This study examines the usefulness of general price level information (GPL) and current cost information (CC) originally provided by SFAS No. 33 as compared to historical cost information (HC) in predicting bankruptcy. The study also examines the usefulness of GPL data versus CC data when each supplements HC data. In addition, this study tests the usefulness of the three types of information systems combined in one model (HC, GPL, and CC) versus HC data in predicting bankruptcy. The study focuses on the predictability of business failure using financial ratios as predictors. A comparison of these predictors is made in order to identify the accounting system that yields a better prediction of bankruptcy. Two multivariate statistical techniques, multiple discriminant analysis (MDA) and logistic regression analysis (LRA), are used to derive the ex—post classification and the ex-ante prediction results. Six functions are developed, based on ratios computed with HC, CC, GPL, the combined HC and GPL, the combined HC and CC, and the combined HC, GPL, and CC. The resulting functions are used to classify 40 firms as failed or nonfailed. The analysis is repeated for three time bases—one, two, and three years before bankruptcy. The main results of the various analyses …

SFAS No. 52, Foreign Currency Translation, was issued in December, 1981, replacing SFAS No. 8, Accounting For the Translation of Foreign Currency Transactions and Foreign Currency Financial Statements. SFAS No. 52 has shifted the impact of translation gains and losses from the income statement to the balance sheet. It was expected that SFAS No. 52 would eliminate the incentive for multinationals to engage in various hedging activities to reduce the effect of the translation process in reported earnings. It was also expected that multinationals would change their foreign exchange risk management practices. The major purpose of this study was to investigate the effect of SFAS No. 52 on foreign exchange risk management practices of U.S. based multinationals.

The purpose of this research is to study and improve decision accuracy in the real world. Spot price prediction of petroleum products, in a budgeting context, is the task chosen to study prediction accuracy. Prediction accuracy of executives in a multinational oil company is examined. The Brunswik Lens Model framework is used to evaluate prediction accuracy. Predictions of the individuals, the composite group (mathematical average of the individuals), the interacting group, and the environmental model were compared. Predictions of the individuals were obtained through a laboratory experiment in which experts were used as subjects. The subjects were required to make spot price predictions for two petroleum products. Eight predictor variables that were actually used by the subjects in real-world predictions were elicited through an interview process. Data for a 15 month period were used to construct 31 cases for each of the two products. Prediction accuracy was evaluated by comparing predictions with the actual spot prices. Predictions of the composite group were obtained by averaging the predictions of the individuals. Interacting group predictions were obtained ex post from the company's records. The study found the interacting group to be the least accurate. The implication of this finding is that even …