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ACCT3015 Semester 2, 2022

Assignment – Part B

PART B: Machine Learning for predicting bankruptcy [30 marks total, worth 60%]

Note, that the 30 marks we award in this part of the assignment, will be rescaled down to 15 marks, which will accountfor 60% of your total assignment grade (i.e., 15 out of 25). Please respond to each of the 5 questions equally, given the mark allocation provided below.

Refer to the general instructions documentforfurther information on word count and submission requirements.

INSTRUCTIONS:

1. (5 marks) Consider the corporate bankruptcy prediction problem, and the list of variables in Appendix A. These variables are currently used to predict the probability of corporate failure for a firm, using annual financial and market data. Recommend additional variables, which are likely to be associated with the prediction of corporate bankruptcy and hence likely to improve the predictive power of the machine learning models. You should outline new variables that can be created from the existing variables (such as financial ratios), as well as external data sources you can collect from different Big Data sources. Justify why you believe they would be appropriate explanatory variables.

2. (5 marks) Recommend and discuss methods of exploratory data analysis that you would undertake to understand and visualize the data. Explain and motivate your choices.

3. (5 marks) Provide a summary analysis of the predictive performance of the CART model outlined in Appendix B, and an analysis of which variables contributed most to the model’s predictive power. Interpret the CART decision tree.

4. (5 marks) Compare results of the gradient boosting model outlined in Appendix C. Describe and explain any difference in model performance. Identify the role and impact of predictor variables in the CART and gradient boosting models.

5. (5 marks) Outline how the model/(s) can be embedded into a business or accounting process, to facilitate or inform decision-making. You may choose any appropriate business or accounting process.

Presentation and Referencing (additional 5 marks)

• Additional marks are awarded for the presentation of your report, as well as cohesiveness and quality of discussion.

• Please reference, where relevant. Your reference list will not be included in the word count.

Appendix A: Covariates included in models

#	Variable Name	Variable Type	Description
1	act	Numeric	Current Assets - Total
2	am	Numeric	Amortixation of Intangibles
3	ap	Numeric	Accounts Payable - Trade
4	at	Numeric	Total Assets
5	bkvlps	Numeric	Book value per share
6	capx	Numeric	Capital Expenditures
7	ceq	Numeric	Common/Ordinary Equity
8	ch	Numeric	Cash
9	che	Numeric	Cash and short-term investments
10	chech	Numeric	Cash and cash equivalents Increase/Decrease
11	csho	Numeric	Common shares outstanding
12	dlc	Numeric	Debt in current liabilities
13	dltt	Numeric	Long Term Debt - Total
14	dt	Numeric	Total Debt including current
15	dvc	Numeric	Dividends Common/Ordinary
16	dvt	Numeric	Dividends - Total
17	ebit	Numeric	Earnings Before Interest and Taxes
18	ebitda	Numeric	Earnings Before Interest and Taxes, Depreciation and amortization
19	fincf	Numeric	Financing activities net cash flow
20	gdwlam	Numeric	Goodwill amortization
21	ivncf	Numeric	Investing activities net cash flow
22	lct	Numeric	Current liabilities - total
23	lt	Numeric	Liabilities - total
24	mkvalt	Numeric	Market value - total
25	ni	Numeric	Net Income (Loss)
26	oancf	Numeric	Operating Activities Net Cash Flow
27	optvol	Numeric	Volatility - Assumption (%)
28	prcc_f	Numeric	Price Close - Annual - Fiscal
29	prch_f	Numeric	Price High - Annual - Fiscal
30	prcl_f	Numeric	Price Low - Annual - Fiscal
31	revt	Numeric	Revenue - Total
32	sale	Numeric	Sales/Turnover (Net)
33	spce	Numeric	S&P Core Earnings
34	spcsrc	Categorical	S&P Quality Ranking - Current
35	teq	Numeric	Stockholders Equity - Total
36	tfva	Numeric	Total fair value assets (TFVA)
37	tfvl	Numeric	Total fair value liabilities (TFVL)