Artificial Intelligence Measurement of Disclosure (AIMD)

Abstract

Empirical research on voluntary disclosure lacks an appropriate measurement technique for quantifying the intensity of firm’s disclosure. In this paper I introduce AIMD, a computerised technique for measuring disclosure using artificial intelligence, which derives disclosure proxies from English-language annual reports for ten different information dimensions without human involvement. Criterion validity tests indicate that, controlling for a robust set of covariates and multiple statistical techniques, AIMD is negatively associated with information asymmetry as proxied by spreads and PIN. Furthermore AIMD has construct validity when compared to the AIMR disclosure rating, Standard & Poor’s Transparency and Disclosure Rating, several proprietary manual disclosure scorings and companies’ own assessment of their level of disclosure as indicated by a survey. I also demonstrate the applicability of AIMD as a cost-effective technique for measuring disclosure using a sample of 127,895 firm-year observations of companies regulated by the SEC.

Keywords: disclosure, measurement, artificial intelligence, annual report

JEL Classification: C80, F3, M41