The Impact of the CEO's Angry Facial Expression on the Future Market Value of the Enterprise

Zhenhong Lu; Xinran Tang; Yi Xu

doi:10.54097/1nysze98

Authors

Zhenhong Lu
Xinran Tang
Yi Xu

DOI:

https://doi.org/10.54097/1nysze98

Keywords:

Expressions; firm value; regression models.

Abstract

With the rapid development of the economy, it is particularly important to accurately calculate and assess the future value of enterprises. It is the basis of judgment for investors to make investment decisions and an essential step in the investment process, which is related to enterprises' future survival and development. There are many factors affecting the future value of an enterprise, including the financial soundness of the enterprise, market share, and market competition. By reviewing the literature, this paper proposes that the future market value of an enterprise has a certain correlation with the Chief Executive Officer's (CEO) facial expression, and this paper mainly focuses on the impact of the CEO's angry expression on the company's future market value, by decomposing the video into a series of sets of expression clips, using the Adaptive Augmented Sequential Feature Extraction in the Transformer system to model and identify the expression clips, and through the in the regression model to study to explore whether the effect of the angry emoji on the market value of Seven Nights is positive or negative, as well as the investors' reaction to the market.

Downloads

Download data is not yet available.

References

Zijian C, Xiaoliang Z. Research on automatic emotional recognition of learners based on facial expressions - appropriateness, current situation, existing problems, and improvement path. Distance education magazine, 2019, 37 (04): 64-72.

Girard M J, Cohn F J, Mahoor H M, et al. Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses. Image and Vision Computing, 2014, 32 (10): 641-647.

Akansu A, Cicon J, Ferris P S, et al. Firm performance in the face of fear: how CEO moods affect firm performance. Journal of Behavioral Finance, 2017, 18 (4): 373-389.

Patrick L, F J C, Iain M, et al. Automatically detecting pain in video through facial action units. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society, 2011, 41 (3): 664-74.

Mingen Z, Hachi H, Zhiqun Y. Experiments on the influence of anger on driving behavior. Journal of Chongqing University of Technology (Natural Science), 2011, 25 (10): 6-11.

Arye L B. Why firms adopt antitakeover arrangements. University of Pennsylvania Law Review, 2003, 152 (2): 713-753.

North S M, Todorov A, Osherson N D. Inferring the preferences of others from spontaneous, low-emotional facial expressions. Journal of Experimental Social Psychology, 2010, 46 (6): 1109-1113.

Yang L, Jincheng Z. CEO facial expression analysis based on neural serendipity networks and its impacts on media attention at press conferences. Data Analysis and Knowledge Discovery, Serendipity, 2023, 7(8): 46-61.

Elsbach K D, Barr P S. The effects of mood on individuals' use of structured decision protocols. Organization Science, 1999, 10(2): 181-198.

Mayew J W, Venkatachalm M. The power of voice: managerial affective states and future firm performance. The Journal of Finance, 2012, 67(1): 1-43.

Yuanyuan L, Wenbin W, Chuanxu F, et al. Expression snippet transformer for robust video-based facial expression recognition. Pattern Recognition, 2023, 138.