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New Machine Learning Model for CEOs Facial Expressions

New Machine Learning Model for CEOs Facial Expressions

Posted August 9, 2021 at 4:11 pm
Quantpedia

Excerpt

Nowadays, it is a standard that fillings such as 10-Ks and 10-Qs are analyzed with machine learning models. ML models can extract sentiment, similarity metrics and many more. However, words are not everything, and we humans also communicate in other forms. For example, we show our emotions through facial expressions, but the research on this topic in finance is scarce. Novel research by Banker et al. (2021) fills the gap and examines the CEOs facial expressions during CNBC’s video interviews about corporate earnings.

The authors utilize a conventional neural network for face detection and facial expression recognition to measure the dynamic hemifacial asymmetry of expressions. The idea is based on neuropsychology, which states that facial asymmetry induces distrust. Therefore, the crucial task is to find whether the CEO´s faces asymmetry influence stocks. The results support the theory from neuropsychology. Dynamic hemifacial asymmetry is negatively connected with the three-day cumulative abnormal return after the interview. There is also a relation with bid-ask spreads suggesting that investors’ opinions are dispersed following the event and tend to be larger. Moreover, the distrust is even stronger when the company has a weaker information environment (high volatility and forecasts dispersion). Overall, several takeaways from the paper could be utilized with already established approaches for textual analysis. 

Authors: Rajiv D. Banker, Hui Ding, Rong Huang and Xiaorong Li

Title: Market Reaction to CEOs’ Dynamic Hemifacial Asymmetry of Expressions — A Machine-Learning Approach

Linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3814689

Abstract:

Neuropsychological studies propose that listeners unconsciously assess speakers’ trustworthiness via their facial expressions. Building on this theory, we investigate how investors respond to CEOs’ dynamic hemifacial asymmetry of expressions (HFAsy) shown on CNBC’s video interviews about corporate earnings. We employ a machine-learning approach of face-detection and facial-expression-recognition based on conventional neural network to measure CEOs’ dynamic HFAsy. Consistent with the neuropsychological prediction that facial asymmetry induces distrust, we document that the stock market reacts negatively to the CEO’s HFAsy shown on the interview video. We also find that the abnormal bid-ask spread around the interview date is positively associated with the CEO’s HFAsy. We further show that these effects are more pronounced for firms with weaker information environments. Finally, we document that analyst forecast revisions are negatively associated with CEOs’ HFAsy. Overall, our study provides evidence that investor trust and trading behavior are affected by the dynamic hemifacial asymmetry of expressions appeared on CEOs’ faces.

Visit Quantpedia to read the full article and learn more about the research: https://quantpedia.com/new-machine-learning-model-for-ceos-facial-expressions/

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