Predictive Dynamics in Cryptocurrency Trading: Unraveling Behavioral and Psychological Influences

Abstract

The rapid expansion of cryptocurrency trading has become a defining feature of contemporary financial markets, attracting a constantly growing group of participants, now surpassing 106 million worldwide. This research focuses on the psychological and behavioral foundations of trading behaviors, investigating how individual psychological states and lifestyle choices impact cryptocurrency trading activities. Using Ordinary Least Squares (OLS) regression, we examine the influence of various factors such as Loneliness, Negative Emotions, Fear of Missing Out (FOMO), Socialization, Healthy Lifestyle Habits, Entertainment Spending, and Sense of Achievement on the frequency of cryptocurrency trades. Our study also includes an analysis of gender differences through Levene’s T-test, thereby increasing the depth of our predictive model. The results of this study aim to fill a gap in existing literature by quantifying the degree to which individual psychological profiles and behaviors can predict trading activities, thereby providing detailed insights into the emotional and cognitive dimensions of the digital trading world. This research not only contributes to the field of behavioral finance but also provides a foundation for developing strategic interventions tailored to various trader segments, ultimately fostering a deeper understanding of the complex dynamics that characterize the crypto market’s volatile landscape.