Everyone’s familiar with class-action lawsuits where a bunch of families sue a pharmaceutical company. However, the same problem often happens for investors as well, leading to a securities class-action lawsuit. Essentially, if a company neglects its fiduciary responsibility to keep investors informed about negative changes in the company, and those eventually impact the company’s stock price when the news becomes public, investors are entitled to sue a company.
Right now, the existing solution is throwing bodies at the problem – plaintiff firms keep tons of lawyers on staff whose job is to read the news and track stocks, and hopefully identify a situation where a securities class-action lawsuit could be filed. This is incredibly manual and time-intensive, and is an impossible process to ensure success – a person is always going to miss some opportunities.
Instead of relying on plaintiff lawyers and industry blogs, like Lyle Roger’s The 10b-5 Daily, to just manually scan and analyze stock price data, we believe there is an opportunity to merge human understanding and machine learning to identify and even predict potential security class action suits. To solve this problem we propose the creation of Classy, a service that predicts potential lawsuits for plaintiff lawyers by combining machine algorithms and human intuition. Currently many of the class action suits brought by firms end up being frivolous and yield limited to no profit for plaintiffs. Classy will help plaintiff firms to mediate this error and increase their efficiency in pursuing the most fruitful cases. Additionally Classy will help plaintiff firms better prioritize their staffing structure, so that there are more lawyers using their time to execute suits rather than searching for potential signs of fraud.
Our product would combine external sensors with machine and human algorithms to predict the likelihood of securities misconduct of various firms and help analyze the success of a suit. The two sensory inputs would be stock prices and news articles. First, we would utilize machine learning to flag precipitous stock price drops throughout the whole market. We would also use natural language processing and sentiment analysis to analyze relevant news items, identifying patterns of negative disclosures by a firm in the past or public apologies issued by CEOs. These sensory inputs would then be analyzed by a machine algorithm, which would use the data to create a likelihood score of disclosure malfeasance by the firm and the predicted settlement value. This information would then be transmitted through a human algorithm – plaintiff lawyers with years of experience and relationship expertise – who would then verify and expand upon the potential suits flagged by the machine algorithm. They would also provide feedback to the machine algorithm in order to improve its efficacy and accuracy over time.