the determinants and consequence of Chinese redacted information
Description
China adopted amendments allowing companies to redact filings without prior approval in 2016. Leveraging this change as a quasi-nature experiment, we explore whether managers utilize redacted information to withhold bad information in the more lenient regulatory environment. Our investigation uncovers a significant shift in managerial behavior: Since 2016, managers incline to employ redactions to obscure negative news rather than safeguarding proprietary data. Furthermore, we find that the poorer firm performance and a higher cost of equity are associated with the redacted disclosures after 2016, suggesting that investors perceive an increase in firm-specific risk attributed to withholding bad news through redactions. To identify firms with redacted information, we searched all annual reports of companies publicly traded on the SSE and SZSE from 2006 to 2019 in the WinGo database for terms, such as “豁免披露”, “披露豁免”, “暂缓披露” or “披露暂缓" in Chinese words. Our sample period starts from 2006 because Chinese public firms have been required to disclose data on research and development since 2006. Then, we manually check annual reports with at least one of the key phrases to ensure that they indeed represent redacted information. The "Original_Redact" dataset comprises information on firms with redacted data, merged with industry codes and categorized based on the types of redacted information. In the "Determinants" dataset, observations of both redacted and non-redacted firms are included. It encompasses proxies for proprietary information, the number of inventory patents, and a binary variable indicating trade secrecy. Additionally, measures of accrual and real earnings management are provided, along with other control variables. The “firm_performance” dataset contains observations with available firm performance measures, TobinQ or Market-adjusted return. The “PMS3_firmper” dataset presents a Propensity Score Matching (PSM) sample with available firm performance measures and control variables. Each redacted firm is matched with three non-redacting firms based on closest propensity scores. In the "Cost_Equity" dataset, observations with available cost of equity measures are provided. We measure cost of equity using PEG model and GLS model respectively. The “psm_coe_reg” dataset presents another Propensity Score Matching (PSM) sample with available cost of equity measures and control variables. The “deter_robust” dataset comprises observations with an alternative accrual earnings management proxy available for analysis. The “car_1”, “car_2” and “car_3” consist of firms issuing announcements regarding redacted information. We calculate the cumulative abnormal return using various models (Market model, Fama French 3 factors model, and Fama French 5 factors model), within the event window [-1, 1], [-1, 2] and [-1, 3]. The event date corresponds to the announcement date.