Investors can experience behaviors that usually are seen entirely irrational from the classical economic principles. The focus of this research is to generate formative or reflective behavioral constructs, which helps to measure the economic effects of investor decisions in the stock market. The foundation of applied algorithms are structural equation models (SEM), and partial least squares (PLS). This approach allows the analysis of dependent and independent variables that are not identically independent o distributed, the advantage of this methodology is that it can be applied to small samples, wlth complex relationships, including categorical variables, improving the reliability and validity of the models by reduction of the random error random term, with an appropriate collinearity handle. The innovation relies on the use of time series for path modeling. Three investor categories were defined: winners, indifferent, and losers that interact in the NASDAQ stock market, deploying seven different phases or emotional stages ranging from financial panic to market euphoria. The results also helped to test the central idea of prospect theory in which the human being tends to be less excited about gains and suffers more from losses in the decision process.