Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal) & Stefanie Di Giandomenico (Senior Legal Research Associate, Blue J Legal), Timing Is Everything: The Step Transaction Doctrine in GSS Holdings, 174 Tax Notes Fed. 1849 (Mar. 28, 2021):
In this article, In this article, Alarie and Di Giandomenico examine the recent decision in GSS Holdings [v. United States, No. 19-728T (Fed. Cl. July 26, 2021),] and use machine learning to evaluate the effect of the selected analytical time frame on the outcome of this step transaction doctrine case. ...
In this article, we explore how tax experts can use machine learning tools to safely test and assess potential litigation strategies before deploying them at trial or on appeal. This can be especially useful for cases involving questions of law that turn on interrelated factors, such as the step transaction doctrine. By way of illustration, we put the recent GSS Holdings Court of Federal Claims decision under our machine learning microscope to see what we can see.
The case is being appealed by the taxpayer, GSS Holdings, to the Federal Circuit. The trial court reached its decision on cross-motions for summary judgment, favoring the government. It applied the step transaction doctrine to step together two transactions, finding them to constitute one asset sale for tax purposes. The court concentrated its analysis on a two-day period leading up to and including the sale, rejecting the taxpayer’s argument that the analysis should begin many years earlier, when the relevant agreements were first negotiated. When following the trial court’s characterization of the relevant time period, Blue J predicts a government win with 65 percent confidence.
On appeal, GSS argues that the trial court did not apply the step transaction doctrine correctly. More specifically, it contends that the court used an inappropriate hybrid test in reaching its decision and did not consider the appropriate time frame.
Machine learning can help us assess how important timing is to the outcome of the case. Without passing judgment on the likelihood of the appellate court adopting GSS’s preferred view of timing, we can explore how successful GSS would be if the longer time period is used as the basis for the step transaction doctrine analysis on appeal.
Our analysis using Blue J’s step transaction doctrine machine learning model suggests that if GSS can convince the Federal Circuit to extend its analysis beyond December 2011, to include the times at which the earlier agreements were entered into, the step transaction doctrine would probably not apply to step together the transactions at issue and the taxpayer would prevail with 94 percent confidence.
Using GSS Holdings, we demonstrate how Blue J’s machine learning technology can be used to test the efficacy of a litigation strategy before it’s employed in court. In this case we tested GSS’s strategy of beginning the step transaction doctrine analysis at the time that the Aaardvark IV LAPA was established, rather than focusing on the last two steps that led to the loss, as originally argued by the government. If the court took the approach proposed by GSS, Blue J predicts with 94 percent confidence that the court would not have applied the doctrine to step together the sale and FLN balance payment for tax purposes. But because the trial court rejected GSS’s approach, it held that the doctrine should apply — a result that Blue J predicted with 65 percent confidence. The Federal Circuit is likely to have the final say on the issue, but if it agrees that GSS’s approach is correct in law then Blue J predicts that there is a high likelihood that the trial court’s decision will be reversed
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