The MSc degree is awarded for demonstration of advanced knowledge of accounting research, skill in knowledge construction, advanced knowledge of a specific field of research, advanced knowledge of data-gathering methods, scholarship in use of sources relied on and a contribution to knowledge development in accounting. BEAM056 provides students with the basic building blocks with which to complete these fundamental aspects of the degree program, and to prepare students for the final dissertation module.
Your assignment concerns corporate failure prediction and you are required to answer ALL of the research questions listed below.
(1) Can accounting data be used to predict the failure of a company?
(2) What are the driving factors that caused the failure of the energy companies within your sample?
(3) Are these factors simply internal or can they be related to the external environment? (e.g. the ability to adapt to regulatory changes).
(4) Can accounting ratio prediction models be improved by including qualitative data such as the rhetoric found within annual reports?
Using the tools provided, you are required to conduct independent research to investigate and answer the research questions. Your final written document should adhere to the following structure:
Abstract Introduction Literature Review Methodology Data
Results Conclusion References
Appendices (where appropriate)
You will be provided with a list of US energy corporations (19) which failed (filed for bankruptcy under Chapter 11) during 2016. These companies will be the main focus of your analyses.
You will also need a sample of non-failed firms with which to compare and contrast with the ones which have failed. The sample of non-failed firms should be chosen and detailed independently. You are required to have a sample of non-failed firms (of your choosing), consisting of at least 100 observations1.
In order to answer research question (4) you are provided with a list of the most popular positive and negative words found within global financial reports. You are required to analyse at least 5 positive and 5 negative words of your choosing from this list. The reasons for your selections should be clearly noted within your report.
Examples and advice will be provided throughout the course.
The assignment and the research questions are deliberately and broadly positioned. Given the voluminous literature and the many different ways authors have attempted to tackle this question in the past, students are granted a free license to answer the questions in any means they feel appropriate.
1 As of September 2017 there are 807 North American energy companies available on the WRDS Compustat database. 520 non-failed companies are listed in the United States and have data available in 2016. Students may also wish to compare different industries of which there are over 4000 companies to choose from. A list of the 520 companies is now provided for you.
A minimum requirement to pass this module is that students should be able to determine if there any significant differences between failed and non-failed firms with regards to a series of single accounting ratios (of your choosing). Using these single (univariate) measures, students should demonstrate the ability to incorporate univariate measures into a single multivariate model. Univariate and multivariate models must be measured for accuracy (or the ability to separate failed and non-failed corporations) in an appropriate manner (e.g. Contingency table, ROC curves).
Students may wish to base their work upon a prior published study (e.g. Altman, 1968), this is acceptable provided that the replication contains a multivariate element. If this approach is taken then inferences must be drawn between the findings of the original paper, and those findings produced by the student. Differences are likely to reside in the country of study, the era in which the study was conducted, and the types of company which were analysed. These must be detailed and explained in detail within the results.
Remember that credit is awarded for originality and that plagiarism is not tolerated by the University of Exeter Business School.
Students will be assessed on their ability to:
· Demonstrate knowledge of the research topic;
· Critically appraise relevant research;
· Provide a clear understanding of the methodology and methods;
· Interpret results;
· Produce a clearly written, well-structured report.
– Beaver, W. (1966). Financial Ratios as Predictors of Failures. Journal of Accounting Research, 4(3)(Supplement), 71-102.
– Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609.
– Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93.
– Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
– Agarwal, V., & Taffler, R. J. (2007). Twenty-five years of the Taffler z-score model: does it really have predictive ability? Accounting and Business Research, 37(4), 285-300.
2 There are many papers relating to corporate failure and its prediction and this list is provided as a starting point for your research. Students are highly encouraged to engage in independent reading of additional sources of material.
– Agarwal, V., & Taffler, R. J. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 32(8), 1541-1551.
– Jackson, R, & Wood, A. (2013) The Performance of Insolvency Prediction and Credit Risk Models in the UK: A Comparative Study. The British Accounting Review, 45 (3), 183-202