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 Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 20 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. Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 21 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. Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 22 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. Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 23 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 Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 24 (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 Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 25 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. Roy, Asian Finance & Banking Review 8(1) (2024), 13-29 26 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. REFERENCES Akinbuli, S. F., & Kelilume, I. (2013). The effects of mergers and acquisition on corporate growth and profitability: Evidence from Nigeria. 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