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Available: Mar 25, 2023 21:00Available: Mar 25, 2023 21:00until May 26, 2023 21:59 until May 26, 2023 21:59

Details

Group Assignment will contribute 20% towards your final grade and each group should have 3 members. The due date to form a group is Friday 14th April, by 11:59 pm, and the due date for submission is Friday 26th May, by 11:59 pm.

What you need to submit:

· Report

· Code

· Group data

1.0 Background

Adjusted Closing Price (ACP), the closing price after adjustments for all applicable splits and dividend distributions, may objectively and accurately reflect the listed company's market value.

In this assignment, your task is to develop three predictive models and forecast the adjusted closing price given its historical values. You need to download the dataset from Jupyter notebook and split training and test sets. Your training set should include all data from Jan 2017 to Jan 2023, and your test set should include all data in Feb 2023.

2.0 Obtaining data by your Group number

For this assignment, you will need to download the Python Jupyter notebook file “00assignment.ipynb”. Once you have downloaded the file, enter your group number in the input line, i.e. change the number ‘86’ in the code “group_num = 86” (For example, your group is “86”, so the “group_num = 86”). Then, run all the commands in the same Jupyter notebook file to retrieve and save your data set, which will be saved into the file "your group number_data.csv".

Download Here: 00Assginment.ipynb Download 00Assginment.ipynb

3.0 Assignment instructions

Written report

The report's purpose is to describe, explain, and justify the findings of your selected company.

Suggested potential outline for the main parts of the report (further details below):

1. Business context and problem formulation.

2. Data processing and primary analysis

3. Analytical approach I

4. Analytical approach II

5. Analytical approach III

6. Result and recommendations

Please note that the ‘Analytical approach’ section would better be the most substantial part of your written report. You may consider breaking down the longer parts into smaller sections.

1. Business context and problem formulation.

Your report gives a detailed description of the problem that is being investigated, providing the context and background for the analysis.

2. Data processing and primary analysis

You describe the data processing steps clearly and in sufficient detail, justifying and explaining your choices and decisions. Your choices and decisions are justified by data analysis, domain knowledge, logic, and trial and error (if necessary). You describe your analysis process, presenting selected results. Your analysis is sufficiently rich, and your visualizations are insightful. You explain the relevance of the analysis results to the underlying business problem. You interpret the statistical outputs that you provide.

3-5. Analytical approach

We recommend you choose at least three methodologies, which has learned this semester, to predict the ACP in Feb 2023 (You need to balance the prediction accuracy and interpretability of models when you choose models). To facilitate the comparison of the predictive ability of different models, RMSE will be used as the error evaluation standard.

You clearly describe and justify the models, methods, and algorithms in your analysis. The choice of methods is logically related to the substantive problem, underlying theoretical knowledge, and data analysis. You interpret the estimated models. You report crucial assumptions and whether they are potentially violated. Your overall analysis is rich, comprehensive, thorough, and logical. You implement a sound model selection process. You obtain a high standard of predictive accuracy in line with what is achievable with the methods at the level of experience expected from students taking this unit. You are not misled by overfitting and explicitly acknowledge the limitations of the data and/or methods.

6. Result and recommendations

The reasoning from the analysis and results to your conclusions and recommendations is logical and convincing. Your conclusions and recommendations are written in plain language appropriate for non-technical audiences. Your recommendations are well-thought-out, carefully developed, and potentially useful.

Jupyter Notebook

Your assignment is required to submit the Python code used for the analysis, as a Jupyter notebook or Python script. The code is submitted separately from the report. The code may be examined to verify that you have done the work. Your code should have comments that clearly indicate which parts correspond to which sections of your report. You should explicitly acknowledge when you borrow pieces of code from external sources. If the training of your model involves generating random numbers, the random seed must be fixed, e.g. ‘np.random.seed (0)’, so that the marker expects to have the same results as you had.

4.0 Submission instructions

· The page limit is 10 pages excluding the cover page, duty declaration page, and appendices (no limitation in font type, size, and line spacing) and your report saves as “ECMT2130_your group number_report”.

· Clearly and appropriately present any relevant graphs and tables.

· All the numerical results are reported up to four decimal places.

· Please use APA 6th or 7th in your report.

· The Jupyter notebook saves as “ECMT2130_your group number_code”. Not submitting your code will lead to a loss of 30% of the assignment marks.

· The individual data save as “ECMT2130_your group number_data”. Not submitting your data will lead to a loss of 30% of the assignment marks.

· Late submissions are subject to a deduction of 5% of the maximum mark for each calendar day after the due date. After ten calendar days late, a mark of zero will be awarded.

5.0 Marks

Marks awarded will be based on the following:

6.0 Help

If you have any questions, feel free to contact the teaching team by email or ask on ED.

Q & A

https://edstem.org/au/courses/10914/discussion/1259527Links to an external site.