Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms

摘要

This paper analyzes financial ratios of 27 consumer discretionary firms listed on the S&P 500 over an eleven-year period from 2006-2016. It adopts a two-step approach wherein first a confirmatory factor analysis (CFA) on the financial time-series is conducted and the resulting constructs' scores are then used to perform a cluster analysis using self-organizing maps (SOMs). The consumer discretionary sector is considered an economic and stock market predictor. It consists of non-essential goods and services which in an economic slump are more likely to be foregone. The suggested approach is expected to be a useful reference guide to help understand the past performance of inter- and intra-sector companies. It also enriches the body of literature on the application of machine learning techniques to the analysis of firm- and sectoral-level performance.