Types of politically connected firms and analysts' earnings forecast
Publication Name
Journal of Applied Accounting Research
Abstract
Purpose: This study examined the effect of different types of politically connected (PCON) Malaysian firms on analysts' forecast accuracy and dispersion. Design/methodology/approach: The study identified different types of PCON firms according to Wong and Hooy's (2018) classification, which divided political connections into government-linked companies (GLCs), boards of directors, business owners and family members of government leaders. The sample covered the period 2007–2016, for which earnings forecast data were obtained from the Institutional Brokers' Estimate System (IBES) database and financial data were extracted from Thomson Reuters Fundamentals. We deleted any market consensus estimates made by less than three analysts and/or firms with less than three years of analyst forecast information to control for the impact of individual analysts' personal attributes. Findings: The study found that PCON firms were associated with lower analyst forecast accuracy and higher forecast dispersion. The effect was more salient in GLCs than in other PCON firms, either through families, business ties or boards of directors. Further analyses showed that PCON firms—in particular GLCs—were associated with more aggressive reporting of earnings and poorer quality of accruals, hence providing inadequate information for analysts to produce accurate and less dispersed earnings forecasts. The results were robust even after addressing endogeneity issues. Research limitations/implications: This study found new evidence of the impact of different types of PCON firms in exacerbating information asymmetry, which was not addressed in prior studies. Practical implications: This study has a significant practical implication for investors that they should be mindful of high information asymmetry in politically connected firms, particularly government-linked companies. Originality/value: This is the first study to provide evidence of the impact of different types of PCON firms on analysts' earnings forecasts.
Open Access Status
This publication is not available as open access
Funding Number
027/2020