This paper analyzes the Mexican Stock Market indicators and their relationships to study the ten most representative stocks in The Mexican Stock Market Index between 2011 to 2020, reflecting behavioral effects using the Mexican Volatility Index. A longitudinal research design of 119 observations sample size is modeled monthly; this sample was transformed into categorical variables to reflect emotional stages. The main objective was to analyze the stock market emotions applying a novel approach to create latent behavioral variables using current Mexican behavioral indicators as reflexive constructs. There is a lack of knowledge in using different techniques to model financial behavior in financial and economic modeling. The typical techniques employed to model market time series have been simple and multiple regression, broadly used in this science. Since behavioral science appliances, there is a need for evolution to modeling the complex nature of behavior. Partial Least Squares and Structural Equation Models (PLS-SEM) can manage different scales, complex relations, reflexive or formative models, non-normal data, and small samples. Possible because of the lack of knowledge al flexibility of PLSSEM models, there is an evident lack in the use of this methodology in modern research and financial teaching, so a new methodology for modeling financial data is exposed that can resolve problems in financial researching and teaching. It is relevant to show a way of modeling emotional financial markets; it is recommended the use of this modeling in different kind of data, different countries, characteristics, sectors, industries, or variables, or any possible application in different sciences with the same problems of assumptions like in economics sciences. Keywords: Investor behavior, risk, market efficiency, Structural Equations, and Partial Least Squares methods