Visualization and Statistical Modeling of Financial Big Data: Log-Linear Modeling With Skew Error
28 Pages Posted: 21 May 2018
Date Written: May 9, 2018
In this paper, we consider the visualization and statistical modeling of financial data (e.g., sales, assets) for many global firms which are listed and delisted. This study presents an exploratory data analysis carried out in the R programming language. The results show that a log-linear model with skew-t error is useful for modeling the total sales volume (in thousands of U.S. dollars) as a function of the number of employees and the total assets (in thousands of U.S. dollars), and is obtained by comparing the Akaike information criteria between several log-linear models with error terms which are independent and identically distributed random variables with skew distributions. These models are also evaluated by cross-validation.
Keywords: Financial Big Data, Exploratory Data Analysis, Data Visualization, Log-Linear Model, Skew Distribution, SparkR
JEL Classification: C13, C21, C44, C46
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