This website uses cookies to collect usage information in order to offer a better browsing experience. By browsing this site or by clicking on the "ACCEPT COOKIES" button you accept our Cookie Policy.

New Machine Learning Model for CEOs Facial Expressions

Quantpedia

Contributor:
Quantpedia
Visit: 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/

Past performance is not indicative of future results.

Any stock, options or futures symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

Disclosure: Interactive Brokers

Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from Quantpedia and is being posted with permission from Quantpedia. The views expressed in this material are solely those of the author and/or Quantpedia and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

In accordance with EU regulation: The statements in this document shall not be considered as an objective or independent explanation of the matters. Please note that this document (a) has not been prepared in accordance with legal requirements designed to promote the independence of investment research, and (b) is not subject to any prohibition on dealing ahead of the dissemination or publication of investment research.

trading top