Thesis (Ph. D.)--University of Western Ontario, 2004. / Vita. Includes bibliographical references (leaves 138-152).

Thesis (Ph. D.)--Ohio State University, 1986. / Includes bibliographical references (leaves 261-272). Available online via OhioLINK's ETD Center.

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Active participation in international trade is a must for the governments and business of today's rapidly changing world. This situation enforces the need to have extensive information on market potential estimation methods. Special factors related to international markets such as data deficiency and unexpected data needs make the market assessment task a much more difficult and complicated issue. In this study, first, an attempt is made to have a compact inventory of existing international forecasting techniques. Different classification schemes from the literature are presented. In order to evaluate the available international forecasting techniques, nine evaluative factors have been defined and used in an overall evaluation attempt. These efforts showed that none of the existing techniques are very applicable and successful in international markets. Second, Multiple Factor Technique is presented as a low-cost, preliminary market assessment technique that can be utilized in international markets. The mechanics of the technique and it's rationale are discussed with a brief developmental background. The technique is tested by using different number of variables with unweighted and weighted market potential indices. The United States is chosen as the reference market while the United Kingdom is the target market. A durable consumer good (VCR) and a non-durable consumer good (beer) are used to test the Multiple Factor Technique with ten years (1974-1983) data. Multiple Factor Technique seem to be more accurate with non-durable products than with durable products. Meanwhile, this author believes that the results may have something to do with the maturity of both reference and target markets. For example, if the target country is immature but the reference country mature, MFT might be effective for future market potential estimates. Furthermore, a Multiple Factor Technique based short term forecasting attempt shows that the technique is as much applicable as it is for a market assessment technique. Future studies will improve our understanding as well as our ability to apply the Multiple Factor Technique. / M.S.

Defensive interval measures, first introduced by Sorter and Benston in 1960, have been largely ignored in the theoretical and applied literature. In this dissertation, the conceptual superiority of these ratios is explored and empirical investigations are undertaken to determine if these measures actually impart information different from the more traditional liquidity position indicators. Correlation tests of the cross-sectional degree of association between liquidity variables were performed. Significant associations between the traditional and defensive ratios were generally found, although the actual parameter estimates were usually quite small. In a number of other cases, statistical independence was established. These results were corroborated by time-series analyses. A literature review of bankruptcy studies indicates the important role that liquidity variables play in discriminating between failed and nonfailed firms. In view of the alleged superiority of the defensive intervals, it was postulated that consideration of these refined liquidity measures might improve discriminatory ability. The primary purpose of this dissertation was therefore to investigate the contribution that defensive intervals make to business failure prediction. Multiple discriminant analysis (MDA) was the basic technique employed to evaluate this contribution. Using ratio sets found to be good predictors in prior research as a starting point, discriminant models were constructed that incorporated various combinations of defensive interval measures. A number of refinements over the typical application of MDA were considered in this model development: a priori odds of group membership were identified; a range of relative costs of misclassification errors was considered; tests of the equality of group dispersion matrices were performed in order to select the appropriate form of statistical analysis; the paired sample design was rejected; and a Bayesian inference approach was adopted to evaluate the models. Various quadratic MDA models were developed and evaluated, Evidence indicates that incorporating defensive interval measures in the analysis does indeed improve discriminatory ability. Most striking was the improvement noted in the correct classification of failed firms. The analysis was extended to a comparison of model predictions and going concern evaluations as reported in auditor opinions on financial statement presentations. Evaluation of a subsample of the failed firm population indicated that the selected quadratic models provided advance signals of going concern problems much more frequently than the auditor opinions. An independent sample was drawn containing companies that had been identified by their auditors as having going concern problems. For those firms that actually filed for bankruptcy, the discriminant models consistently outperformed the auditor opinions in terms of correct classification of going concern status. This advantage extended up to three years prior to the actual filing date. For those firms that did not file for bankruptcy, the models generally indicated going concern problems earlier than the auditor opinions. Discriminant models which incorporate defensive interval measures can provide some important input to the auditor's going concern review, As demonstrated in this dissertation, these models often provide early signals of imperiled continuing operations and thus may offer the auditor a valuable alternative perspective to consider in going concern evaluations. / Ph. D.

Copper econometric models currently existing in the open literature incorporate the demand side of the market only as an aggregated demand function. In this thesis, copper demand was disaggregated into its end-use industrial sectors and linear demand equations estimated for each sector. The correlation among the error terms of the various sectoral equations was explicitly taken into account in the estimation. Elasticities were computed at the means for the price and activity variables. A comparison with results using the Ordinary Least Squares method is also provided. Exogenous variables used in each sectoral equation were forecast separately and copper demand was subsequently forecast for each end-use sector. / Master of Arts

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 though an interacting group may be desirable for information synthesis, evaluation, or working toward group consensus, it is undesirable if prediction accuracy is critical. The accuracy of the environmental model was found to be the highest. This suggests that apart from random error, misweighting of cues by individuals and groups affects prediction accuracy. Another implication of this study is that the environmental model can also be used as an additional input in the prediction process to improve accuracy.

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 indicate that the combined HC and CC model has more discriminant power than does the HC, the GPL, or the combined HC and GPL models in each of the three years before bankruptcy. The results also show that there are significant differences in the overall classification rate derived from the combined HC, GPL, and CC model and the HC model, the GPL model, or the combined HC and GPL model . The differences between the combined HC and CC and the combined HC, GPL, and CC models are not significant in each of the three years before bankruptcy. The results also indicate that the differences in the the performance of MDA and LRA are not significant except in the second year before bankruptcy when the combined HC and GPL model is used.

A research report submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Management, specialising in Entrepreneurship and New Venture Creation Johannesburg, 2017 / This study examined the relationship between business failure and macroeconomic fluctuations within business cycles of South Africa’s economy for the time period 1980 to 2016. The study also sought to understand where, if any, immediate and lag correlations between fluctuations and business failure could be established. To understand this connection, this study used longitudinal data sets of different macroeconomic factors and studied their influence on business failure. The vector error correction model (VECM) was used to determine the long-term relationship between failure and each of the other variables. Additionally, Granger Causality was applied to establish whether the macroeconomic variables investigated in this study can be constructed to predict the probability of business failures. Three classes of macroeconomic predictor variables were considered. Firstly, well-known international variables in the form of GDP and CPI were used. Secondly, the study incorporated the three Composite Business Cycle indicators- leading, coincident and lagging. Lastly, behavioural indicators were used to incorporate the views of the actual businesses and their customers, which for this the study were the Business and Consumer Confidence Indices. After examining the effects the 7 macroeconomic variables had on business failure, the study found that there is a long-run relationship between the Composite Lagging Business Cycle indicator, the Business Confidence and Consumer confidence, which influenced Business Failure. Additionally, it was noted that Business Failure influence the Composite Lagging Business Cycle indicator in the long-run. The study additionally found that Business Failure may Granger Cause the Composite Leading Business Cycle indicator Outcomes of the study are potentially vital for entrepreneurs to understand the timing of entry into markets based on macroeconomic fluctuations through their cycles in certain industries. Business owners can make proactive financial and strategic decisions vital for survival of their business through the expansion and especially in the contraction cycles of the macroeconomic environments. / MT2017

I examine the presence of earnings management at pre-IPO and lockup periods. Motivated by significant post-lockup insider sales documented in prior research, I investigate whether insiders (managers and venture capitalists) inflate earnings around the lockup period in order to increase share price and maximize personal wealth from selling shares at lockup expiration. I also compare levels of earnings management in the pre-IPO and lockup periods with those in the post-lockup period. Prior research also documents that auditor quality mitigates earnings management behavior. I explore the impact of auditor quality in the unique setting of IPO lockups. ... Cross-sectional analysis reveals that my sample IPO firms also utilize real-activities manipulation, but only in the early pre-IPO period. The results are robust with respect to alternative abnormal accruals and real-activities measures. I also find that IPO firms that hire prestigious auditors experience less earnings management in the lockup period than firms with lower-quality auditors, after controlling for the monitoring role of venture capitalist and underwriter reputation. / by Lizhong Hao. / Thesis (Ph.D.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.