New York – Many research providers are thinking about how to grow their research products. Of course the Holy Grail client base is the institutional money managers, but many ARPs are looking for ways to serve the sell-side as well. There is one sweet spot that might be exploited by research providers that produce primary or specialized research. This is in providing trading ideas to the buy side. However, this is much more easily said than done.
A good trading idea is one that is based in deep knowledge of a company or sector, is not widely distributed, and is contrarian with regard to the expectations of the street. This concept is not news to anyone in the financial services industry, but it is news to software providers, industry consultants and many data providers. Given the definition of a good trading idea, research platforms that form a conduit of research ideas from a number of sources, can only be useful if the distribution of the ideas is limited to a small number of accounts.
Yes, we are taking license with this term, which has become a theory of thermal dynamics (and information theory), but it can also be interpreted in its Greek translation “a turning towards”. In this sense we define entropy as the tendency of an idea to float to the top probabilistically. When institutions seek an idea engine, they may use a qualitative filter to assess the entropy of an idea and then translate it into a trade. In order to establish mind share, a research provider competing in the research ideas game must be able to isolate trading ideas that have this character of maximal entropy.
One very clear example of this challenge is within the industry consultant field. Many industry consultants have mountains of data and analysis that they sell back to their own industry. As a result, when investment professionals talks to industry consultants, they see the value of the content right away. Reliably translating this into good trading ideas proves much more challenging. Because of the glut of data and analysis, it is nearly impossible for the industry consultant to determine which ideas are “investment worthy” and which are not. As well, the analysis is slanted toward corporate clients rather than toward financial services. As a result, the industry consultants and the money managers are speaking different languages.
One way to solve the problem of qualifying ideas is to overlay the datasets with translators, who can speak both languages. This would probably be an analyst that also has industry experience. Indeed, there are firms that are implementing this approach with some success.
The concept of trading ideas and entropy is easy to conceptualize, but difficult to implement. First, there is need to produce consistently good (and hopefully contrarian) ideas—a huge challenge in itself. Second, is the process by which ideas are unearthed is a critical element. Third, is building the trust with a client that any communication from research provider “x” is a must-take call. Finally, the distribution of the ideas to a limited set of institutions is critical from an informational standpoint for the money manager and from a pricing standpoint for the research provider.