




































ASIAN FINANCE & BANKING REVIEW 8(1) (2024), 13-29

 

13 

 

FINANCE & BANKING REVIEW 
  ASFBR VOL 8 NO 1 (2024) P-ISSN 2576-1161   E-ISSN 2576-1188 

Journal homepage: https://www.cribfb.com/journal/index.php/asfbr 

Published by Asian Finance & Banking Society, USA 

EVALUATING FINANCIAL SYNERGY IN BANK MERGER: 

RANKING MERGER OPTIONS AND ANALYZING INFLUENTIAL 

FACTORS                                                            

 
 Gourav Roy (a)1    

 

(a) Lecturer, Bangladesh Institute of Capital Market (BICM), Dhaka, Bangladesh; E-mail: gouravroy.du@gmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 5th March 2024 

Reviewed & Revised: 6th March  

to 26th June 2024 
Accepted: 30th June 2024 

Published: 4th July 2024 

 
Keywords: 

Merger, Synergy, Financial Factors, 
Simulation, Sensitivity, Valuation, 

Banks, Emerging Economy, 

Bangladesh. 

 
      JEL Classification Codes:  

 

      G34, G32, C51, G21 

       

      Peer-Review Model:  
 

      External peer-review was done through  

      double-blind method. 

 

 
A B S T R A C T 

 
 

Given the excess number of banks, the Central Bank of Bangladesh recently saw mergers as a good 

solution for economic development in an emerging economy like Bangladesh. Still, the question 

remained: which bank should merge with whom to create value, known as financial synergy? The study 

investigates which mergers add value to financial synergy and which do not. Additionally, the study 

scrutinizes the financial factors that influence the financial synergies resulting from the mergers of the 

participating banks. This study employs fifty-five possible cases of mergers found in eleven banks, 

including government, non-government, and specialized banks, to conduct financial synergy valuations 

on the average of five years of financial information. The methodology employs simulation, sensitivity, 

trend, scenario, Ordinary Least Squares (OLS), and Mixed Effect Generalized Linear Model (MEGLM) 

to solve the research questions. The results reveal that mergers between BKB and RAKUB, EXIM and 

Padma, NBL, and UCB can result in positive financial synergy among the six cases proposed by the 

central bank. The results also show that financial factors including debt to capital, reinvestment rate, 

return on capital, cost of debt, and revenues significantly impact the financial synergy. The findings of 

the study suggest the central bank merge based on the ranking provided in the study, considering the 

influential factors in mergers among banks. These findings contribute to the existing field of study by 

optimizing the synergy valuation strategies for bank mergers in a complex environment of bank types. 

 
 

© 2024 by the authors. Licensee Asian Finance & Banking Society, USA. This article is an open 
access article  distributed under the terms and conditions of the Creative Commons Attribution (CC 

BY) license (http://creativecommons.org/licenses/by/4.0/). 

                                                                  

 

INTRODUCTION 

Synergy is the idea that when different business units inside complex organizations operate together as a unified system, 

they can create more value than if they were to function independently. This means that if two companies named A and B 

merge, and the merged company is named AB, the resultant comparative value will be: 

 

V(AB) > V(A) + V(B)      (i) 

 

From equation (i), if the value of synergy is to be determined, the equation for synergy will be: 

 

     V(Synergy) = V(AB) – V(A) – V(B)     (ii) 

 

Recently, Bangladesh Bank (The central bank of Bangladesh) has decided to amalgamate weak banks with strong 

banks (Dhaka Tribune, March 13, 2024). After that, Bangladesh Bank published a guideline for the merger of the banks on 

April 04, 2024. Later, the question of which banks to merge with whom was uncovered with eleven banks namely Sonali 

Bank PLC to merge with Bangladesh Development Bank PLC (Case 1), Bangladesh Krishi Bank (BKB) to merge with 

Rajshahi Krishi Unnayan Bank (RAKUB) (Case 2), BASIC Bank PLC to merge with City Bank PLC (Case 3), EXIM Bank 

Limited to merge with Padma Bank PLC (Case 5), and National Bank Limited to merge with United Commercial Bank PLC 

(Case 6) (Somoy Business Desk, 2024). It was also though earlier that BASIC Bank PLC could be merged with Agrani 

Bank PLC (Case 4) (TBS Report, 2024). The study is relevant because, Bangladesh has more scheduled banks in number 

                                                      
1Corresponding author: ORCID ID: 0000-0001-9782-9103 

© 2024 by the authors. Hosting by Asian Finance & Banking Society. Peer review under responsibility of Asian Finance & Banking Society, USA.  

https://doi.org/10.46281/asfbr.v8i1.2219 

 

To cite this article: Roy, G. (2024). EVALUATING FINANCIAL SYNERGY IN BANK MERGER: RANKING MERGER OPTIONS AND 

ANALYZING INFLUENTIAL FACTORS. Asian Finance & Banking Review, 8(1), 13-29. https://doi.org/10.46281/asfbr.v8i1.2219 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/asfbr.v8i1.2219
https://orcid.org/0000-0001-9782-9103


Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

14 
 

than required to serve the money market. The growing Non-Performing Loans (NPLs), shortage of liquidity, depositors’ 

lacking confidence, and financial distress are making the existence of most of these banks vulnerable. The central bank 

assumed this problem and decided to merge among banks. From the dilemma of which bank to merge with whom, the 

problem statement of the study is the picking of the best options out of the fifty-five possible solutions of valuation. Also, 

there exists a concern that which financial factors actually contribute most to the financial synergy, because such factors 

can be controlled and optimized for increasing synergy value. The study aims to provide a solution of ranking based on 

financial synergy out of the existing possible options of banks. Additionally, the study estimates the factors that impact the 

financial synergies of the merged banks, which is its second research objective.The novelty of the study is using synergy 

valuation and econometric techniques to rank and identify influential financial factors in emerging economy banks, 

providing a basis for further research in merger and acquisition.  

The study begins by introducing the concept and its relevance, followed by a review of existing literature from key 

research objectives and theoretical backgrounds. Next, the materials and methods explain the techniques used to determine 

synergy values for different options and factor analysis. Later, the results and discussions provide the outcomes and 

explanations that align with the objectives. Finally, the study concludes with key insights, novelty, and future research 

avenues. 

LITERATURE REVIEW 

Mergers tend to form across the world under different dimensions. To generate financial synergy out of it, forecasting 

becomes a great task to solve, also the factors influencing these values should be identified properly. This section details 

earlier studies that meet the research objectives criterion.  

 

Mergers and Financial Synergies 

Mergers are a common practice in corporate finance and restructuring that enhance a company's growth and competitiveness 

(Sui et al., 2016; Gaughan, 2018). The merger is the process of combining the assets of two companies who have decided 

to merge their activities (Ben Letaifa, 2017). The merger enhances revenue and reduces costs by fostering synergy between 

the acquiring and target companies (Majumdar et al., 2012). Firms engage in mergers and acquisitions (M&A) primarily to 

expand their operations, as growth is essential for their survival (Akinbuli & Kelilume, 2013). A study on power plants of 

USA was done where 5% synergy in operating efficiency has been observed (Demirer & Karaduman, 2022). A study on 

US banks found that mergers are inefficient in improving X efficiency and scale efficiency, and that factors affecting 

performance also affect their performance (Peristiani, 1997). A study on the context of North Macedonia showed that banks’ 

efficiency falls 83.33% to 70.06% after merger in 2011 and to 66.36% in 2020 (Fotova Čiković et al., 2022). A study on 

merger of 52 horizontal bank in Europe from 1994 to 1998 shows that merger don’t contribute to greater market power 

(Huiziniga et al., 2001). Another study from 1994 to 2001, done on 134 individual banks on 11 EU countries to understand 

the impact of merger on banks prove that merger and consolidation is beneficial in technical aspects (Ebodume & Omarov, 

2007). A study on European Commission institutions over 492 takeovers show that mergers between domestic and cross-

border banks of similar size have a substantial positive impact on the performance of the merged institutions (Vennet, 1996). 

If the scenario is shifted to Indian economy, analysis conducted on bank mergers from 2019 to 2020 reveals a rise in the 

financial value of the banks being acquired, benefiting their owners (Herwadkar et al., 2023).  

In Bangladesh, merger in banking industry is being experienced after a long time, thus, the question is highly 

relevant whether such mergers will cause synergistic value, and if yes, which mergers will do so.  

 

Factors Affecting Mergers and Synergies 

The value of synergy has been properly modeled in a paper where synergy has been categorized in operating synergy and 

financial synergy (Damodaran, 2005). In this study, the author has provided a beautiful elaboration of how financial synergy 

can be calculated. The factors that have been considered are mostly the inputs to the calculation of the financial synergy. In 

a study on understanding the impact of different factors on mergers and acquisitions (Mucenieks, 2018), the author identified 

few financial factors which contribute the M&A. In Nepal, its’ been found that the factors that are the inputs to calculation 

of financial synergy are significantly impactful to financial synergy (Sharma, 2018). A study investigated the determinants 

of the anticipated synergy resulting from a merger or acquisition, based on an analysis of previous mergers and acquisitions 

in the banking sector of a European Union country. Two out of the five elements have been modeled using dynamic 

simulation based on high-quality research and found significant impacting synergy value (Yiannis et al., 2007).  A study on 

the mechanical engineering companies of Czech Republic indicated a statistically significant correlation between the 

indicators derived from cash flow and the value of synergy (Režňáková & Pěta, 2018). A study on SAARC and ASEAN 

regions found that Free Cash Flows have positive impact on synergy while firm size is insignificant (Khan & Bin Tariq, 

2023). 

 

Theoretical Background 

The financial synergy is mainly generated from diversification, cash slack and tax benefits (Damodaran, 2005). To consider 

the fragmentation of all these factors, financial statements’ outputs were considered. A study was done to find out factors 

impacting financial synergy where key financial factors are chosen as independent variables to judge the synergy (Darayseh 

& Alsharari, 2022). Simulation strategy was used in measuring impacts of variables on synergy (Yiannis et al., 2007).) A 

study used key financial factors to compare against synergy to find the significance (Mucenieks, 2018). 

This study focuses on banks in emerging economies, including government and non-government commercial and 

specialized banks, as previous literature only focuses on pre-event and post-event analysis, lacking simulation methodology 

to judge every dimension of values from synergy valuation. 



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

15 
 

 

Purpose of the Study and Hypothesis 
 

The purpose of the study is to prepare a ranking list of best options of banks, mergers between which will result in maximum 

financial synergy. The study afterward finds the influential financial factors that contribute to the financial synergy. For 

meeting the second objective, the hypothesis will be as follows: 

 

Ha: There exists significant relationship between different financial factors and the synergy value. 

 

MATERIALS AND METHODS 
 

Research Design (First Research Objective) 

 
Figure 1. Research Design for First Research Objective 

 

The research design in Figure 1 employs financial inputs for calculating the synergy value which finally contribute as 

independent variables in Figure 2. 

 

 

Which Mergers 
Create Synergy?

Determination of 
Inputs

Finding proxies of the 
banks for Beta

Risk-free Rate

Country Risk 
Premium

Risk Premium

Valuation of the 
Banks (Stand Alone 

and Combined)

Pre-tax Cost of Debt

Tax Rate

Debt/Capital Ratio

Revenues

EBIT

Pre-tax Return on 
Capital

Reinvestment Rate

Length of Growth 
Period

Calculation of 
Synergy

Outputs (Stand Alone 
and Comnbined)

Cost of Equity

Afetr-tax Cost of 
Debt

Cost of Capital

Afetr-tax Return on 
Capital

Reinvestment Rate

Expected Growth 
Rate

Value of Synergy

Value of Individual 
Firms

Synergy Calculation

Best Synergy Case

Analysis

Simulation

Sensitivity

Trend Analysis

Scenario Analysis



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

16 
 

Research Design (Second Research Objective)  

 
Figure 2. Research Design for Second Research Objective 

 

The Figure 2 provides categories of variables, and tests to be performed to test the hypothesis. 
 

Data 

The data is the audited financial statements of the banks for the five years from 2018 to 2022. For Risk-free Rate calculation, 

average of 5 years’ 10-year Treasury Bond cut-off yield data collected from the Bangladesh Bank is used. For country risk 

premium, as of a study in NYU (Damodaran, 2024), the data has been considered for Bangladesh. The risk premium is 

calculated from the average DSEX return for the last five years. 
 

Variables 

The second research objective requires the same independent and dependent variables including few control variables.  

Which Financial Factors 
Impact the Financial 

Synergy?

Variables

Dependent

Independent

Control

Diagonostic Tests before 
Regression

Descriptive Statistics

Correlation with P Values

Empirical Analysis

OLS

MEGLM

Diagonostic Tests after 
Regression

Test of Heteroskedasticity

Test of Multicollinearity



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

17 
 

 
Figure 3. Identification of Variables 

 

The inputs to synergy have been considered independent variables (Damodaran, 2005). Studies have found that 

firm size has been a significant variable in determining synergy (Moeller et al., 2004; Ellis, 2005; Susanti & Restiana, 2018; 

Utami, 2023). Firm size significantly impacts the success of a merger (Chen, 1991; Fama & French, 1993). A study was 

done on firm’s value and firm structure that incorporate year of establishment as a control variable (Al-Saidi & Al-

Shammari, 2014; Susanti & Restiana, 2018). There is a good correlation between number of branches and banks’ 

performances that finally contribute to the banks’ enterprise value (Hirtle, 2005). A study on understanding branch network 

structure and bank’s profitability tried to implicate the impact of branch networks on profitability that meets the enterprise 

value (Fuchs et al., 2024). A study on the relation between human capital and firm value (Sisodia et al., 2021) revealed a 

significant relation between the employee size and firm value (Sisodia et al., 2021). Thus, the study has incorporated firm 

size, date of establishment, number of branches, number of districts of operation, and number of employees as control 

variables.  

 
 

Methodology for Determining Variables 
 

Table 1. Characteristics of the Data 
 

Type of data Quantitative 

Scale of data Ratio level 

Source of data Audited financial statements from 2018 to 2022 

Model based variables Cross-sectional 

Dependent variable One 

Independent variables Nine 

Control variables Five 
 

The formulas of the research are derived from (Damodaran, 2005), (CFI, 2024) and authors’ own analysis. 

Variables

Research 
Objective 1

Forecast 
Varibale

Financial 
Synergy

Predictor 
Assumptions

Risk-Free Rate
Country Risk 

Premium

Risk Premium Beta

Pre-Tax Cost of 
Debt

Tax Rate

Debt/Capital 
Ratio

Revenues

EBIT
Pre-Tax Return 

on Capital

Reinvestment 
Rate

Length of 
Growth Period

Research 
Objective 2

Dependent 
Variable

Financial 
Synergy

Independnet 
Variables

Beta 
(Combined)

Pre-Tax Cost of Debt 
(Combined)

Tax Rate 
(Combined)

Debt/Capital 
(Combined)

Revenues 
(Combined)

EBIT 
(Combined)

Pre-Tax Return on 
Capital (Combined)

Reinvestment Rate 
(Combined)

Length of Growth 
Period (Combined)

Control 
Variables

Firm Size
Date of 

Establishment

Number of 
Branches

Number of Districts of 
Operation

Number of 
Employees



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

18 
 

Table 2. Derivation of Dependent Variable 
 

Particular Formula 

Financial Synergy (Value of the merged firms) – ∑Value of the firms standalone 

 

Table 3. Derivation of Formulas for Independent Variables 
 

Particulars Formula 

Risk-free Rate (Rf) 
∑ 10 𝑦𝑒𝑎𝑟 𝐵𝐺𝑇𝐵 𝐶𝑢𝑡𝑜𝑓𝑓 𝑌𝑖𝑒𝑙𝑑2022

2018

5
 

Country Risk 

Premium 
6.58% 

Risk Premium (Rp) 
∑ 𝐷𝑆𝐸𝑋 𝐼𝑛𝑑𝑒𝑥 𝑅𝑒𝑡𝑢𝑟𝑛 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑅𝑖𝑠𝑘 𝑃𝑟𝑒𝑚𝑖𝑢𝑚 − 𝑅𝑖𝑠𝑘𝑓𝑟𝑒𝑒 𝑅𝑎𝑡𝑒2022

2018

5
 

Beta (β) 

(Standalone) 

∑ 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐵𝑒𝑡𝑎 𝑓𝑜𝑟 𝑃𝑟𝑜𝑥𝑦 𝐵𝑎𝑛𝑘𝑠 𝐿𝑖𝑠𝑡𝑒𝑑 𝑖𝑛 𝐷𝑆𝐸𝑋2022
2018

5
 

Pre-Tax Cost of 

Debt (Standalone) 

∑
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑒𝑥𝑝𝑒𝑛𝑠𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑛𝑔𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡
2022
2018

5
 

Tax Rate (Tc) 
(Standalone) 

∑
𝑇𝑎𝑥 𝑒𝑥𝑝𝑒𝑛𝑠𝑒𝑠

𝐸𝑎𝑟𝑛𝑖𝑛𝑔 𝐵𝑒𝑓𝑜𝑟𝑒 𝑇𝑎𝑥 (𝐸𝐵𝑇)
2022
2018

5
 

Debt/Capital Ratio 
(D/C) (Standalone) 

∑
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡

𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙
2022
2018

5
 

Revenues 

(Standalone) 

∑ 𝑇𝑜𝑡𝑎𝑙 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠2022
2018

5
 

Earnings before 
Interest and Taxes 

(EBIT) 

(Standalone) 

∑ 𝐸𝐵𝐼𝑇2022
2018

5
 

Pre-Tax Return on 

Capital 
(Standalone) 

∑
𝐸𝐵𝐼𝑇

𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙
2022
2018

5
 

Reinvestment Rate 

(Standalone) 

∑
𝑁𝑒𝑡 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 + 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑁𝑒𝑡 𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝐶𝑎𝑝𝑖𝑡𝑎𝑙

𝐸𝐵𝐼𝑇(1 − 𝑇𝑎𝑥 𝑅𝑎𝑡𝑒)
2022
2018

5
 

Length of Growth 

Period (Standalone) 
The continuing period of profit or, diminishing rate of loss. 

Beta (Combined) 
[
 
 
 
 
 
 

𝛽1

1 + {(1 − Tc1) ×

𝐷
𝐶

1

1 −
𝐷
𝐶

1
}

 × 
𝐸𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒 𝑉𝑎𝑙𝑢𝑒 (𝐸𝑉) 1

𝐸𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒 𝑉𝑎𝑙𝑢𝑒 (𝐸𝑉)1 + 𝐸𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒 𝑉𝑎𝑙𝑢𝑒(𝐸𝑉) 2

]
 
 
 
 
 
 

+

[
 
 
 
 
 
 

𝛽2

1 + {(1 − Tc2) ×

𝐷
𝐶

2

1 −
𝐷
𝐶

2
}

 × 
𝐸𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒 𝑉𝑎𝑙𝑢𝑒 2

𝐸𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒 𝑉𝑎𝑙𝑢𝑒 (𝐸𝑉) 1 + 𝐸𝑛𝑡𝑒𝑟𝑝𝑟𝑖𝑠𝑒 𝑉𝑎𝑙𝑢𝑒 (𝐸𝑉) 2

]
 
 
 
 
 
 

 

Pre-Tax Cost of 

Debt (Combined) 

(𝑃𝑟𝑒𝑡𝑎𝑥 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐷𝑒𝑏𝑡1 × 𝐸𝑉1) + (𝑃𝑟𝑒𝑡𝑎𝑥 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐷𝑒𝑏𝑡2 × 𝐸𝑉2)

𝐸𝑉1 + 𝐸𝑉2
 

Tax Rate 

(Combined) 

(𝑇𝑐1 × 𝐸𝑉1) + (𝑇𝑐2 × 𝐸𝑉2)

𝐸𝑉1 + 𝐸𝑉2
 

Debt/Capital Ratio 

(Combined) 

(𝐷/𝐶1 × 𝐸𝑉1) + (𝐷/𝐶2 × 𝐸𝑉2)

𝐸𝑉1 + 𝐸𝑉2
 

Revenues 

(Combined) 
Revenue 1 + Revenue 2 

EBIT (Combined) EBIT 1 + EBIT 2 

Pre-Tax Return on 

Capital (ROC) 
(Combined) 

(𝑃𝑟𝑒𝑡𝑎𝑥 𝑅𝑂𝐶1 × 𝐸𝑉1) + (𝑃𝑟𝑒𝑡𝑎𝑥 𝑅𝑂𝐶2 × 𝐸𝑉2)

𝐸𝑉1 + 𝐸𝑉2
 

Reinvestment Rate 

(RR) (Combined) 

(𝑅𝑅1 × 𝐸𝑉1) + (𝑅𝑅2 × 𝐸𝑉2)

𝐸𝑉1 + 𝐸𝑉2
 

Length of Growth 

Period (n) 
(Combined) 

Average of the banks’ growth. 

Here, “1” stands for 1st company and “2” stands for 2nd company applicable for merger. 



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

19 
 

In case of standalone valuation, the inputs to these calculations are of individual banks. On the other hand, in case 

of combined valuation or merger, the inputs are those which are calculated for the combined cases. 

 

Table 4. Outputs to Calculate Financial Synergy (Both for the Cases of Standalone and Combined) 
 

Particular Formula 

Cost of Equity (Ke) Rf + β × Rp 

After-Tax Cost of Debt (Kd) Pre-Tax Cost of Debt × (1-Tc) 

Cost of Capital (Kc) {Ke × (1-D/C)} + (Kd × D/C) 

After-tax Return on Capital Pre-Tax Return on Capital × (1- Tc) 

Expected Growth Rate (g) Reinvestment Rate × After-tax Return on Capital 

PV of FCFF 
{𝐸𝐵𝐼𝑇 × (1 − 𝑇𝑐) × (1 − 𝑅𝑅)} × (1 + 𝑔) × {1 −

(1 + 𝑔)𝑛

(1 + 𝑘𝑐)𝑛
 

Terminal Value (TV) 

𝐸𝐵𝐼𝑇 × (1 − 𝑇𝑐) × (1 + 𝑔)𝑛 × (1 + 𝑅𝑓) ×

(1 − 𝑅𝑓)
𝐾𝑐

𝐾𝑐 − 𝑅𝑓
 

Enterprise Value (EV) PV of FCFF +
𝑇𝑉

(1+𝐾𝑐)𝑛
 

Value of the Firm (Standalone) EV1+EV2 

Here, “1” stands for 1st company and “2” stands for 2nd company applicable for merger. 

 

Methodology for Analysis for Research Question 1 

Crystal Ball, a valuation software, determines financial synergy for multiple cases, with a total of fifty-five possible cases 

for synergy valuation using the combination method (iii). 

 

C (n, r) = 
𝑛!

𝑟!(𝑛−𝑟)!
      (iii) 

 

The study evaluates the financial synergy of six merger proposals from Bangladesh Bank through 10,000 

simulations. Sensitivity analysis identifies sensitive factors, trend analysis predicts maximum and minimum synergy values, 

and scenario analysis determines changes in synergy value for 0% to 100% changes in independent variables. 
 

Methodology for Analysis for Research Question 2 

Table 5. The Definition and Codes for the Variables 
 

Codes Definition Codes Definition 

Beta_C The combined Beta RR_C The combined reinvestment rate 

COD_C The combined pre-tax cost of debt lgr_C The combined length of growth period 

Tax_C The combined tax rate FirmSize The average of the firm sizes of merging banks 

D/C_C The combined debt to total capital Est The average of the banks’ years of establishments 

Revenues_C The combined revenues Branch The average of the banks’ number of branches 

EBIT_C The combined Earnings before Interest 

and Taxes 

Districts The average of the number of districts the banks 

have operation 

ROC_C The combined pre-tax return on capital HR The average of number of employees of the banks 

The Model Estimation 

The research question 02 required two models to estimate. One is the Ordinary Least Square (OLS) and the other is Mixed 

Effect Gaussian Generalized Linear Model (MEGLM). The basic OLS model is shown below: 

 

Y = α + βixi + ε       (iv) 

 

From equation (ii), the derived OLS model for this research is shown below: 

 

Y = α + β1Beta_c + β2ln_COD_C + β3Tax_C + β4D/C_C + β5Revenues_C + β6EBIT_C + β7ROC_C + β8RR_C + β9lgr_C 

+ β10ln_FirmSize + β11ln_Est + β12Branch + β13Districts + β14ln_HR + ε     (v) 

 

For ensuring normality of data principle, cost of debt, date of establishment, HR and Firm Size are log normalized 

in OLS and only cost of debt in MEGLM. From equation (iii), for building a model for MEGLM, almost everything in the 

OLS is reiterated except for link and identity functions. The basic MEGLM function is shown below: 

 

 g(E(Yi)) = E(Yi) = α + βixi + ε      (vi) 

 

From equation of the regarding link and identity, the following parameters are conventionally used: 

 

Table 6. GLM Specification 
 

Distribution Natural Parameter Canonical Link 

Gaussian (Normal) µ Identity 



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From the Table 3, the final model to be followed is shown below: 

g(µ) = α + β1Beta_C + β2ln_COD_C + β3Tax_C + β4D/C_C + β5Revenues_C + β6EBIT_C + β7ROC_C + β8RR_C + β9lgr_C 

+ β10FirmSize + β11Est + β12Branch + β13Districts + β14HR ++ ε      (vii) 

Here, 

Y and g(µ) are the representatives of value of synergy which is the dependent variable. The α stands for the constant 

terms, βi stands for the coefficients and ε stands for the random error terms.  

 

RESULTS 

Results from Research Question 01 

The results of six cases of mergers are summarized below: 

Simulation Results for Six Cases (Figures in Crore of BDT) 

 

  
Case 01. SBL Merges BDBL Case 02. BKB Merges RAKUB 

  
Case 03. BASIC Merges City Bank Case 04. BASIC Merges Agrani 

  
Case 05. EXIM Merges Padma Case 06. UCB Merges NBL 

Figure 4. Simulation Results for Six Cases of Proposed Mergers (Figures in Crore of BDT) 

 

The simulation using 10,000 trials in Crystal Ball, results from Figure 4 show that case 2, 5, and 6 result in positive financial 

synergy while others end in negative financial synergy.  

 

 



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Sensitivity Results for Six Cases 

 

  
Case 01. SBL Merges BDBL Case 02. BKB Merges RAKUB 

  
Case 03. BASIC Merges City Bank Case 04. BASIC Merges Agrani 

  
Case 05. EXIM Merges Padma Case 06. UCB Merges NBL 

Figure 5. Sensitivity Results for Six Cases of Proposed Mergers 

 

The sensitivity using 10,000 trials in Crystal Ball, results from Figure 5 show that the most sensitive factors in all cases have 

been debt to capital ratio, pre-tax cost of debt, EBIT, and risk-free rate.  

 

 

 

 

 



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Trend Analysis for Six Cases 

 

  
Case 01. SBL Merges BDBL Case 02. BKB Merges RAKUB 

  
Case 03. BASIC Merges City Bank Case 04. BASIC Merges Agrani 

  
Case 05. EXIM Merges Padma Case 06. UCB Merges NBL 

Figure 6. Trend Analysis for Six Cases of Proposed Mergers 

 

The results, using 10,000 trials in Crystal Ball in Figure 6, show that case 2, 5, and 6 have positive and less risky spectrum 

of trend of synergy value. On the other hand, case 1, 3, and 4 have negative and bigger spectrum of trend of synergy value. 
 

 

 

 

 

 



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Scenario Analysis (In Crore of BDT) 
 

Table 7. Scenario Analysis for the Six Cases 
 

Cases Mean Standard Deviation Minimum Maximum 

Case 01: SBL Merges BDBL -640.8 113.2 -1,392.90 -254.7 

Case 02: BKB Merges RAKUB 1,704.70 461.1 -70.8 6,423.70 

Case 03: BASIC Merges City Bank -1,556.00 739.1 -4,998.00 5,279.40 

Case 04: BASIC Merges Agrani -59.4 246.8 -2,206.90 1,035.50 

Case 05: EXIM Merges Padma 4,297.90 1,904.80 -6,748.70 13,831.10 

Case 06: NBL Merges UCB 2,303.90 1,871.00 -33,092.50 29,892.20 

 

The scenario, using 10,000 trails in Crystal Ball, analysis shows that in case of 0.1% change takes place in each of the 

independent variables, the mean stands negative for case 1, 3, and 4. The variability is higher in case 5 and 6. The optimum 

synergy is found in case 2, 5, and 6. 

 

Possible Merger Solution of Forty-Nine Cases for 10,000 Trials Each Case (In Crores of BDT) 
 

Table 8. Ranking of Value of Possible Mergers 
 

Number 

of 

Simulated  

Cases 

Merger 

Parties 

Mean 

Synergy 

Minimum 

Synergy 

Maximum 

Synergy 

Most 

Sensitive 

Factor 

(Positive) 

Most 

Sensitive 

Factor 

(Negative) 

Synergy 

Range 

(90% 

Confidence) 

Synergy 

Range 

(75% 

Confidence) 

Rank 

01 Agrani & 
City Bank 

(1,089.9) (18,425.9) 2,682.2 Debt/Capital 
Ratio (Agrani) 

Debt/Capital 
Ratio (City 

Bank) 

(1,000) to 
(1,100) 

(900) to 
(1,300) 

41 

02 Agrani & 
EXIM 

(872.98) (4,541.7) 452.6 Debt/Capital 
Ratio (Agrani) 

Debt/Capital 
Ratio (EXIM 

Bank) 

(820) to 
(890) 

(780) to 
(950) 

38 

03 Agrani & 

Padma 

1,468.8 

 

(77.1) 

 

3,587.5 

 

Risk-Free Rate EBIT 

(Padma) 

1,420 to 

1,510 

180 to 1,580 29 

04 Basic & 

EXIM 

(923.8) (2,544.5) 3,516.1 Debt/Capital 

Ratio (EXIM 

Bank) 

Debt/Capital 

Ratio (Basic) 

(850) to 

(950) 

(810) to 

(1,010) 

39 

05 Basic & 
Padma 

1,681.1 3,418.6 790.8 Risk-Free Rate Pre-tax Cost 
of Debt 

(Padma) 

1640 to 
1710 

1,590 to 
1,790 

26 

06 BDBL & 
Agrani 

23.8 (206.8) 356.4 Tax Rate 
(BDBL) 

Debt/Capital 
Ratio 

(BDBL) 

10 to 30 02 to 39 34 

07 BDBL & 
Basic 

358.5 95 881.8 Debt/Capital 
Ratio (Basic) 

Pre-tax Cost 
of Debt 

(Basic) 

348 to 365 330 to 378 33 

08 BDBL & 

BKB 

1,719 788.1 8,568.6 Risk-free Rate Pre-tax Cost 

of Debt 
(BKB) 

1,680 to 

1,730 

1,590 to 

1,820 

25 

09 BDBL & 

City Bank 

(1,138.2) (1,857.8) (495) Pre-tax Cost of 

Debt (City 
Bank) 

Debt/Capital 

Ratio (City 
Bank) 

(1,100) to 

(1,150) 

(1,050) to 

(1,200) 

42 

10 BDBL & 

EXIM 

(734.6) (2,332.2) (230) Pre-tax Cost of 

Debt (EXIM) 

EBIT 

(EXIM) 

(710) to 

(740) 

(680) to 

(780) 

37 

11 BDBL & 
NBL 

3,273.2 
 

1,695.5 
 

6,578.4 
 

Risk-free Rate Pre-tax Cost 
of Debt 

(NBL) 

3,190 to 
3,320 

3,100 to 
3,420 

14 

12 BDBL & 

Padma 

1,736.1 738.6 4,117.9 Risk-free Rate Pre-tax Cost 

of Debt 
(Padma) 

1,690 to 

1760 

1,620 to 

1830 

24 

13 BDBL & 

RAKUB 

622.6 253.4 1,796 Debt/Capital 

ratio 
(RAKUB) 

Pre-tax Cost 

of Debt 
(RAKUB) 

605 to 640 580 to 660 31 

14 BDBL & 

UCB 

(608.5) (879) (434.2) Pre-tax Cost of 

Debt (UCB) 

Debt/Capital 

Ratio (UCB) 

(595) to 

(613) 

(584) to 

(625) 

35 

15 BKB & 

Agrani 

1,469.2 433.3 5,894.4 Risk-free Rate Pre-tax Cost 

of Debt 

(BKB) 

1,410 to 

1,500 

1,370 to 

1,580 

28 

16 BKB & 
Basic 

1,530.2 425.3 6,827.2 Risk-free Rate Pre-tax Cost 
of Debt 

(BKB) 

1,490 to 
1,560 

1,400 to 
1,640 

27 

17 BKB & City 

Bank 

2,897.8 (23,058.5) 26,138 Pre-tax Cost of 

Debt (City 
Bank) 

Pre-tax Cost 

of Debt 
(BKB) 

2,400 to 

3,100 

1,800 to 

3,600 

17 

18 BKB & 

EXIM 

5,256.3 (5,479.5) 26,049.2 Pre-tax Cost of 

Debt (EXIM) 

Pre-tax Cost 

of Debt 
(BKB) 

4,900 to 

5,500 

4,500 to 

5,900 

08 

19 BKB & 

NBL 

5,290.1 1,234.5 21,538.7 Risk-free Rate Pre-tax Cost 

of Debt 

5,100 to 

5,400 

5,000 to 

5,600 

07 



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(BKB) 

20 BKB & 

Padma 

3,258.9 1,063.8 19,664.4 Risk-free Rate Pre-tax Cost 

of Debt 

(BKB) 

3,180 to 

3,300 

3,020 to 

3,460 

15 

21 BKB & 

UCB 

6,006 (24,080.5) 22,357.1 Risk-free Rate Pre-tax Cost 

of Debt 

(BKB) 

5,700 to 

6,200 

5,300 to 

6,600 

04 

22 City Bank & 
EXIM 

(1,891.7) (26,665.3) 2,617.5 Debt/Capital 
Ratio (City 

Bank) 

Debt/Capital 
Ratio 

(EXIM) 

(1,800) to 
(1,950) 

(1,700) to 
(2,080) 

45 

23 City Bank & 
Padma 

1,925.6 (33,634.9) 11,562.5 Pre-tax Cost of 
Debt (City 

Bank) 

Debt/Capital 
Ratio (City 

Bank) 

1,700 to 
2,200 

1,200 to 
2,600 

22 

24 NBL & 
Agrani 

2,944.8 6,295.8 1,047.9 Risk-free Rate Pre-tax Cost 
of Debt 

(NBL) 

2,900 to 
2,980 

2,850 to 
3,100 

16 

25 NBL & 

Basic 

3,290.1 1,575.7 6,947.4 Risk-free Rate Pre-tax Cost 

of Debt 
(NBL) 

3,220 to 

3,340 

3,160 to 

3,470 

13 

26 NBL & City 

Bank 

16,952.1 (21,337.4) 39,378.8 Pre-tax Cost of 

Debt (City 
Bank) 

EBIT (NBL) 16,400 to 

17,200 

156,00 to 

18,000 

02 

27 NBL & 

EXIM 

2,335.6 (11,013.2) 8,486.1 Debt/Capital 

Ratio (NBL) 

Debt/Capital 

Ratio 

(EXIM) 

2,200 to 

2,450 

1,800 to 

2800 

20 

28 NBL & 

Padma 

4,738.8 2,055.2 9,801.8 Risk-free Rate Pre-tax Cost 

of Debt 

(Padma) 

4650 to 

4810 

4,500 to 

4,950 

10 

29 RAKUB & 
Agrani 

1,313.4 (2,458) 6,212.3 Pre-tax Cost of 
Debt 

(RAKUB) 

Debt/Capital 
Ratio 

(RAKUB) 

1,260 to 
1,320 

1,180 to 
1,440 

30 

30 RAKUB & 
Basic 

502.3 195.6 1,779.9 Risk-free Rate Pre-tax Cost 
of Debt 

(RAKUB) 

485 to 510 460 to 530 32 

31 RAKUB & 

City Bank 

(5,719.7) (32,048.5) (527.7) Pre-tax Cost of 

Debt (City 
Bank) 

Debt/Capital 

Ratio (City 
Bank) 

(5,500) to 

(5,900) 

(5,200) to 

(6,300) 

49 

32 RAKUB & 

EXIM 

(3,361.2) (11,656) 4,191.8 Pre-tax Cost of 

Debt (EXIM) 

Debt/Capital 

Ratio 
(EXIM) 

(3,150) to 

(3,400) 

(2,960) to 

(3,700) 

48 

33 RAKUB & 

NBL 

3,761.8 1,678.4 7,737.3 EBIT (NBL) Risk-free 

Rate 

3,750 to 

3,800 

3,600 to 

3,980 

12 

34 RAKUB & 
Padma 

2,019.9 743.2 5,219.1 EBIT (Padma) Risk-free 
Rate 

1,990 to 
2,060 

1,900 to 
2,100 

21 

35 RAKUB & 

UCB 

(2,611.5) (16,653.5) 2,985 Debt/Capital 

Ratio (UCB) 

Pre-tax Cost 

of Debt 
(UCB) 

(2,580) to 

(2700) 

(2,490) to 

(2,850) 

47 

36 SBL & 

Agrani 

2,503.2 (353.9) 5,666.9 Pre-tax Cost of 

Debt (SBL) 

Risk-free 

Rate 

2,420 to 

2,530 

2,330 to 

2,680 

19 

37 SBL & 
Basic 

2,861.3 1,263.9 6,154.3 Pre-tax Cost of 
Debt (SBL) 

Risk-free 
Rate 

2,820 to 
2,900 

2,740 to 
3,060 

18 

38 SBL & BKB 6,204.8 (1,180.7) 31,519.2 Pre-tax Cost of 

Debt (BKB) 

Risk-free 

Rate 

5,900 to 

6,500 

5,500 to 

6,800 

05 

39 SBL & City 
Bank 

16,731.5 (45,430.3) 143,583.6 EBIT (SBL) Pre-tax Cost 
of Debt (City 

Bank) 

16,300 to 
16,800 

15,800 to 
17,000 

03 

40 SBL & 

EXIM 

1,910.3 (16,199.2) 7,129.5 Debt/Capital 

Ratio (SBL) 

Debt/Capital 

Ratio 
(EXIM) 

1,800 to 

2,100 

1,500 to 

2,300 

23 

41 SBL & NBL 5,805.2 1,565.4 13,260.6 Risk-free Rate Pre-tax Cost 

of Debt 
(NBL) 

5,700 to 

5,850 

5,500 to 

6,200 

06 

42 SBL & 

Padma 

4,300.7 417.7 9,558.9 Risk-free Rate Pre-tax Cost 

of Debt 

(Padma) 

4,220 to 

4,480 

3,900 to 

4,620 

11 

43 SBL & 

RAKUB 

(2,412.7) (7,735.9) 12,244.8 Risk free Rate Debt/Capital 

Ratio (SBL) 

(2,300) to 

(2,500) 

(2,110) to 

(2,690) 

46 

44 SBL & UCB 19,839.7 1,387.6 39,630.3 Pre-tax Cost of 
Debt (UCB) 

EBIT (SBL) 19,600 to 
20,100 

18,700 to 
20,900 

01 

45 UCB & 

Agrani 

(698.2) (5,434.7) 1,108.3 Debt/Capital 

Ratio (UCB) 

Debt/Capital 

Ratio 

(Agrani) 

(660) to 

(720) 

(590) to 

(810) 

36 

46 UCB & 

Basic 

(950.7) (3,466.4) 8,964 Debt/Capital 

Ratio (UCB) 

Debt/Capital 

Ratio (Basic) 

(880) to 

(1,020) 

(780) to 

(1,110) 

40 

47 UCB & City (1,801.6) (9,246.2) 1,050.4 Pre-tax Cost of 

Debt (City 
Bank) 

Debt/Capital 

Ratio (City 
Bank) 

(1,780) to 

(1,820) 

(1,560) to 

(1,950) 

44 



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48 UCB & 

EXIM 

(1,471.4) (5,448.4) 50.3 Pre-tax Cost of 

Debt (EXIM) 

Debt/Capital 

Ratio (City 
Bank) 

(1,440) to 

(1,500) 

(1,380) to 

(1,570) 

43 

49 UCB & 

Padma 

5,033.8 (6,858.9) 14,364.8 Pre-tax Cost of 

Debt (UCB) 

Pre-tax Cost 

of Debt 
(Padma) 

4,800 to 

5,200 

4,600 to 

5,500 

09 

 

The ranking in Table 8 allows the opportunity to provide solution to the decision of which bank should merge with 

whom. The table provides insights about mean, minimum, and maximum synergy values for each case. In case of sensitivity, 

both negative and positive factors are summarized in the table. Basing on 25% and 75% confidence, the range of synergy is 

shown for each case. Finally, the ranks are shown in the last column. 

 

Table 9. Solution to Research Question 01 (The Best Mergers Possible) 
 

Merging Bank Best Case Position Merging Bank Best Case Position 

Sonali Bank PLC UCB 1 BASIC Bank PLC NBL 13 

Bangladesh Development Bank PLC NBL 14 United Commercial Bank PLC SBL 1 

Agrani Bank PLC NBL 16 EXIM Bank Limited BKB 8 

Bangladesh Krishi Bank UCB 4 National Bank Limited City Bank 2 

Rajshahi Krishi Unnayan Bank NBL 12 Padma Bank PLC UCB 9 

City Bank PLC NBL 2   

 

Table 9 shows the best merging option for each bank with the other bank. It may happen from Table 8 that one 

bank is suited for merger for many banks. But, Table 9 allows the merging suitability for every bank with the other. 
 

Table 10. Solution to Research Question 01 (Based on Proposed Mergers by Bangladesh Bank) 
 

Proposed Cases Synergy Value 

(In Crore of BDT) 

Which Plausible Solutions are Synergistic 

SBL and BDBL -639.2 Negative 

BKB and RAKUB 1,650.39 Beaten (BKB-UCB) 

BASIC and City Bank -1,604.20 Negative 

BASIC and Agrani -57.4 Negative 

EXIM and Padma 4,317.40 Positive 

UCB and NBL 2,517 Positive 

 

Based on the proposed 6 cases of the central bank, Table 10 shows which of these six cases hold the positive 

synergy. It’s seen that case 2 (BKB-RAKUB merger) not only is positive, but also beats rank 4 option (BKB-UCB merger).  

 

Results from Research Question 02 
 
 

In this segment, “***” “**”, and “*” indicate “significance at 99%, 95%, and 90% confidence interval respectively. 
 

Table 11. Descriptive Statistics 
 

Variable Obs Mean Std. Dev. Min Max 

Synergy 55 2208.453 4591.168 -5719.71 19839.74 

Beta_C 55 0.010264 0.026387 -0.03711 0.105892 

COD_C 55 0.06695 0.028571 -0.07662 0.16903 

Tax_C 55 0.032156 0.508118 -2.10363 1.016814 

DC_C 55 1.061882 0.33539 0.076925 2.292402 

Revenues_C 55 3199.862 2141.443 237.752 10610.75 

EBIT_C 55 -1178.21 1949.99 -6991.82 1636.093 

ROC_C 55 -0.06387 0.130302 -0.80718 0.156549 

RR_C 55 0.004807 0.028564 -0.0401 0.198211 

lgr_C 55 3.454545 1.408548 1 10 

FirmSize 55 6.13E+11 4.05E+11 7.72E+10 1.60E+12 

Est 55 1987.455 9.121237 1972 2011 

Branch 55 408.8182 288.0571 54.5 1133.5 

Districts 55 46.16364 10.35618 19 64 

HR 55 5836.909 3147.027 929.5 14465.5 

 

Table 11 provides the summary of the data where it’s seen that the data set has a great level of variability. The 

minimum and maximum values have huge distances with symmetric distribution in values. There are 55 observations and 

all will be applicable in regression analysis.  

 

 

 
 

 



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Table 12. Corelation with P Values 
 

  Synergy Beta_

C 

DC_C EBIT_

C 

ROC_

C 

RR_C lgr_

C 

ln_CO

D_C 

ln_Tax_

C 

Revenues

_C 

ln_Est ln_HR Branc

h 

District

s 

FirmSiz

e 

Synergy 1.0                             

                                

Beta_C 0.0 1.0                           

  0.8                             

DC_C 0.1 0.5*** 1.0                         

  0.4 0.0                           

EBIT_C -0.6*** 0.1 0.1 1.0                       

  0.0 0.6 0.6                         

ROC_C -0.3** 0.0 -0.4** 0.2 1.0                     

  0.0 0.8 0.0 0.1                       

RR_C -0.2* -0.1 -0.6*** 0.2 0.5*** 1.0                   

  0.1 0.5 0.0 0.1 0.0                     

lgr_C 0.1 0.1 -0.2 0.1 0.1 0.6*** 1.0                 

  0.6 0.5 0.2 0.4 0.5 0.0                   

ln_COD_

C 

0.5*** 0.1 0.6*** -0.4** -0.7*** -0.8*** 0.2 1.0               

  0.0 0.5 0.0 0.0 0.0 0.0 0.2                 

ln_Tax_

C 

-0.5** -0.1 -0.4* -0.1 0.6** 0.4 0.0 -0.4* 1.0             

  0.0 0.7 0.1 0.8 0.0 0.1 1.0 0.1               

Revenues

_C 

0.1 0.0 -0.1 0.2 0.1 0.0 0.0 -0.3** 0.2 1.0           

  0.4 0.7 0.6 0.2 0.4 1.0 0.8 0.0 0.5             

ln_Est -0.2* -0.1 -0.1 0.2 -0.3** -0.1 -0.1 0.1 -0.3 -0.6*** 1.0         

  0.1 0.5 0.7 0.2 0.0 0.4 0.5 0.4 0.3 0.0           

ln_HR 0.3** -0.1 -0.1 -0.2* 0.2 0.1 0.0 -0.1 0.1 0.6*** -0.9*** 1.0       

  0.0 0.7 0.7 0.1 0.2 0.5 0.9 0.3 0.8 0.0 0.0         

Branch 0.3* 0.2 0.1 -0.3* 0.1 0.0 0.1 -0.1 0.0 0.5*** -0.8*** 0.9*** 1.0     

  0.1 0.1 0.3 0.1 0.3 0.9 0.7 0.6 1.0 0.0 0.0 0.0       

Districts 0.1 -0.2* -0.2 -0.2 0.4** 0.2 0.1 -0.1 -0.2 0.5** -0.6*** 0.6*** 0.4** 1.0   

  0.4 0.1 0.1 0.2 0.0 0.1 0.3 0.5 0.4 0.0 0.0 0.0 0.0     

FirmSize 0.1 -0.3** -0.2 0.0 0.2 0.1 0.1 -0.2 -0.1 0.6*** -0.5** 0.6*** 0.4*** 0.6*** 1.0 

  0.4 0.0 0.1 1.0 0.2 0.5 0.4 0.2 0.6 0.0 0.0 0.0 0.0 0.0   

 

The results from Figure 12 show that the variables are not properly correlated to each other that reduces the chance 

of multicollinearity. 

 

Table 13. Regression Analysis by OLS (with Variance Covariance Estimator) 
 

Number of Observations P Value R-squared 

55 0.0000*** 0.6186 

 

Table 14. Variable-wise Regression Analysis by OLS (with Variance Covariance Estimator) 
 

 

Variables Coefficients Robust Std. Error t statistics P Values 

Beta_C 13519.11 17265.91 0.78 0.438 

DC_C -10411.12 5208.03 -2.00 0.053* 

EBIT_C -0.31 0.35 -0.89 0.379 

ROC_C 27711.32 10741.04 2.58 0.014** 

RR_C -262469.40 100453.40 -2.61 0.013** 

lgr_C 409.61 599.19 0.68 0.498 

ln_COD_C 10336.42 4196.39 2.46 0.018** 

Revenues_C 0.64 0.33 1.93 0.061* 

ln_Est -113398.70 220693.60 -0.51 0.61 

ln_HR 769.11 3097.43 0.25 0.805 

Branch -0.85 4.61 -0.18 0.855 

Districts -135.00 73.38 -1.84 0.073* 

Tax_C -2827.76 3013.21 -0.94 0.354 

ln_FirmSize 418.82 963.64 0.43 0.666 

constant 889784.20 1693895.00 0.53 0.602 

 

From Table 13 it is found that there exists significant relationship between different financial factors and financial 

synergy value, and the null hypothesis can be rejected. The R-squared shows that changes in financial synergy value can be 

predicted by 61.86% by the changes in the independent variables. In Table 14, it’s found that combined debt to capital, 

revenues, districts are significant at 90% confidence interval, while combined return on capital, reinvestment rate, and log 

normal value of cost of debt are significant at 95% confidence interval. 
 



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

27 
 

Table 15. Test of Multicollinearity 
 

Variable VIF 1/VIF 

RR_C 9.28 0.11 

ROC_C 7.19 0.14 

ln_COD_C 7.00 0.14 

DC_C 6.32 0.16 

Tax_C 4.05 0.25 

ln_HR 3.90 0.26 

EBIT_C 3.81 0.26 

ln_Est 3.64 0.27 

Branch 3.52 0.28 

ln_FirmSize 3.24 0.31 

Districts 3.21 0.31 

Revenues_C 2.80 0.36 

Beta_C 1.87 0.53 

lgr_C 1.20 0.83 

Mean VIF 4.36 

 

The mean VIF score is 4.36 found from Table 15 that shows less scope of multicollinearity in the model. Using 

Breusch-Paga / Cook-Weisberg test for heteroskedasticity, the p value is 0.2967 that accepts the null hypothesis for 

homoskedasticity or, constant variance. 

 
Table 16. Mixed Effect Generalized Linear Model with Variance-Covariance Estimator (VCE) 
 

Number of Observations P Value Wald chi2 

55 0.0000*** 127.52 

Table 17. Variable-wise Mixed Effect Generalized Linear Model with Variance-Covariance Estimator (VCE) 

 

Variables Coefficients Robust Std. Error z Statistics P Values 

Beta_C 26135.76 13157.09 1.99 0.047** 

DC_C -8697.77 3955.14 -2.20 0.028** 

Revenues_C 0.59 0.30 1.96 0.05** 

EBIT_C -0.35 0.28 -1.26 0.21 

ROC_C 25257.98 8407.77 3.00 0.003*** 

RR_C -235175.30 84752.21 -2.77 0.006*** 

lgr_C 565.06 504.91 1.12 0.26 

FirmSize 0.00 0.00 -0.52 0.60 

HR 0.87 0.64 1.36 0.17 

ln_Est -74757.55 142221.70 -0.53 0.60 

ln_COD_C 9557.37 3271.60 2.92 0.003*** 

Tax_C -2374.56 2486.13 -0.96 0.34 

Branch -7.03 5.00 -1.41 0.16 

Districts -129.43 61.20 -2.11 0.034** 

constant 606984.80 1089697.00 0.56 0.58 

 

The MEGLM with VCE results in Table 16 and 17 show that the null hypothesis can be rejected. Also, beta, debt 

to capital, revenues, and districts are significant at 95% confidence interval, while return on capital, reinvestment rate, log 

normal value of cost of debt are significant at 99% confidence interval. 

 

DISCUSSIONS 

The study has found that out of the proposed mergers, case 2, 5, and 6 result in positive synergy, while case 2 is the most 

optimum synergy option. Out of the ranking, Table 9 provided the best matching solutions for merger by meeting the 

research question 1. Though especially using financial inputs to predict financial synergy no exact studies have been done 

yet, still studies of (Mucenieks, 2018), (Darayseh & Alsharari, 2022), (Sharma, 2018), and (Yiannis et al., 2007) which 

worked to identify impact of financial factors on financial synergy are worthy of mentioning. The studies outlined significant 

relationship between dependent and independent variables. In the research objective 2, this study rejects the null hypothesis 

by accepting that financial factors significantly impact the financial synergy.  

Findings from Figure 05 and Table 8 represent that synergy values of almost all of the cases are mostly positively 

sensitive to Pre-tax Cost of Debt by 35% and, Risk-free Rate by 32.72%., Debt/Capital ratio by 23.36% and 8.92% by other 

factors on average. On the other hand, synergy values are mostly negatively sensitive to Pre-tax Cost of Debt by 43.36%, 



Roy, Asian Finance & Banking Review 8(1) (2024), 13-29

  

28 
 

Debt/Capital ratio by 36.36%, and 20.28% by other factors on average. The trend results from figure 6 show at 10% and 

25% confidence, how much the synergy values can fluctuate which contribute to solutions found at Table 9 and Table 10. 

Table 7 represents the scenario of the cases with mean, maximum, minimum, and standard deviation variabilities.   

The OLS with VCE output shown in Table 13 represents that there exists a significant relation between the 

dependent and independent variables where, DC_C, ROC_C, RR_C, ln_COD_C, Revenues_C and control variable Districts 

are significant which ultimately rejects the null hypothesis. The MEGLM with VCE results also affirm the OLS results with 

VCE robustness showing significant relation in the model by 99% confidence interval. Here, additionally one independent 

variable Beta_C is also significant. Thus, the null hypothesis can be rejected concluding that there exists significant relation 

between the financial factors and synergy value. 

The study has confirmed that of the models used, both the OLS and MEGLM provide almost identical results for 

predicting the synergy value. Finally, it can be said that the methodologies employed in the study meet both of the research 

objectives, and the ideas are well-conceived.  

There are some findings:- 
 

 The study has found that out of the proposal suggested by Bangladesh Bank in merger, only case 2, case 5, and 

case 6 add value to synergy after merging. And, considering the combination options, the case 2 beats the rank 04 

merger between BKB and UCB. Thus, the solution is to proceed the three cases of mergers with maximum priority 

to implement case 2. 

 The study rejects the null hypothesis by accepting that there exists significant relationship between different 

financial factors and the synergy value. The study found that the merged independent variables debt to capital ratio, 

return on capital, reinvestment rate, and cost of debt, revenues, and control variable districts are significant to 

predict the changes in the value of financial synergy after merger. In OLS with VCE regression model and gaussian 

MEGLM with VCE, debt to capital ratio, reinvestment rate and districts are negatively sloped to the synergy value 

of mergers while other significant variables are positively sloped to the synergy value of mergers.  

 The study affirms that in an emerging economy like Bangladesh, mergers in the banking sector can result in positive 

synergy value. 

CONCLUSIONS 

The study reveals that out of the central bank's proposed mergers, cases 2, 5, and 6 significantly enhance financial synergy 

after merging, and financial factors significantly impact the value of financial synergy. This article introduces a unique 

approach to the existing research domain by utilizing a combination of simulation and econometric analysis to assess the 

merger options of various bank types, such as government, non-government, and specialized banks, and by implementing a 

ranking methodology to determine the optimal merger solution. Additionally, the study presents a novel approach to 

identifying the financial factors that influence the value of financial synergy. The study has validated the theory of synergy 

through mergers in the banking sector of an emerging economy such as Bangladesh, and it recommends more mergers based 

on the ranking strategy provided by the study using proper methodology. The study makes minimal assumptions about the 

mergers' growth forecasts, which may pose a constraint in situations where external factors influence the economy. The 

study reveals opportunities for complex merger evaluations across other industries in emerging economies. 
 

Author Contributions: Conceptualization, G.R.; Methodology, G.R.; Software, G.R.; Validation, G.R.; Formal Analysis, G.R.; Investigation, G.R.; 
Resources, G.R.; Data Curation, G.R.; Writing – Original Draft Preparation, G.R.; Writing – Review & Editing, G.R.; Visualization, G.R.; Supervision, 

G.R.; Project Administration, G.R.; Funding Acquisition, G.R. Author has read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study due to that the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: The author received no direct funding for this research.  

Acknowledgements: I acknowledge the cooperation from Bangladesh Bank, the central bank of Bangladesh for allowing the financial statements of the 
concerned banks on unavailability in websites. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                

                                                                                                                                                                                                                    

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