Construction of a classification model to predict the probability of default
$10-30 USD
Pagado a la entrega
Goal: Construction of a classification model to predict the probability of default for a company based on various financial ratios.
Data: Historical data on corporate financial ratios and default events for public companies over the 1995-2004 period.
1. Create a folder. You’ll use 1 external data file as input – containing historical financial ratios and default events for public companies.
2. Import the input dataset, use the [login to view URL]() function to convert the variable Default into a factor (this will tell R to understand its values as categorical, not as continuous). The variable Default indicates the default status of the company in a particular year – it equals 1 if the company defaults on its debt, and 0 otherwise. The variable ID serves as a company identifier, since most companies are observed over multiple years, and the variable Year designates the year of observation.
3. There are 5 financial ratios: WC2TA (working capital-to-total assets), RE2TA (retained earnings-to-total assets), EBIT2TA (earnings before interest and taxes-to-total assets), ME2TA (market value of equity-to-total assets), and S2TA (sales-to-total assets). Compute summary statistics for the data:
4. Compute and report the overall default rate for the sample (number of default events divided by the number of observed companies).
5. Use boxplots to visualize the distribution of each one of the 5 financial ratios by default status. Here is an example with 3 of the ratios:
6. Based on the plots, can you hypothesize the sign of the coefficient that each one of the financial ratios would have in a classification regression, where the default status is the dependent variable and the specific financial ratio is one of the explanatory variables (predictors)?
7. Demonstrate and explain what would happen if we fit a linear regression model (instead of a logistic model), where the default status is the dependent variable and the financial ratios are the explanatory variables (predictors)?
8. Use the function glm()to fit logistic regression models, where the default status indicator is the dependent variable and the financial ratios are the explanatory variables. Try all 16 combinations of explanatory variables, where you have 3 or more predictors in a model.
9. Use a cut-off of 50% probability to assign a model-predicted class (e.g. default or non-default) and create a table distributing the in-sample observations into correctly classified defaults and non-defaults, and incorrectly classified defaults and non-defaults.
10. Based on the table in item 9. compute what percentage of all observations are correctly classified by the model? What is the Type I error rate? What is the power (sensitivity) of the classification model?
11. Repeat steps 9. and 10. with cut-offs of 10% and 90% and report the table, percentage accuracy, Type I error rate, and power (sensitivity) of the model. How does the power (sensitivity) of the model change with the change of the cut-off? Why do you think it changes this way?
Nº del proyecto: #33557000
Sobre el proyecto
13 freelancers están ofertando un promedio de $49 por este trabajo
Hi I am a very experienced statistician, data scientist and academic writer. I have completed several PhD level thesis projects involving advanced statistical analysis of data. I have worked with data from several comp Más
Hello! I'm statistician and will glad to help you with this project. I'm reliable man and you can be sure, that my work will have proper quality. Tell me details! -- Looking forward for working with you, Vladimir
hi there I' m an expert in data science for the past 5 years. I can confirm to you i can deliver this project perfectly please lets discuss .................................................
Hi, I have a lot of experience with the R language. I also have a master's degree in data science. My reviews prove to you that I worked well on R projects. Your project is a challenge for me. Let's discuss it. For th Más
Hello! My name is Aldo. I am an actuarial student. I have many experience with R, Python, SQL, VBA, finances and statistics. I always deliver high quality work within accepted time limit and budget. Please start the Más
Hello! I am interested in your project Construction of a classification model to predict the probability of default I have completed similar papers in the past and can assure you of exceptional and original work withi Más
Construction of a classification model to predict the probability of default Being MBA and having a professional experience of 7 years in different types of writing services, I can better assist you with my budget frie Más
Construction of a classification model to predict the probability of default I am a supreme writer who has full command over Academic Writing including; SWOT, PESTEL, Porter Five Forces Analysis, 7Ps, summaries, Case Más
yes i can completing this project in minimum days and i am certified for this job because i am data entry operator
