Do investors' personalities predict market winners? Experimental setting and machine learning analysis

Area of Science:

• Behavioral Finance
• Psychology
• Financial Markets

Background:

• Understanding investor behavior is crucial for financial market analysis.
• Previous research has explored demographic and psychological factors in trading, but their interplay remains complex.
• The influence of personality traits on financial decision-making requires further investigation.

Purpose of the Study:

• To investigate the predictive power of Big-five personality traits, social behaviors, self-attribution, and demographics on equity trading performance.
• To explore the association between social and ethical virtues and trading effectiveness.
• To utilize machine learning for clustering personal features and understanding their interconnection with socioeconomic determinants in financial decisions.

Main Methods:

• Experiments and surveys involving 146 participants in equity trading.
• Analysis of Big-five personality traits, social behaviors, self-attribution, and demographic characteristics.
• Application of machine learning techniques to cluster personal features.

Main Results:

• Investors exhibiting higher openness and neuroticism achieved returns exceeding the market benchmark.
• Social traits, including awareness of fairness and politeness, were linked to effective stock trading.
• Machine learning revealed interconnections between personal characteristics and financial decisions.

Conclusions:

• Personality traits, specifically openness and neuroticism, are significant predictors of trading performance.
• Social and ethical awareness positively influences trading effectiveness.
• Clustering personal features using machine learning provides novel insights into the relationship between individual characteristics and financial decision-making.