e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits (2020)

Abstract: In recent years, the focus of e-Commerce research has been on better understanding the relationship between the internet marketplace, customers, and goods and services. This has been done by examining information that can be gleaned from consumer information, recommender systems, click rates, or the way purchasers go about making buying decisions, for example. This paper takes a very different approach and examines the companies themselves. In the past ten years, e-Commerce giants such as Amazon, Skymall, Wayfair, and Groupon have been embroiled in class action security lawsuits promulgated under Rule 10b(5), which, in short, is one of the Securities and Exchange Commission’s main rules surrounding fraud. Lawsuits are extremely expensive to the company and can damage a company’s brand extensively, with the shareholders left to suffer the consequences. We examined the Management Discussion and Analysis and the Market Risks for 96 companies using sentiment analysis on selected financial measures and found that we were able to predict the outcome of the lawsuits in our dataset using sentiment (tone) alone to a recall of 0.8207 using the Random Forest classifier. We believe that this is an important contribution as it has cross-domain implications and potential, and opens up new areas of research in e-Commerce, finance, and law, as the settlements from the class action lawsuits in our dataset alone are in excess of $1.6 billion dollars, in aggregate.

## introduction since 1990, over four thousand securities class action lawsuits have been filed alleging violations of section 10b of the securities exchange act of 1934. while 10b is a very broad section, its main foci are manipulative and deceptive practices in relation to securities. in their communications with stakeholders, companies often refer to financial measures that do not conform with the generally accepted accounting principles (gaap). these non-gaap measures (ngms) have been shown to positively increase a document's tone, which could be overinflating the company's prospective performance. consequently, if actual performance falls short, overly favourable wording could be part of the instigation of securities lawsuits. to evaluate if we could use a ngms approach to classifying securities class action lawsuits, we use the financial filings submitted to the u.s. securities and exchange commission (sec) for ninety six random lawsuits, half settled and half dismissed, over the alleged damage period, as our dataset. we propose a novel use of sentiment analysis by examining a key section of the quarterly and annual reports submitted to the sec in two states: first, the unaltered report as filed with the sec (x ), and second, the report without selected ngms (x). we then calculated the change in the tone or sentiment (as we use these terms interchangeable) as (x -x ) for each report and used it as an input to our prediction model. we found that we are able to predict the outcome of the lawsuits for the aggregate dataset with a recall of 0.8207 using the calculated sentiment (tone) change alone using the random forest classifier. when the tone change is used in conjunction with other features, we find that we are able to predict the outcome with a recall of 0.9142, again using the random forest classifier. securities lawsuits are extremely expensive to companies: the settlements from our sample alone are in excess of $1.6 billion dollars, in aggregate. to our knowledge, this use of natural language processing, in particular the change in sentiment analysis of the ngms in financial reports and approach to potential lawsuit classification has not been done before. we believe that this is an important contribution as it has cross-domain impli-cations and potential, and opens up new areas of research in e-commerce, finance, and law. ## related work the recurring theme of research that supports the use of ngms is altruism -that they provide additional, relevant information that gaap cannot @xcite @xcite . these measures are everywhere in the financial ecosphere and have become accepted as part of the fundamental financial narrative. while the use of ngm has its supporters, there are many more detractors who cite evidence that strongly suggests that the motives are opportunistic rather than altruistic. earnings targets are a fundamental part of measuring corporate goals. companies set these objectives to help the company grow, but also demonstrably communicate to investors that the company is worth investing in. researchers have found that there is a higher percentage of companies that are meeting or beating their earnings targets relative to those that do not. this strongly suggests that there is some degree of financial "management" @xcite @xcite @xcite @xcite @xcite , and one of the tools available to do that are ngms. research has also found ngms, even as supplementary measures, are misleading given their persuasive nature @xcite as the company is essentially implying, through the adjustments that they make, that its actual performance is different (and in some cases starkly different) from its audited performance. alee et al. also raises the concern that non-gaap earnings, in particular, may confuse and mislead the average investor @xcite when non-gaap profits are created through adjustments from what was originally a gaap loss @xcite . @xcite found that when management discloses information to stakeholders, it tends to use "flexibility" in the tone in order to limit the damage by framing the negativity in positive ways @xcite , which speaks to corporate motivation. @xcite found that this motive entices writers to re-frame negativity into positivity because the impact of negative words on shareholders (or potential shareholders) is inex-orable @xcite . therefore, careful use of word constructs can help to avoid, or at least, significantly limit the pervasive affect brought on by negative wording. this idea is also echoed by @xcite who indicate that overly optimistic tones can be catalysts for securities class action lawsuits @xcite . wongchaisuwat, klabjan, and mcginnis used clustering classification models to determine the likelihood of patent litigation. if litigation was determined to be likely, sec financial data was then incorporated into the model to predict the timeline to litigation @xcite . gruginskie and vaccaro also researched lawsuit lead time based on data provided by the tribunal regional federal da 4 a regiã from 2016 @xcite . their model was broken down into four time frames: up to 1 year; from 1 to 3 years; from 3 to 5 years; and more than 5 years. overall, support vector machines and random forest returned the best f1 measure performance of 83.85 and 83.33, respectively, for results up to 1 year. alexander et al. examined features extracted from source documents such as the lawsuit itself, the trial docket, summary judgments, and the magistrate's report to predict the outcomes of a series of lawsuits @xcite . using a random forest model, they varied the number of features used in prediction to see which model would provide the most insight. the model that used the full range of features provided the best performance, resulting in 94% accuracy @xcite . ## conclusion our research provided the novel approach of performing an extractive sentiment analysis using the tone change between financial reports containing ngms with those that do not for prediction of the outcome of securities class action lawsuits promulgated under rule 10b(5). we conducted our ogy on different aspects of e-commerce, such as buying decisions, based on different user-groups understanding and interpretations of keywords, and how words that characterize and contextualize affect sentiment. we also suggest that the paradigm be shifted from focusing intently on how customer information can be used to drive bottom-line performance, to incorporating how e-commerce companies communicate with their stakeholders to determine if there is alignment between what the company says and, ultimately, does, as evidenced in their regulatory and financial filings.