AbstractThis study examines whether geographic information disclosed at an increasingly disaggregated level (specifically, consolidated vs. continent vs. country) results in increased predictive ability of company operations (specifically, sales, gross profit, and earnings). Multinational corporations (MNCs) are formed using a simulated merger approach by combining the annual operating results of six individual firms, one from each of six countries. This approach makes it possible to compare the forecasting accuracy of data disclosed at the country, continent, and consolidated levels, not possible using current geographic segment disclosures. Previous studies using year‐ahead forecast models implicitly assume the predictive factors included in the models are significant in forecasting operating results. Using regression forecast models, this study tests whether the predictive factors included in the models are effective in forecasting operating results by examining the direction, size, and significance of the regression coefficient estimates. The coefficients provide evidence that exchange rate changes, inflation, and real GNP growth are useful in forecasting annual sales and gross profit. Whereas, at least for this sample and this time period, exchange rate changes, inflation, and real GNP growth are not significant variables in forecasting annual earnings. The results indicate that the accuracy of forecasts increase as sales and gross profit are disclosed at a more disaggregated geographic level. The hypothesized relationship between consolidated, continent, and country levels, while holding strongly under perfect foresight, holds to a lesser extent using forecasts of exchange rates, inflation, and real GN