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Title: Emotions affect investors' willingness to take risks during trading sessions: A machine learning experimental research Authors:  Sofia Poggi - Sapienza University (Italy) [presenting]
Abstract: Analyzing human emotions is fundamental in decision-making, considering that they determine more than 90\% of our behaviors. The aim is to capture emotions during trading decision-making leveraging cutting-edge technology: artificial intelligence can transform micro-facial expressions into emotions. To better understand whether fear could be responsible for the change in risk aversion and to better identify the emotional channel, we rely on treatment and control in field experiments on an actual trade market. 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. In a few words, the purpose is to detect the impact of emotion on investors' willingness to take risks during trading sessions by applying a Machine learning algorithm able to see facial micro-expression and transform it into data.