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Tag Archives: statistical tests
Financial reporting fraud. 2022 Best
This assignment surrounds fraud detection and asks you to investigate the application of Benford Law as a tool to detect financial reporting fraud.
Financial reporting fraud.
Assignment Option (B) – Fraud Detection Requirement. This assignment surrounds fraud detection and asks you to investigate the application of Benford Law as a tool to detect financial reporting fraud. A list of 45 companies has been provided for you. These companies have been determined by Audit Analytics (available through WRDS) to have not only misrepresented their accounts; but have done so in a fraudulent manner. The details of each fraud and the account-years which are affected are provided.5 Using financial data, your task is to provide empirical analysis and establish whether these fraudulent accounts could have been predicted by Benford Law.
Financial reporting fraud.
In particular, you should analyze these fraudulent reports and compare the results to companies that are deemed to be clean. The clean companies can be any of your choosing, but the clean sample must be comparable to those which are fraudulent (e.g. in terms of size, industry, profitability). 6 Your analysis should include a comparison of MAD, KS and Chi-squared test for both groups – including tests of difference. You are also required to perform separate analyses, not only on the financial data as a whole, but according to type of information i.e. Balance sheet, Income Statement, and Cashflow items. Comparisons should be made to the findings of Amiram et al (2015).
Financial reporting fraud.
You may also wish to take the type of fraud being committed into account when performing any analysis. No financial data is provided – you will need to manually collect this yourselves through, for example, Compustat (WRDS). Using your python skills acquired within this module (and elsewhere), your report should seek to provide (but not limited to): An introduction – Discussion of Benford Law, the potential application to fraudulent account detection, what your research goals are, and what you hope to achieve through your analysis. A literature review – You have two strands of literature to discuss, namely Benford Law, and fraud detection.
Financial reporting fraud.
Remember that a literature review should be a cohesive discussion of extant literature, how it relates to your research, and is the main indicator of whether you understand the subject area or not. Utilise the skills that you have learned within BEAM068 to paint a rounded picture of the subject area. Methodology – What statistical tests/models are you going to perform and why? Data – A description of the data and its origin, including summary statistics of the key variables. Univariate analysis – The inclusion of (but not limited to) tests of difference between the two groups of firms in terms of the financial data acquired, along with separate analysis of balance sheet, income and cashflow statement items.
Financial reporting fraud.
Multivariate analysis – No multivariate analysis is required but you may wish to investigate Amiram et al’s proposition that misstatements are more likely to occur in smaller, younger, more volatile, growing firms. This may be done by using key independent variables in order to explain your Benford conformity measures by way of a regression. Remember that the companies I have given you represent fraudulent misstatements only, and therefore may not adhere to the findings of Amirim et al (2015). You may wish to uncover evidence as to the type of companies that fraudulent misstatements are more likely to occur.
Financial reporting fraud.
Conclusion – What are your key findings and what are the implications? Python code – A code (*.py) file which documents each stage of your analysis (uploaded as a separate file). You may expand or explore this topic in any way you feel appropriate – provided that the key areas highlighted above are covered. https://youtu.be/c8AVe776g7s
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Applied Empirical Accounting and Finance. 2022 Best
This paper explores applied empirical accounting and finance. In 2015/2016 the US market saw a record high number of Chapter 11 Bankruptcy filings across the mining industry – unprecedented in recent times.
Applied Empirical Accounting and Finance.
Requirement This assignment incorporates several elements of the BEAM079 module and requires that you perform an empirical investigation and discussion of the following scenario – building upon the work you may have carried out in BEAM078 Applied Empirical Accounting and Finance. In 2015/2016 the US market saw a record high number of Chapter 11 Bankruptcy filings across the mining industry – unprecedented in recent times. 1, 2 Using financial data, sentiment analysis, and neural networks, your task is to provide empirical analysis and establish whether these bankruptcies could have been predicted.
Applied Empirical Accounting and Finance.
Utilizing a model that can be used to predict mining industry bankruptcies one year prior to failure. Incorporating a mixture of financial and textual data, you are tasked with analysing the annual reports of 27 bankrupt mining companies, and 73 non-bankrupt companies (the training sample) during the years 2015/2016. The list of companies, the annual reports, and some financial data has been provided3 . Your primary analyses will be based on this “training” sample of 100 firms. Using your python skills acquired within this module (and elsewhere), your report should seek to provide (but not limited to):
Applied Empirical Accounting and Finance.
An introduction – Discussion of the mining industry and recent bankruptcies, what are the potential causes, and what are your research goals? A literature review – You have 3 strands of literature to discuss namely: Bankruptcy prediction literature, sentiment analysis literature, neural network literature. Remember that a literature review should be a cohesive discussion of extant literature, how it relates to your research, and is the main indicator of whether you understand the subject area or not. Utilise the skills that you have learned within BEAM068. Methodology – What statistical tests/models are you going to perform and why?
Applied Empirical Accounting and Finance.
Data – A description of the data and its origin, including summary statistics of the key variables. Univariate analysis – The inclusion of (but not limited to) tests of difference (t-tests) between the two groups of firms in terms of both financial and textual data (tone and readability); correlation – ensure/demonstrate that there are no extreme correlations that will affect your multivariate neural network; a comparison of bankrupt and non-bankrupt word clouds. Multivariate analysis – Neural Network(s).
Applied Empirical Accounting and Finance.
A predictive model that uses both financial and non-financial data. How accurate is it (ROC AUC)? Some extensions might include – Is it better having textual data in the model rather than just having financial variables? Does it retain its accuracy when applied to the validation sample? How much more accurate is a neural network than a logit model – in terms of both training and validation? Weighted vs unweighted sentiment (advanced). Conclusion – What are your key findings and what are the implications?
Applied Empirical Accounting and Finance.
Python code – A code (*.py) file which documents each stage of your analysis (uploaded as a separate file). Notes: • The provided annual reports were collected from Perfect Filings (UoE database library). • The provided financial data was collected from WRDS compustat. • The list of failed companies was collected from the Lopucki bankruptcy database.4 • The list of sentiment words from the Loughran McDonald dictionary is provided. Feel free to collect any additional information that you feel is necessary to your study. https://youtu.be/DYg2jT9aUG4
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