Tuesday, May 17, 2022

If you’re a hardcore, process-oriented analyst, this post is for you. Chris Petrescu is the founder and CEO of CP Capital. As a data strategist and a former corporate finance manager, he has a long-standing relationship with financial data. We talked with Petrescu about Calcbench’s point-in-time data and how you can leverage it for a competitive edge.

Q. Tell us about Calcbench’s point-in-time data. What does it do? A. Calcbench’s point-in-time data allows you to take a snapshot of a company’s past. Analysts use point-in-time analysis to assess the evolution of a particular data point.

Q. How does point-in-time analysis help you achieve more accurate financial analysis? A. Point-in-time data isn’t altered by reclassifications or restatements. For historical market analysis, using point-in-time data allows you to remove certain biases that can undermine your research.

Point-in-time data helps you avoid “survivorship bias.” Some datasets will remove companies that have gone bankrupt, or overwrite companies that have been acquired. For systematic firms, this is an issue. Your historical analysis will look rosier because you’re only analyzing companies that have “survived” into the present time.

In addition, point-in-time data helps you avoid “look-ahead bias.” Vendors can store data delivery information incorrectly. For example, a vendor might time-stamp data delivered at 9:30 a.m., when it may have been known to the public markets, but it was actually not delivered until 11:00 a.m. If you’re calibrating a model to trade at the market open, you need to build a model that is trading on data known at that time.

Point-in-time data controls for these biases by accurately storing all company data, regardless of current-day status, and accurately time-stamps the data.  It sounds simple, but some of the largest data vendors in the world don’t grasp these concepts, or don’t care to.

Q. What pitfalls should analysts watch out for? A. Certain hedge funds are more prone to point-in-time data issues than others.  The farther back in time you look, the greater potential for point-in-time data issues.  So, a discretionary investor analyzing a handful of names over a few quarters is far less likely to uncover these issues, while a systematic firm analyzing 3,000 stocks over 5 years most certainly could detect such issues.

Q. What advice would you give an investor looking to integrate point-in-time data into their financial analysis? A. For starters, ask your financial data company how they handle their historical data. Also run high-level analytics to check if your data vendor is in fact accurately storing its data correctly.  It’s extremely important to know this about a dataset early on in the research process, so you can give yourself the opportunity to evaluate alternatives.

Q. How can you get point-in-time data from Calcbench? A. Calcbench subscribers get point-in-time standardized data by downloading the Calcbench API client Python package, and then calling the standardized method. (See screenshot below.) For a demo, contact us@calcbench.com.

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