August & September  2019, 12(4&5): 1015-1025. doi: 10.3934/dcdss.2019069

Enterprise inefficient investment behavior analysis based on regression analysis

1. 

Dalian Maritime University, Dalian 116025, China

2. 

Dalian Commodity Exchange, Dalian 116023, China

* Corresponding author: Wei Li

Received  September 2017 Revised  January 2018 Published  November 2018

Inefficient investment will affect enterprise's survival and long-term development, and ultimately lead to the decline in corporate value. In order to promote the efficiency of the enterprise investment, in this paper, we aim to effectively analyze enterprise inefficient investment behavior, which has great significance in both enterprise management and social resources allocation. Firstly, we propose and analyze some typical enterprise investment theories, such as 1) MM enterprise investment theory, 2) Jorgensen investment theory, and 3) Tobin's q theory. Secondly, we propose a novel enterprise inefficient investment behavior analysis method based on regression analysis. Finally, to demonstrate the effectiveness of the proposed method, we conduct a series of experiments based on the CCER database. Experimental results show that the economy fluctuates across states due to the aggregate cash-flow shock driving the level of aggregate liquidity. Furthermore, we also can see that the particular sample path starts with a series of positive shocks, which can increase the capital value and decrease the cash value.

Citation: Wei Li, Yun Teng. Enterprise inefficient investment behavior analysis based on regression analysis. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1015-1025. doi: 10.3934/dcdss.2019069
References:
[1]

S. N. AwondoE. G. Fonsah and D. J. Gray, Incorporating structure and stochasticity in muscadine grape enterprise budget and investment analysis, Horttechnology, 27 (2017), 212-222.   Google Scholar

[2]

S. S. Chen and I. J. Chen, Ineficient investment and the diversification discount: evidence from corporate asset purchases, Journal of Business Finance & Accounting, 38 (2011), 887-891.   Google Scholar

[3]

J. N. CooperD. L. LodwickB. AdlerC. LeeP. C. Minneci and K. J. Deans, Patient characteristics associated with differences in radiation exposure from pediatric abdomen-pelvis CT scans: A quantile regression analysis, Computers in Biology and Medicine, 85 (2017), 7-12.   Google Scholar

[4]

J. J. Cordes, Using cost-benefit analysis and social return on investment to evaluate the impact of social enterprise: Promises, implementation, and limitations, Evaluation and Program Planning, 64 (2017), 98-104.   Google Scholar

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W. Dobson and s China, State-owned enterprises and canada s foreign direct investment policy, Canadian Public Policy-Analyse De Politiques, 43 (2017), S29-S44.   Google Scholar

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C. FumagalliM. Motta and T. Ronde, Exclusive dealing: Investment promotion may facil-itate inefficient foreclosure, Journal of Industrial Economics, 60 (2012), 599-608.   Google Scholar

[7]

O. Hart and L. Zingales, Liquidity and inefficient investment, Journal of the European Eco-nomic Association, 13 (2015), 737-769.   Google Scholar

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Z. G. He and P. Kondor, Inefficient investment waves, Econometrica, 84 (2016), 735-780.  doi: 10.3982/ECTA11788.  Google Scholar

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C. W. HsuJ. H. WangY. H. Kung and M. C. Chang, What is the predictor of surgical mor-tality in adult colorectal perforation?, The Clinical Characteristics and Results of a Multivariate Logistic Regression Analysis, Surgery Today, 47 (2017), 683-689.   Google Scholar

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X. Y. JiH. YeJ. X. Zhou and W. L. Deng, Digital management technology and its appli-cation to investment casting enterprises, China Foundry, 13 (2016), 301-309.   Google Scholar

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S. W. LiT. HuP. J. Wang and J. G. Sun, Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments, Computa-tional Statistics & Data Analysis, 110 (2017), 75-86.  doi: 10.1016/j.csda.2016.12.011.  Google Scholar

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Y. LiuC. McMahan and C. Gallagher, A general framework for the regression analysis of pooled biomarker assessments, Statistics in Medicine, 36 (2017), 2363-2377.  doi: 10.1002/sim.7291.  Google Scholar

[13]

M. MainouA. V. MadenidouA. LiakosP. PaschosT. KaragiannisE. BekiariE. VlachakiZ. WangM. H. MuradS. Kumar and A. Tsapas, Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma, A systematic review and meta-regression analysis, European Journal of Haematology, 98 (2017), 563-568.   Google Scholar

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[15]

R. MeisterA. JansenM. HarterY. Nestoriuc and L. Kriston, Placebo and nocebo reactions in randomized trials of pharmacological treatments for persistent depressive disorder, A Meta-Regression Analysis, Journal of Affective Disorders, 215 (2017), 288-298.   Google Scholar

[16]

A. Olaya-AbrilL. Parras-AlcantaraB. Lozano-Garcia and R. Obregon-Romero, Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis, Science of the Total Environment, 592 (2017), 134-143.   Google Scholar

[17]

D. PhungD. ConnellS. Rutherford and C. Chu, Cardiovascular risk from water arsenic exposure in Vietnam: Application of systematic review and meta-regression analysis in chemical health risk assessment, Chemosphere, 177 (2017), 167-175.   Google Scholar

[18]

X. H. QuZ. J. LiuY. L. WangY. FangM. Y. Du and H. He, Dentofacial traits in association with lower incisor alveolar cancellous bone thickness: A multiple regression analysis, Angle Orthodontist, 87 (2017), 409-415.   Google Scholar

[19]

R. RajanH. Servaes and L. Zingales, The cost of diversity: The diversification discount and inefficient investment, Journal of Finance, 55 (2000), 35-80.   Google Scholar

[20]

G. A. RibaroffE. WastnedgeA. J. DrakeR. M. Sharpe and T. J. G. Chambers, Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis, Obesity Reviews, 18 (2017), 673-686.   Google Scholar

[21]

D. S. Scharfstein and J. C. Stein, The dark side of internal capital markets: Divisional rent-seeking and inefficient investment, Journal of Finance, 55 (2000), 2537-2564.   Google Scholar

[22]

S. H. Seog and Y. S. Baik, Inefficient investment, information asymmetry, and competition for managers, Journal of Public Economic Theory, 14 (2012), 971-995.   Google Scholar

[23]

S. VermaM. E. NongpiurE. AtalayX. WeiR. HusainD. GohS. A. Perera and T. Aung, Visual field progression in patients with primary angle-closure glaucoma using pointwise linear regression analysis, Ophthalmology, 124 (2017), 1065-1071.   Google Scholar

[24]

A. Voss, How disagreement about social costs leads to inefficient energy-productivity investment, Environmental & Resource Economics, 60 (2015), 521-548.   Google Scholar

[25]

T. M. Whited, Is it inefficient investment that causes the diversification discount?, Journal of Finance, 56 (2001), 1667-1691.   Google Scholar

[26]

X. YeY. M. KangB. Zuo and K. Zhong, Study of factors affecting warm air spreading distance in impinging jet ventilation rooms using multiple regression analysis, Building and Environment, 120 (2017), 1-12.   Google Scholar

[27]

B. YildizJ. I. Bilbao and A. B. Sproul, A review and analysis of regression and machine learning models on commercial building electricity load forecasting, Renewable & Sustainable Energy Reviews, 73 (2017), 1104-1122.   Google Scholar

[28]

R. S. YoonM. J. GageD. K. GalosD. J. Donegan and F. A. Liporace, Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis, Injury-International Journal of the Care of the Injured, 48 (2017), 1165-1169.   Google Scholar

[29]

G. L. YuL. ZhuY. LiJ. G. Sun and L. L. Robison, Regression analysis of mixed panel count data with dependent terminal events, Statistics in Medicine, 36 (2017), 1669-1680.   Google Scholar

[30]

Y. F. ZhangM. ZhangY. Liu and R. Nie, Enterprise investment, Local Government Inter-Vention and Coal Overcapacity: The Case of China, Energy Policy, 101 (2017), 162-169.   Google Scholar

show all references

References:
[1]

S. N. AwondoE. G. Fonsah and D. J. Gray, Incorporating structure and stochasticity in muscadine grape enterprise budget and investment analysis, Horttechnology, 27 (2017), 212-222.   Google Scholar

[2]

S. S. Chen and I. J. Chen, Ineficient investment and the diversification discount: evidence from corporate asset purchases, Journal of Business Finance & Accounting, 38 (2011), 887-891.   Google Scholar

[3]

J. N. CooperD. L. LodwickB. AdlerC. LeeP. C. Minneci and K. J. Deans, Patient characteristics associated with differences in radiation exposure from pediatric abdomen-pelvis CT scans: A quantile regression analysis, Computers in Biology and Medicine, 85 (2017), 7-12.   Google Scholar

[4]

J. J. Cordes, Using cost-benefit analysis and social return on investment to evaluate the impact of social enterprise: Promises, implementation, and limitations, Evaluation and Program Planning, 64 (2017), 98-104.   Google Scholar

[5]

W. Dobson and s China, State-owned enterprises and canada s foreign direct investment policy, Canadian Public Policy-Analyse De Politiques, 43 (2017), S29-S44.   Google Scholar

[6]

C. FumagalliM. Motta and T. Ronde, Exclusive dealing: Investment promotion may facil-itate inefficient foreclosure, Journal of Industrial Economics, 60 (2012), 599-608.   Google Scholar

[7]

O. Hart and L. Zingales, Liquidity and inefficient investment, Journal of the European Eco-nomic Association, 13 (2015), 737-769.   Google Scholar

[8]

Z. G. He and P. Kondor, Inefficient investment waves, Econometrica, 84 (2016), 735-780.  doi: 10.3982/ECTA11788.  Google Scholar

[9]

C. W. HsuJ. H. WangY. H. Kung and M. C. Chang, What is the predictor of surgical mor-tality in adult colorectal perforation?, The Clinical Characteristics and Results of a Multivariate Logistic Regression Analysis, Surgery Today, 47 (2017), 683-689.   Google Scholar

[10]

X. Y. JiH. YeJ. X. Zhou and W. L. Deng, Digital management technology and its appli-cation to investment casting enterprises, China Foundry, 13 (2016), 301-309.   Google Scholar

[11]

S. W. LiT. HuP. J. Wang and J. G. Sun, Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments, Computa-tional Statistics & Data Analysis, 110 (2017), 75-86.  doi: 10.1016/j.csda.2016.12.011.  Google Scholar

[12]

Y. LiuC. McMahan and C. Gallagher, A general framework for the regression analysis of pooled biomarker assessments, Statistics in Medicine, 36 (2017), 2363-2377.  doi: 10.1002/sim.7291.  Google Scholar

[13]

M. MainouA. V. MadenidouA. LiakosP. PaschosT. KaragiannisE. BekiariE. VlachakiZ. WangM. H. MuradS. Kumar and A. Tsapas, Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma, A systematic review and meta-regression analysis, European Journal of Haematology, 98 (2017), 563-568.   Google Scholar

[14]

N. Matthews and S. Motta, Chinese state-owned enterprise investment in mekong Hy-dropower: Political and economic drivers and their implications across the water, Energy, Food Nexus, Water, 7 (2015), 6269-6284.   Google Scholar

[15]

R. MeisterA. JansenM. HarterY. Nestoriuc and L. Kriston, Placebo and nocebo reactions in randomized trials of pharmacological treatments for persistent depressive disorder, A Meta-Regression Analysis, Journal of Affective Disorders, 215 (2017), 288-298.   Google Scholar

[16]

A. Olaya-AbrilL. Parras-AlcantaraB. Lozano-Garcia and R. Obregon-Romero, Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis, Science of the Total Environment, 592 (2017), 134-143.   Google Scholar

[17]

D. PhungD. ConnellS. Rutherford and C. Chu, Cardiovascular risk from water arsenic exposure in Vietnam: Application of systematic review and meta-regression analysis in chemical health risk assessment, Chemosphere, 177 (2017), 167-175.   Google Scholar

[18]

X. H. QuZ. J. LiuY. L. WangY. FangM. Y. Du and H. He, Dentofacial traits in association with lower incisor alveolar cancellous bone thickness: A multiple regression analysis, Angle Orthodontist, 87 (2017), 409-415.   Google Scholar

[19]

R. RajanH. Servaes and L. Zingales, The cost of diversity: The diversification discount and inefficient investment, Journal of Finance, 55 (2000), 35-80.   Google Scholar

[20]

G. A. RibaroffE. WastnedgeA. J. DrakeR. M. Sharpe and T. J. G. Chambers, Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis, Obesity Reviews, 18 (2017), 673-686.   Google Scholar

[21]

D. S. Scharfstein and J. C. Stein, The dark side of internal capital markets: Divisional rent-seeking and inefficient investment, Journal of Finance, 55 (2000), 2537-2564.   Google Scholar

[22]

S. H. Seog and Y. S. Baik, Inefficient investment, information asymmetry, and competition for managers, Journal of Public Economic Theory, 14 (2012), 971-995.   Google Scholar

[23]

S. VermaM. E. NongpiurE. AtalayX. WeiR. HusainD. GohS. A. Perera and T. Aung, Visual field progression in patients with primary angle-closure glaucoma using pointwise linear regression analysis, Ophthalmology, 124 (2017), 1065-1071.   Google Scholar

[24]

A. Voss, How disagreement about social costs leads to inefficient energy-productivity investment, Environmental & Resource Economics, 60 (2015), 521-548.   Google Scholar

[25]

T. M. Whited, Is it inefficient investment that causes the diversification discount?, Journal of Finance, 56 (2001), 1667-1691.   Google Scholar

[26]

X. YeY. M. KangB. Zuo and K. Zhong, Study of factors affecting warm air spreading distance in impinging jet ventilation rooms using multiple regression analysis, Building and Environment, 120 (2017), 1-12.   Google Scholar

[27]

B. YildizJ. I. Bilbao and A. B. Sproul, A review and analysis of regression and machine learning models on commercial building electricity load forecasting, Renewable & Sustainable Energy Reviews, 73 (2017), 1104-1122.   Google Scholar

[28]

R. S. YoonM. J. GageD. K. GalosD. J. Donegan and F. A. Liporace, Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis, Injury-International Journal of the Care of the Injured, 48 (2017), 1165-1169.   Google Scholar

[29]

G. L. YuL. ZhuY. LiJ. G. Sun and L. L. Robison, Regression analysis of mixed panel count data with dependent terminal events, Statistics in Medicine, 36 (2017), 1669-1680.   Google Scholar

[30]

Y. F. ZhangM. ZhangY. Liu and R. Nie, Enterprise investment, Local Government Inter-Vention and Coal Overcapacity: The Case of China, Energy Policy, 101 (2017), 162-169.   Google Scholar

Figure 1.  Price of capital
Figure 2.  Marginal value of cash
Figure 3.  Marginal value of capital
Figure 4.  Cash to capital ratio
Table 1.  Descriptive statistic of different variabless
Variable Minimum Maximum Average Standard deviation
Age(t-1) 3 17 8.81 2.25
Size(t-1) 15.68 25.74 22.09 1.09
Growth(t-1) -1.17 185.21 0.485 5.17
Lev(t-1) 0.0077 54.27 0.963 2.08
TBQ(t-1) 0.0001 15.92 0.674 0.591
Cash(t-1) 0 0.854 0.257 0.126
Ret(t-1) -0.925 6.38 0.254 0.683
IVV1(t) 0 0.624 0.054 0.147
IVV1(t-1) 0 0.552 0.051 0.068
IVV2(t) -1.39 1.22 0.008 0.163
IVV2(t-1) -1.27 1.05 0.027 0.129
Variable Minimum Maximum Average Standard deviation
Age(t-1) 3 17 8.81 2.25
Size(t-1) 15.68 25.74 22.09 1.09
Growth(t-1) -1.17 185.21 0.485 5.17
Lev(t-1) 0.0077 54.27 0.963 2.08
TBQ(t-1) 0.0001 15.92 0.674 0.591
Cash(t-1) 0 0.854 0.257 0.126
Ret(t-1) -0.925 6.38 0.254 0.683
IVV1(t) 0 0.624 0.054 0.147
IVV1(t-1) 0 0.552 0.051 0.068
IVV2(t) -1.39 1.22 0.008 0.163
IVV2(t-1) -1.27 1.05 0.027 0.129
Table 2.  Regression results summarization
Variable IVV1(t) IVV1(t) IVV1(t) IVV2(t) IVV2(t)
Constant -0.072
(-2.134**)
-0.081
(-2.336**)
-0.080
(-2.317**)
-0.395
(-3.819**)
-0.386
(-3.742**)
$Age_{t-1} $ 6.954E-5
(0.115)
0.001
(0.782)
0.000
(0.659)
-0.001
(-0.581)
-0.021
(-0.883)
$Size_{t-1} $ 0.004
(0.115)
0.004
(0.782)
0.004
(0.659)
0.017
(-0.588)
0.017
(-0.883)
$Growth_{t-1} $ 0.000
(-0.415)
-9.78E-5
(-0.355)
0.000
(-0.372)
0.000
(0.463)
0.017
(-0.883)
$TBQ_{t-1} $ -0.011
(-1.968**)
-0.013
(-1.742**)
-0.014
(-1.696**)
-0.049
(-3.741)
-0.046
(-3.691)
$Lev_{t-1} $ 0.000
(-0.338)
0.000
(-0.416)
0.003
(1.524)
-0.002
(-0.957)
0.016
(-3.752***)
$Cash_{t-1} $ 0.052
(3.749***)
0.066
(4.125***)
0.064
(4.121***)
0.268
(3.654***)
0.165
(3.627***)
$Ret_{t-1} $ 0.005
(2.025***)
0.008
(1.028)
0.003
(1.114)
0.028
(3.457***)
0.028
(3.364***)
$IVV1_{t-1} $ 0.475
(19.965***)
0.472
(18.527***)
0.453
(18.508***)
$IVV2_{t-1} $ 0.136
(3.652***)
0.135
(3.827***)
$Adj-R2$ 0.258 0.274 0.283 0.097 0.106
$F\;value$ 73.85*** 27.71*** 28.54*** 7.96*** 8.72***
Variable IVV1(t) IVV1(t) IVV1(t) IVV2(t) IVV2(t)
Constant -0.072
(-2.134**)
-0.081
(-2.336**)
-0.080
(-2.317**)
-0.395
(-3.819**)
-0.386
(-3.742**)
$Age_{t-1} $ 6.954E-5
(0.115)
0.001
(0.782)
0.000
(0.659)
-0.001
(-0.581)
-0.021
(-0.883)
$Size_{t-1} $ 0.004
(0.115)
0.004
(0.782)
0.004
(0.659)
0.017
(-0.588)
0.017
(-0.883)
$Growth_{t-1} $ 0.000
(-0.415)
-9.78E-5
(-0.355)
0.000
(-0.372)
0.000
(0.463)
0.017
(-0.883)
$TBQ_{t-1} $ -0.011
(-1.968**)
-0.013
(-1.742**)
-0.014
(-1.696**)
-0.049
(-3.741)
-0.046
(-3.691)
$Lev_{t-1} $ 0.000
(-0.338)
0.000
(-0.416)
0.003
(1.524)
-0.002
(-0.957)
0.016
(-3.752***)
$Cash_{t-1} $ 0.052
(3.749***)
0.066
(4.125***)
0.064
(4.121***)
0.268
(3.654***)
0.165
(3.627***)
$Ret_{t-1} $ 0.005
(2.025***)
0.008
(1.028)
0.003
(1.114)
0.028
(3.457***)
0.028
(3.364***)
$IVV1_{t-1} $ 0.475
(19.965***)
0.472
(18.527***)
0.453
(18.508***)
$IVV2_{t-1} $ 0.136
(3.652***)
0.135
(3.827***)
$Adj-R2$ 0.258 0.274 0.283 0.097 0.106
$F\;value$ 73.85*** 27.71*** 28.54*** 7.96*** 8.72***
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