Study On Crisis Warning System Of Listed Companies From The Perspective Of The R&D Investment

In the recent years, the study on corporate crisis has gained more and more attention of scholars, and also obtained a wealthy of research results. At present, the guideline of China's economic development is "promoting the upgrading of industrial structure". It requires the enterprises to increase scientific and technological investment to cultivate their own R&D capabilities, and take the road of independent innovation. So, we focused on how the ability of science and technology investment affects the business performance and viability. At present, in the field of pre-warning of corporate crisis there are few people discuss the distinction between crisis and non-crisis enterprises, and the ability of the technological input ability to predict the crisis. Therefore, it's of great importance to forecast the failure of them for keeping away crisis and improving existing pre-warning model, to help market participants to prevent risks. At the same time, with the increasing standardization of the securities market and the standardization of information disclosure, it becomes possible to build a reasonable model.On the basis of reviewing the extensive literature about crisis warning model, this dissertation discussed this problem from both theoretical and empirical aspects. From the theory related to crisis warning, this dissertation discussed both the mechanism and precursors of failure, then built a pre-warning indicator system. In empirical studies, it selected 100 listed companies in China as samples. Combining with the impacts of technological innovation on the business performance and the relevant theories of financial analysis, this dissertation selected the scientific and technological input capacity, solvency, profitability, growth capacity and cash flow targets as indexes. Using paired T test, partial correlation analysis, factor analysis and Logistic regression, it built a crisis early-warning model of listed companies for this new situation. In this model, it contained debt factors, scientific and technological input factors, cash flow factors, profitability factor, growth factors and technology growth factor as independent variables, and its effective predictive ability was 76.6%, while the effective predictive ability was 70.5% if the R&D ability was not considered.