New York - Thomson Reuters has added to its solutions portfolio for quantitative analysis with the launch of I/B/E/S Point in Time (Point in Time), which offers the buy-side another tool for financial backtesting and historical analysis. Point in Time is built on the Thomson Reuters I/B/E/S Quantitative File System and I/B/E/S historical databases.
Forward-looking bias in investing occurs when investment professionals incorrectly assume they knew something at a particular point in time. Point in Time introduces the concept of a point date, which is the date of the file that the data appeared in. By utilizing a point date, a client could be able to see when that data was known - eliminating the need for a lag or to make time assumptions about the data. A user of Point in Time can follow all changes in EPS and Recommendations throughout time since no data is overwritten or deleted - a particularly useful tool when watching for recommendation scale changes. Point in Time users can see the changes as they happen and see what the scale was before the change and what the recommendation scale became after.
Point in Time's market coverage includes 80,000 companies from over 100 countries. Thomson Reuters has a global focus and aims to ensure that data, methodology, and collection rules are consistent; i.e., one database and one methodology for all companies, irrespective of geography.
Point in Time can also help eliminate "survivorship bias," which occurs when performance backtests have incomplete data. This happens when the universe of available companies only represents the present. Thomson Reuters provides an ongoing company coverage database that never expires, even if the company has been delisted. The I/B/E/S Point in Time product is a daily snapshot database of the QFS and History files with a daily time series starting in Jan 1, 2000. I/B/E/S Point in Time also has data with activation dates as early as January 1980.
Thomson Reuters has introduced a time series of the Expected Report date for a company. By utilizing this feature, a user can see companies who have a history of delaying reports and if this correlates negatively or positively with their earnings.