Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .Tốc độ điều chỉnh cấu trúc vốn mục tiêu của các doanh nghiệp niêm yết tại Việt Nam .
MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM HO CHI MINH UNIVERSITY OF BANKING NGUYEN VAN DIEP SPEED OF ADJUSTMENT TOWARDS TARGET CAPITAL STRUCTURE: EVIDENCE FROM VIETNAMESE LISTED FIRMS SUMMARY OF DOCTORAL DISSERTATION Major: Finance – Banking Code: 9.34.02.01 Academic Advisor: Prof Dr Nguyen Ngoc Thach HO CHI MINH CITY-2023 CHAPTER 1: INTRODUCTION 1.1 Background and Motivation of the Study Financial economists have devoted considerable attention to explaining the capital structure of firms However, it is still difficult to explain capital structure fully because specific theories have different perspectives Corporate financial managers try to adjust the capital structure of companies to achieve an optimal target level of leverage, but it is difficult or impossible for them to achieve this because of problems, e.g., adverse selection, the presence of relative costs of issuing debt and equity that change over time, or a tradeoff between the costs and benefits of raising equity capital (Baker & Wurgler, 2002; Myers & Majluf, 1984) In fact, the capital structure adjustment process involves a number of factors, and corporate financial managers will carefully consider the company's financial situation when making adjustments to their capital structure companies In a perfect financial market, companies will achieve their target capital structure very quickly However, it may take several years for firms to adjust to their desired leverage ratio due to the existence of adjustment costs The COVID-19 pandemic originated in Wuhan, China, at the end of 2019 and has quickly spread to China's neighboring countries and the whole world On January 30, 2020, the World Health Organization (WHO) declared a global health emergency (WHO, 2020) The worldwide pandemic of COVID-19 has created a major macroeconomic shock for corporations (Bui et al., 2022) Vo, Mazur, and Thai (2022) reported that the COVID-19 outbreak accelerated the speed of adjustment (SOA) to the target leverage ratio of publicly listed firms In addition, the outbreak of the COVID-19 pandemic has significantly impacted the health of the world's population and economic activities in all countries, with significant impacts on supply chains across the world The blockade has disrupted supply and demand for many goods in terms of product flow At the same time, financial flows are affected, with serious consequences spreading throughout supply chains around the world (Moretto & Caniato, 2021) The COVID-19 outbreak has given a new look at supply networks and created pressure to fill gaps, heal "cracks" in global supply chains, and increase the role of supply chain finance (SCF) in the world economy Scholars argue that SCF has emerged as a new flexible financing vehicle to alleviate the impact of financial constraints Pan, Xu, Li, Ling, and Zheng (2023) established the link between SCF and capital structure adjustment of core firms through a data set of firms listed on the Shanghai and Shenzhen Stock Exchanges in China Their study shows that SCF can increase capital structure SOA significantly, especially for overleveraged firms, which will have a faster SOA than underleveraged firms They concluded that SCF can effectively improve cooperation between banks and companies, effectively distribute financial resources among companies in the supply chain, and significantly improve competitiveness Furthermore, trade credit (specifically trade payables) is often used by companies, especially in countries with underdeveloped financial markets (Booth, Aivazian, Demirguc-Kunt, & Maksimovic, 2001) Casey and O'Toole (2014) observe that firms are more likely to use trade credit and that interfirm credit acts as a substitute for bank financing, especially during periods of crisis Because times of crisis can exacerbate problems arising from information asymmetry and corporate non-transparency, financial institutions may reduce the provision of credit to companies In such conditions, trade credit (trade payables) appears useful to mitigate credit constraints Abuhommous (2021) and Cao and Cui (2021) find that trade credit has an influence on the capital structure adjustment process For example, Cao and Cui (2021) conclude that trade credit accelerates the capital structure adjustment process of enterprises in China Both works show that the positive effect of trade credit on the capital structure of SOA is more pronounced for over-leveraged firms Overall, the results of these two works imply that firms use trade credit to conserve cash flow and restore leverage to the target capital structure Developing countries, such as Vietnam, have greater information asymmetry, and this can lead to higher transaction costs when companies use external financing In addition, Vietnamese firms are heavily dependent on bank credit and less diverse sources of external funding, making businesses more vulnerable to adjusting debt ratios Therefore, the speed of adjusting their capital structure may also be slow after considering adjustment costs and related benefits The outbreak of the COVID-19 epidemic has posed unprecedented challenges and has had a significant impact on economic development because Vietnam is highly dependent on other economies With three main impacts of COVID-19 on growth, investment, and trade; disruption of important production value chains (supply chain of processed and manufactured goods such as electricity, electronics, machinery, and equipment; supply chain of seafood and agricultural products; and supply chain of textiles and garments); consumption decline has a major impact on services and tourism SCF activities in Vietnam have been of interest to a number of banks and large companies Many chain links of retail and high-tech corporations, after applying financial solutions based on the supply chain model, have helped internal suppliers convert receivables and inventory into cash and overcome problems partly overcome the shortage of working capital However, the rate of Vietnamese firms participating in supply chains is quite low compared to regional markets (Thach Binh, 2018) Besides, Vietnamese firms have a very high level of dependence on trade credit as a source of funding because Vietnam has a poorly developed financial market and financial constraints (Le My, 2022) Survey results on payment practice trends released for the first time by Atradius for the Vietnamese market show that business-to-business (B2B) deferred payment sales transactions are on the rise in the Vietnamese market; about 58% of the total transaction value is carried out in the form of deferred payment sales (Atradius Vietnam, 2020) This survey result suggests that Vietnamese firms can make reasonable use of B2B payments in the context of certain cash flow limitations due to limited credit capital from banks (Bach Ngoc Thang, 2018) Additionally, Vietnamese companies often follow TOT in their capital structure decisions (Thai & Burlacu, 2021) According to the survey, the majority of Vietnamese companies consider the costs and benefits of different funding sources in their capital structure decisions Trade credit is important to meet the financing needs of companies as an alternative to debt financing and can also provide companies with a lowcost means of adjusting leverage to their target in Vietnam Given the unprecedented economic impacts of COVID-19, this study will investigate the speed of adjustment towards the target capital structure in the Vietnamese context Besides, Vietnam represents an ideal context to examine the influence of SCF and trade credit (trade payables) on the SOA of leverage Although some studies have shown that COVID-19, SCF, and trade credit have an impact on the SOA of the capital structure of firms, previous studies have not been conducted in the Vietnamese context At the same time, previous studies all used the frequency method, so there will be different results when evaluating the SOA of the capital structure of Vietnamese enterprises In this thesis, the Bayesian method is the appropriate approach because it has higher reliability and overcomes many disadvantages of the frequency method (Wasserstein, Schirm, & Lazar, 2019) For theoretical and practical reasons, this study will focus on understanding the SOA of target-oriented capital structures Specifically, this study examines the impact of the COVID-19 pandemic, SCF, and trade credit (trade payables) on the capital structure adjustment process of companies in the institutional context of Vietnam To the best of the author's knowledge, this is the first study to explore the convergence rate for target leverage of Vietnamese enterprises under the impact of COVID-19, SCF, and trade credit 1.2 Research Objectives The overall objective of the study is to analyze the SOA of the capital structure towards the target level of listed enterprises in Vietnam Based on the research results, the author proposes policy implications for financial managers of Vietnamese listed enterprises to have policies affecting SOA so that businesses can quickly achieve their target capital structure To achieve the general goal, the research will in turn address the following four specific goals: First, the thesis explores the impact of the COVID-19 pandemic on the SOA of the target capital structure of listed enterprises in Vietnam Second, the thesis explores the impact of SCF on the SOA of the target capital structure of listed enterprises in Vietnam Third, the thesis explores the impact of trade credit (trade payables) on the SOA of the target capital structure of listed enterprises in Vietnam Propose some policy implications for relevant subjects to help Vietnamese businesses achieve their target capital structure 1.3 Research Questions To achieve the research goal, this thesis will focus on answering the following research questions: How does the COVID-19 pandemic impact the SOA of the target capital structure of listed companies in Vietnam? How does SCF impact the SOA of the target capital structure of listed companies in Vietnam? How does trade credit (trade payables) impact the SOA of the target capital structure of listed enterprises in Vietnam? What are the policy implications for relevant stakeholders to help Vietnamese businesses achieve their target capital structure? 1.4 Object and Scope of Research Object of Research: The research subjects are the target capital structure, SOA of the target capital structure, and the impact of the COVID-19 pandemic, SCF, and trade credit (trade payables) on the capital structure adjustment process Scope of Research: Research on the process of adjusting the target capital structure of businesses listed on the Ho Chi Minh City Stock Exchange and Hanoi Stock Exchange in the period 2010–2021 1.5 Research Methods To achieve the overall research goal of the thesis, which is to explore the capital structure adjustment speed of listed enterprises in Vietnam, the author applies qualitative methods combined with quantitative methods Specifically: With the qualitative research method, the author uses descriptive statistics to summarize the data with the mean value, standard deviation, minimum value, and maximum value of the variables in the model With quantitative methods, the author will use a Bayesian approach to measure the adjustment speed of the capital structure of listed enterprises in Vietnam 1.6 New Contribution of The Thesis 1.6.1 New scientific contributions The main theoretical contributions of the thesis are as follows: First, the thesis applies a new estimation method (namely BML) and reveals new insights into the relationship between SOA and the degree of leverage The author has considered both types of leverage, including book leverage and market leverage Specifically, using a dynamic model, this study finds that the SOA of Vietnamese companies is 9.2% to 12.5%/year Second, given the unprecedented economic impacts of COVID-19, this thesis also contributes to the literature on the impact of crises on firm behavior by exploring the impact of crises COVID-19 and the capital structure adjustment of companies The findings show that SOA leverages faster during the COVID-19 crisis than during this non-pandemic period Third, the results of this study shed light on the emerging link between SCF and corporate capital structure adjustment in the Vietnamese market Specifically, this study shows that companies with a high commitment to SCF have a higher rate of convergence to the target debt ratio than companies with a low commitment or no commitment to SCF Also, this research can contribute to discussions on sustainable supply chain management This study also sheds light on the emerging link between trade credit (trade payables) and firms' capital structure adjustments Specifically, this study shows that companies with a high use of trade credit (a high ratio of trade payables) will have a higher rate of convergence to the target debt ratio than companies with a low use of trade credit Sixth, the results of the thesis show that there is asymmetry in the impact of SCF and trade credit (trade payables) on SOA Specifically, for companies with leverage above the target level, SCF and trade credit will accelerate the process of adjusting the capital structure to the target level more than for companies with leverage below the target level 1.6.2 New practical contributions This thesis has policy implications for both policymakers and business managers in Vietnam This study is useful for corporate finance managers in Vietnam in understanding the important factors that are influencing financing decisions, especially debt levels and SOA, thereby determining debt levels and SOA tracking Based on the findings from this study, policymakers in Vietnam can also better understand corporate financing decisions, implement equity and bond capital market development policies, develop favorable macroeconomic policies to increase SOA, and develop mechanisms to monitor corporate financing decisions to avoid influencing corporate bankruptcy 1.7 Organization of the Thesis This study is structured into five chapters, specifically as follows: Chapter 1: Overview Chapter 2: Literature Review Chapter 3: Research models and methods Chapter 4: Empirical Findings and Discussion Chapter 5: Conclusion and Recommendations CHAPTER 2: LITERATURE REVIEW 2.1 Overview of capital structure 2.1.1 Capital structure Capital structure refers to the way in which a company's assets are financed (Myers, 2001) Specifically, Myers (1984) argues that capital structure is the combination of debt and equity that a business uses to finance its operations 2.1.2 Target capital structure A company's target capital structure refers to the capital structure the company is trying to achieve In other words, target capital structure describes the combination of debt, preferred stock, and common equity that is expected to optimize a company's stock price When a company raises new capital, it will focus on maintaining this target or optimal capital structure 2.1.3 Speed of Adjustment In the optimal case, a firm's actual capital structure is equal to its target capital structure However, in practice, some companies cannot immediately adjust their leverage to the target level This is the case when adjustment costs are high or when the financial system simply cannot meet the financial needs of businesses If firms have target leverage, they will return to target leverage after such shocks The actual leverage may then be only partially aligned with the target leverage The rate at which firms adjust to their target leverage is likely to be higher for firms that are further away from their target leverage level, known as the speed of adjustment (SOA) of leverage 2.2 Theories related to capital structure 2.2.1 Modorliani-Miller theory 2.2.2 Pecking Order Theory 2.2.3 Trade-off Theory 2.2.4 Market Timing Theory 2.3 COVID-19 pandemic and capital structure 2.3.1 Concept The SARS-CoV-2 coronavirus and its variants are the cause of the global COVID19 pandemic, an infectious acute respiratory disease (WHO, 2023) 2.3.2 Economic impact of COVID-19 The COVID-19 outbreak has hit businesses around the world, affecting almost every business sector and industry While some countries enforced restrictions on exports and imports, the economy took a hit in the Gulf countries Unsurprisingly, the quarantine measures imposed have harmed business, trade, exports, imports, and other forms of consumption According to Alhawel, Nurunnabi, and Alyousef (2020), 99.5% of companies were severely affected by the coronavirus outbreak It was found that 46% of businesses claimed that their annual revenue was reduced by 100%, 17% of businesses claimed that their revenue was affected by 90%, and 52% of businesses had to close The productivity of companies seems to be greatly affected due to the pandemic and lockdown measures, ultimately reducing their performance The study also shows that a company's performance depends on the ratio of total investment capital to sales revenue Many studies have assessed the impact of COVID-19 on businesses and found that COVID-19 has negatively affected company performance, dividends, liquidity, stock prices, and leverage (Khatib & Nour, 2021; Nguyen Van Diep & Le Duy Khang, 2021; Shen, Fu, Pan, Yu, & Chen, 2020) Most countries are expected to see a sharp decline in economic growth, thereby reducing gross domestic product 2.3.3 Empirical studies on COVID-19 and capital structure 2.4 Supply chain finance and capital structure 2.4.1 Concept 20 (MTB), profitability (PROF), non-debt tax shield, also known as depreciation (DEP), corporate income tax (TAX), corporate risk/earnings volatility (RISK), and industry average annual leverage at book value (IndBLEV) or at value market (IndMLEV) 3.4 Estimation Methodology 3.4.1 Bayesian Statistics 3.4.2 Advantages of Bayesian analysis 3.4.3 Bayesian linear regression Summary of Chapter This chapter outlines the research design, including the research model, measurement of variables in the model, data sources, and sampling methods The study uses a partial adjustment model and applies the Bayesian linear regression (BML) estimation technique to examine the SOA of leverage 21 CHAPTER 4: EMPIRICAL FINDINGS AND DISCUSSION 4.1 Descriptive Statistics Table 4.1 Descriptive statistics of variables Variable Obs Mean Std dev Min Max BLEV 6,575 0.4836 0.2187 0.0006 0.9919 MLEV 6,575 0.5045 0.2538 0.0003 0.9956 PROF 6,575 0.0897 0.0853 -0.7729 0.9970 SIZE 6,575 27.2972 1.5778 23.3216 33.6910 TANG 6,575 0.2400 0.2134 0.0000 0.9697 DEP 6,575 0.0251 0.0318 0.0000 0.3143 MTB 6,575 1.1022 0.6332 0.1451 12.4129 TAX 6,575 0.1943 0.1265 0.0000 0.9970 RISK 6,573 0.0755 0.1625 0.0005 4.2427 IndBLEV 6,575 0.4904 0.0764 0.3063 0.6244 IndMLEV 6,575 0.5138 0.1060 0.2668 0.7593 Source: author's calculations In table 4.1, the total number of observations (obs), minimum value (min), maximum value (max), average value (mean), and standard deviation (std dev.) of this study are shown Descriptive statistics show that the total number of observations used for the current study is 6,575, which is large enough for the purpose of the current study and ensures the generalizability of the study results The present study uses the book value (BLEV) and market value (MLEV) of leverage From the descriptive statistics table, the minimum values of BLEV and MLEV are 0.06% and 0.03%, respectively, while their maximum values are 99.19% and 99.56%, respectively The standard deviation of book leverage (21.87%) and market leverage (25.38%) shows that the dispersion ratio of market leverage is higher than the dispersion ratio of book leverage Furthermore, the average values of book and market leverage are 48.36% and 50.45%, respectively, meaning that the average market leverage is 1.04 times larger (0.5045/0.4836) relative to book leverage To be more 22 specific, the author presents the value of the average leverage of businesses over the years in the research sample in Figure 4.1 0.7000 0.6000 0.5000 0.4000 0.3000 0.2000 0.1000 0.0000 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 BLEV 0.4955 0.5098 0.5061 0.4997 0.4877 0.4798 0.4714 0.4773 0.4799 0.4749 0.4723 0.4703 MLEV 0.4744 0.6207 0.6014 0.5587 0.5059 0.4940 0.4840 0.4753 0.5060 0.5125 0.4867 0.3972 BLEV MLEV Figure 4.1: Average leverage between 2010 and 2021 Figure 4.1 presents the change in two leverage proxies, including book leverage (BLEV) and market leverage (MLEV), over about 12 years (from 2010 to 2021) after the GFC The chart shows that, on average, listed companies in Vietnam use significant levels of debt over time, according to both market and book measures 4.2 Correlation analysis 4.3 Estimation Results 4.3.1 Adjustment speed of capital structure Case 1: Book leverage (BLEV) as a measure of debt ratio Specifically, Table 4.8 reports Bayesian estimation results for a proxy for book leverage (BLEV) Table 4.8 shows that the average parameter of the lagged variable BLEV has β = 0.9084, and the probability that this lagged variable has a positive impact on book leverage is 100% The coefficient of the lagged dependent variable (BLEV) is interpreted as 23 indicating the existence of a dynamic capital structure, and it provides an estimate of the SOA Specifically, the coefficient of the lagged variable BLEV ranges from to and has a strong and positive impact, so this implies that Vietnamese-listed companies in the research sample follow a debt adjustment policy to a certain extent target book debt ratio This result shows that hypothesis 1a is accepted Case 2: Market leverage (MLEV) as a measure of debt ratio Table 4.9 reports Bayesian estimation results for a proxy for market leverage (MLEV) Table 4.9 shows that the average parameter of the lagged variable MLEV has β = 0.8748, and the probability that this lagged variable has a positive impact on book leverage is 100% This result implies that the lag of the dependent variable MLEV is important in the dynamic capital structure model Specifically, the coefficient of the lagged variable MLEV ranges from to and has a strong and positive impact, so this implies that Vietnamese-listed companies in the research sample follow the debt adjustment policy to the target level of the market debt ratio This result shows that hypothesis 1b is accepted SOA is calculated by subtracting from the mean parameter of the lag of the BLEV and MLEV variables The results of the study, in the case of the leverage variable being BLEV, show that the SOA is 0.0916 (take 1–0.9084), meaning that only 9.16% of the variation between target BLEV and actual BLEV is accounted for adjusted annually In the case of leveraged MLEV, the SOA is 0.1252 (take minus 0.8748), meaning the SOA is 12.52% per year for businesses in Vietnam These SOAs correspond to half-lives of leverage shocks of about 7.2 years (for book leverage) and 5.2 years (for market leverage), respectively The results of the present study also show that non-financial listed companies in Vietnam adjust leverage to their target leverage relatively slowly This result from this thesis is similar to the SOA results reported by previous empirical works (Aỗar, 2021; Aderajew, Trujillo-Barrera, & Pennings, 2019; Bui Huu Phuoc & Ngo Van Toan, 2019; Ho, Lu, & Bai, 2021; Mocking & Steegmans, 2017; T Nguyen, Bai, Hou, & Truong, 2021; Thai & Burlacu, 2021; Vo et al., 2022) 24 4.3.2 Factors affecting capital structure 4.3.3 The COVID-19 pandemic and the speed of adjustment of the target capital structure Case 1: Book leverage (BLEV) as a measure of debt ratio Table 4.10 shows the SOA of book leverage for businesses before and during the COVID-19 crisis In the pre-pandemic period, the average parameter of the lagged variable BLEV has β = 0.9075, and the probability that this lagged variable has a positive impact on book leverage is 100% This result implies that the lag of the dependent variable BLEV is important in the dynamic capital structure model SOA book leverage of businesses is quite low; SOA = 1–0.9075, which is about 9.2% per year (less than 10%) This SOA corresponds to a half-life of pre-Covid-19 book leverage shocks of 7.1 years Meanwhile, during the COVID-19 pandemic, the estimation results show that the average parameter of the lagged variable BLEV has β = 0.9026, and the probability that this lagged variable has a positive impact on book leverage is 100% SOA book leverage of businesses is also quite low in this case: SOA = - 0.9026, which is about 9.7% per year (less than 10%), corresponding to the half-life cycle of book leverage shocks during the COVID-19 period of 6.8 years However, SOA during the pandemic was higher than SOA during the pre-pandemic period Thus, the SOA of book leverage during the economic crisis caused by COVID-19 is faster than the SOA of book leverage in the pre-crisis period This result shows that hypothesis 2a is accepted The results of the thesis are compatible with the conclusions of Vo et al (2022) in the case of book leverage Case 2: Debt ratio measured by market leverage (MLEV) Table 4.11 shows the SOA of market leverage for businesses before and during the COVID-19 crisis In the pre-pandemic period, the average parameter of the lagged variable MLEV had β = 0.8672, and the probability that this lagged variable has a positive impact on book leverage is 100% This result implies that the lag of the dependent variable MLEV is important in the dynamic capital structure model The SOA market leverage of businesses 25 will be equal to minus 0.8672 = 0.1328, which is about 13.3% per year This SOA corresponds to a half-life of pre-Covid-19 market leverage shocks of 4.9 years In contrast, during the COVID-19 pandemic, the estimation results show that the average parameter of the lagged variable MLEV has β = 0.8502, and the probability that this lagged variable has a positive impact on book leverage is 99.06% Therefore, SOA = – 0.8502 = 0.1498, meaning that the SOA of firms' market leverage is approximately 15% per year, corresponding to the half-life of market leverage shocks in the past year The COVID-19 period is 4.3 years Thus, SOA of market leverage during the economic crisis caused by COVID-19 is faster than SOA of market leverage in the pre-crisis period This result shows that hypothesis 2b is accepted Previous empirical research examining the impact of the COVID-19 outbreak on the SOA of market leverage of businesses around the world also showed similar results (Vo et al., 2022) 4.3.4 Supply chain finance and the speed of adjustment of target capital structures 4.3.4.1 Supply chain finance and the speed of adjustment of the target capital structure for the full sample First, for book leverage, the BML regression results show that the average parameter of the DevLEV variable has a value of 0.0107, and the probability that this variable has a positive impact on the change in book leverage is 86.35% This result implies that this baseline SOA, without considering the impact of SCF, is about 1.07% per year The positive sign of the mean parameter of the variable DevLEV implies that these companies have a faster SOA Therefore, there is evidence of a strong and positive relationship between SOA and the gap between the target debt ratio and the firm's actual debt ratio at book value Therefore, companies whose actual book leverage is far from the target book leverage will always want to reach the optimal level quickly, so the SOA will be larger These results are consistent with the findings of dynamic trade-off research in the capital structure literature (Ho et al., 2021; Mukherjee & Mahakud, 2010; Pan et al., 2023) 26 The results in this table also show that the average value of the parameter SCFxDevLEV (interaction variable between SCF and deviation from the target leverage ratio) is 0.0058, and the probability that this parameter has the product extreme to a change in book leverage of 73.34% Therefore, the author concludes that there is a positive relationship between SCF and SOA This result shows that, compared to companies not committed to SCF, companies highly committed to SCF will have an SOA at book value faster by 0.0058/0.0107 = 0.5410, which is about 54.10% per year compared to companies with low SCF commitments In other words, how long does it take, on average, for a firm with an SCF commitment to correct half the deviation between actual and target leverage (corresponding to the half-life of book leverage shocks)? Thus, SCF accelerates the SOA of the book capital structure of listed companies in the Vietnamese market The author's findings imply that firms with a high commitment to SCF will have lower transaction costs, lower overall adjustment costs, and a high leveraged SOA The results are consistent with the authors' expectations and with findings from previous empirical research for the Chinese market (Pan et al., 2023) Overall, this result supported the author's hypothesis that SCF increases SOA leverage Therefore, hypothesis 3a is accepted Next, for market leverage, the estimation results using BML show that the average parameter of the variable DevLEV has a value of 0.0577, and the probability that this variable has a positive impact on the change in the leverage market is 100% This result implies that this baseline SOA, without considering the impact of SCF, is about 5.77% per year The positive sign of the mean parameter of the variable DevLEV implies that these companies have a faster SOA Therefore, there is evidence of a strong and positive relationship between SOA and the gap variable between the firm's target debt ratio and its actual debt ratio by market value Therefore, companies whose actual leverage is far from the target leverage (according to market value) will always want to reach the optimal level quickly, so the SOA will be larger These results are consistent with the findings of dynamic trade-off research in the capital structure literature (Ho et al., 2021; Mukherjee & Mahakud, 2010; Pan et al., 2023) 27 The results in this table also show that the average parameter of the variable SCFxDevLEV has a value of 0.0398, and the probability that this parameter has a positive effect on the change in book leverage is 100% This result shows that, compared to companies not committed to SCF, companies committed to SCF will have an SOA of capital structure at market value 69.08% faster per year (=0.0398/0.0577 =.6908) In other words, how long does it take, on average, for a firm with an SCF commitment to correct half the deviation between actual and target leverage (corresponding to the half-life of book leverage shocks)? Thus, SCF accelerates SOA of capital structure according to the market value of listed companies in the Vietnamese market This result shows that hypothesis 3b is accepted 4.3.4.2 Supply chain finance and the speed of adjustment of leverage for over- and under-leveraged firms The results show that companies with leverage above target will have a faster SOA than companies with leverage below target, with a difference of 0.61 percentage points These results show that SOA decreases faster than SOA increases, meaning that there exists an asymmetry in the SOA of capital structure in both measures of book leverage and market leverage In addition, this result also implies the impact of SCF on SOA, meaning that SCF can positively promote both upward and downward adjustments in the capital structure of listed companies in the Vietnamese market SCF causes firms with above-target leverage to have their SOA fall to the target level faster than firms with below-target leverage to have their SOA rise to the target level In addition, further support is provided for H4a,b, which states that SCF improves a firm's ability to adjust its capital structure to maximize profits and enhance competitiveness In other words, SCF can provide companies with options for faster upward adjustments in their capital structure In particular, SCF reduces SOA to the target leverage level more than increases SOA to the target leverage level The authors' results are consistent with the results of previous studies (Pan et al., 2023) Therefore, hypotheses H4a and H4b are both accepted 4.3.5 Trade credit and the adjustment speed of the target capital structure 28 4.3.5.1 Trade credit and the speed of adjustment of the target capital structure for the full sample First, for book leverage, the BML regression results show that the average parameter of the DevLEV variable has a value of 0.0129, and the probability that this variable has a positive effect on the change in book leverage is 94.51% This result implies that this baseline SOA, without considering the impact of TCH, is about 1.29% The positive sign of the mean parameter of the variable DevLEV implies that these companies have a faster SOA Therefore, there is evidence of a strong and positive relationship between SOA and the gap between the target debt ratio and the firm's actual debt ratio at book value Therefore, companies whose actual book leverage is far from the target book leverage will always want to reach the optimal level quickly, so the SOA will be larger These results are consistent with the findings of dynamic trade-off research in the capital structure literature (Ho et al., 2021; Mukherjee & Mahakud, 2010; Pan et al., 2023) The results in this table also show that the average parameter of the variable TCHxDevLEV (the interaction variable between TCH and deviation from the target leverage ratio) has a value of 0.0044 and a probability that this parameter has the product extreme to a change in book leverage of 69.85% Therefore, the author concludes that there is a positive relationship between TCH and SOA This result shows that compared to companies using low trade credit, companies in the group using high trade credit will have an SOA of the capital structure according to book value 34.39% faster (= 0.0044/0.0129) per year In other words, on average, it takes a firm in the high trade credit utilization group to correct half the deviation between actual and target leverage (corresponding to the halflife of its book leverage shock) takes about 1.6 years Thus, trade credit has accelerated the SOA of the book capital structure of listed companies in the Vietnamese market The author's findings imply that firms with high trade credit will have lower transaction costs, lower overall adjustment costs, and a highly leveraged SOA The results are consistent with the author's expectations and with findings from previous empirical research for the 29 Chinese market (Cao & Cui, 2021) Overall, this result supports the author's hypothesis that trade credit increases SOA leverage Therefore, hypothesis 5a is accepted Next, for market leverage, the estimation results using BML show that the average parameter of the variable DevLEV has a value of 0.0493 and the probability that this variable has a positive impact on the change in leverage Market leverage is 100% The positive sign of the mean parameter of the variable DevLEV implies that these companies have a faster SOA Therefore, there is evidence of a strong and positive relationship between SOA and the gap variable between the firm's target debt ratio and its actual debt ratio by market value Therefore, companies whose actual leverage is far from the target leverage (according to market value) will always want to reach the optimal level quickly, so the SOA will be larger These results are consistent with the findings of dynamic tradeoff research in the capital structure literature (Ho et al., 2021; Mukherjee & Mahakud, 2010; Pan et al., 2023) The results in this table also show that the average parameter of the variable TCHxDevLEV has a value of 0.0159, and the probability that this parameter has a positive effect on the change in book leverage is 100% This result shows that compared to companies in the high trade credit usage group, they will have SOA of capital structure according to market value 32.29% faster (=0.0159/0.0493) per year In other words, on average, it takes a company in the high-trade credit group to correct half the deviation between its actual and target leverage (corresponding to the half-life of its leverage) book leverage shock) takes about 1.8 years Thus, commercial credit accelerates SOA capital structure according to the market value of listed companies in the Vietnamese market This result shows that hypothesis 5b is accepted 4.3.5.2 Trade credit and the speed of adjustment of leverage for over- and underleveraged firms The results show that, compared to companies with a low amount of trade credit, companies in the high trade credit group will have SOA of their capital structure about 23%–52% faster per year In other words, how long does it take, on average, for a firm in 30 the highly commercial credit group to correct half the deviation between actual and target leverage? It takes about 0.9 to 2.6 years Thus, TCH accelerates the SOA of the capital structure of firms with leverage below the target level Summary of Chapter This chapter presents research results related to the size of SOA toward the optimal debt ratio, factors affecting the target debt ratio, and the impact of the COVID-19 pandemic, SCF, and trade credit on SOA toward target debt 31 CHAPTER 5: CONCLUSION AND RECOMMENDATIONS 5.1 Conclude This study uses a sample of 654 firms with a total of 6,575 firm-year observations over the period 2010–2021 The study uses an unbalanced panel data set of non-financial companies listed on HoSE and HNX The research results are presented briefly in Table 5.1 Table 5.1: Summary of Research Results Research hypothesis Content Results H1a Listed companies in Vietnam have target (book) leverage and will adjust the business's book leverage to the target (book) leverage Accept H1b Listed companies in Vietnam have target (market) leverage and will adjust the business's book leverage to the target (market) leverage Accept H2a The SOA of companies towards target (book) leverage before the COVID-19 pandemic crisis period will be slower than the SOA during the COVID-19 pandemic crisis period Accept H2b The SOA of companies towards target (market) leverage before the COVID-19 pandemic crisis period will be slower than the SOA during the COVID-19 pandemic crisis period Accept H3a Companies with a high commitment to SCF will have a faster process of adjusting their book leverage Accept H3b Companies with a high commitment to SCF will have a faster process of adjusting their market leverage Accept No 32 H4a SCF accelerates the process of adjusting the capital structure according to the book value of companies with leverage above the target level rather than companies with leverage below the target level Accept H4b SCF accelerates the process of adjusting the capital structure according to the market value of companies with leverage above the target level rather than companies with leverage below the target level Accept H5a Companies with a high amount of trade credit will have a faster process of adjusting their book leverage Accept 10 H5b Companies with a high amount of trade credit will have a faster process of adjusting their market leverage Accept 11 H6a For a firm with book leverage above the target, leverage SOA towards the target will be faster than for firms with low amounts of trade credit Accept H6b For a firm with market leverage above the target, leverage SOA towards the target will be faster than for firms with low amounts of trade credit Accept 12 Source: author's compilation 5.2 Policy implications 5.2.1 Policy implications related to the COVID-19 pandemic The results of the thesis provide further empirical evidence that COVID-19 will make the SOA of target debt levels for Vietnamese companies faster Therefore, financial managers need to prepare optimal financing solutions in case similar epidemic crises occur in the future 5.2.2 Policy implications related to supply chain finance The findings from this study further imply that to accelerate SOA to target levels, managers should leverage SCF to optimize capital structures After the COVID-19 pandemic, capital pressure is weighing heavily on the business community, leading to 33 declining sales, depleted capital, and long payment delays, "rupturing" business cash flow SCF is a useful tool to bridge the financing gap by advancing cash and closing the cycle of purchases and receivables At the same time, in order to limit negative impacts when supply chains are interrupted, businesses must identify risks in their supply chains and find ways to resolve them by diversifying sources or stockpiling important raw materials or items 5.2.3 Policy implications related to trade credit The results of the study also show that there is a positive relationship between trade credit and SOA leverage This shows that when businesses encounter difficulties, trade credit can help them adjust leverage according to their target capital structure The findings from this study further imply that to accelerate SOA toward its goals, managers should leverage trade credit to optimize capital structure and enhance firm value and competitiveness Therefore, businesses need to increase information transparency and implement the best corporate governance practices in developed countries For accounts payable, business managers should transparently use trade credit agreements with their suppliers to effectively manage costs associated with financing and also consider the commercial advantages of trade credit, such as the ability to evaluate the quality of goods before paying the supplier In addition, businesses also need to pay their bills on time, should not cut off suppliers without a valid reason, keep lines of communication open, and inspire trust with all business partners and suppliers 5.3 Limitations and suggestions for future research directions First, this study is based on a sample of 654 non-financial listed companies (HoSE and HNX) in Vietnam It is therefore likely that the financial decisions of companies in different industries may be influenced by a number of other factors To better understand the financing behavior of firms in Vietnam, it is important to consider firms in a specific 34 industry in upcoming studies on capital structure decisions and SOA to help clearly understand the dynamics of the capital structure of these companies Second, this study only focuses on examining the impact of the COVID-19 pandemic, SCF, and trade credit on the target capital structure SOA of companies in Vietnam Therefore, future studies may choose to add other factors, and this will be able to help better understand the capital structure adjustment behavior of Vietnamese companies and the factors involved in SOA Third: In this thesis, the author uses the cash-to-cash cycle (C2C) to measure SCF Collaboration of businesses in the chain and coordination of appropriate activities play a key role in the SCF management process, and the expected impacts are achieved through better capital allocation in the chain or through better management of financial flows Therefore, future studies may suggest using additional methods to measure SCF Fourth: As stated above, this study uses secondary data from non-financial companies listed in Vietnam To better understand the financing decisions, target debt levels, and SOA of companies in Vietnam, it is important to shift the focus from empirical research based on secondary data to research based on data Primary data (survey) to understand the behavior of corporate financial managers regarding capital structure decisions Summary of Chapter This chapter concludes this study by summarizing the main findings of the study on SOA in the period before and during the COVID-19 outbreak Also, the relationship between SCF and SOA, trade credit, and SOA was also found Implications for corporate finance managers are also presented Finally, this chapter continues to highlight the limitations of the study and presents recommendations for future research