Emotions affect investors’ willingness to take risks during trading sessions. A machine learning & facial recognition experimental research

Analyzing human emotions is fundamental in decision-making, considering that they determine more than 90% of our behaviors.
Nowadays, it is increasingly essential for companies to measure emotions to deeply understand and optimize people's decision-making processes and avoid wasting economic resources. Theoretically, according to traditional finance researchers such as Sharpe (1964), Modigliani, Miller (1958), Malkiel, and Fama (1970) , investors are considered rational decision-makers regarding trading in stock markets. However, looking into economic literature over the last 30 years, an extensive body of literature has shown the effects of cognitive and behavioral biases on financial decisions. Growing evidence suggests that behavioral factors affect individual economic behavior, and some of these factors demonstrably influence financial markets. Firstly, this study intends to capture emotions during the trading decision-making leveraging a cutting-edge technology: artificial intelligence can transform micro-facial expressions into emotions. Secondly, the study is a field experiment on real trade market rather than a laboratory experiment to directly capture and test the causal impact of emotions on changes in individual risk aversion and whether the observed changes in risk aversion on the real market were caused or empathized by a negative emotion such as fear as tested by Guiso, Sapienza, and Zingale, 2013 in a laboratory experiment.To better understand whether fear could be responsible for the change in risk aversion and to better identify the emotional channel, we rely on a treatment and control framework. Half of the participants will watch a short horror video before a trading session. Since the subject will be randomly assigned to watch the video, the idea is that this difference in treatment should entirely drive the difference in risk aversion and emotion intensity detected between the groups. For example, watching a horror movie triggers an emotional and physical response similar to those produced by a severe financial loss. So differentially from the Guiso et al. experiment, the real experiment should mimic the situation of investors who are emotionally and financially affected by the stock market crash rather than the investors affected only financially. In a few words, the research aims at detecting the impact of emotion on investors’ willingness to take risks during trading sessions through the application of a Machine learning algorithm able to see facial micro-expression and transform it into data.