Reference no: EM132296882

Data Mining Modeling Project -

For this Discussion Post: We are going to bring together all that you have learned in this course. For your last assignment in this course, you will complete a data modeling project provided to you on the Final Project: The Details page. Once you have analyzed the data and modeled it, you will share your results with the class in a narrated presentation. This assignment is broken down into three parts.

Part 1: The Scenario

Assume you are a Business Analyst of a big financial firm - where your job is to analyze the financial statements of different corporations and develop predictive models to generate early warnings about any potential financial risks (such as probable bankruptcy) of a corporation.

In one of the cases, you have the historical bankruptcy data and you need to develop a model to predict the of a corporation, what are the steps you would be taking to meet the goals? (Hint: go through the questions provided on the Final Project: The Details page).

Part 2: The Presentation

Once you have the model, prepare a PowerPoint Presentation to convince your executives that - this model addresses company's business needs and the company can rely on the model predictions (i.e., how trustworthy the model is?).

NOTE: You DO need to do a narrated presentation for this assignment. If you don't know how to do this, view the page how to present your work online page before getting started.

Part 3: Create and Share

Once you have created the PowerPoint presentation and are ready to share it:

• Attach your PowerPoint presentation AND your HTML R Code in the discussion.
• Share your thoughts on why you think the model you have created does or does not meet the business needs of your company and why, as well as whether or not you think the model is trustworthy. Be detailed, drawing on all that you have learned in this course.

Discuss and Engage:

• Share your conclusions and presentation discussing what you learned with your peers.

Predicting Bankruptcy -  

Data analysts are often asked to predict corporate bankruptcies.

The Scenario

Just as doctors check blood pressure and pulse rate as vital indicators of the health of a patient, business analysts scour the financial statements of a corporation to monitor its financial health. Whereas blood pressure, pulse rate, and most medical vital signs, however, are measured through precisely defined procedures, financial variables are recorded under much less specific general principles of accounting. A primary issue in financial analysis, then, is how predictable is the health of a company?

One difficulty in analyzing financial report information is the lack of disclosure of actual cash receipts and disbursements. Users of financial statements have had to rely on proxies for cash flow, perhaps the simplest of which is income (INC) or earnings per share. Attempts to improve INC as a proxy for cash flow include using income plus depreciation (INCDEP), working capital from operations (WCFO), and cash flow from operations (CFFO). CFFO is obtained by adjusting income from operations for all non-cash expenditures and revenues and for changes in the current asset and current liabilities accounts.

A further difficulty in interpreting historical financial disclosure information is caused whenever major changes are made in accounting standards. For example, the Financial Accounting Standards Board issued several promulgations in the middle 1970s that changed the requirements for reporting accruals pertaining to such things as equity earnings, foreign currency gains and losses, and deferred taxes. One effect of changes of this sort was that earnings figures became less reliable indicators of cash flow.

In the light of these difficulties in interpreting accounting information, just what are the important vital signs of corporate health? Is cash flow an important signal? If not, what is? If so, what is the best way to approximate cash flow? How can we predict the impending demise of a company?

To Get Started: The Context

To begin to answer some of these important questions, we conducted a study of the financial vital signs of bankrupt and healthy companies. We first identified 66 failed firms from a list provided by Dun and Bradstreet. These firms were in manufacturing or retailing and had financial data available on the Compustat Research tape. Bankruptcy occurred somewhere between 1970 and 1982.

For each of these 66 failed firms, we selected a healthy firm of approximately the same size (as measured by the book value of the firm's assets) from the same industry (3 digit SIC code) as a basis of comparison. This matched sample technique was used to minimize the impact of any extraneous factors (such as industry) on the conclusions of the study.

The study was designed to see how well bankruptcy can be predicted 2 years in advance. A total of 24 financial ratios were computed for each of the 132 firms using data from the Compustat tapes and from Moody's Industrial Manual for the year that was 2 years prior to the year of bankruptcy. The table lists the 24 ratios together with an explanation of the abbreviations used for the fundamental financial variables. All these variables are contained in a firm's annual report with the exception of CFFO. Ratios were used to facilitate comparisons across firms of various sizes.

Predicting Corporate Bankruptcy: Financial Variables and Ratios

What This Table Explains

The first four ratios using CASH in the numerator might be thought of as measures of a firm's cash reservoir with which to pay debts. The three ratios with CURASS in the numerator capture the firm's generation of current assets with which to pay debts. Two ratios, CURDEBT/DEBT and ASSETS/DEBTS, measure the firm's debt structure. Inventory and receivables turnover are measured by COGS/INV and SALES/REC, and SALES/ASSETS measures the firm's ability to generate sales. The final 12 ratios are asset flow measures.

Using This Data and R, Your Job Is To:

• Decide on what data mining technique(s) would be appropriate in assessing whether there are groups of variables that convey the same information and how important that information is? Conduct such an analysis.
• Comment in your presentation on the distinct goals of profiling the characteristics of bankrupt firms versus simply predicting (black box style) whether a firm will go bankrupt and whether both goals, or only one, might be useful. Also comment on the classification methods that would be appropriate in each circumstance.
• Explore the data to gain a preliminary understanding of which variables might be important in distinguishing bankrupt from nonbankrupt firms. (Hint: As part of this analysis, use side-by-side boxplots, with the bankrupt/not bankrupt variable as the x variable.)
• Using your choice of classifers, use R to produce several models to predict whether or not a firm goes bankrupt, assessing model performance on a validation partition. Based on the above, comment on which variables are important in classification, and discuss their effect.

Once Your Data Analysis is Complete:

Once you have your data analysis complete, you are ready to create, display and present your results in a narrated slideshow. Move on to the next page to learn how to do this.

Attachment:- Assignment File.rar