Industry trends are constantly diverting attention away from small and mid-cap equity research. At this time, the rise of index and passive investing causes large-cap concentration on the buyside, and cost pressures force sellside firms to reduce their coverage of smaller names. With the introduction of MiFID II, these trends are likely to continue. Now institutional buyside investors are turning to external data providers to find ways to incorporate the use of technology to find alpha.
Canalyst: What is your fund’s investment style?
Analyst: We are a long/short, value, growth, multi-asset fund.
C: What is your title and what are your responsibilities?
A: I’m an Analyst, Generalist. My main job is to review companies, build financial models, and get their quarterly updates.
C: What was your process prior to gaining access to Canalyst?
A: I was mostly building in-house, with some model exports from other financial data vendors.
C: What were some of the challenges you were facing?
A: The main challenge was time constraint. We also found models from other sources to not be linked up or have accurate, if any, operating data.
C: What types of solutions were you looking for to solve this problem?
A: We were using an in-house programming process to build our models automatically to deal with the time constraints and attention to detail.
C: What initially made Canalyst an attractive option?
A: The ability to pull up a model with the historical and operating data, and the drivers linked up has been very helpful. The numbers in Canalyst models are exactly what we see in the company reports.
C: What features or requirements were important in making your decision to choose Canalyst?
A: The functionality of the product. I download a Canalyst model and then build off of that.
C: What do you like about the Canalyst database?
A: There’s data integrity, the models are detailed, clean, and consistent, and your wide coverage. Our ability to pull up 20-30 models that are up-to-date and ready for use is very helpful.
C: What benefits have you experienced since implementing Canalyst?
A: I’ve found myself with more time to do other things. Updates are very fast during earnings, in fact, Canalyst updates faster than if I had to do it myself.
C: How many people are using Canalyst in your organization? What are their roles? How has it helped them do their jobs better?
A: We currently have an analyst and portfolio manager using Canalyst. Saving time has allowed us to cover more names, helping us increase our holdings.
C: How has Canalyst enabled your company to achieve its business objectives?
A: Canalyst has given us a competitive edge.
C: Is your usage of Canalyst different from what you expected?
A: No, but the pre-IPO models is an interesting feature.
C: What do you like about Canalyst as a company?
A: Canalyst Support is user friendly and acts very quickly. Whenever I request a model on a new company that is not on the database, it is built within a couple of days.
C: How would you describe Canalyst to someone who’s never heard of us?
A: For professionals with experience, it is an analyst or assistant to help them to build high quality models – a PM doesn’t need to hire 3 to 5 analysts, they can just hire Canalyst. For those with limited experience, it can help them learn what a good model is and should look like.
C: What do you mean by good?
A: [Canalyst] is an industry standard.
Institutional investors have never had an ideal solution for their modeling needs. They turn to data vendors who cover a large number of equities but rely heavily on outsourcing their data entry. Or they have hand-built models that capture individual company details and drivers but are difficult to compare. Canalyst provides a forward-looking database that covers 4000+ North American equities and accurately models how companies operate as opposed to just providing historical financial data. Every model is built and updated in Vancouver, Canada, going through three rounds of review, including a review by a senior equity research sector specialist, and an additional layer of data validation and error checking before becoming available for clients. The database empowers managers to look at names across multiple sectors without compromising the quality of a model they use to make their investment decisions.