Empirical analysis of corporate innovation,investor focus and stock slumps risk based on fuzzy mathematics and function optimization

Abstract

Support vector machine need to choose kernel function according to data distribution characteristics, while iterative algorithm of function parameter optimization can effectively improve the validity of data analysis. Using a sample of A-share listed firms in China from 2007–2017, this paper discusses the impact of corporate innovation on stock price crash risk and the moderating effect of investors’ attention on the relationship between the two under the triple effect of enhancing confidence, interpreting information and releasing panic. The results show that: (1) the innovation output is negatively correlated with the stock price crash risk, and the inhibition of substantive innovation is more significant than that of strategic innovation; Investors’ focus mainly has the effect of enhancing investor confidence, thus strengthening the negative correlation between the two. (2) R&D is positively correlated with the stock price crash risk. Investors’ focus helps to alleviate the information asymmetry and play the effect of information interpretation, thus weakening the positive correlation between the two; (3) the capitalization of development expenses in R&D has not really promoted the enterprise value but has become a means for insider to reduce their holdings and cash. The stock price crash risk is aggravated by the widespread sell-off caused by panic reaction of investors. The research in this paper has certain theoretical and practical significance to improve the substantial innovation of corporates, restrain insider trading behavior, protect the interests of investors and then maintain the stable development of capital market.

Keywords

Fuzzy mathematics stock price crash risk corporate innovation investor focus

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References

Aboody and

Lev , Information asymmetry, R&D, and insider gains, The Journal of Finance55(6), 2000, 2747–2766.

Aghion ,

Bloom ,

Blundell , et al., Competition and innovation: An inverted u relationship, Quarterly Journal of Economics120(2) (2005), 701–728.

B.M.

Barber and

Odean , All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors, Review of Financial Studies21 (2008), 758–818.

Basu , The conservatism principle and the asymmetric timeliness of earnings, Journal of Accounting & Economics24(1) (1997), 3–37.

Basu , Conservatism research: Historical development and future prospects, China Journal of Accounting Research2(1) (2009), 1–20.

P.C.

Godfrey ,

C.B.

Merrill and

J.M.

Hansen , The relationship between corporate social responsibility and shareholder value: An empirical test of the risk management hypothesis, Strategic Management Journal30(4) (2009), 425–445.

P.C.

Godfrey , The relationship between corporate philanthropy and shareholder wealth: A risk management perspective, Academy of Management Review30(4) (2005), 777–798.

Hirshleifer ,

Low and

S.H.

Teoh , Are overconfident CEOs better innovators, The Journal of Finance7(4) (2012), 1451–1498.

Holmstrom , Agency cost and innovation, Journal of Economic Behaviar and Organization (12) (1989), 305–327.

10.

A.P.

Hutton ,

A.J.

Marcus and

Tehranian , Opaque financial reports, R2 and crash risk, Journal of Financial Economics94(1) (2006), 67–86.

11.

Jiang ,

Cai and

Zhu , Multiple major shareholders and stock price crash risk, Accounting Research (01) (2018), 68–74.

12.

Jin and

S.C.

Myers , R2 around the world: New theory and new test[J], Journal of Financial Economics9(2) (2006), 257–292.

13.

J.B.

Kim and

Zhang , Financial reporting opacity and expected crash risk: Evidence from implied volatility smirks, Contemporary Accounting Research31(3) (2014), 851–875.

14.

J.B.

Kim ,

Li and

Zhang , Corporate tax avoidance and stock price crash risk: Firmlevel analysis, Journal of Financial Economics100(3) (2011a), 639–662.

15.

J.B.

Kim ,

Li and

Zhang , CFOs versus CEOs: Equity incentives and crashes, Journal of Financial Economics101(3) (2011b), 713–730.

16.

Kim and

Zhang , Accounting conservatism and stock price crash risk: Firm-level evidence, Contemporary Accounting Research33(1) (2016), 412–441.

17.

Luo and

Du , Media reports, institutional environment and stock price crash risk, Accounting Research2014(09), 53–59+97.

18.

Pan ,

Dai and

Lin , Information opacity, analyst concern and stock collapse risk, Financial Research2011(09), 138–151.

19.

Tian and

Wang , Tolerance for failure and corporate innovation, Review of Financial Studies27(1) (2014), 211–255.

20.

Wang ,

Cao and

Ye , Supervision or tunneling: The shareholding ratio of major shareholders and the risk of stock price crash, Management World (02) (2015), 45–57.

21.

Xiao and

Song , Information disclosure quality and stock price crash risk: The intermediary role of analyst prediction, Financial Research (02) (2017), 110–121.

22.

Xu ,

Li ,

Yuan and

K.C.

Chan , Excess Perks and Stock Price Crash Risk: Evidence from China, Journal of Corporate Finance25 (2014), 419–434.

23.

Xu ,

Yu and

Yi , Herd behavior of institutional investors and stock price crash risk, Management World (07) (2013), 31–43.

24.

Ye ,

Cao and

Wang , Can the disclosure of internal control information reduce the risk of stock price crash?Financial Studies2015(02), 192–206.

25.

Zhou ,

Zhang and

Yang , Innovation input and market performance of gem listed companies: Based on the internal and external perspectives of the company, Economic Research (11) (2017), 135–141.

26.

Esteva ,

Kuprel ,

Novoa ,

Ko and

Swetter , Dermatologist-level classification of skin cancer with deep neural networks, Nature542(7639) (2017), 115–118.

27.

Fan and

Xiao , A framework for knowledge discovery in massive building automation data and its application in building diagnostics, Automation in Construction50 (2015), 81–90.

28.

Liang ,

Hong and

Shen , Occupancy data analytics and prediction: A case study, Build Environ102(2) (2016), 179–192.

29.

Kaneko , A new measure of regression model accuracy that considers applicability domains, Chemometr Intell Lab171(2) (2017), 1–8.

30.

Wang , Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach, Appl Energy146 (2015), 92–103.

31.

Pingkuo and

Zhongfu , How to develop distributed generation in China: In the context of the reformation of electric power system, Renewable & Sustainable Energy Reviews66 (2016), 10–26.

32.

Zhou ,

Wang and

Jiang , Predicting the gas-liquid critical temperature of binary mixtures based on the quantitative structure property relationship, Chemometrics & Intelligent Laboratory Systems167 (2017), 190–195.

33.

Salem ,

Shaffer and

Kublik , Microstructure-informed cloud computing for interoperability of materials databases and computational models: Microtextured regions in Ti alloys, Integrating Materials & Manufacturing Innovation6(1) (2017), 111–126.