基于DEA-Tobit方法的人工智能行业上市公司融资效率研究
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摘要
人工智能产业发展对实现我国总体产业升级和供给侧结构性改革具有重要意义。该领域投资态势向好,但融资渠道较为混乱。本文结合我国37家人工智能产业上市公司2013～2016年的融资数据,首先采用了DEA方法对我国人工智能产业上市公司融资效率进行测度,同时采用Malmquist指数法从动态角度来反映人工智能产业上市公司融资变化,然后通过Tobit方法构建融资效率影响因素模型。从实证结果可以看出,人工智能产业融资效率不高,大多数企业并未达到DEA有效,综合技术效率的不高主要是由于规模效率较低引起的;资本结构、企业的营业能力和成长性与融资效率具有显著的相关性,是影响融资效率的重要因素。最后从提高融资资金利用率、优化融资结构两个方面提出了相关建议。
The development of artificial intelligence industry is of great significance to the realization of overall industrial upgrading and supply-side structural reform in China. The investment trend in this field is good,but the financing channels are more confused. Based on the financing data in 2013 ～ 2016 of 37 listed companies of artificial intelligence industry in China,we first use the DEA method to measure the financing efficiency of 37 listed companies of the artificial intelligence industry in China. At the same time,we use Malmquist index method to reflect the financing changes of the listed companies of artificial intelligence industry from the dynamic perspective,and then we construct the model of the influencing factors of the financing efficiency by Tobit method. From the empirical results,it can be seen that the financing efficiency of the artificial intelligence industry is not high,and the most of the companies are not effective in DEA. The low comprehensive technical efficiency is mainly due to the low scale efficiency. And the capital structure,the business ability and growth of the enterprise,which are the important factor that affects the financing efficiency,are remarkably related to the financing efficiency. Finally,some suggestions are put forward from two aspects: raising the utilization rate of financing funds and optimizing the financing structure.
The development of artificial intelligence industry is of great significance to the realization of overall industrial upgrading and supply-side structural reform in China. The investment trend in this field is good,but the financing channels are more confused. Based on the financing data in 2013 ～ 2016 of 37 listed companies of artificial intelligence industry in China,we first use the DEA method to measure the financing efficiency of 37 listed companies of the artificial intelligence industry in China. At the same time,we use Malmquist index method to reflect the financing changes of the listed companies of artificial intelligence industry from the dynamic perspective,and then we construct the model of the influencing factors of the financing efficiency by Tobit method. From the empirical results,it can be seen that the financing efficiency of the artificial intelligence industry is not high,and the most of the companies are not effective in DEA. The low comprehensive technical efficiency is mainly due to the low scale efficiency. And the capital structure,the business ability and growth of the enterprise,which are the important factor that affects the financing efficiency,are remarkably related to the financing efficiency. Finally,some suggestions are put forward from two aspects: raising the utilization rate of financing funds and optimizing the financing structure.
引文
[1]王玉荣.中国上市公司融资结构研究[D].对外经济贸易大学,2004.
[2] Romano C A,Tanewski G A,Smyrnios K X. Capital structure decision making:a model for family business[J]. Journal of Business Venturing,2001,16(3):285-310.
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[10]刘满凤,李圣宏.基于三阶段DEA模型的我国高新技术开发区创新效率研究[J].管理评论,2016,28(01):42-52.
[11]查勇,宋阿丽,杨宏林,梁樑.考虑决策者风险偏好的机会约束DEA模型[J].管理科学学报,2014,17(01):11-20.
[12]朱南,谭德彬.我国财务公司资金使用效率、动态变化及影响因素研究——基于DEA方法的实证分析[J].金融研究,2015,01:177-192.
[13]李春好,苏航,佟轶杰,孙永河.基于理想决策单元参照求解策略的DEA交叉效率评价模型[J].中国管理科学,2015,23(02):116-122.
[14] Charnes A,Cooper W W,Rhodes E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research,1978,2(6):429-444.
[15] Farrell M J. The measurement of productive efficiency[J]. Journal of the Royal Statistical Society,1957,120(3):253-290.
[16]王小波.全要素生产率的指数估计与分解[J].统计研究,1992,02:58-65.
[17] Malmquist S. Index numbers and difference surfaces[J]. Trabajos De Estadistica,1953,4(2):209-242.
[18] Caves D W,Christensen L R,Diewert W E. The economic theory of index numbers and the measurement of input, output, and productivity[J]. Econometrica,1982,50(6):1393-1414.
[19] Tobin J. Estimation of relationships for limited dependent variables[J]. Econometrica,1958,26(1):24-36.
[2] Romano C A,Tanewski G A,Smyrnios K X. Capital structure decision making:a model for family business[J]. Journal of Business Venturing,2001,16(3):285-310.
[3] Almeida H,Wolfenzon D. The effect of external finance on the equilibrium allocation of capital[J]. Journal of Financial Economics,2005,75(1):133-164.
[4] Eisdorfer A,Giaccotto C,White R. Capital structure,executive compensation,and investment efficiency[J].Journal of Banking&Finance,2013,37(2):549-562.
[5] Kaffash S, Marra M. Data envelopment analysis in financial services:a citations network analysis of banks,insurance companies and money market funds[J].Annals of Operations Research,2017. 1-38.
[6]陈燕武.基于复合DEA和Malmquist指数的科技投入产出效率评价[J].运筹与管理,2011,20(06):196-204.
[7]王赫一,张屹山.两阶段DEA前沿面投影问题研究——兼对我国上市银行运营绩效进行评价[J].中国管理科学,2012,20(02):114-120.
[8]董锋,刘晓燕,龙如银,周德群.基于三阶段DEA模型的我国碳排放效率分析[J].运筹与管理,2014,23(04):196-205.
[9]杨德权,薛云霞.基于交叉效率DEA和熵IAHP对物流企业绩效评价[J].运筹与管理,2015,24(03):172-178.
[10]刘满凤,李圣宏.基于三阶段DEA模型的我国高新技术开发区创新效率研究[J].管理评论,2016,28(01):42-52.
[11]查勇,宋阿丽,杨宏林,梁樑.考虑决策者风险偏好的机会约束DEA模型[J].管理科学学报,2014,17(01):11-20.
[12]朱南,谭德彬.我国财务公司资金使用效率、动态变化及影响因素研究——基于DEA方法的实证分析[J].金融研究,2015,01:177-192.
[13]李春好,苏航,佟轶杰,孙永河.基于理想决策单元参照求解策略的DEA交叉效率评价模型[J].中国管理科学,2015,23(02):116-122.
[14] Charnes A,Cooper W W,Rhodes E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research,1978,2(6):429-444.
[15] Farrell M J. The measurement of productive efficiency[J]. Journal of the Royal Statistical Society,1957,120(3):253-290.
[16]王小波.全要素生产率的指数估计与分解[J].统计研究,1992,02:58-65.
[17] Malmquist S. Index numbers and difference surfaces[J]. Trabajos De Estadistica,1953,4(2):209-242.
[18] Caves D W,Christensen L R,Diewert W E. The economic theory of index numbers and the measurement of input, output, and productivity[J]. Econometrica,1982,50(6):1393-1414.
[19] Tobin J. Estimation of relationships for limited dependent variables[J]. Econometrica,1958,26(1):24-36.