Dynamic Identification and Early Warning of Investment Inefficiency of Listed Companies in China--Based on the Perspective of Model Building and Upgrading
- https://doi.org/10.2991/msmi-18.2018.38How to use a DOI?
- Investment inefficiency, Identification; Early warning, The two-tier stochastic frontier model, China.
Investment is the driving force of economic growth, but inefficient investment behavior can be seen everywhere. How to identify the inefficient investment, and grasp the early warning technology for inefficient investment behavior is very practical significance. Based on the construction and upgrading of the two-tier stochastic frontier model to overcome the shortcomings of previous practices, we have carried on the thorough analysis to the Current topic, and has drawn the conclusion: (1) The result of model comparison before and after upgrade is that:Compared to the annual growth rate, using of Tobin’s Q to measure the investment opportunities of the enterprise is more valuable. (2) The result of identifying inefficient investment behavior is that: From a macro perspective, the overall performance of Listed Companies in China is lack of investment; but on the micro level, the investment offset caused by investment game is not balanced in different years. (3) The result of early-warning inefficient investment behavior is that:When exogenous variables are introduced into the upgraded model , the newly synthesized model is able to recognize the interference intensity of game factors; At the present stage of China, the improvement of the corporate debt ratio is not a magic weapon to restrict the management's wanton investment.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hong-Tao LIU PY - 2018/04 DA - 2018/04 TI - Dynamic Identification and Early Warning of Investment Inefficiency of Listed Companies in China--Based on the Perspective of Model Building and Upgrading BT - Proceedings of the 2018 5th International Conference on Management Science and Management Innovation (MSMI 2018) PB - Atlantis Press SP - 215 EP - 221 SN - 2352-5428 UR - https://doi.org/10.2991/msmi-18.2018.38 DO - https://doi.org/10.2991/msmi-18.2018.38 ID - LIU2018/04 ER -