Winning, Defined? Text-Mining Arbitration Decisions

66 Pages Posted: 25 Mar 2020 Last revised: 26 Mar 2020

See all articles by Charlotte Alexander

Charlotte Alexander

Georgia State University – Institute for Insight; Georgia State University College of Law

Nicole G. Iannarone

Drexel University Thomas R. Kline School of Law

Date Written: February 26, 2020

Abstract

Who wins in consumer arbitration? Historically, this question has been nearly impossible to answer, as most arbitration proceedings are a private black box, and arbitral forums release only limited summary statistics. One exception is the Financial Industry Regulatory Authority (FINRA), which arbitrates virtually all disputes between investors and stockbroker-dealers, and makes all of its nearly 60,000 written arbitration decisions publicly available in an online database. This Article is the first to use computational text analysis tools to study these decisions, and to construct a measure of the claimants’ win, loss, and settlement rates. It is the first installment in an original data analytics project that aggregates dispersed public data and document sets to assess the efficacy of arbitration outcome transparency as an investment protection measure. This Article makes three main contributions. First, the results of our novel study provide a more granular picture of customer experiences in the FINRA forum. We identify settlement as the most frequent outcome, followed by claimant losses, and then wins. In twenty percent of cases, we identify the presence of multiple outcomes per arbitration decision, where a claimant lost some claims but won or settled others, for example. This suggests a greater complexity and nuance in the notion of a “win” than FINRA’s outcome measure – and previous scholarship – have recognized. Second, we discovered that the structure of FINRA’s written arbitration decisions prevents further exploration of the amounts of compensatory damages that claimants recover, if any, compared to the amounts requested. Our final contribution is therefore a set of recommendations to FINRA – applicable to other private dispute resolution forums as well – to increase data access, usability, and transparency.

Keywords: FINRA, Dispute Resolution, Computational Analysis, Text Mining, Data Analytics, Textual Analytics, Legal Analytics, Consumer, Transparency, Data, Arbitration

Suggested Citation

Alexander, Charlotte and Iannarone, Nicole G., Winning, Defined? Text-Mining Arbitration Decisions (February 26, 2020). Cardozo Law Review, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3546266

Charlotte Alexander

Georgia State University – Institute for Insight ( email )

Tower Place 200, Third Floor
3348 Peachtree Road NE
Atlanta, GA 30326
United States

Georgia State University College of Law ( email )

P.O. Box 4037
Atlanta, GA 30302-4037
United States

Nicole G. Iannarone (Contact Author)

Drexel University Thomas R. Kline School of Law ( email )

3320 Market Street
Philadelphia, PA 19104
United States

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