Original analysis, methodology notes, and technical commentary on forensic data analytics, securities investigations, and expert witness practice.
When trades are fragmented across brokers, routed through nominee accounts, or deliberately obscured by wash activity, standard profit calculations systematically undercount economic gains. This piece examines FIFO trade reconstruction, wash trade detection, and multi-leg profit attribution—including functional Python code for each—drawing on the market microstructure literature from Kyle (1985) through Putniņš (2012) on the quantification of manipulation profits.
The analytical rigor that makes a financial expert's conclusions correct is rarely, by itself, what makes them persuasive in a courtroom. This piece examines the structural principles of expert report drafting, the communication of statistical uncertainty to non-specialist factfinders, and the design of litigation exhibits—with Python and R code for annotated event-study charts and cross-examination-resilient sensitivity analyses.
Securities litigation frequently hinges on methodological choices that are invisible to non-specialists but can dramatically alter the conclusions drawn from identical data. This piece catalogues the most consequential pitfalls in event study design, damages calculation, and statistical fraud testing—including misspecified estimation windows, the bundled-disclosure problem, event-induced variance, and the multiple testing problem—with code implementing the Benjamini-Hochberg FDR correction.
Benford's Law—that leading digits in naturally occurring datasets follow a logarithmic distribution—underpins one of forensic accounting's most cost-effective screening methodologies. This piece covers first- and second-digit analysis, duplicate transaction detection, and earnings threshold manipulation testing (following Burgstahler and Dichev, 1997), including a fully functional Python implementation of the complete digital analysis suite with MAD conformity scoring per Nigrini (2012).
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