Similar to Assignment 1, this assignment also gives you an opportunity to leverage on the existing knowledge in the financial performance research and your newly acquired skill in machine learning. The emphases in this assignment are your critical analysis skill to evaluate and provide insightful critics on data, and data analytics estimators of both the conventional statistical approaches and the machine learning algorithms. We are not expecting you to be an avid computer programmer to create an AI. We want you to leverage on the AI to teach an AI. Yes! You can leverage on ChatGPT to assist you with Python code for the data analysis. Be mindful that, ChatGPT or other generative AI are tools to get things done. They can hallucinate and give false responses. You are still responsible for the accuracy and the final writing.
Bankruptcy prediction
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§ Bankruptcy prediction using machine learning. Data source: Company Bankruptcy Prediction | KaggleLinks to an external site.
Sentiment analysis
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§ Sentiment analysis on product/service review. Data source: British Airways Passenger Reviews (2016 - 2023) | KaggleLinks to an external site.
Sustainability and profitability
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· Emission and profitability. Data source: 10-year ASX data: emission data asx all.xlsx
1. Once you have decided on a project, download the appropriate dataset from their respective repositories. Also, take a look at this recording to get some ideas on how to approach this assignment. This is a high level overview.
2. Go to Science Direct collection at ECU Library. You will need your ECU access credential to log into the database. Here is the link to the repository.
3. Search for research articles related to your project. You will use some of them as a foundation for your own analysis. Use the following questions to guide your endeavour. These questions will assist you to make your analysis relevant.
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1. What are the findings?
2. Where applicable, what are the variables, including the control variables, they use and why?
3. What are the limitations of these existing studies?
4. How would your analysis extend the existing research?
4. Using appropriate machine learning algorithms and conventional statistical methods, write a report on the followings.
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1. The estimators you use in the data analytics. This is the core of your discussion.
1. Which estimators you use for the analysis? E.g., neural network, logistic regression, k-nearest neighbour.
2. Explain why you choose them. What are their strengths and limitations?
3. How well each estimator performs such as their accuracy?
4. Discuss any limitations in the data, how these affect the estimators' performance, and how you address them.
2. Extract inisghts from the data. Discuss the implications of your findings within a business decision context. Position yourself as an advisor for a group of investors. See the examples below.
1. Bankruptcy prediction: Which metrics (financial ratios) are important and why they are relevant to your client's investment decision.
2. Sentiment analysis: How strong the correlation between the sentiment and the variables of interest? What would be your advice for your clients?
5. Write a 2000-word report on your analysis. The professional report is to be presented to an intelligent, non-specialist audience. You can use these headings to structure your report.
1. Introduction.
2. Methodology.
3. Results, insights, discussion, and recommendation.
4. Limitations and conclusion.
5. References.
Your report is intended for managerial level decision makers. They don’t need standardised beta and p-values. They need actionable results. Include persuasive data visualisation where necessary