A quantitative analysis of statistical data utilized in the investment decision-making process with application on Southern California commercial banks: A statistical stud

The problem. This study was conducted for the following purposes: (1) To report the values of various financial variables utilized in the investment decision-making process of commercial banks. (2) To investigate how the three different estimations methods used in the study differ in terms of error percentage in the prediction of the dependent variables. (3) To suggest ways in which any findings identified in (1) or (2) above may be useful in improving investment decision-making processes in commercial banks of Southern California.;Method. The research approach adopted for this study involved the application of: (1) Linear multiple regression analysis. (2) Log Linear multiple regression analysis. (3) Stepwise regression analysis on the selected variables.;In addition to compare the estimation results of: (1) Linear multiple regression. (2) Log Linear multiple regression. (3) James-Stein estimator. In terms of error percentage.;Results. The study found the following independent variables to be the most significant predictors of the dependent variables: (1) Payroll expenses as a percentage of total operating income. (2) Leverage measure 2 (Total Long Term Debt/Total Assets). (3) Consumer loans as a percentage of total loans.;The results also showed that the estimated values of the variables differ from one variable to another depending on the estimation method used. The James-Stein estimator generally had the minimum percentage of error.;Finally, the results of this study imply that the investment decision-making process is significantly constrained by factors that fall within the control of management. The importance of the role that quantitative analysis play in improving the investment decision-making process of banks (and thus their overall performance) has been demonstrated.