Enterprise inefficient investment behavior analysis based on regression analysis

Wei Li; Yun Teng

doi:10.3934/dcdss.2019069

Article Contents

2019, Volume 12, Issue 4&5: 1015-1025. Doi: 10.3934/dcdss.2019069 open access

This issue Previous Article Uniform $L^1$ stability of the inelastic Boltzmann equation with large external force for hard potentials Next Article Error minimization with global optimization for difference of convex functions

Enterprise inefficient investment behavior analysis based on regression analysis

Wei Li^1, , and
Yun Teng^2,

1.
Dalian Maritime University, Dalian 116025, China
2.
Dalian Commodity Exchange, Dalian 116023, China

* Corresponding author: Wei Li
* Corresponding author: Wei Li

Received: August 31, 2017

Revised: December 31, 2017

Published: March 2019

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Abstract

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.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. Price of capital

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Figure 2. Marginal value of cash

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Figure 3. Marginal value of capital

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Figure 4. Cash to capital ratio

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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

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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***

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References

[1]	S. N. Awondo, E. G. Fonsah and D. J. Gray, Incorporating structure and stochasticity in muscadine grape enterprise budget and investment analysis, Horttechnology, 27 (2017), 212-222.
[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.
[3]	J. N. Cooper, D. L. Lodwick, B. Adler, C. Lee, P. 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.
[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.
[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.
[6]	C. Fumagalli, M. Motta and T. Ronde, Exclusive dealing: Investment promotion may facil-itate inefficient foreclosure, Journal of Industrial Economics, 60 (2012), 599-608.
[7]	O. Hart and L. Zingales, Liquidity and inefficient investment, Journal of the European Eco-nomic Association, 13 (2015), 737-769.
[8]	Z. G. He and P. Kondor, Inefficient investment waves, Econometrica, 84 (2016), 735-780. doi: 10.3982/ECTA11788.
[9]	C. W. Hsu, J. H. Wang, Y. 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.
[10]	X. Y. Ji, H. Ye, J. X. Zhou and W. L. Deng, Digital management technology and its appli-cation to investment casting enterprises, China Foundry, 13 (2016), 301-309.
[11]	S. W. Li, T. Hu, P. 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.
[12]	Y. Liu, C. 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.
[13]	M. Mainou, A. V. Madenidou, A. Liakos, P. Paschos, T. Karagiannis, E. Bekiari, E. Vlachaki, Z. Wang, M. H. Murad, S. 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.
[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.
[15]	R. Meister, A. Jansen, M. Harter, Y. 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.
[16]	A. Olaya-Abril, L. Parras-Alcantara, B. 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.
[17]	D. Phung, D. Connell, S. 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.
[18]	X. H. Qu, Z. J. Liu, Y. L. Wang, Y. Fang, M. 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.
[19]	R. Rajan, H. Servaes and L. Zingales, The cost of diversity: The diversification discount and inefficient investment, Journal of Finance, 55 (2000), 35-80.
[20]	G. A. Ribaroff, E. Wastnedge, A. J. Drake, R. 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.
[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.
[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.
[23]	S. Verma, M. E. Nongpiur, E. Atalay, X. Wei, R. Husain, D. Goh, S. 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.
[24]	A. Voss, How disagreement about social costs leads to inefficient energy-productivity investment, Environmental & Resource Economics, 60 (2015), 521-548.
[25]	T. M. Whited, Is it inefficient investment that causes the diversification discount?, Journal of Finance, 56 (2001), 1667-1691.
[26]	X. Ye, Y. M. Kang, B. 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.
[27]	B. Yildiz, J. 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.
[28]	R. S. Yoon, M. J. Gage, D. K. Galos, D. 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.
[29]	G. L. Yu, L. Zhu, Y. Li, J. G. Sun and L. L. Robison, Regression analysis of mixed panel count data with dependent terminal events, Statistics in Medicine, 36 (2017), 1669-1680.
[30]	Y. F. Zhang, M. Zhang, Y. Liu and R. Nie, Enterprise investment, Local Government Inter-Vention and Coal Overcapacity: The Case of China, Energy Policy, 101 (2017), 162-169.