Data and dimension reduction for visual financial performance analysis. (April 2015) - Sarlin, Peter
This article assesses the suitability of data and dimension reduction methods, and data–dimension reduction combinations, for visual financial performance analysis. Motivated by no comparable quantitative measure of all aspects of dimension reductions, this article attempts to capture the suitability of methods for the task through a qualitative comparison and illustrative experiments. While the discussion deals with differences of data–dimension reduction combinations in terms of their properties, the experiments illustrate their general applicability for financial performance analysis. The main conclusion is that topology-preserving data–dimension reduction combinations with predefined, regular grid shapes, such as the self-organizing map, are ideal tools for this task. We illustrate advantages of these types of methods with a visual financial performance analysis of large European banks.