Revenue recognition timing and attributes of reported revenue: The case of software industry's adoption of SOP 91-1 [An article from: Journal of Accounting and Economics]
Book Details
Author(s)Y. Zhang
PublisherElsevier
ISBN / ASINB000RR598Y
ISBN-13978B000RR5984
AvailabilityAvailable for download now
Sales Rank11,047,278
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Journal of Accounting and Economics, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
I examine how revenue recognition timing affects attributes of reported revenue, using a sample of software firms that adopted Statement of Position 91-1 in the early 1990s. I find early recognition yields more timely revenue information, as evidenced by higher contemporaneous correlation with information impounded in stock returns. However, such early recognition diminishes the extent to which accounts receivable accruals map into future cash flow realizations and lowers the time-series predictability of reported revenue. Overall, the results suggest early revenue recognition makes reported revenue more timely and more relevant, but at the cost of lower reliability and lower time-series predictability.
Description:
I examine how revenue recognition timing affects attributes of reported revenue, using a sample of software firms that adopted Statement of Position 91-1 in the early 1990s. I find early recognition yields more timely revenue information, as evidenced by higher contemporaneous correlation with information impounded in stock returns. However, such early recognition diminishes the extent to which accounts receivable accruals map into future cash flow realizations and lowers the time-series predictability of reported revenue. Overall, the results suggest early revenue recognition makes reported revenue more timely and more relevant, but at the cost of lower reliability and lower time-series predictability.

