Reference no: EM133547539 , Length: word count:2000
Assignment: 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 Al. We want you to leverage on the Al to teach an AI. Yes! You'll leverage on ChatGPT to assist you with data analysis using your preferred machine learning approach.
Artificial neural network
1. Bankruptcy prediction.
Data source: Company Bankruptcy Prediction | Kaggle
Sentiment analysis
1. Media sentiment and share price. Data source:
Twitter and Refinitiv
2. Media sentiment and about products, firms, or industries.
Data source: Twitter, Refinitiv, and IBIS World.
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.
2. Once you have decided on a project, download the appropriate dataset from their respective repositories.
3. 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.
4. Search for research articles related to your project. E.g., if you choose Media sentiment and share price, you would likely to search for 'sentiment analysis and share price' articles. 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.
A. What are the findings?
B. Where applicable, what are the variables, including the control variables, they use and why?
C. What are the limitations of these existing studies?
D. How would your analysis extend the existing research?
5. Using appropriate machine learning algorithms and conventional statistical methods, write a report on the followings.
A. The estimators you use in the data analytics. This is the core of your discussion.
I. Which estimators you use for the analysis? E.g., neural network, logistic regression, k-nearest neighbour.
II. Explain why you choose them. What are their strengths and limitations?
III. How well each estimator performs such as their accuracy?
IV. Discuss any limitations in the data, how these affect the estimators' performance, and how you address them.
B. Discuss the implications of your findings
Witnin a business Decision context.
Position yourself as an advisor for a group of investors. See the examples below.
I. Bankruptcy prediction: Which metrics (financial ratios) are important and why they are relevant to your client's investment decision.
II. Media sentiment and share price: How strong the correlation is? What would be your advice for your clients?
III. Media sentiment and about products, firms, or industries: How strong the correlation is? What would be your advice for your clients?
6. 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.
A. Executive summary.
B. Introduction.
C. Methodology.
D. Results, discussion, and recommendation.
E. Limitations and conclusion.
F 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.