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2019, vol. 16, br. 2, str. 1-58
Predviđanje vrste revizorskog mišljenja - statistika, mašinsko učenje ili kombinacija navedenih?
Univerzitet Singidunum, Beograd

e-adresatradojevic@singidunum.ac.rs
Ključne reči: mišljenje revizora; finansijski izveštaji; generalizovani linearni mešoviti modeli; random forest (drveće odlučivanja); statistički paket GRRF (Guided Regularized Random Forest); skupovi
Sažetak
Cilj ovog istraživanja je prevazilaženje metodoloških ograničenja uočenih u prethodnim istraživanjima u oblasti predviđanja vrste revizorskog mišljenja i izvlačenje pouzdanih zaključaka o uporedivim prediktivnim performansama različitih metoda koje se koriste u te svrhe. Prediktivne performanse dvanaest modela iz oblasti statistike i mašinskog učenja su ocenjene u dva različita praktična scenarija: a) kada su prethodne informacije (vrste revizorskih mišljenja) o klijentu dostupne i mogu se koristiti za predikciju i b) kada su prethodne informacije nedostupne (npr. novoosnovana društva). Rezultati pokazuju da, u prvom scenariju, nekoliko metoda iz obe grupe prediktivnih metoda ostvaruju uporedive performanse u vrednosti od 0,89, mereno površinom ispod krive (eng. Area under the curve). U drugom scenariju, međutim, algoritmi mašinskog učenja, posebno oni zasnovani na drveću odlučivanja, kao što je random forest, ostvaruju značajno bolje rezultate od statističkih metoda, i to u vrednosti od 0,79. Razvili smo i ocenili performanse dva hibridna modela, koji za cilj imaju da iskoriste prednosti statističkih metoda (interpretabilnost rezultata) i metoda mašinskog učenja (obrada velikog broja objašnjavajućih varijabli i veća preciznost). Celokupna procedura je prikazana na reproducibilan način, uz korišćenje najvećeg empirijskog skupa podataka korišćenog u dosadašnjim istraživanjima ovog tipa, koji obuhvata 13.561 par godišnjih finansijskih izveštaja i korespondirajućih revizorskih izveštaja. Procedure opisane u ovom članku omogućavaju revizorskim kućama i finansijskim službenicima širom sveta da razviju i testiraju prediktivne modele koji podržavaju procedure revizorskog planiranja i ocenu rizika ispravnosti podataka u finansijskim izveštajima.
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O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/EJAE16-21832
objavljen u SCIndeksu: 27.10.2019.
metod recenzije: dvostruko anoniman
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