Determinants of tobin’s q the case of listed construction and material companies in vietnam

Policies and Sustainable Economic Development | Determinants of Tobin’s q: The Case of Listed Construction and Material Companies in Vietnam VU THI LAN PHUONG ERP FPT Service Limited Company phuong.vulanphuong@gmail.com NGUYEN KIEU HUNG Joint Stock Commercial Bank for Investment and Development of Vietnam (BIDV) - hungnk16@gmail.com Abstract This paper aims to identify the determinants of Tobin’s q Exploring a sample of 152 construction and material companies listed on the Ho Chi Minh Stock Exchange (HOSE) and Ha Noi Stock Exchange (HNX), the study applies the method by James Tobin (1968) to evaluate Tobin’s q of the Vietnamese listed firms from 2013 to 2015 The multiple regression model is employed to estimate impacts of the main factors on Tobin’s q We find significantly positive effects of leverage and firm age on Tobin’s q, but a negative influence of firm size on this ratio The paper caters for the overall portrait of construction and material in Vietnam in order to clarify and reconcile the results Keywords: Tobin’s q; firm performance; construction and material sector Introduction Tobin’s q, which is a ratio devised by James Tobin in 1969, plays an important role in assessing the firm performance by specific periods Defined as the ratio of the market value of a firm to the replacement cost of asset, Tobin’s q shows the comparison between the cost to replace a firm’s asset and the value of its stocks Tobin’s q is compared to in order to assess the firm value When it is below 1, the firm value is undervalued denoting that the book value of the firm’s assets are higher than their expected market value and when Tobin’s q is higher than 1, the value of its assets are expected to be higher than book value (Tobin, 1969) Hence, by using Tobin’s q which comparing the book value and market value of companies, portraits of firms are more complete and support the decision making of investors or stakeholders Tobin’s q compensates weaknesses of other indicators measuring firm performance such as ROE and ROA which impossibly indicate the market value of firms, hence, the combination between Tobin’s q and other ratios provides more adequate information about firm value Therefore, it is likely to support investors and researchers to investigate firm performance, assessing firm value or predict the development of firms and sectors Additionally, the correlations between Tobin’s q and determinants are different in diversity of samples depending on specific firms, sectors, and economies in periods Such the importance suggests us to investigate determinants of Tobin’s q in specific cases in Vietnam Therefore, the main objective of this research is to investigate the determinants of q of listed construction and material companies in Vietnam We also perform the package data, methodologies to measure the value of q, as well as the correlation between q and its determinants including financial and non-financial ratios Furthermore, we cater for the prediction about development of construction and material sector in Vietnam after data analysis, reconciliation with other firm performance ratios and sector background, which plausibly contributes to support decision-making procedure of investors and stakeholders The remainder of the study is structured into five sections Section presents the literature review, followed by methodologies in section Section demonstrates results and discussion whilst section concludes the research Literature review A firm performance is derived from the value that firms create through their operation activities based on stakeholder perspective (Freeman, 1984) Additionally, Hartzell and Starks (2003) argue that institutional investors are likely to impact CEO behaviors and firm performance Ferreira and Matos (2008) also indicate that firms with higher levels of ownership by independent investors may have lower expenses and better governance, which is reflected on a better firm performance Therefore, firm performance has been examined for the consideration of investors, stakeholders, and researchers by virtue of several justifications Firstly, firm performance is affected by the financial circumstance, especially the financial distress that might harm the firm’s survival through cost increasing (Taffler, 1982) Recession possibly causes the weakness exposure of corporations leading to losses of profits and market share (Opler & Titman, 1994) Secondly, firm performance caters for the set of ratios to measure the “financial health” and corporate governance Most of investors have aspiration that effective management can support the firm to overcome the challenges, stabilize the growth, and assure the financial condition in period Thus, firm performance plausibly performs expectation from market perspective to the firms (Ehikioya, 2009) There are two classifications of firm performance, in which the first is accounting-based class, mostly depending on accounting data and being criticized because of backward-looking data and limited capacity of forecasting The second classification is the market-based measurement which Tobin’s q belongs to It is considered in various previous researches because of its merits For instance, market price indicates the aggregate expectation for the firm growth in the future (Stickney et al., 2007); operating cash flow per share ratio demonstrates cash covering capital expenditure and dividends for each share (Bernstein, 1993) To reconcile the advantages and disadvantages of two types of measure, AlMatari et al (2014) suggest that it is advised to use a combination measure of the firm performance including accounting and market-based measures The question on how to calculate the Tobin’s q is concerned by many scholars with the purpose to find out the most accurate and appropriate formula The earliest method raised by James Tobin and William Brainard was published in 1968 Tobin’s q was defined as the ratio with the numerator is the firm market valuation made up sum of the market value of equity and book value of liabilities while the denominator is the replacement or substitution cost, which is measured by total book value of equity and liabilities (Tobin, 1969) Lindenberg and Ross (1981) build a computation of q, also known as LR algorithm in order to measure Tobin’s q and to examine relationship between q and other indicators measuring monopoly power They divided firm assets into three components including plant and equipment, inventory, and other assets Although L-R algorithm was common estimation techniques for q, it was asserted to be too complex and cumbersome to conduct and combine with other ratios to make the financial decisions (Sang, 1998) However, it is evident that L-R algorithm has been admitted and adopted by researches of Tamrineia et al (2013), Pruitt et al (1994), and Lang et al (1989) On the other hand, the proposal of recursive model by Hall et al (1988) to calculate Tobin’s q was reputed to modify the drawbacks of L-R algorithm This method made the difference by using the age structure to adjust market value and replacement cost It is not only straightforward to measure but also convenient to access to the database However, the model by Hall et al (1988) takes a disadvantage that it might cause the upward bias by inflating the bond value estimation Due to the disadvantages mentioned above, this paper focuses on the simple calculation that was developed by James Tobin and used widely to determine the value of q: Tobin’s q= ������ ������ �����+����������� ������ ����� ������ ���� �����+����������� ���� ����� 620 | Policies and Sustainable Economic Development Various factors that can impact Tobin’s q are considered as follows: Leverage Leverage refers to the use of debt to acquire additional assets and is calculated by total liabilities to sum of equity and total liabilities (total assets) It can be simplified to state how much debt can be used to buy a currency unit of asset Therefore, the higher the financial leverage is the higher amount of debt will be spent on acquiring new assets Financial leverage is preferred when the benefits generated by debt are greater than the interest expense associated with the debt Many companies use financial leverage instead of acquiring more equity, which could reduce the earnings per share of existing shareholders However, in the financial distress which can trigger the sharp decreases in revenue, distribution, or market shares, the high financial leverage possibly causes the risks and burden to the firms due to the high interest Along with the risk, most of investors expect that corporations having better financial condition can take the larger market shares with appropriate strategy and overcome the difficulties (Opler & Titman, 1994) Therefore, does financial leverage not only have benefits generally but also has more crucial role in firm’s survival and development in recession Similarly, papers by Jensen (1986) and Bolton and Scharfstein (1990) propose that financial leverage can significantly affect firm performance because it can provide managers incentives to make better investment decisions, which indicates the positive correlation between debt ratio and firm performance However, studies by Sunder and Myers (1999) and Abbasali et al (2012) show that there is a strong negative and significant relationship between debt ratio and firm performance in Iran The similar results are demonstrated in research by Onaolapo and Kajola (2010) while they observe the data of 30 companies listed on Nigeria Stock Exchange Tangibility Tangibility is measured by the ratio of total tangible assets to total assets Campello (2007) argues that tangibility is used to identify a causal link between financing and firm performance Because of higher capacity for reselling and repossessing, the more tangible asset, the more valuable Gilson et al (1990) suggest that high tangibility makes creditors to choose asset liquidation over contract renegotiation when firms underperform and become distress Sunder and Myers (1999) demonstrate a significant positive relationship between tangibility and debt ratio and a negative relationship between debt ratio and firm performance; thus, it is likely to infer that tangibility and firm performance, proxied by Tobin’s q, have negative correlation However, Abbasali et al (2012) argue that tangibility and firm performance have a significantly positive correlation Growth rate Growth rate of firm is measured by percentage of change in sales Frank (1988) suggests that growth is a good indicator of the expectation of firm’s performance; hence, it implies a positive correlation between growth and firm’s survival as well as firm performance Besides, a research by Brush et al (2000) investigates the relationship between growth rate and firm performance that was under the hypothesis with and without free cash flow It is revealed that the firms that have strong governance with free cash flow that trigger the significantly positive relationship between sale growth and Tobin’s q Indeed, rarely the firms operate without free cash flow, especially in construction and material companies, thus their results are more cases Policies and Sustainable Economic Development |7 likely to be referred in many Firm size Firm size data is collected by total assets or the natural logarithm of assets The viewpoints of relationship between firm size and firm performance are various under many research aspects and authors’ hypotheses For instance, under the investigation of listed companies in Tehran Stock Exchange, Abbasali et al (2012) identify a significant and positive correlation between firm size and financial performance This can be explained by the expectation of researchers and investors that the larger firms are more likely to use flexible manufacturing systems and have more opportunities to access the public resources and bank loan than small ones Nevertheless, other scholars hold contrast results that there is no relationship between firm size and firm performance when investigating the information technology companies (Kalkan et al., 2011) Therefore, the relationship between firm size and firm performance should be researched and conclude by specific sectors Moreover, Nametag et al (2015) emphasize that firm size has an ambiguous effect on the firm performance It is argued that as the size increases, efficiency may be reduced due to the decreases of strategic management and operational activities, then causing the negative relationship In other cases, the size has a positive effect on firm performance as firms with more assets tend to raise their capital Age of firm There are many theoretical models presenting the same fundamental concept about relationship between firm age and firm performance and how firm age affects firm performance Abbasali et al (2012) indicate that age and Tobin’s q have a positive relationship However, the quantile regression analysis in studies by Reichstein et al (2010) and Serrasqueiro et al (2010) reveals that firm age has a significantly negative impact on firm growth and performance The contrast results can be explained by the changes in firm behaviors when they become older According to Berger and Udell (1998), when firms become more experienced, they are more likely to have access to public equity or long-term debt financing, which implies a positive correlation Likewise, Coad et al (2010) both demonstrate the positive relationship and clarify the deterioration of firm performance with age The latter is explained that younger firms are more successful at converting employment growth into growth of sales, profits, and productivity Furthermore, Loderer and Waelchli (2010) indicate that cost of goods sold and overhead expenses go up with age and growth slows down, which cause the negative correlation between age and firm performance To conclude, our study aims to examine the influences of financial leverage, tangibility, growth rate, firm size and firm age on Tobin’s q of listed construction and material companies in Vietnam Our research is expected to provide a deeper understanding of construction and material sector from 2013-2015 and explain its characteristics based on the empirical results as well as the previous researches 3.Methodology 3.1 Overview of the Construction and Material Sector in Vietnam Construction and material sector is field that has strong interaction and distinct characteristics 10 | Policies and Sustainable Economic Development Firstly, construction and material firms often have large scale of tangible asset such as plants, machines, and equipment Generally, the construction and material companies having good performance possibly improve revenue by their efficiency of using tangible assets The appropriate rate of asset tangibility is one of signs that can indicate the potential in firm performance and survival in each company (Le & Truong, 2007) In Vietnam, the ratio of fixed asset turnover (FATO) is widely used to measure the efficiency of tangible asset investment.) Secondly, firms in the construction and material sector in the period 2013-2015 had a significant financial leverage that is defined by the ratio of total liabilities to total assets This ratio, which fluctuated from 66 percent to 75 percent, indicates that the preferring debts can lead to high risks in companies It is pointed out that this ratio will cause risks if it is higher than 40 (Le & Truong, 2007) It will also refer to a hazard for firms if the ratio is over 70 percent Thirdly, a noticeable feature of the listed construction and material companies in Vietnam is that most of them have small size with the low capitalization which account for percent of the capitalization of the national market Among those listed in the stock markets, there are 20 biggest companies that make up 79 percent of total sector capitalization (Construction Sector Analysis of FPTS, 2015) Additionally, the level of capitalization of the Vietnam construction companies in Vietnam is low when they are compared to those in ASEAN This is regarded as a drawback of the sector in this period Fourthly, the construction and material sector follows to the economic cycle The period 2013- 2015 witnessed the post-crisis recovery of the economy by the growth rate increasing and stabilized inflation Although sales growth of the sector slightly increased in this period, it may suggest a potential growth in the future after the economic recession The summary of financial ratios of construction and material sector is described in Table Earnings per share (EPS) indicate the profit allocated to each common share - the higher EPS is likely to raise the stock price Although the negative value of EPS in 2013 indicates that owning stocks of construction and material companies in this period cannot bring profits to investors, this ratio increases annually to 110.37 percent in 2015 That performs the expectation of optimistic profitability and development in the future of sector Return on Assets (ROA) and Return on equity (ROE), which demonstrate the percentage of how profitable a company relates to its assets and equity correspondingly, have an upward tendency from 2013 to 2015 That means the management board of construction and material companies are more efficient in using its assets and equity to generate earnings Beside the profitability ratios, Table shows the financial conditions and efficiency of companies in construction and material sector in period 2013-2015 Fixed asset turnover (FATO) and tangibility are proportions which illustrate how likely company is to generate revenue from fixed asset investment and portion of tangible asset on total asset respectively FATO increases from 2.29 to 4.16 before falling to 2.6 in 2015, which indicates that the capacity of generating revenue from tangible asset in 2015 is lower than that in 2014, whilst the percentage of tangible asset rises from 14.83 percent to 24.54 percent Therefore, despite the boost of tangibility, the efficiency in using fixed assets to generate sales decreases sharply The ratio of debt to revenue in this period is higher than 1, which demonstrates that these companies depend on large amount of debt to generate revenue This illustration may be a sign of risk that the companies have been facing Table Financial ratios of Construction and material sector from 2013 to 2015 in Vietnam Year EPS ROA ROE FATO Tangibility Debt/ Revenue 2015 2014 110.37% 38.72% 2.44% 0.82% 11.83% 3.10% 2.60 4.16 24.54% 14.83% 1.14 1.19 2013 -26.86% -0.72% -2.22% 2.29 31.82% 1.33 Moreover, the structure of sector is indicated through the value of subsectors Civil construction and infrastructure construction make up with the largest proportion of 41.2 percent and 40.6 percent respectively whilst the industry construction accounts for the remainder with 18.2% The period 2013 – 2015 indicates the most significant portion of 43 percent of North in geographic structure followed by South and the Middle with 32.4 percent and 24.6 percent Additionally, private companies account for the largest percentage of more than 80 percent in sector structure and remain the upward tendency (see Figure 1) 100% 4.30% 4.40% 4.10% 3.60% 3.50% 4.10% 4.00% 72.90% 75.70% 79.90% 82.30% 83.50% 84.00% 20.20% 16.70% 14.20% 12.40% 12.00% 9.90% 2009 2010 2011 2012 2013 2014 6.50% 90% 80% 70% 60% 61.50% 50% 83.60% 40% 30% 20% 34.20% 22.70% 10% 0% 2007 2008 State enterprises Private enterprises Foreign - investment enterprises Figure Proportion of participants in construction and material sector from 2007 – 2014 Source: General Statistics Office of Vietnam Beside the characteristics, it can be seen the prospective of the construction and material industry through the impacts of government policies on civil construction, industrial construction and infrastructure construction Firstly, the Law of Housing passed in 2014 has permitted the foreigners who work in Viet Nam to have right to own their houses Furthermore, the government conducted both policies including consumer stimulus package and decreasing the basic rate of interest in order to encourage consumption and uplift the sale of construction and material sector Therefore, by the decisions and policies of the government in the period 2013-2015, the industry of civil construction has opportunities to improve their performance and market shares Secondly, the negotiations of six free trade agreements (FTAs), especially the success of Trans-Pacific Partnership Agreement (TPP), contribute to the development of this sector through the increase of Foreign Direct Investment (FDI) Investment for industrial construction accounts for 40 percent of total FDI capital structure, which is equivalent to – billion dollar per year Thirdly, the statistical data indicates that 40 percent of traffic infrastructure has poor standard of quality (Construction sector analysis of FPTS, 2015) Hence, the budget for upgrading may occupy 48 – 60 billion dollar till 2020, which leads to the potential development in the infrastructure construction 3.2 Data source The data set used in this paper is collected from websites cophieu68.com and vndirect.com.vn, which covers financial statements of 152 construction and material companies from 2013 to 2015 in Vietnam They are listed on Ho Chi Minh and Ha Noi Stock Exchange (HOSE and HNX correspondingly) To guarantee the accuracy and objectivity analysis, we apply some rules in data collection Firstly, we collected and cleaned up the data from audited financial statements For instance, data from financial statements is extracted from the audited balance sheets and income statements of firms, while other data about outstanding shares is collected from the audited financial statement reports Secondly, we only select stocks that have been traded at least one year to the beginning of the research period in order to assure that all the observations have values of firm age that are different from zero Additionally, we remove companies that have tangibility ratios approximate zero or their share prices are valueless 3.3 Method To identify the determinants of Tobin’s q, this research uses a multiple regression specified as follows: q = β0 + β1Leverageit + β3Tang1it + β4Tang2it + β5Git + β6Sizeit + β7Ageit +εit (i: entity and t: time) q: Tobin’s q (q) that is measured as: ������ ������ �����+����������� ������ ����� Tobin’s q= ������ ���� �����+����������� ���� ����� Due to the unavailability of data on Liabilities Market Value in Vietnam market, this value will be replaced by Liabilities Book Value that is equal to total liabilities in balance sheet (Lai & Vo, 2015) Equity Market Value is identified as share price multiplies by number of outstanding shares while Equity Book Value is owner’s equity in balance sheet Thus, the formula to determine Tobin’s q as follows: Tobin’s q= ������ ������ �����+����������� ���� � ���� ������ ���� �����+����������� ���� ����� Leverage: financial leverage information is collected from balance sheets of = total liabilities/ total asset This companies Tang1 = Tangibility*code Code is a dummy variable which takes the value of if a firm is listed in HNX, of otherwise Tang2 = Tangibility* Firm Age G = growth* Firm Age This variable is measured by the sales variation in current year compared to that in previous year and is collected from the Income statements in 2012, 2013, 2014 and 2015 Firm Size is measured by logarithm of total assets Firm Age is the number of years since a firm started to operate and was permitted in Operation Registration Certification to the year of research 4.Results and discussion 4.1 Descriptive statistics Table presents the summary of descriptive statistics of the listed companies in the period 2013- 2015 As seen in Table 2, the average value of Tobin’s q (q) is 0.93, which means that market value and performance was underestimated However, the values of its minimum and maximum are 0.46 and 2.61 correspondingly, which shows the fluctuation of q in three years Thus, the q in this period has had evident variability The statistical description of Leverage showing the minimum value of 0.059 and the maximum of 0.93 illustrates the variation of firms’ preferable level in using debts of construction and material companies There are various companies which prefer using debts to acquire assets; even there are firms to conduct approximately 93 percent of asset acquisition by using credit However, others which tend to restrict to use mortgage in asset investment will have lower financial leverage The input variables Tang1 and Tang2 are the multiplication of Tangibility with code and firm age of corporations respectively G is calculated by multiplying firm growth and firm age The large range of these variables shows the diversity of sample that covers the companies have both uptrend and downtrend in average sale and various firms that have high and low asset tangibility ratio in research period The descriptive statistics of size and the age demonstrate the variety of samples spreading small, medium, and large companies and various ages such as long-standing and new ones Table Summary of descriptive statistics Variable Mean q Leverage Tang1 0.9302 0.6129 0.1274 4.2470 Tang2 G Size 8,575.9250 3,910,725.0000 13.0722 Age 4.2 Standard Deviation Minimum Maximum 0.2537 0.1988 0.1668 0.4608 0.0589 0.0000 2.6125 0.9306 0.8371 5.3639 0.0000 39.4210 183,136.5000 22.8092 -130.1759 1.3368 10.2744 16.9489 13.6321 3.0000 59.0000 Correlation matrix Table presents the correlation matrix among the variables used for the multiple regression model to investigate the dependence between multiple variables As indicated in Table 3, Leverage and Tang1 have negative correlation with q while the remainders have positive relationship with Tobin’s q Notably, firm size has a significant relation with q at the 99 percent confidence level Moreover, the correlation coefficients of variables applied to the regression are at the value less than 0.8, which illustrates that the probability of getting multi-collinearity in the model is significant low Table Correlation matrix q Leverage Tang1 Tang2 G Size q 1.0000 Leverage Tang1 -0.0493 -0.0189 1.0000 -0.0368 1.0000 Tang2 0.0081 -0.0460 0.6227*** 1.0000 G 0.0092 0.0147 -0.0048 -0.0227 1.0000 -0.0181 0.1443*** -0.0259 1.0000 0.0836* 0.4822*** -0.0269 0.0896* Size Age 0.1382*** 0.4413*** 0.0461 0.0734 Age 1.0000 Note: ***, ** and * denote the significance at 99%, 95% and 90% confidence levels, respectively To provide better understanding on the relationship between Tobin’s q and other factors namely leverage, tangibility, growth, size and age of companies, this research applies econometric models to find out determinants 4.3 Multiple regression The regression result which illustrates the factors affecting Tobin’s q is shown in Table In the case of listed construction and material companies in Vietnam, the fixed-effects model (FEM) is applied after running the Hausman test (see Table in the Appendix) FEM assumes that the individual specific effect is correlated with the independent variable As indicated in Table 4, the predictor variable Leverage has a positive correlation with q, which is clarified that per unit of increasing in Leverage leads to an increase of approximate 0.654 of q Because Leverage is measured as total debt divided by total assets, the value of coefficient of Leverage demonstrates that those preferring asset acquisition by loan have higher value of q than the counterparts This result is consistent with previous researches that bank loans can have positive effect on firm performance (Degryse & Ongena, 2001; Slovin & Glascock, 1992) Table Results from fixed-effects panel regression: Determinants of Tobin’s q Variables Coefficients Leverage Tang1 0.6540*** 0.4858* Standard Errors 0.1514 0.2721 Tang2 -0.0196** 0.0096 -9 46.9×10-9 G 4.13×10 Size -0.1132*** 0.0420 Age 0.0498*** 0.0093 Intercept 0.8937 0.4922 Notes: The dependent variable is Tobin’s q (q) ***, ** and * denote the significance at 99%, 95% and 90% confidence levels, respectively Number of observations: 456 The coefficients of Tang1 and Tang2 indicate the significant effects of tangibility on Tobin’s q but in adverse directions Accordingly, Tang1 has a positive correlation with q, which implies that firms with both higher ratio of tangibility and trade in HNX have higher Tobin’s q of 48.57 percent than their counterparts The result is in line with a research by Abolfazl et al (2013) who indicate that tangibility is one of the factors with strong effect on Tobin’s q Firms having higher level of tangible assets are more likely to use debt and to affect the firm performance (Mackie & Mason, 1990) Tang2, which is measured as an interaction of tangibility and age of companies, has a negative correlation with q at the 95 percent confidence level The negative coefficient means that the companies which have higher tangibility and longer time of operation are more probably to have lower value of Tobin’s q than their counterparts Actually, the firms that have long-running activities plausibly slowly innovate and their manager boards impossibly control overall strategic and operating activities (Reichstein et al., 2010) Additionally, companies having high tangibility have high average debt ratios, which requires good manageable capacity in order to guarantee the satisfactory liquidity, operation, and profitability Hence, in old companies, the high asset tangibility is likely to cause the inefficient governance However, it is found that growth of firms have no significant impact on Tobin’s q Importantly, firm size has a negative influence on Tobin’s q, which demonstrates that a one- percent decrease of the total assets leads to 11.32 percent increase of Tobin’s q This result supports the finding by Rajput and Bharti (2015) that when the firm size increases, the board of managers has lower efficiency of operational and strategic management which is plausible to cause the lower value of Tobin’s q At the 99 percent confidence level, the positive coefficient of firm age implies that older companies have 4.98 percent higher Tobin’s q This result is consistent with previous papers by Lamont (1972) and Ronstadt (1988) who indicated that the more experience the companies accumulated the higher firm performance is In some cases of old firms, the profitability increases with age because their experiences and reputation support them to gain access to public equity or long-term debt financing (Berger & Udell, 1998) 5.Conclusion This paper investigates the effects of factors on Tobin’s q in the case of listed construction and material companies in Vietnam This research concludes that Tobin’s q in construction and material sector during the period of 2013-2015 is significant affected by financial leverage, tangibility asset, growth rate, firm size and age of companies By applying multiple regression model in dataset, it is shown the positive effects of leverage and age on q, and the negative correlation between firm size and Tobin’s q The interactions of tangibility and stock exchange code as well as firm age cause the positive and negative impacts on Tobin’s q correspondingly However, growth rate multiplied by age has an insignificant relationship with Tobin’s q Although the average value of Tobin’s q of construction and material sector in Vietnam from 2013 to 2015 is lower than which indicates the pessimistic development of sector, the Government’s policies will encourage and support the improvement of this sector Beside the financial performance, the current demand and growth tendency of construction and material sector are fit to development in the future The tendency and strength in effects of determinants are evident, but the results inferred from regression model have some inconsistencies with previous researches That can be explained by the specific characteristics of samples, companies, sectors, and economy in countries Additionally, this paper focuses on only listed companies in HOSE and HNX but disregard data of other construction and material firms Hence, it is necessary to have further researches determining the specific circumstances that leverage, tangibility, size and age of companies have positive or negative correlation with Tobin’s q Furthermore, the research data should cover all companies in construction and material sector broadly Appendix Table Hausman test (b) fe (B) re (b-B) Difference sqrt(diag(V_bV_B)) S.E Leverage 0.6540 -0.0101 0.6641 0.1231 Tang1 0.4857 -0.0196 0.36 74 -0.0136 0.2363 Tang2 0.118 -0.0062 G 4.13x10 Size -0.1131 -9 7.27x10 0.0262 -9 -3.14 x10 -0.1393 0.0082 -9 0.0393 Age 0.0498 0.002 0.04 0.0091 Notes: b = consistent under Ho and Ha; obtained from xtreg (fixed effects model) 75 B = inconsistent under Ha, efficient under Ho; obtained from xtreg (random effects model) Test: Ho: difference in coefficients not systematic chi2 = (b-B)'[(V_b-V_B)(-1)](bB) = 47.12 Prob > 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