Abstract. This paper empirically examines whether labor strikes affect the forecasting and information content of quarterly earnings numbers. We address two issues regarding financial analyst forecast (FAF) superiority: whether FAF superiority increases when a strike occurs and if so, whether the increase in FAF superiority is sustained immediately after the strike ends. We also examine two issues regarding information content: whether strikes affect the coefficient mapping unexpected earnings into stock prices and whether strikes affect the variance of stock price changes. We suggest that strikes affect both the forecasting and information content of quarterly earnings numbers. Résumé. Cet article examine de façon empirique si les grèves des travailleurs influencent la prévision et le contenu informatif des résultats trimestriels. Nous abordons deux questions relatives à la supériorité des prévisions d'analystes financiers (PAF): à savoir si la supériorité des PAF s'accroît lorsqu'une grève se produit et, dans l'affirmative, si l'accroissement de cette supériorité se maintient immédiatement après la fin de la grève. Nous étudions également deux questions relatives au contenu informatif: à savoir si les grèves influencent le coefficient incorporant l'impact des bénéfices imprévus sur le cours des actions et si les grèves affectent la variance des fluctuations du cours des actions. Les grèves affecteraient donc à la fois la prévision et le contenu informatif des résultats trimestriels.

This paper provides evidence of security analyst (SA) superiority relative to univariate time-series (TS) models in predicting firms' quarterly earnings numbers and shows that SA forecast superiority in our sample is attributable to: (1) better utilization of information existing on the date that TS model forecasts can be initiated, a contemporaneous advantage; and (2) use of information acquired between the date of initiation of TS model forecasts and the date when SA forecasts are published, a timing advantage.

This study examines the association between abnormal returns and five alternative proxies for the market's assessment of unexpected quarterly earnings. We examine the role that measurement error potentially has in multiple regression tests of abnormal returns (occuring around the time of earnings announcements) on an unexpected earnings proxy and other non-earnings variables. The results indicate a potential measurement error interpretation of such multiple regression tests. We examine three procedures which reduce, to an unknown degree, the measurement error problem. Our procedures appear to be more (less) effective at reducing measurement error for small (large) firms and recent (non-recent) forecasts.