Fund managers have been capitalizing on methods to refine and analyze big data to assist investment decisions. What types of alternative data are being used to gain new insights? Sources include: e-commerce receipts and credit-card transaction data; sensors from internet-connected machines or smart devices; and online data collected via “screen scraping” (or “web scraping” or “spidering”).
Yet alternative data does not come without risks. For example, data collected as a result of web scraping may be considered material nonpublic information (MNPI). If that data were collected in a manner considered deceptive, then trading on that information might implicate the anti-fraud provisions of the securities laws. Circumventing security protocols or disguising a scraper’s identity on a site (where required), among other behaviors, could be viewed as misrepresentations or “deceptive devices” under Section 10(b) of the Securities Exchange Act.d
Hedge fund managers and other financial services firms using information in this new environment should therefore understand the legal risks and fashion appropriate policies and procedures (both internal and with respect to vendor diligence). We have made the materials from Proskauer’s presentations available here, outlining the complex regulatory, compliance and contractual issues raised by the data aggregation, web scraping and other data science methods. These topics include:
- Types of Data Aggregators
- Web Scraping and Other Data Collection Methods
- Securities Law Concerns, Including Deceptive Conduct and MNPI
- Best Practices for Due Diligence and Internal Processes
- Compliance with Other Federal Laws and Regulations