We develop a panel intensity framework for the analysis of complex trading activity datasets containing detailed information on individual trading actions in different securities for a set of investors. A feature of the model is the presence of a time-varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. We contribute to the literature on market microstructure and behavioral finance by providing new results on the disposition effect and on the manifestation of risk aversion on the high-frequency trading level. These novel insights are made possible by the joint characterization of not only the decision to close (exit) a position, usually considered in isolation in the literature, but also the decision to open (enter) a position, which together describe the trading process in its entirety. While the disposition effect is defined with respect to the willingness to realize profits/losses with respect to the performance of the position under consideration, we find that the performance of the total portfolio of positions is an additional factor influencing trading decisions that can reinforce or dampen the standard disposition effect. Moreover, the proposed methodology allows the investigation of the strength of these effects for different groups of investors ranging from small retail investors to professional and institutional investors.