Static traffic reaches 5 - 6% of urban land area. Promote public transport, especially in Hanoi and Ho Chi Minh City, strive to meet 50% of travel demand.
Implement policies to create favorable conditions, especially in urban areas, industrial zones and disadvantaged rural areas, encourage housing development: ensure the average housing target of 8m2 of floor space/person by 2000, 10m2 of floor space/person by 2010, and 10-12m2 of floor space/person by 2020 according to the regional housing development program to provide housing types suitable for social groups with different needs and income levels. Build public service works, ensure 3-5m2 of land/person, meeting the material and spiritual needs of people in each urban area.
* Increase investment capital for construction
According to estimates, the capital investment demand for urban technical infrastructure development is on average about 2 billion VND to 5 billion VND per 1000 people, depending on the scale of the urban area. Based on the urban growth rate, from now until 2020, the average urban population will increase by about 1 million people per year. Thus, the annual capital investment demand must reach from 3000 billion VND to 5000 billion VND. Therefore, in the coming time, the policy of mobilizing domestic and foreign capital must pay more attention to capital sources from the private sector, international aid and foreign direct investment (FDI) through the forms of BOT, BT, BTO projects, using land funds to create capital for infrastructure construction, etc. At the same time, there must be measures to effectively use state budget capital, including foreign loans, to focus on building key urban infrastructure, especially infrastructure projects that are not for business purposes or are not capable of directly recovering capital.
* Increase scientific and technical capacity in construction
Self-construction of complex, high-tech works and complexes requiring modern equipment. Raising the level of local construction companies to be able to carry out complex, large-scale works. Construction enterprises
The central government has the qualifications to be a general contractor for construction projects, including the provision of construction materials and technological equipment.
Construction material production technology is at the world's advanced level. Production and manufacture of main construction materials for raw and finished construction works, product quality is competitive in the market and meets export standards. Construction mechanical industry meets the requirements of construction equipment, production, replacement and repair parts.
* Promote international economic integration in the field of construction and improve the quality of training and fostering of management, technical and worker staff.
Building an international economic integration strategy for service sectors related to the construction industry including: architectural services, construction engineering consulting services and synchronous construction engineering consulting, urban planning and landscape architecture services, construction services, real estate buying, selling and leasing services.
The Ministry of Construction is taking advantage of ODA aid from some countries such as France, Japan, Korea... to build vocational training centers, at the same time send staff to study at universities or post-graduate levels abroad, or create conditions for many scientific and technical staff to participate in classes, conferences, and related international forums.
The implementation of the above objectives creates many production and business opportunities for construction enterprises in general and listed construction joint stock companies in particular. At the same time, it creates favorable conditions in terms of capital, human resources, science and technology to enhance asset management at enterprises. Therefore, in the coming time, listed construction joint stock companies need to seize opportunities and increase the rate of return for shareholders of the enterprise.
4.2 Group of direct solutions to enhance asset management at listed construction joint stock companies in Vietnam
4.2.1 Assessing the impact of asset management on ROA, ROE and Z-score of listed construction joint stock companies in Vietnam
4.2.1.1 Testing the impact of asset management on ROA of listed construction joint stock companies in Vietnam
As assessed in chapter 3, currently, the assets of listed construction joint stock companies have not been strictly and scientifically managed, clearly demonstrated through indicators reflecting the results of cash management, receivables, inventories and fixed assets. Therefore, to strengthen asset management at these units, first of all, it is necessary to determine the specific impact level of the management results of each type of asset on the overall ROA, as a scientific basis for planning the company's asset management, as well as prioritizing decisions. To address this requirement, a verification model was built based on the 2010 financial data of 104 listed construction joint stock companies in Vietnam, following the following steps.
* State the hypothesis
First applied in 1920 by the DUPONT company, this is essentially a technique of decomposing a composite ratio into a series of causally related ratios, in order to explain the change of the composite index through its component indexes. Accordingly, the enterprise's return on assets (ROA) is analyzed into:
Profit after tax | Revenue | ||
ROA = | ------------------------- | x | ------------------------ (4.1) |
Revenue | Total assets |
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1
ROA = Revenue Profit X ---------------- (4.2)
Total Assets Revenue
Assuming that the four types of assets Cash, Receivables, Inventory and Fixed Assets account for an absolute proportion of the total assets at the enterprise, (4.3) will be equivalent to the following formula:
Business1 | ||
ROA = profit business | X | ---------------------------------------- (4.3) Cash + Receivables + Reserves + Fixed Assets |
collect | Revenue |
ROA =
Profit
1
X ------------------------------------------------- --------------- (4.4)
business1 | + | Period | + | 1 | + | 1 | |
collect | Cashflow | average revenue army | Product rotation inventory | Fixed Asset Turnover |
According to the above formula, Cash Turnover, Inventory and Fixed Assets positively affect ROA, Average Collection Period negatively affects.
So, the model hypothesis is set as follows:
ROA is influenced by the following factors: Cash Turnover, Average Collection Period, Inventory Turnover and Fixed Asset Utilization Efficiency (a group of indicators reflecting the efficiency of managing each type of asset of the enterprise). In addition, there is Revenue Profit, proven through the DUPONT model with a 1:1 relationship. The size of total assets is also added to the model for testing. In theory, companies with many assets will have an advantage in scale, so ROA can be larger than other businesses (the theory of economic efficiency according to scale of Alfred Marshall (1842 - 1924) ). According to theoretical analysis, the funding structure is one of the factors affecting asset management in construction enterprises. At the same time, officials at listed construction joint stock companies also confirmed the impact of this factor in practice, so the independent variable of debt ratio can be added.
* Determine the regression model
Based on the DUPONT model (by transforming the equation in chapter 1), it can be inferred that the relationship between the above factors and ROA is linear (except for Revenue Profit), so the regression equation has the following form:
ROA i = β 1 + β 2 X 2i + β 3 X 3i +β 4 X 4i + β 5 X 5i + β 6 X 6i + e i (4.5)
In which, the values X 2, X 3 … X 6 are respectively Debt ratio, Total asset size, Short-term payment capacity (replacing cash turnover), Average collection period, Inventory turnover and Fixed asset profitability ratio (replacing Fixed asset utilization efficiency because it is more statistically significant, consistent with the theory analyzed in the previous section). On the other hand, because the main objective of the study is to understand the relationship between asset management efficiency and ROA of the enterprise, the debt ratio and asset size are chosen as control variables. If the variables Short-term payment capacity, Average collection period, Inventory turnover, Fixed asset profitability ratio are added, the ability to explain ROA increases, the study is meaningful.
* Check the distribution of the dependent variable
Figure 4.1 Value distribution graph of ROA indicator in 2010
The Histogram graph shows that the data of the ROA variable in 2010 are all concentrated around the mean value = 4% with a low standard deviation of 0.034. Graph format
bell-shaped, quite symmetrical, so it can be affirmed that the dependent variable has a normal distribution, this is a basic condition that must be satisfied in a linear regression model.
* Estimating model parameters and testing hypotheses
The method used to estimate the parameters is “Least Squares”. Combined with T-test to test the model hypothesis (with 95% confidence interval).
Ho: β k = 0 (meaning X k has no relationship with ROA) H1: β k # 0 (meaning Xk has a relationship with ROA)
Because of the presence of control variables in the model, the estimation and testing procedures are divided into two stages. Stage 1 examines the ability to explain the variation of ROA through the control variables of debt ratio and Total assets. Stage 2 examines the ability to explain ROA after adding independent variables reflecting the management results of each type of asset.
Using SPSS software with a data set of 104 listed construction joint stock companies in 2010, the results of testing and estimating parameters in the model are as follows:
Model summary table
Model | R | R 2 | R 2 adjust | Estimated error | Statistical values change | |||||
R 2 replace change | F replace change | df1 | Df2 | Sig. F instead change | ||||||
Integer part = 0 | 1 | .586a | .34 | .33 | .02497 | .34 | 26.13 | 2 | 100 | .000 |
2 | .686b | .47 | .44 | .02288 | .13 | 5.79 | 4 | 96 | .000 | |
a. Independent variables: (Constant), total assets, debt ratio | ||||||||||
b. Independent variables: (Constant), total assets, debt ratio, Current solvency, Average Collection Period, Inventory Turnover and Return on Fixed Assets Ratio | ||||||||||
The correlation coefficient R = 0.586 shows a close relationship between the group of 2 control variables Total Assets and Debt Ratio with the enterprise's ROA. R 2 = 0.34 shows that Total Assets and Debt Ratio together explain 34% of the change in ROA, however
It is unclear how much each variable explains. The F value is large (26.13) and statistically significant (sig = 0.000).
After adding the variables Current Liability, Average Collection Period, Inventory Turnover, Fixed Asset Return on Assets, the values of R, R 2 and adjusted R 2 all increased. In which, adjusted R 2 = 0.44, meaning that when adding the above 4 variables, the entire model explained 44% (an increase of 10%) of the change in ROA. The change in the F index was significant (sig F change = 0.000), proving that adding variables reflecting asset management efficiency to the model was reasonable and necessary.
Estimated value table (a)
Model | Estimated value Not adjusted | Estimated adjust | T | Sig. | ||
Beta | Error | Beta | ||||
1 | (Constant) | 0.113 | 0.010 | 11,217 | 0.000 | |
Debt ratio | -0.103 | 0.015 | -0.577 | -7,065 | 0.000 | |
Total assets | -5.163E- 10 | 0.000 | -0.056 | -0.686 | 0.494 | |
2 | (Constant) | 0.098 | 0.015 | 6,548 | 0.000 | |
Debt ratio | -0.093 | 0.015 | -0.519 | -6,195 | 0.000 | |
Total assets | -3.645E- 10 | 0.000 | -0.040 | -0.528 | 0.599 | |
Payment ability short term | 0.010 | 0.005 | 0.171 | 1,950 | 0.054 | |
Collection period average | -7.242E-5 | 0.000 | -0.301 | -3,765 | 0.000 | |
Spin inventory | 0.002 | 0.001 | 0.256 | 3,233 | 0.002 | |
Profitability ratio Fixed assets | 8.415E-6 | 0.000 | 0.031 | 0.406 | 0.686 | |
a. Dependent variable: ROA | ||||||
From the table of parameter estimates, it can be seen that in both models (including only control variables and with all variables), there is not enough basis to confirm that the total asset size has an impact on the enterprise's ROA (because p is greater than 0.05). In addition, the parameter estimate corresponding to the variable Profitability of fixed assets is not statistically significant (if replaced by the variable Profitability of fixed assets, the result is still similar). The remaining parameter estimates are all significant, inferring that there is a relationship between Debt ratio, Short-term payment capacity, Average collection period, Inventory turnover with ROA in the actual operations of listed construction joint stock companies. The impact direction of these factors is consistent with the initial hypothesis.
The Tolerance values in the test are all greater than 0.17, indicating that there is no autocorrelation between variables affecting the beta parameter estimation results.
* Write the correlation regression equation
Based on the magnitude and sign of the adjusted beta in the Estimated Value table, the mathematical equation representing the dependence of ROE is written as follows:
ROE i = - 0.519*HSno i + 0.171*TTNH i – 0.301*KTTien i + 0.256*VquayHTK +e i
(4.6)
The meaning of this model: when fixing the remaining variables, if a variable changes by 1 unit, ROA will change on average by:
- Debt ratio increases by 1%, ROA decreases by 0.519% and vice versa.
- Short-term solvency increases by 1%, ROA increases by 0.171% and vice versa.
- Collection period increases by 1%, ROA decreases by 0.301% and vice versa.
- Inventory turnover increases by 1%, ROA increases by 0.256% and vice versa.
4.2.1.2 Testing the impact of asset management on ROE and bankruptcy risk of listed construction joint stock companies in Vietnam
The results of the above model testing show that each type of asset is managed closely and scientifically, while reducing the proportion of debt in total capital will have a positive impact on the ROA of listed construction joint stock companies in Vietnam. However, according to the assessment of interviewed managers (section 3.3.3.3), the shortcomings in funding policies and the incorrect perception of company leaders about the importance of