Visualization and statistical modeling of financial big data: double-log modeling with skew-symmetric error distributions

Abstract

This study considers the visualization and statistical modeling of financial data (e.g., sales, assets, etc.) for a large data set of global firms that are listed and delisted. We present exploratory data analysis carried out in the R programming language. The results show that a double-log model with a skew-t error distribution is useful for modeling a firm’s total sales volume (in thousands of U.S. dollars) as a function of its number of employees and total assets (in thousands of U.S. dollars). This result is obtained by comparing the Akaike information criteria of several double-log models with independent and identically distributed random error terms with skew-symmetric distributions and by further evaluating the models using cross-validation.