To take the simplest example, imagine a hedge fund that employs analysts to make models that tell the fund to buy, sell, or hold a stock. How does the analyst make that model? You can’t depend on public information from the SEC alone, since everyone has access to that information; you need to get an edge by tapping into independent research that is available to the public but not actually read by everyone. Enter the world of sell-side research, the job of which is to identify what information, data, or insights are out there that have a material impact on a stock and to present those in reports to analysts who work inside those funds (the “buyside”).
A sell-side report traditionally has a basic shape. At the top is the analyst’s details, the company producing the report, and a headline. There will be a price target and a recommendation (often buy, sell, or hold)–those are the points that get the most public attention. They’re also the parts of the reports that buy-side analysts care the least about.
In reality, the detail that follows is what matters. This is usually organized into a few sections with bolded headlines, and the entire report itself is summarized by bullets at the top of the report. The bullets give a general overview of the detail in the report, and the details therein serve to inform the buy-side analysts of how the market and the companies inside that market are changing. These insights can vary widely from involving scientific data, market research about consumer interests, even analysis of political changes that will affect companies in the future.
Historically, sell-side researchers had finance backgrounds and came from investment banks. They would apply their understandings of net profit margins and interest-rate swap derivatives to companies’ balance sheets to provide a financial analysis of companies. Since so much of this research is math oriented, much of it is being automated and these kinds of sell-side analysts are finding a smaller and smaller market for their work.
More recently, sell-side research has begun to involve specialist knowledge and new methods of collecting and packaging data. The most famous example is Palantir (PLTR), which provides (among other things) research to investment firms about changes in marketplaces that will affect stock prices in one way or another. This research involves significant data sets, many of which are hard to access or hidden behind paywalls; PLTR is in the job of collecting that data, synthesizing it, and producing actionable insights for clients.
This is known as “alternative data,” although alt-data isn’t so alternative these days as it is used more and more by more funds and investment banks, and it is becoming an important part of their workflow. Crucially, the skillset involved in creating alt-data doesn’t come from finance at all, but from data science, mathematics, statistics, engineering, biochemistry, and even journalism.
This is both good and bad for the finance industry, as it provides a lot of opportunities for people from non-conventional backgrounds to find a niche while also challenging old conceptions of what research really is and what kind of research brings value to the table. For the aspiring financier, it also means one must be much more creative in breaking into the world of sell-side research than ever before.