Analyzing the Dimensions and Components of an Optimal Investment Model Based on Stock Return Predictors and Risk Factors of Disruptive Traders

This study analyzes the dimensions and components of an optimal investment model based on parameters for predicting stock returns and the risk factors associated with disruptive traders. The research adopts an applied post-event survey approach. Initially, the study identifies stock return predictor variables, followed by an examination of disruptive traders' risk factors, which are determined using behavioral errors and beta differences in trading. Subsequently, the study tests its assumptions by applying a combined regression model, integrating the risk factors of disruptive traders and the predictor variables of stock returns. Principal Component Analysis (PCA), the Generalized Supremum Augmented Dickey-Fuller (GSADF) test, and the logit method are utilized to assess the influence of disruptive traders on bubble formation in the Tehran Stock Exchange. The findings indicate that disruptive traders have a positive and significant effect on bubble occurrence. Specifically, a one-unit increase in optimistic sentiment, coupled with market disruption, raises the likelihood of bubble formation.