An Empirical Study on the Impact of Operational Asset Quality on Firm Value Wen-yan DONG 1,a, Huan-huan HUO 1,b and Xin-zhong BAO 1,c,*

2017 4th International Conference on Economics and Management (ICEM 2017) ISBN: 978-1-60595-467-7 An Empirical Study on the Impact of Operational Asset Quality on Firm Value Wen-yan DONG 1,a, Huan-huan HUO 1,b and Xin-zhong BAO 1,c,* 1 School of Management, Beijing Union University, Beijing, China a 161120210301@buu.edu.cn, b 2420803390@qq.com, c xinzhongbao@vip.sina.com *Corresponding author Keywords: Operational Asset Quality, Enterprise Value, Panel Data. Abstract. Based on the financial data of new energy listed companies from 2011 to 2015, this paper studies the impact on the of the enterprise by using the panel data to have an empirical research, and constructs the regression model of Tobin s Q from four aspects the existence, the turnover, the -added and the realization. The results show that the characteristic attribute of operating assets has a significant effect on the enterprise of new energy listed companies. 1. Introduction The of the enterprise depends largely on the development of enterprises, and its development depends largely on the enterprise s the situation of core production and operation. The decisive factor that will influence the enterprise is much complicated, but the relevant situation of operating assets is an important index to evaluate the quality of production and operation. In this paper, we choose forty new energy listed companies as the research object, and study the relationship between operating assets and business from the quantitative aspect [1]. Based on the operational asset quality attributes and the reasonable index system used to measure the four characteristics attributes and enterprise that we established, we used the regression model established by qualitative attribute classification to verify the assumption of the impact of the operational quality of the new energy listed companies on the enterprise. 2. Research Design 2.1 Research hypothesis 2.1.1 The Assumption of the Existence Quality of Operating Assets The higher proportion of high-quality assets in operating assets, indicates that the operating assets of the financial assets can provide more reliable accounting information for the financial statements users, and also shows that the overall physical quality of the enterprise s operating assets is higher, this will give the enterprise the more lasting cash flow from operating activities [2]. Therefore, we come up with the hypothesis 1: the enterprise s badly managed asset rate is negatively related to its own. The core competitiveness of the new energy industry is patented technology, special equipment and so on which can bring sustainable technology to its development. The proportion of non-current operating assets can reflect the strength of the enterprises market competitiveness in some degree, and can also explain the production scale, production capacity. Therefore, we come up with the hypothesis 2: non-current operating assets ratio is positively related to the of the enterprise. Whether it is a new energy listed company, or other enterprise, its operating liquidity assets should be in a lower level on average for a period in order to improve its production and sales efficiency. Therefore, we come up with the hypothesis 3: the ratio of operating liquidity assets is negatively correlated with firm. Operating assets can maintain that their own does not fall and the physical quality does not be seriously damaged in a certain period of time, so it can provide lasting effective and effective for 700

the daily production and the management of enterprises. Therefore, we come up with the hypothesis 4: the hedge rate of operating assets is positively related to the enterprise. 2.1.2 The Assumption of Operating Assets Operational Quality In terms of operating assets, the higher the turnover rate is (especially the operating liquidity assets), the higher the using efficiency will be, and the higher turnover rate indicate that the operating assets have provide a more efficient work in the production and operation of enterprises, so as to accelerate the appreciation of enterprises [3]. Therefore, we come up with the hypothesis 5: operating asset turnover rate, commercial debt turnover rate, effective operating liquidity ratio, inventory turnover rate, and operating non-current assets turnover rate are all positively related to the enterprise. 2.1.3 The Assumption of the Value of the Valuation of Operating Assets The characteristic of the asset is to bring the profit for the enterprise, and the gross profit margin and the core profit rate reflect the profit level of enterprise s daily production and operation, which reflects the -added effect of the operating assets brought to the enterprise [4]. Therefore, we come up with the hypothesis 6: core profit margins are positively related to firm. At the same time, the return on operating assets represents the level of profit brought by the operating assets which enterprise holds. The higher the return on operating assets is, the higher the profitability of the enterprise relative to the operating assets will be. Therefore, we come up with the hypothesis 7: the return on operating assets is positively related to the enterprise. 2.1.4 The Assumption of the Presentation Property of Operating Assets Cash is king, this sentence fully reflects the importance of cash to the enterprise. Financial distress and corporate bankruptcy are both caused by the monetary funds shortage or insolvency [5]. Whether it is the core profit rate of change, or operating assets realized rate, the higher the is, the more corporate s cash inflow will be, so it can provides a sufficient sustained cash flow to enterprises future production and operation. Therefore, we come up with the hypothesis 8: core profit realized rate, operating assets realized rate and business are positively related. 2.2 Research variables and data sources 2.2.1 Variable Design The design of variables in this paper and their ings are shown in Table 1. Variable ing Explained variable (Corporation ) (The existence of operating assets) (Operating assets turnover) (Value-added of operating assets) (The realization of operating assets) (Control variable) Table 1. The variables in the model. Tobin s Q ratio Variable name Variable symbol TQ Predict the correlation Bad operating assets rate NPO Negative correlation Operating assets hedge rate OAH Positive correlation non-current Operating assets ratio UOA Positive correlation Operating liquidity ratio COA Negative correlation Commercial debt turnover rate BLT Positive correlation Inventory turnover rate ITR Positive correlation Effective operating current assets turnover rate VOC Positive correlation non-current Operating assets turnover rate FOA Positive correlation Operating assets turnover rate OAT Positive correlation Gross margin GPR Positive correlation Core profit margins CPR Positive correlation Return on operating assets OAP Positive correlation Core profit realized rate, CPL Positive correlation operating assets realized rate OAL Positive correlation Company Size CS Positive correlation Financial Leverage FL Positive correlation 701

This paper selected the annual financial statements data of 40 new energy listed companies from 2011 to 2015 as the research object. The data used in this paper is derived from the CSMAR series research databases. The data processing in this paper is done by using EVIEWS8.0 and Microsoft Office Excel2007 software. 3. Empirical Analysis and Interpretation of Results 3.1 Descriptive statistics We analysis the operating assets of the new energy listed companies from 2011 to 2015 in four aspects the existence, turnover, -added and realization, the results are shown in Table 2. Table 2. Statistical Analysis of Operational Asset Quality of New Energy Listed Companies. 2011 2012 2013 2014 2015 Standar d deviati on Standard deviatio n Standard deviatio n Standard deviatio n Standar d deviati on NPO 0.0069 0.0079 0.0063 0.0073 0.0078 0.0123 0.0084 0.011 0.0085 0.01 OAH 0.9947 0.0086 0.9947 0.0054 0.993 0.0118 0.9849 0.037 0.9803 0.05 UOA 0.5231 0.2256 0.5217 0.2398 0.5055 0.2209 0.5347 0.218 0.5413 0.22 COA 0.4769 0.2256 0.4783 0.2398 0.4945 0.2209 0.4653 0.218 0.4587 0.22 BLT 9.4787 12.178 165.78 983.53 1285.1 8064.2 1095.5 6857 917.44 5721 ITR 8.0235 11.337 16.118 56.267 233.59 1434.3 2308 14558 16.502 50.4 VCO 1.6323 1.1643 1.7914 1.2581 1.7965 1.5362 1.7406 1.387 1.9032 1.68 FOA 1.5805 1.4855 1.6858 1.478 1.69 1.5881 1.4339 1.618 1.4662 1.77 OAT 0.6031 0.4445 0.6531 0.4413 0.6745 0.5384 0.5996 0.487 0.6091 0.54 GPR 0.1786 0.1422 0.1944 0.1012 0.1752 0.141 0.1737 0.094 0.2 0.1 CPR 0.008 0.2582 0.0394 0.16 0.0165 0.1868-0.021 0.145 0.0088 0.15 OAP 0.0249 0.0567 0.0284 0.0465 0.0188 0.0548-0.007 0.062-0.002 0.08 CPL 51.071 317.92 1.4592 2.897-0.46 10.75 69.785 422.4 0.3079 3.64 OAL 0.0701 0.0608 0.0396 0.0578 0.0146 0.081 0.0407 0.065 0.0512 0.06 (1) The quality of the existence of operating assets. First of all, bad operating assets rate of the new energy listed companies on the average remained at about 0.758%, but there is a small upward trend, indicating that the overall size of the operating assets in the new energy industry had a slight increase, but did not deteriorate. Secondly, the operating assets hedge ratio is 98.82% on average, and the standard deviation is low, but the ratio decreased year by year indicating that the new energy listed companies can generally guarantee that the operating assets do not de, but the ability is subtle weakened. Finally, on the one hand, the operating liquidity assets and operating non-current assets are both evenly distributed. On the other hand, the new energy listed companies maintain a relatively high agreement on the liquidity management of operating assets as a whole. (2) Operating assets turnover quality. First, the average turnover rate of commercial claims of new energy listed companies is maintained at about 69465.68%, and the average number of commercial claims revolutions is maintained at 0.52 days. Second, the average inventory turnover rate is 51643.77%, and inventory turnover period is 0.71 days on average. Next, the average turnover rate of effective operating assets is about 177.28%, and the average turnover period is 205.89 days. The turnover rate is on the rise in general, and the standard deviation is relatively small, indicating that the new energy companies can effectively use the operating assets which play a key role in enterprises production and operation, and is expected to be able to maintain this ability in the future. Finally, the average turnover rate of operating assets is 62.79%, the overall trend is stable and the standard deviation is low, indicating that its operating asset turnover rate can represent most of the new energy listed companies, and the industry can effectively use the business assets to lay the foundation for production and sales. 702

(3) Value-added of operating assets. Firstly, the average gross margin is 18.44%, the overall trend is upward, and its standard deviation is small, indicating that the new energy industry can maintain and enhance their profitability in the future. Combined with the commercial debt turnover rate, and inventory turnover analysis, the majority of new energy listed companies improves their bargaining position in the industrial chain gradually, continuously improve the supply chain financing capacity, and maintain their own profitability. Secondly, the core profit margins have a certain fluctuation, indicating that it is difficult for the new energy listed companies to maintain a stable core profit level in an unstable economic environment. In the end, the average return on operating assets was around 1.27%. Which was similar to the core profit margin, the return on operating assets increased first and then increased, it was still negative at 2015, and its standard deviation was low, reflecting a decline on asset -added. (4) Liquidity of operating assets. The average realization rate of core profit of new energy listed companies is 2443.27%, the volatility is generally fluctuated, and the standard deviation is not stable, which indicates that the quality of the core profit is lower. The average realization rate of operating assets remained at around 4.32%. The realization rate decreased by 5% from 2011 to 2015, and rebounded to 5.12% in 2013, and the standard deviation was low, indicating that the new energy listed companies had the ability to operate assets into real estate funds in their urgent need for cash flow. NP O OA H UO A CO A BL T IT R VC O FO A OA T GP R CP R OA P CP L 3.2 Relevance test Table 3. Analysis of the correlation between independent variables. OAH UOA COA BLT ITR VCO FOA OAT GPR CPR OAP CPL OAL -0.05-0.31 0.313-0.01 0.439 0.134 0.335 0.407-0.04 0.017-0.05 0.023-0.13 0.531 0 0 0.919 0 0.058 0 0 0.569 0.816 0.454 0.751 0.068 0.065-0.07 0.034 0.023 0.116 0.047 0.058 0.137 0.214 0.65 0.049 0.06 0.361 0.361 0.637 0.747 0.102 0.506 0.411 0.053 0.002 0 0.487 0.4-1 0.072 0.027 0.574-0.56-0.25-0.13-0.1-0.12-0.08 0.341 0 0.314 0.7 0 0 0 0.063 0.182 0.094 0.29 0-0.07-0.03-0.57 0.564 0.245 0.132 0.095 0.119 0.075-0.34 0.314 0.7 0 0 0 0.063 0.182 0.094 0.29 0-0.01-0.03-0.09-0.09 0.116 0.041 0.01-0.01 0.032 0.887 0.634 0.193 0.197 0.103 0.564 0.889 0.915 0.653 0.219 0.105 0.255-0.1-0.01-0.02-0.01 0.06 0.002 0.139 0 0.141 0.945 0.747 0.856 0.395 0.179 0.52-0.1 0.171 0.173-0.03 0.346 0.011 0 0.143 0.016 0.014 0.72 0 0.841-0.06 0.183 0.35 0.018-0.02 0 0.379 0.01 0 0.801 0.774-0.16 0.186 0.285 0.015 0.108 0.023 0.008 0 0.832 0.128 0.777 0.516 0.1 0.245 0 0 0.158 0 0.637-0 0.327 0 0.955 0-0.02 0.3 0.777 0 0.064 0.366 According to the Table 3, it can be found that the correlation coefficient between the operating nonprofit asset rate and the operating liquidity ratio is -1, so there is a strong negative correlation between them in the confidence interval of 95%. The correlation coefficient between operating non-current asset turnover and operating asset turnover is 0.841, and there is a strong positive 703

correlation between the two operating factors in the confidence interval of 95%. The correlation coefficient between the gross profit margin and the core profit rate and the correlation coefficient between the core profit rate and the return on operating assets are both higher than 0.6, so they both had the strong positive correlation in the confidence interval of 95%. In addition to the above-mentioned variables with strong correlation, the highest absolute of the correlation coefficient between the other explanatory variables is 0.574, which is lower than 0.6, so there is no serious multiple collinearity. As a result of that we will use the quality of assets as a standard to establish a regression model subsequently, we remove the operating non-current assets, operating non-current assets turnover rate, and the core profit margin which had the high correlation coefficient and remain 11 independent variables for multiple regression. 3.3 Model establishment Test the basic panel data, choose the applicable regression model, and do the regression analysis. In this paper, we use the index system which measures the quality of operating assets as the classification standard, and the regression model is established to test the influence of different attributes of the asset quality on the enterprise [6]. The model 1 examines the impact of the existence quality of the existing assets of on the new energy listed companies. The model 2 examines the impact of the turnover of the operating assets on the new energy listed companies. Model 3 examines the impact of -added of the operating assets on the new energy listed companies, and model 4 examines the impact of operating assets of the realization quality on the new energy listed companies. Model 1: TQ it =β 0 +β 1 NPO+β 2 OAH+β 3 COA +β 4 CS +β 5 FL+α i +γ t +u it Model 2: TQ it =β 0 +β 1 BLT+β 2 ITR +β 3 VCO+β 4 OAT +β 5 CS +β 6 FL +α i +γ t +u it Model 3: TQ it =β 0 +β4 1 GPR +β 2 OAP +β 3 CS +β 4 FL+α i +γ t +u it Model 4: TQ it =β 0 +β 1 CPL +β 2 OAL+β 3 CS+β 4 FL+α i +γ t +u it TQ it represents the of the i-th company at year t; α i and γ t represent the unacceptable heterogeneity of the company s individual; U it is a time dummy variable that changes with time but does not change with the company s individual. 3.4 Regression analysis According to the model established above, the relevant financial indicators of the new energy listed companies are estimated, and we use the F test and the Hausman test to test the four models. The results both show that the fixed model is more effective in reflecting the relationship between variables than the hybrid effect model or the random effect model. The results of the regression are shown in Table 4. According to Table 4, in four models, when the significance level is 0.05, the fitting degrees between the independent variable and the dependent variable are all more than 0.7, and the four models all showed a strong regression fit effect. Operating assets turnover had the lowest fit degree with the Tobin s Q, and the of R 2 is 0.723186. Liquidity of operating assets had the highest fit degree with the Tobin s Q, and the of R 2 is 0.799457. Table 4. An Analysis of the Correlation between the Management Asset Quality and the Enterprise Value of New Energy Listed Companies. The quality of the existence of operating assets Operating assets turnover quality Value - added of operating assets Liquidity of operating assets Model 1 Model 2 Model 3 Model 4 F test and of P 12.37876 12.42703 12.48078 12.48078 (OLS or FE) [4] 0 0 0 0 Hausman test and of P 13.85275 10.193794 13.45852 13.79543 (FE or RE) -0.0031-0.017-0.0037-0.0032 704

Model choosing FE FE FE FE C [5] 1.829665 0.620939 1.3884 2.336728 0 0 0 0 NPO -4.84903 OAH 1.090499 COA -0.94833 BLT -4.48E-05 ITR -4.46E-05 VCO 0.222135 OAT 0.873418 GPR 1.546288 OAP 1.189036 CPL -0.00023 OAL 0.139376 CS 6.48E-12-2.79E-12 6.54E-12 6.54E-12 FL 0.192123 0.78569 0.01771-0.54774 R-squared 0.788213 0.723186 0.78958 0.799457 Adjusted R-squared 0.724538 0.649134 0.726316 0.739163 Durbin-Watson stat 2.008582 2.026706 2.002452 2.042783 The number of enterprises 40 40 40 40 Observations 2000 2000 2000 2000 3.5 Empirical analysis 3.5.1 Operational Asset Existence Model There is a significant negative correlation between bad operating asset rate and firm, so the hypothesis 1 is established. There is a significant positive correlation between operating asset and corporate Tobin s Q, this is in compliance with the hypothesis 2. There is a significant negative correlation between operating liquidity ratio and Tobin s Q, which s that there is a significant positive correlation between operating non-current asset ratio and firm, which is consistent with hypothesis 3 and hypothesis 4. The four assumptions on the existence of operating assets is established which show that for the new energy listed companies, maintaining the physical status of operating assets can help listed companies to maintain the current market of the enterprise, so as to further stabilize the market for corporate identity support. This also s that new energy companies can improve the physical quality of operating assets to enhance the overall of the enterprise. 3.5.2 Operational Asset Turnover Model There is a significant negative correlation between commercial debt turnover rate and Tobin s Q, and there is also a significant negative correlation between inventory turnover rate and Tobin s Q, but the negative correlations are both a little bit weak, this is not consistent with the hypothesis 5 which assumed that there is a positive correlation between commercial debt turnover rate and inventory turnover. And the effective operating liquidity ratio and the operating asset turnover rate separately had a significant positive correlation with the Tobin s Q, which is consistent with the hypothesis 5. In theory, because of the different credit policies and sales policies adopted by the different enterprises, there may be a negative correlation between the commercial debt turnover rate and the inventory turnover rate. And in the regression model, the commercial debt turnover rate and the inventory turnover rate also should have an opposite correlation coefficient with the enterprise Tobin s Q. But it still has the possibility that the commercial debt turnover rate and the inventory turnover rate are different from the assumptions 5, this situation can be caused by the method we used in processing the raw data. On the other word, we calculated the receivables and notes receivable average of data at the beginning and end of the year when we process the 705

original date of the receivables and notes receivable in the financial statements. It can also be caused by the new energy listed companies operating assets relative to the operating non-current assets, commercial claims and inventory does not have a promotion on the enterprises production and operation, that is to say the operating non-current assets is more important to the production and operation in new energy companies. 3.5.3 Value-added Model of Operating Assets The gross profit margin and the return on operating assets both had the significant positive correlations with the Tobin s Q. Because of the significant positive correlation between the core profit margins and the gross margin, there must be a significant positive correlation between the core profit margin and the Tobin s Q, which is consistent with the assumption 6 and 7. The two assumptions about the - added of operating assets show that the management of new energy listed companies can start from improving the profitability of enterprises and then leverage the operating assets as a lever to improve the corporate. 3.5.4 Operational Asset Realization Model There is a significant negative correlation between the core profit rate and Tobin s Q, but the negative correlation is weak, which is not consistent with the hypothesis 8. There is a significant positive correlation between the realized asset realization rate and the Tobin s Q, which is consistent with the hypothesis 8. The core profit realized rate had a negative correlation with the of the enterprise; this may be due to the poor liquidity of the administrative expenses, the finance charge and the selling expenses, at the same time, the three expenses and the business tax and surcharges accounted for a larger proportion. The positive correlation between the realized rate of operating assets and the of the enterprise shows that the new energy listed companies should focus on the asset management on the operating assets to increase the market of listed companies. 4. Conclusion Based on the financial data of new energy listed companies from 2011 to 2015, this paper study the impact on the of the enterprise by establishing the index system to measure the quality of operating assets and constructing the regression model of Tobin s Q from four aspects - the existence, the turnover, the -added and the realization. The results are as follows. Firstly, there are significant differences in the attribute indexes of the operational assets quality of the new energy listed companies. The new energy industry has the characteristics that the different enterprises had the different development levels of maturity within the industry, which determines that the operating assets of the quality indicators will be different, and the different quality characteristics have the significant different impact on corporate, especially the operating assets turnover efficiency and the liquidity capacity. Secondly, the operating assets turnover rate, the effective operating liquidity ratio, the operating assets return rate and the operating assets realized rate have a significant positive effect on the enterprise of the new energy listed companies. The bad management assets rate significantly restricts the new energy companies to enhance the of listed companies. Acknowledgements This research is partially supported by the National Social Science Foundation of China (no. 14BGL034), the Key Program of Beijing Social Science Foundation of China (no. 15JGA003) and College innovation ability promotion projects of Beijing municipal education commission (PXM2016_014209_000018_00202730_FCG). References [1] Qi Chen. A Study on the Evaluation and Growth of Management Assets of Listed Companies in 706

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