We examine reporting delay of start-up firms using two measures. The first measure is based on firms’ discretion over the length of the first reporting period. Greek start-up firms that were obliged to follow double entry accounting system (in accordance with local generally accepted accounting principles (GAAP) or International Financial Reporting Standards (IFRS)) could determine their first year-end for external financial reporting and influence the length of their first accounting period by choosing either a +/- twelve-month reporting period. In addition to factors proposed in the accounting literature (i.e., auditing and level of performance), we find that liquidity constraints, costs of preparation, and weakened reporting role of accounting information are associated with a start-up firm’s reporting choice. Given the tendency of policymakers towards harmonization of accounting, our results point to increasing need to assess the effect of common reporting choices under country-specific and non-harmonized institutional tax arrangements.
Our second measure is based on delay in the filing of financial reports in the first reporting period. We further deconstruct our second measure into the period (ASM_period) between the fiscal year-end (FYE) and approval of financial statements at the shareholders general meeting (ASM) and the period between convergence of the ASM and the filing date (FD) of approved financial statements (FD_period). We find that variation of the ASM_period is influenced by the level of equity capital, the size of the board of directors, and employment of auditing firms that are variables aligned with shareholders’ information asymmetry. The length of the FD_period is negatively related to firm’s performance and leverage and is positively related to the length of the ASM_period.
The datasource is the database of “ICAP” covering the period 2002-2010. We hand-collected data from summaries of founding agreements and firms’ announcements from the National Printing House. We apply logistic regressions and ordinary least squares regression using consistent and robust estimators.