CHAPTER 1: INTRODUCTION 1.1 Rationale Telecommunications has been an integral part of Vietnam’s development from resistance war against invaders, liberation of the country to the renovated era and until now Nowadays, the participation of many large telecommunications groups such as VNPT, Viettel, FPT… in telecommunications has contributed an outstanding development to this industry overall In 2016, Ministry of Information and Communications published a record of 365.500 billion VND (16.5 billion USD) in the revenue of telecommunications with 7.5% growth compared to 2015 This means that telecommunications contributed 50.396 billion VND to State Budget In 2017, the industry welcomed many opportunities including the Fourth Industrial Revolution for telecommunications companies benefiting numerous favorable conditions Nevertheless, these enterprises also face various challenges such as 4G network supply for users switching to a new provider without changing their phone number, market saturation… These obstacles require telecommunications companies to focus on resources’ investment for technological innovation as well as infrastructure to catch up with market changes The investment and development of network infrastructure also necessitate telecommunications firms to invest large amount of capital Concurrently, the payback speed mainly depends on each company’s ability as well as the rapid changes of telecommunication technology result in numerous risks faced by these companies during their operation In the current Vietnam Stock Exchange market, there is a total of 14 telecommunications companies listed on Hanoi Stock Exchange (HNX) and Ho Chi Minh Stock Exchange (HOSE) The scale of these firms is large, however compared to the number of unlisted telecommunications enterprises, the quantity is too small which leads to the inconsiderable competence in the market The thesis author estimated that during the period of 2010-2016, Return on sales (ROS) of listed telecommunications companies listed on Hanoi Stock Exchange was only – 0,007 due to in 2011 and 2012, this ratio was just – 0,097 and – 0,209 respectively Other years’ ratio was also lower than the mean of Information Technology companies Return on equity of telecommunications companies shared the similar situation The mean of ROE in this period was always lower than that of Information Technology firms Profitability of telecommunications compared with other industries was considerably lower and along with the interest rate at that period had created many difficulties for the industry’s companies attract investment capital to develop business As a result, telecommunications companies are forced to face many risks and notably, financial risk Financial risk can lead to various many financial consequences such as bankruptcy… Therefore, the author chose “Analysis of financial risk in telecommunications companies listed on Vietnam's stock market” as the topic for the doctoral thesis 1.2 Thesis objectives In order to support telecommunications companies in discovering solutions to prevent and reduce financial risk as well as improve business performance, the thesis concentrated on these following objectives: (1) Identifying financial risk in aspects such as debt structure, solvency, profitability, business performance, financial structure (2) Establishing the model on factors’ influence on financial risk (3) Measuring the degree and identify trends of factors’ influence on financial risk in telecommunications companies such as debt structure, solvency… (4) Proposing suitable solutions to control financial risk in telecommunications companies 1.3 Research questions To achieve all above objectives, the study focused on answering these following specific research questions: (1) What are primary characteristics of financial risk in telecommunications companies? (2) What are factors affecting financial risk in telecommunications companies and methods to measure them? (3) What are neccessary policies that should be applied to control financial risk in telecommunications companies? 1.4 Research subject and research scope - Research subject: The thesis studied financial risk in telecommunications companies listed on Vietnam's stock market - Research scope: + In terms of contents: The thesis focused on studying issues related to financial risk in telecommunications companies including (1) Introduction and overview of financial risk and financial risk analysis, (2) Research methods and models on financial risk analysis as well as research theories, (3) Findings and discussion of financial risk in telecommunications companies and (4) Conclusion and recommendations for telecommunications companies + In terms of spatial aspect: The study concentrated on telecommunications companies listed on Vietnam's stock market + In terms of temporal aspect: Data for the study’s analysis was collected from audited financial statements, annual reports and other reliable sources dated from 2010 to 2016 1.5 Research procedure of the thesis To carry out the thesis, the fellow followed these following stages: Overviewing: Identifying research questions; Establishing theoretical framework; Structuring the research; Collecting and analyzing data: Discussing research findings; Concluding and proposing solutions 1.6 Stucture of the thesis In addition to introduction, conclusion and recommendation, appendix, figures, references, the thesis is structured to five chapters as follows: Chapter 1: Introduction Chapter 2: Overview Chapter 3: Methodology Chapter 4: Research findings and discussion Chapter 5: Recommendations and conclusion Chapter 2: OVERVIEW 2.1 Studies on financial risk 2.1.1 Defintion of financial risk Taking financial risk’s definition and viewpoints from previous studies into account, the author proposed the definition of financial risk as unfavorable outcomes resulted from unexpected and negative circumstance Also taking other researchers’ viewpoint into consideration, the author defined financial risk is potential outcomes the company is likely to suffer due to the influence of internal and external factors on financial circumstance and may lead to the company’s bankruptcy 2.1.2 Classification of financial risk Brow (2015) in Financial Risk Management for Dummies classified financial risk into seven types including market risk, credit risk, operational risk, liquidity risk, funding risk, reputational risk and political risk Karen (2005) divided financial risks to seven types including interest risk, foreign exchange risk, commodity risk, credit risk, operational risk, equity price risk, and liquidity risk Financial risk, according to Nguyen Minh Kieu (2014), consists of credit risk, interest risk and exchange rate risk Vu Thi Hau (2003) claimed that financial risk is the possibility in which the company is insolvent with due debts due to the use of financial leverage Based on explanations by Trinh Thi Phan Lan (2016), financial risk is classified with two criteria When being considered in terms of its origin, financial risk includes internal risk and external risk Internal risk is the unusual status in the company’s financial activities affected by subjective factors such as the company’s qualifications, capital structure, profitability… External risk is the unusual status in the company’s financial activities affected by objective factors such as prices, inflaction, interest, foreign exchange rate Through detailed research of previous studies, the author suggested financial risk be divided into these following types which are solvency risk, profitability risk, operational risk, credit risk, interest risk, debt structure risk and financial structure risk 2.1.3 Factors influencing financial risk Based on many companies’ actual operation, it can be noted that there are numerous factors affecting financial risk including internal ones and external ones - Internal factors within the company: They are factors originating from within the firm, or subjective factors, which have considerable impacts on financial risk They contain risks such as leaders’ management qualifications; debt structure; financial structure; operational performance; profitability and solvency - External factors outside the company: They are factors originating from outside the company and the company absorbs their influence objectively They consist of risks affecting the company’s financial circumstance such as the natural environment, political environment, sociocultural environment, legal environment, financial economic environment (including interest, inflation, and foreign exchange rate), technological environment and industry environment It can be concluded that Financial risk analysis is the application of tools and appropriate analysis skills to identify, evaluate as well as measure and control financial risk And based on that deployment, recommendations are proposed to limit, discover and predict financial risk in companies 2.2 Theoretical frameworks of financial risk 2.2.1 M&M theory Modern capital structure theory dated back to 1958 was suggested by two researchers Franco Modigliani & Merton Miller (M&M theory) and eidted by later studies by Modigliani and Miller (1963), Miller (1977) They proved that the company’s value is not affected by capital structure In other words, how the company funds their activities does not impact their value And the correlation between Return on Equity (ROE) and financial leverage is positive Clauses of the theory is divided into two circumstances: the company pays corporate income tax or it does not When companies paying corporate income tax use large amount of capital, financial distress cost will increase Also financial distress cost has a positive correlation with debt ratio When financial distress cost outvalue the effectiveness of tax shield, the company value will decrease M&M theory stated that whether the firm pay corporate income tax or not, ROE still has a positive correlation with debt-to-equity ratio This is the cause of enterprises using more capital to increase the efficient of equity However, the debt abuse will decrease companies’ profitability and operational income Concurrently, firms are likely to be insolvent and easily exposed to bankruptcy and this increase financial risk This is also considered shortcomings of M&M theory and soon to be resolved in later theories related to capital structure 2.2.2 Static Trade-off Theory Trade-off theory by M&M claimed that companies trade tax benefits from debts for issues originated from the possibility of bankruptcy This means that benefits and cost of financial debts offer the optimal capital structure for a company In addition, it is necessary for financial debts’ positive and negative impacts to exist in order for business results to achieve Wu & Wang (2005) proposed static trade model, which states that companies balance their debts and equity via the trade-off between tax shield along with interest and bankruptcy cost of financial crisis Scott (2005) claimed that the increase in debts compared with equity enhances the financial position of the firm In that scenario, the tax was deducted from debts but not from equity Specifically, the theory stated that the value of a company with debts equals to the value of a company without debts added with tax shield’s current price and excluded from financial distress cost When debt ratio increases, benefits of tax shield as well as other financial cost will increase At one point, for every debt ratio that increases, benefits of tax shield will be smaller or equal to the value of other financial cost This leads to the company’s use of debts being ineffective, the company value decreases and financial risk increases 2.2.3 Pecking Order Theory The theory states that company managers decide how to finance company operations based on a hierarchy where they first use retained earnings (internal financing), then debt financing, then equity financing The two types of companies theorized are “good” ones and “bad” ones These two types of firms both want to increase their capital and their first choice is to issue stocks with suitable pricing “Bad” companies will assume they are “good” ones through issuing stocks as the first means of financing - a sign proves the company’s quality in the market However, “good” companies will use their hierarchical internal financing before stock issuance 2.2.4 Optimum Asset Structure Theory The theory claims that the firm achieves the optimal asset structure when ROA reaches its highest value Melnyk et al (1984) also modified and clearly stated that when the company does not focus on investing in fixed assets, low value of fixed assets can cause the production to not meet the demand This leads to the firm’s profits and ROA being negative (Figure 2.1) as the revenue cannot make up for incurred cost, especially fixed cost such as depreciation, insurance When the enterprise expands its production, invests more in fixed assets, the productivity will increase If the new productivity matches with the demand, operational performance will be optimal and ROA will increase to A point When ROA increases, operational performance and solvency will increase and financial risk will reduce However, the productivity exceeds the demand, ROA will decrease 2.3 Research on financial risk analysis 2.3.1 Research on identification and evaluation of financial risk Feng (2016) in his research on financial risk of listed manufacturing companies in China analyzed financial risk via its four types including investment risk, payback risk, income distribution risk and financial risk Defang & Murong (2005) defined financial risk from a narrow perspective They claimed that financial risk connects with debt scale and debt structure that the enterprise is responsible for They distinguished financial risk via leverage ratio and claimed that financial risk has a negative correlation with profitability and operational performance of the enterprise MacKay & Phillips (2005) and Vijitha Gunarathna (2016) shares similar opinions when they identified financial risk through leverage Brigham & Houston (2009) classified financial risk into seven types including interest risk, reinvestment rate risk, default risk, risk/reward ratio… in a clear manner Madura (2012) presented a five-part study with the aim to provide a comprehensive picture of international finance analysis and management of exchange rate risk Li Zhe et al (2012) recognized financial risk via five indicators which are solvency, managing qualification, developing qualification, profitability and paying ability Nevertheless, their research only applied for Lifeng Electricity Company from 2008 to 2009 Vu Thi Hau (2013), in her thesis, identified financial risk based on five-indicator group reflecting slovency consisting of feneral solvency, short-term solvency, fastsolvency quick, solvency for long-term debts and ability to pay off interest In addition, she mentioned the criteria group used to evaluate financial risk including debt structure, operational performance, asset and capital structure, profitability, financial leverage, variance and standard deviation Trinh Thi Phan Lan (2016) also shared the same viewpoint with Vu Thi Hau when distinguishing financial risk by financial leverage and liquidity risk The author mentioned other signs to identify financial risk such as exchange rate risk, interest rate risk, commercial credit risk, material price volatility risk and bankruptcy risk In addition, Nguyen Ngoc Quang (2002) collected data by investigating and carrying out surveys to propose directions and solutions to complete the system of financial analysis criteria in construction enterprises However, he only provided preliminary arguments on the influence of financial risk on profitability of firms and his recommendations had not yet to offer deep analysis of financial risk Pham Xuan Kien (2011) applied the qualitative method in his research His study analyzed and assessed the current state of financial analysis in Vietnamese transportation enterprises The research results have supplemented and completed the criteria, content and methods of financial analysis Financial risk analysis was also mentioned in the content and solutions to perfect the analysis were offered by the author Nguyen Ngoc Tra Vy & Nguyen Van Cong (2013) identified the risk of bankruptcy in pharmaceutical companies listed on Vietnam's stock market through the Z index ” determined via finance criteria They applied Altman's Z" model with support tools in statistics and Microsoft office software to calculate and display the results graphically The, two researchers analyzed and offered forecasts on bankruptcy risk as well as recommendations to minimize the risk of bankruptcy in pharmaceutical enterprises Lam Minh Chanh (2007) established the concept and calculation methods of the Z index for each company type according to Altman's model (2000) However, the author only studied signs to identify bankruptcy risk and based on that to rank the credit rating but not distinguish financial risk specifically Huynh Cat Tuong (2008) applied the Z index of Altman in the prediction of financial distress domestically and internationally through various aspects such as: law, economics, finance His thesis focused on forecasting financial distress at the macro level instead of concentrating on a specific type of enterprise Khong Thanh Hoa (2008) also applied results of Altman's study in identifying bankruptcy risk with a 70% of relevance The main reason for this percentage is that the author conducted the research only for a relatively short period of time, only in 2007 Additionally, in 2007, Vietnam's debt and bond markets were not developed, hence the results were not accurate Dao Thi Thanh Binh (2013) used the index to identify default risk and credit ranking for manufacturing companies in Vietnam The net profit criteria was collected in four quarters to determine the status of enterprises The results have an accuracy rate of 86%, but low applicability due to small sample size Yao et al (2003) estimated the probability of financial risk at different degrees when households carry out savings and investment Their research thesis used multivariate analysis to analyze households' acceptance of financial risk through periods of time Other factors that affect their finances are age, race and marital status However, the study's analysis of financial risk only remained at the household level Zhu (2007) with "Application of asymmetric Laplance laws in financial risk measures and time series analysis" offered traditional methods in financial risk analysis and time series analysis based on theories related to normal status The thesis mentioned VaR as one of the most important measures in modern financial risk management and proposed that financial risk include market risk, credit risk and operational risk Tran Duy Hien (2009) modelized raw data and analyzed financial risk His thesis' main contributions were presented as follows: (1) The research used the theory of continuous grids to provide a coherent and consistent framework for models of random data sets such as authentic random factor in probability theory, in which the distributions are characterized by Choquet formula functions (2) The thesis also focused on the statistical decision theory regarding risks in financial economics The thesis provided a general and consistent approach to the theory popularizing random dominance by developing approximations of utility functions as well as applying investigative methods for statistical inference (3) The study focused on statistical theory to consider risks in financial economics It used concepts of competency and Choquet integral to measure risks closely in investment decisions Then, the thesis offered statistical inference based on empirical data that can be appropriately developed, such as V-statistics based on non-parameter estimation of distribution functions However, the research only indicated statistical inference procedures but not investigate systematically as well as collect raw data describing V- (Von Mises) estimates Carr (2014) conducted a review of the evaluation: Studying factors that contribute to a comprehensive assessment of financial risk The study used quantitative methods to measure factors' influence on the target assets' distribution The thesis developed a comprehensive method of risk assessment to estimate an individual's overall risk as well as establish a assessment process for prudent risk and incorporate it into a model regarding target assets' distribution Nguyen Minh Kieu (2014) in "Financial risk management" (Risk Management) presented her primary views on financial risk The researcher proposes definitions of risks and financial risk which defines three types, which are credit risk, interest rate risk, and exchange rate risk 2.3.2 Research on measurement of financial risk Internationally and domestically, there have not been many research on financial risk analysis In order to establish a suitable research model and research methods to measure financial risk, the author had reviewed previous studies Typical risk models can be listed as follows: 2.3.2.1 Classic model He concluded that there are three key indicators in determining a company's financial crisis They are: total debt/total assets, net income/total assets, cash flow/total debt Beaver used simple indices and separate assessments in analysis so his research was applicable and time-saving for analysis However, this is also his drawback when Altman (1968) argued his views If a business has low profitability but still is able to cover its debts, it will be classified as an enterprise with potential bankruptcy Therefore, Altman proposed Z model that uses multiple disciminant analysis to provide a better prediction on bankruptcy - Altman’s bankruptcy forecast model: Z model was launched and has been applied in reality since 1968 with five combined indicators to identify bankruptcy risk and interval of public manufacturing companies (WC/TA) X1 = Working capital/Total assets (RE/TA) X2 = Retained earnings/Total assets X3 = Earnings before interest and taxes/Total assets (EBIT/TA) (MVE/TL) X4 = Market value of equity/Total liabilities X5 = Sales/ Total assets (S/TA) The author used statistical methods to analyze multi-dimensional differences of the indicators, and built the Z model as follows: Z = 1,2X1 + 1,4X2 + 3,3X3 + 0,6X4 + 0,999X5 Results showed that if 1.81 Fix model defects and provide theoptimal model If fixing is not possible, the model must be changed khác Select the optimal model Figure 4.18 Procedure of regression analysis Source: The author summarized based on research results 4.3.2 Analysis results After conducting stages in the analysis procedure in Section 4.3.1, the achieved results were presented as follows: - Test the standard distribution of dependent variables: The dependent variable does not have standard distribution, so this must be resolved to make the dependent variable have standard distribution 19 - Descriptive statistical analysis: Described statistical indicators include: Obs (Observation) - number of observations; Mean Std - Mean; Dev (Standard Deviation) Standard deviation; Min - Minimum value; Max - Maximum value - Correlation analysis: Results showed that: CR variable has a strong correlation with QR and IGS, correlation coefficients are: 0.94 and 0.77 times respectively The QR and IGS variables have a very large correlation coefficient being up to 0.72 times Therefore, the research model only chose one of the three variables which was CR , to represent solvency risk The TAT variable has a great correlation with the RT variable Hence, it is also not necessary to use both TAT and RT simultaneously as the large correlation will affect the research resultsl The author selected the RT variable for the study due to the characteristic of telecommunications companies that must provide the service first then collect money Therefore, receivable productivity is an important factor in the research model - Select the appropriate model: Through correlation analysis, the author removed some highly correlated variables such as QR, IGS and TAT The research model was left with 12 variables, including a dependent variable and 11 independent variables To select the right model, the author performed the following steps: + The results showed that the independent variables can explain 93.47 % of significance for the dependent variables Independent variables including DS, CR, ROS, ROA, IT, RT, ES, FAS, IR, AGE are statistically significant with the coefficient p> |z| ranging from 0,000 to 0.046 |z| ranging from 0.0000 to 0.046 ≤ 0.05 Selecting the appropriate model between FEM and REM showed that chi2 (12) = 309.36; Prob> chi2 = 0.0000 |z| ranging from 0.0000 to 0.028 ≤ 0.05 - Analysis results: Research results of the model are shown in Table 4.6 as follows: Table 4.6 Results of parameter estimation via using FEM model lnFRit Coef Robust Std Err P>z DS CR ROS ROA IT -0,3220862 0,326324 0,2632173 0,7759081 -0,0044114 0,2314884 0,0910536 0,0710555 0,4123442 0,0024977 0,1640 0,0000 0,0000 0,0600 0,0770 20 Figure 4.19 Trends of mean of Frit in telecommunications companies from 2010 to 2016 Source: The author processed via Stata14 4.4.2 Regarding factors’ influence - Firstly, variables reflecting financial structure including ES and FAS have a positive linear correlation with Frit The capital structure was represented via the ES variable It showes that when the self-financing factor increases once, Frit increases 277.63%, financial risk decreases and vice versa Asset structure has a positive correlation with Frit but it is not statistically significant - Secondly, interest rate variable is satistically significant and has a linear positive and negative correlation with FRir and financial risk respectively It is because telecommunications companies themselves had used very little amount of long-term debts to invest in business activities In addition, the State Bank raises interest rates leads to telecommunications companies being required to reduce external debts (both short-term and long-term debts) and mainly use equity to invest in business activities Especially in the period from 2010 to 2012 there are telecommunications companies that did not use long-term debts such as CMT, KST, SMT, and TIE - Thirdly, profitability variables include ROS and ROA are all related to Frit but they have different statistical siginificance ROS variable ROS has a positive correlation and is statistically significant with FRit In other hand, ROA variable has a negative correlationand is not statistically significant with FRit - Fourthly, short-term solvency variable - CR has a positive relationship with Frit but not with financial risk The more short-term solvency increase, the more financial risk will decrease - Fifthly, operational performance variables are not consistently related to FRit Variables such as IT, FAS are not statistically significant when estimating the parameters RT variable has a positive effect on FRit but negative on financial risk and is statistically significant This conclusion shows that telecommunications companies face high credit risk as customers cannot pay off their debts During, telecommunications companies' receivabless are mainly short-term - Sixthly, the AGE variable has a positive effect on FRit, but not on financial risk and is statistically significant - Seventhly, the SIZE variable has a negative correlation with FRit but is not statistically significant - Eighthly, the research have not found the connection between debt structure and FRit as the debt structure of telecommunications companies exists in an unbalanced state Chapter 5: CONCLUSION AND RECOMMENDATIONS 5.1 Conclusion 5.1.1 Research ideas The research idea derived from the overview of previous related studies The thesis' objective was to find solutions to prevent and limit financial risk in telecommunications companies To achieve that objective, the author carried out research on characteristics of financial risk and and analyzed it in the context of telecommunications enterprises.Next, the research identified the factors affecting financial risk in telecommunications companies and applied quantitative methods to verify factors that have a correlation with financial risk Finally, the author proposed solutions to prevent and limit inancial risk in telecommunications enterprises 5.1.2 Research findings By using FEM model on Stata14 software, the following conclusions can be drawn: - Regarding financial structure: Financial structure has a negative correlation with financial risk in telecommunications companies This conclusion is partly consistent with hypothesis H5 in the thesis The ES variable showed that when equity increased by one unit, value of Frit increased by 277.63%, financial risk in telecommunications companies will reduce and vice versa This conclusion is consistent with the M&M theory, static trade theory and pecking order theory FAS variable has a linearly neagtive correlation but is not statistically significant with FRit This conclusion is different from the theory of optimal asset structure - Regarding interest rate: IR variable is statistically significant and has a linear positive correlation with Frit but not with financial risk in telecommunications companies Research results not support the hypothesis H6 21 22 lnFRit Coef FAT RT ES FAS IR AGE SIZE _cons -0,0002034 0,0183816 2,776256 0,6342943 1,385981 0,0165634 -0,0166297 -0,9333735 Robust Std Err P>z 0,0001730 0,2400 0,0083771 0,0280 0,3533987 0,0000 0,3638673 0,0810 0,5526848 0,0120 0,0043594 0,0000 0,0113383 0,1420 0,3474447 0,0070 Source: The author processed via Stata14 Results of the FEM model estimating parameters of independent variablea that affect the dependent variable lnFrit are presented as follows: DS variable has a negative impact and is not statistically significant with lnFrit; CR variable has a positive and statistically significant correlation with lnFrit; Variables reflecting profitability have a heterogeneous impact on values representing financial risk: The ROS variable has a positive and statistically significant correlation with lnFrit; The ROA variable has a positive effect and is not statistically significant with lnFrit; Variables reflecting operational performance also have an heterogeneous impact on lnFrit: IT and FAT have a negative impact and are not tatistically significant, RT variable has a positive effect and are statistically significant; Variables reflecting the financial structure have different influence: The ES variable has a positive correlation and is statistically significant with lnFrit, the FAS variable has a positive correlation and is not statistically significant with lnFrit; The IR variable has a positive correlation and is statistically significant with lnFrit; The AGE variable has a positive correlation and is statistically significant with lnFrit; The SIZE variable has a negative correlation but is not statistically significant with lnFrit 4.4 Discussion of research findings 4.4.1 Regarding financial risk The fluctuation trend of the mean of Frit of telecommunications companies in the period of 2010 - 2016 is shown more clearly in Figure 4.19 Frit reached its highest mean in 2012 (3,664 times), then fluctuated around the mean in the whole period (3,024 times) and hit the bottom in 2015 (2,416 times) 4.000 3.000 2.000 1.000 0.000 2009 2010 2011 2012 2013 Frit bình quân 2014 2015 2016 2017 Bình qn tồn giai đoạn - Regarding profitability:The profitability of telecommunications companies has a negative relationship with financial risk, especially with ROS This is in accordance with hypothesis H3, as well as M&M theory and static trade theory - Solvency: Results demonstrated that when CR increases by one unit, the value of Frit increases by 32.63% Financial risk in telecommunications companies will decrease and vice versa Findings are similar with M&M theory Hypothesis H2 was approved - Regarding operational performance: The study concluded that financial risk in telecommunications companies does not have a correlation with inventory performance and fixed asset performance Particularly, RT variable has a negative impact on financial risk and is statistically significant This corresponds with hypothesis H4 - Regarding the company's years of operation: Findings illustrated that AGE variable has a positive impact on FRit, negative on financial risk and is statistically significant (p> |z| = 0,000) When AGE increases by one unit, the value of FRit will increase by 1.66% and financial risk will reduce Model results correspond with hypothesis H7 - Regarding the company size: SIZE variable have negative influence on FRit and is not statistically significant This does not support H8 hypothesis - Regarding debt structure: DS variable does not have any correlation with financial risk Therefore, hypothesis H1 was not approved and this does not correspond with M&M theory and static trade theory 5.1.3 New contributions of the thesis The research had provided new contributions for financial risk analysis in telecommunications companies which can be listed as follows: - In terms of analysis methods: the author used FRit model by Bathory to analyze financial risk - In terms of analytical models, in addition to the usual independent variables that foreign and domestic researchers used, the author also added financial and non-financial variables such as the company size, interest rates and years of operation - Regarding research results, some new findings were made and listed as follows: No correlation between ROA and Frit and RRTC was found; The relationship between RT, IR, AGE and RRTC was indentified and is statistically significant Previous studies were not able to confirm this correlation - Based on research findings, the author proposed solutions to complete the financial risk analysis in telecommunications companies To implement these solutions, the author offered recommendations to the State and governing bodies, exchanges and telecommunications enterprises 5.1.4 Shortcomings of the thesis The thesis faced the following limitations: survey sample; information source and application of non-financial variables 5.1.5 Directions for future studies Taken above-stated shortcomings, the author offered some directions for future studies as follows: (1) Build a financial risk analysis model for other types of enterprises in the economy (2) Develop a financial risk forecast model that can be applied to different types of enterprises and their business characteristics (3) Expand the system of criteria used to identify financial risk to ensure that enterprises in general and telecommunications companies in particular not miss any financial risk in their financial risk analysis process (4) Expand the study scope to review unlisted telecommunications enterprises, telecommunications enterprises trading on UPCOM This will increase the sample size and the observational value which leads to research results bein more accurate (5) Pilot testing the research model for financial risk in different areas with different operating conditions, then determine characteristics and the 23 popularity in order to increase the generality of the model (6) Add non-financial variables such as information access, prudent supervision, effectiveness of the industry, market conditions to increase the understandability and results of the research model 5.2 Recommendations 5.2.1 In terms of financial risk control Based on research findings, the thesis proposes the control measures to prevent and limit financial risk in telecommunications companies as follows: - Improve financial risk analysis: Financial risk analysis must follow a standard procedure of analysis including many steps such as identifying financial risk; measuring financial risk and control financial risk - Complete the process of identifying accurately financial risk: Based on research results, the author proposes a system of criteria to identify financial risk which can include financial structure, interest rates, solvency, profitability, operational performance Complete measurement and evaluation of financial risk: Improve solvency; profitability; operational performance; Ebstablish a reasonable capital structure; Build a reasonable asset structure; and Improve the qualifications of telecommunications companies' administrators - Enhance the process of controlling financial risk: 5.2.2 In terms of solutions to financial risk The author presented measures to solve differebr types of risks such as solvency risk; profitability risk; credit risk; capital structure risk and interest rate risk 5.2.3 Regarding conditions to implement financial risk control solutions in telecommunications enterprises listed on Vietnam's stock market 5.2.3.1 For the State and governing bodies The State and governing bodies should review and improve the legal system in order to create a healthy competitive environment between domestic telecommunications enterprises and international partners It is also neccessary to establish mechanisms to support telecommunications development in remote areas complete the legal framework and facilitate the development of the derivative financial market For the Ministry of Information and Communications, it is essential to quickly establish Vietnam Telecommunications Association 5.2.3.2 For Stock Exchanges In the process of collecting data and researching management functions of Stock Exchanges, the author found that in order to support telecommunications companies in reducing financial risk, stock exchanges should supplement more average financial indicators in the data system, focus on the function of inspection and supervision in the management of listed companies, publish standard criteria to classify enterprises on both exchanges 5.2.3.3 For telecommunications companies listed on Vietnam's stock market Telecommunications companies should enhance their knowledge of financial risk and financial risk management, financial accounting, perfect the management bodies, apply advanced technology in financial risk analysis, employ consultants as well as depploy longterm strategies and objectives in financial risk management 24 ... environment, political environment, sociocultural environment, legal environment, financial economic environment (including interest, inflation, and foreign exchange rate), technological environment... Analysis of profitability risk Profitability risks are assessed based on two indicators: return on assets (ROA) and return on sales (ROS), return on equity (ROE) (Table 4.3) The mean of ROA was lowest... technology group ROE and ROS shared the same situation with ROA Table 4.3 Mean of return of telecommunications companies from 2010 to 2016 Year 2010 2011 2012 2013 2014 2015 2016 Average ROA (times)