Reference no: EM133034456
TFIN605 Data Analytics in Finance - Australian National Institute of Management and Commerce
Analysis of Corporate Debt Maturity in Australia and the U.K.
For the alternative final project you will conduct analysis of the debt maturity of companies from the following two countries: Australia and the U.K.
Relevant financial data is available in one excel file: debt_au_uk.xlsx. You will have to upload this file to your Jupyter notebook and then conduct your analysis using Pandas and other Python Libraries. You will have to submit the Jupyter notebook you used to do the analysis for this project.
You will have to write up your analysis in a report of up to 2,000 words. Your report should also include tables and graphs from your analysis. These tables and graphs have to be produced using Python and you will have to submit all the relevant codes in a Jupyter notebook.
The objectives of your analysis are as follows:
- Document and discuss the distribution and trends in debt maturity ratio (long-term debt/total debt) over time in each country (Australia and the U.K.):
o Use long-term debt to total debt (short-term debt plus long-term debt) ratio as the measure of debt maturity.
o Debt maturity ratio measures the percentage firm's debt that is long-term debt.
o Debt maturity ratio is not a meaningful measure in the following case, so you need to deal with this case in the data pre-processing step:
When a firm has no debt (zero debt), the debt maturity ratio is not a meaningful measure.
- So exclude observations (rows) with zero or missing total debt from your sample
o Also, cap the debt maturity variable and the key firm characteristics (e.g., as firm size, profitability, growth opportunity etc.) at the top and bottom 1 percentile level (see the cash ratio example).
o You will conduct the analysis for Australia and the U.K. and you will discuss how the debt maturity ratios of the two countries compare with each other and if they show similar or different trends over time.
o You will document the distribution of debt maturity ratio in each country in 2007 and 2017 to see if the distribution has changed over time. You can use histograms, kernel density plots and percentile plots to show the distributions.
• Analyse the determinants of debt maturity ratio in each country. So you will have two sets of results.
o Initially, explore the relations between various firm characteristics (such as firm size, profitability, growth opportunity etc.) and debt maturity ratio using scatter plot.
o You will then conduct correlation analysis to determine if there are significant correlations between these characteristics and debt maturity ratio.
o Then use simple linear regressions to quantify the relation between debt maturity and these characteristics one at a time. Here you will use regressions with one independent variable (see lecture 7).
o Finally you will use multiple linear regression analysis to consider the effects of all the different firm characteristics on debt maturity ratio.
o You will compare and contrast the results you get from the above analysis for the two countries in your sample: Australia and the U.K.
• Finally, you should estimate two Machine Learning models and evaluate the predictive performance of these models.
o The first model will try to predict the debt maturity ratio of a firm. You can use the Boston House Price example as a template for this analysis and do similar analysis on debt maturity ratio (instead of house price).
» As X (or independent) variables, use the four firm characteristics we used in the group project: Firm size (Logsale), Profitability, Tangibility and Market to book ratio.
» The y variable or dependent variable in your model would be the debt maturity ratio.
» You should to the train-test split and evaluate the model's performance on the test dataset and interpret the results.
You should read the paper by Fan et al. for this assignment. This paper analyses debt maturity ratio, and it will help you understand the general research background and how to interpret the results. As independent variables in your analysis, you should use the same firm characteristics that we used in the leverage analysis in the group project (such as firm size (Logsales), Profitability etc.).
You should also do additional research via google on the determinants of debt maturity ratio and use those sources as references in your report.
You will summarise you main finding is a report of 2000 words. The report will:
1. Summarise the relevant literature (research papers) and research question.
2. Report and discuss descriptive data analysis and data visualisation.
3. Report and discuss correlation and regression analysis
4. Report and discuss Machine Learning (ML) analysis of debt maturity ratio using the linear regression model (LinearRegression: covered in lecture 11 in the Boston House Price example). Fit an ML model to predict debt maturity ratio and evaluate the performance of the model.
6. Draw inference and conclusion and relate the findings to existing research.
Article - An International Comparison of Capital Structure and Debt Maturity Choices by Joseph P. H. Fan, Sheridan Titman, and Garry Twite
Attachment:- Data Analytics in Finance.rar