FINANCIAL DATA PATTERN RECOGNITION EMPLOYING BENFORD LAW DIAGNOSTIC FORENSIC APPROACH

Data authenticity has become a pressing issue with the rapid evolution of the big data technology, particularly in financial markets. Benford’s Law, which models the distribution of significant leading digits, has proven effective for assessing data authenticity by anticipating a logarithmic distribution pattern across diverse datasets. This study employs Benford’s Law as a diagnostic forensic tool to evaluate the pattern reliability of stock market indices during the Black Swan period, spanning from March 2020 to March 2021. Focusing on the distribution of the first significant leading digits, the study utilizes the chi-square conformity test to assess compliance with Benford’s Law, and conducts a Monte Carlo sensitivity analysis to validate the robustness of these findings. Empirical evidence from the study indicates that certain indices, particularly the FTSE Bursa Malaysia EMAS Shariah Index (EMAS) and the Shanghai Stock Exchange Composite Index (SSEC), displayed significant deviations from Benford’s expected distribution. These deviations suggest irregular trading patterns, potentially reflecting heightened volatility and uncertainty in response to the pandemic. In contrast, other indices, including those from the United States, Japan, and Indonesia, maintained greater alignment with Benford’s Law, signaling relative stability in market behavior despite global financial disruptions. The findings underscore the utility of Benford’s Law as a forensic tool for detecting disparities in stock market behavior, particularly during crisis periods. This analysis reveals that major crises like the COVID-19 pandemic amplify irregularities and contribute to herding behaviors among investors, which can destabilize markets. The study concludes that Benford’s Law provides valuable insights into financial market stability and investor behavior under extreme conditions and suggests that future applications of Benford’s Law could extend to monitoring broader financial instruments to improve crisis detection and response.