Kim Presents Algorithmic Tax Ownership At Vanderbilt

Young Ran (Christine) Kim (Cardozo; Google Scholar) presented Algorithmic Tax Ownership (with Dmitry Erokhin (International Institute for Applied Systems Analysis; Google Scholar)) at Vanderbilt yesterday as part of its Law School Faculty Workshop Series:

Ownership for tax purposes is distinct from ownership under property or contract law, as it looks beyond mere form to the economic substance of the transaction to identify who must bear the tax burdens and reap the tax benefits. Tax ownership is essential in determining the tax consequences of complex transactions, yet it is notoriously convoluted. Neither Congress nor the IRS has provided taxpayers with a clear formula for identifying tax ownership. Rather, courts have been left to develop their own methods, resulting in a complex “patchwork of rules that appear to lack a unifying principle (or set of principles).” An example is the twenty-six factors in Frank Lyon Co. v. United States.

This paper offers clearer guidance on the relevant importance of factors in tax ownership analysis using machine learning.

My previous research doctrinally analyzed tax ownership case law and literature and distilled the excessive number of factors (in the context of litigation financing) to two primary factors—economic risk and legal control of the underlying asset. However, it had limitations, including the limited number of available cases in litigation finance contracts. If there are enough cases regarding tax ownership analysis, creating a formula and assessing the relative importance of criteria is possible. We believe that more established financial transactions, such as repo, present this opportunity. Furthermore, machine learning can process a large number of data quickly, produce models automatically, and offer more accurate results. Once the models for tax ownership analysis are established, we plan to expose the models to new data to test the performance and reliability of the models.  

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