000			01951nam a22002537a 4500
005			20250422100210.0
008			250422b ph ||||| |||| 00| 0 eng d
022			_a2380-2154
040			_cOCT
100			_aKhavis, Joshua
240			_aJournal of Financial Reporting / _hFall 2024
245			_aCollaborating with data aggregators and the estimize.com setting / _cJoshua Khavis and Han-Up Park
300			_aVol 9 (2) pages 71-101 : _billustrations ; _c27 cm
500			_aABSTRACT: Our paper aims to assist researchers interested in generating new data and conducting field experiments to devise strategies for collaborating with startups and online platforms such as Estimize.com (Estimize). Specifically, we provide advice on collaborating with data aggregators in general and share past experiences working with Estimize, an online platform that crowdsources forecasts of earnings, revenue, key performance indicators (KPls), and economic indicators. We inform academics about the opportunities and challenges of collaborating with online platforms such as Estimize by documenting prior successful and unsuccessful collaboration attempts and by sharing Estimize's responses to our questions regarding what they deem important for collaboration. We also present details on the unique archival datasets currently available through Estimize, discuss important events impacting the platform, explain potential ways to generate new data by collaborating with the platform, highlight how the setting's distinguishing features can help test accounting theories, and discuss limitations. Data Availability: Data are available from the public and proprietary sources cited in the text.
650			_aEstimize
650			_acrowdsourcing
650			_aexperiments
650			_adata aggregator
650			_aearnings forecasts
650			_aanalysts
650			_aconsensus forecast
700			_aPark, Han-Up
942			_2ddc _cCR _n0
999			_c10354 _d10354