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The current study aims to examine the relationship between delay in the announcement of quarterly forecasts of annual earnings and the type of earnings news in a unique context. Running a multiple linear regression on data collected from Rahavard Novin software and the companies’ financial statements, is the method of this study to investigate this relationship. Consistent with the pattern of good news early, bad news late, it was found that there is a positive relationship between the bad news type and the amount of delay in announcing quarterly forecast of annual earnings so that the firms with negative adjustments in earnings forecast (bad news), on average, have 12 more days delay in the announcement. Considering other variables showed that as coverage percentage - a sign of success - increases, the amount of delay in announcing earnings forecast decreases, but companies with losses per share, on average, have an additional delay of about 6 days. The results obtained indicate that at least, in some industries there is certain time for reporting. Finally, it became clear that in the period after the adoption of the new disclosure instruction, despite the increased deadline, the amount of delay in earnings announcement has declined by about 2 days. In this study, for the first time in Iran, one of the company’s financial news (quarterly forecasts of annual earnings), have been classified into good and bad, based on comparison with the market expectation, and the relationship between the news type and the amount of delay in announcing the news, has been examined. Cilj prikazane raziskave je v specifičnem kontekstu proučiti povezavo med zakasnitvijo objave četrtletnih napovedi dohodka in vsebino objave. Uporabili smo multiplo regresijsko analizo, podatke, ki jih je zbral programski paket Rahavard Novin, in finančna poročila podjetij. V skladu z vzorcem »dobre novice prej, slabe kasneje« ugotavljamo, da obstaja pozitivna povezava med vrsto slabe novice in velikostjo zakasnitve pri objavi prognoze letnih zaslužkov. Firme, pri katerih so napovedi zaslužka negativne (slabe novice) so objavljene s poprečno zakasnitvijo 12 dni. Če upoštevamo še druge spremenljivke, je pokazalo naslednje: če se poveča odstotek pokritja – znak uspešnosti – je zakasnitev manjša, toda pri firmah z izgubo se zakasnitev poveča za dodatnih približno 6 dni. Dobljeni rezultati tudi kažejo, da je za nekatere gospodarske panoge uveljavljen nek čas za poročanje.

Decision-making problems in the area of financial status evaluation are considered very important. Making incorrect decisions in firms is very likely to cause financial crises and distress. Predicting financial distress of factories and manufacturing companies is the desire of managers and investors, auditors, financial analysts, governmental officials, employees. Therefore, the current study aims to predict financial distress of Iranian Companies. The current study applies support vector data description (SVDD) to the financial distress prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 3-fold cross-validation to find out the optimal parameter values of kernel function of SVDD. To evaluate the prediction accuracy of SVDD, we compare its performance with fuzzy c-means (FCM).The experiment results show that SVDD outperforms the other method in years before financial distress occurrence. The data used in this research were obtained from Iran Stock Market and Accounting Research Database. According to the data between 2000 and 2009, 70 pairs of companies listed in Tehran Stock Exchange are selected as initial data set. Odločanje na področju evaluacije finančnega statusa podjetij so zelo pomembne: napačne odločitve zelo verjetno povzročijo pretres in finančno krizo podjetja. Predvidevanje finančnih kriz in pretresov v proizvodnih podjetjih je pomembno za manager­je, investitorje, revizorje, finančne analitike, državne uradnike in zaposlene. Cilj tega članka je analizirati predvidevanje finanč­nih pretresov v iranskih podjetjih. Naša študija uporablja metodo SVDD (Support Vector Data Description) za predvidevanje finančnih pretresov in predlaga nov bolj stabilen model predvidevanja z večjo močjo razlage. V ta namen smo uporabili tehni­ko preiskovanja mreže in uporabili 3-kratno prečno validacijo, da smo poiskali parametre jedrne funkcije SVDD. Da bi ocenili natančnost predvidevanja SVDD, smo jo primerjali z metodo FCM (fuzzy c-means). Rezultati eksperimenta so pokazali, da je SVDD uspešnejša od drugih metod v letih pred pojavam finančnega pretresa. Podatke, ki smo jih uporabili v naši študiji, smo dobili s teheranske borze in baze podatkov računovodskih raziskav. V skladu s podatki iz obdobja 2000 in 2009 smo izbrali 70 parov družb, ki so bile uvrščene na teheransko borzo.