did not encourage alliance.

Table 5: Bad Alliance Partner of ASS

OTGHEU_2021_v8n3_527_t0005.png 이미지

5. Conclusion

In general, the securities market in Vietnam has exhibited marked variation of its operational performance during 2016–2019, as computed by the negative super-SBM model. The empirical results of this modeling presented a separate efficiency score and ranking for each of the securities companies examined in the study, both before and after alliancing.

The study applied technical alliance selection into the process of seeking out valued partners and improvement of the efficiency score. This was especially true for the possibility of inefficient securities companies in the future when based on the efficiency score in 2019. Optimal partner selection is fundamental to the support of an enterprise to foresee a chance for extending improved operational performance in the future. The main findings contribute to maintaining and developing sustainable securities industry in Vietnam and towards giving an effective operational orientation in the future.

The overall performance and ranking of securities companies in Vietnam is presented to a greater extent, but the study still has limitations. Future research is necessary to compare the Vietnamese securities industry with other Southeast Asian nations such as Indonesia, Thailand, and others in order to have a larger overview of securities industry and a comparison for the growing region.

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