Do Director Networks Help Managers Forecast Better?

I examine whether directors' superior access to information through their board network improves the accuracy of firms' forecasting. Managers may benefit from well-connected directors (i.e., board centrality) as they may have limited insight into market developments or decision-making processes of other firms beyond knowledge specific to their firm. Employing a sample of U.S.-listed companies, I separately examine the effect of within-firm variation in direct and indirect board connections on management earnings forecast accuracy. The study contributes by showing that higher-degree connections can have an economically significant effect on the accuracy of management forecasts, regardless of firms' board interlocks. Further analyses point toward well-connected directors' ability to provide managers with valuable advice in a forecasting context, which complements directors' more extensively studied role in preventing managerial expropriation.

Societal Trust and Management Earnings Forecasts

ABSTRACT We investigate the relationship between societal trust and managers' decisions to voluntarily issue earnings forecasts. We reason that managers are more likely to issue earnings forecasts in high-trust countries than in low-trust countries because investors view these voluntary disclosures as more credible information about the firm's future profitability. We find evidence consistent with these predictions, suggesting that societal trust fosters corporate voluntary disclosure. We also document that societal trust works as a substitute for country-level formal institutions in terms of its implications for management earnings forecast (MEF) issuance. Additionally, we find a stronger relationship between firm-level commitment to credible disclosure and MEFs in low-trust countries, suggesting that country-level societal trust relates to the effectiveness of firm-level credibility-enhancing mechanisms. Finally, we show that firms from countries with higher societal trust issue more precise and accurate MEFs that contain more information about multiple items.