Supplementary Data for Mahalanobis-Based Ratio Analysis and Clustering of U.S. Tech Firms

Note: All supplementary files are provided as a single compressed archive named dataset.zip. Users should extract this file to access the individual Excel and Python files listed below. This supplementary dataset supports the manuscript titled “Mahalanobis-Based Multivariate Financial Statement Analysis: Outlier Detection and Typological Clustering in U.S. Tech Firms.” It contains both data files and Python scripts used in the financial ratio analysis, Mahalanobis distance computation, and hierarchical clustering stages of the study. The files are organized as follows: ESM_1.xlsx – Raw financial ratios of 18 U.S. technology firms (2020–2024) ESM_2.py – Python script to calculate Z-scores from raw financial ratios ESM_3.xlsx – Dataset containing Z-scores for the selected financial ratios ESM_4.py – Python script for generating the correlation heatmap of the Z-scores ESM_5.xlsx – Mahalanobis distance values for each firm ESM_6.py – Python script to compute Mahalanobis distances ESM_7.py – Python script to visualize Mahalanobis distances ESM_8.xlsx – Mean Z-scores per firm (used for cluster analysis) ESM_9.py – Python script to compute mean Z-scores ESM_10.xlsx – Re-standardized Z-scores based on firm-level means ESM_11.py – Python script to re-standardize mean Z-scores ESM_12.py – Python script to generate the hierarchical clustering dendrogram All files are provided to ensure transparency and reproducibility of the computational procedures in the manuscript. Each script is commented and formatted for clarity. The dataset is intended for educational and academic reuse under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).