SEMESTER 2 2023/24

COURSEWORK BRIEF:

This assessment relates to the following module learning outcomes:

A. Knowledge and Understanding

A1. Solutions and technologies specifically designed for handling and extracting patterns from big data.

A2. Interpret the output of advanced analytics techniques used for complex data analytics applications.

B. Subject Specific Intellectual and Research Skills

B1. Identify the statistical models appropriate for analysing the various decisions with complex/big data

B2. Assess the relevance of statistical package outputs to the decisions being addressed.

B3. Work with current software packages to create models using complex data sources.

C. Transferable and Generic Skills

C1. Critically analyse practical difficulties that arise when implementing advanced data analytics methods.

C2. Demonstrate an ability to use software for data analytics and to interpret its output.

Coursework Brief:

(50/100 marks) Part A.

A company specializing in recommending digital content to online users is considering adopting a data-driven solution to enhance their business and user experience both operationally and strategically.

• A1: One area they would potentially be interested in, is to measure the sentiment of the content they provided (using A1_standard.csv dataset). What model would you use and why it is preferred than other methods? Kindly support your answer with experimental results, including predictive accuracy and statistical comparisons with alternative models.

• A2: In the future, what additional features would add value to the system using additional data and advanced analytical techniques? Please provide two different features. What are the implications for adding these additional features? You are not expected to write code for those features, but you are expected to include the source of the data and the techniques if any, for instance the literature and/or the documentation.

(50/100 marks) Part B.

It is time to demonstrate your knowledge by applying machine learning in finance!

Please use ten years of data from 10 assets selected from NASDAQ shares (preferably using a systematic asset selection approach) between the range of 2014.01.01-2024.01.01, run the necessary data pre-processing and processing techniques to:

• Develop a momentum strategy as a simple algorithmic trading strategy.

• Backtest the built trading strategy and evaluate the pros and cons of your Strategy.

• Optimize your strategy to make it perform. better and evaluate your strategy’s performance and robustness.

• Compare the results between the shares/industries and explain which one recovers from the covid in earliest time.

• Construct a portfolio and optimised it using different scenarios.

• You must not use any shares you previously submitted for coursework.

Helpful tips:

You may contact [email protected] for part A and [email protected] for part B.

For all Parts:

You are recommended to use the writing style. suggested below and please be consistent about these settings.

• Fixed font and size: Times new roman, size = 11 for body text

• Use headings for different sections

• 1.5 line/paragraph spacing

• Add captions to the tables and figures

• Use good-quality images for figures

• Cite references properly using Harvard

• For reproducibility, please set random seed using yourstudent number

• The Programming language: Python, others are not allowed.

Carefully report the various steps of your methodology and discuss your results in a rigorous way! Please do not include code in your report. You can put your code (in ipynb or py format) into a zip file and submit via Turnitin using an additional file submission link, other than the “REPORT ONLY” link.

Nature of Assessment: This is a SUMMATIVE ASSESSMENT. See ‘Weighting’ section above for the percentage that this assignment counts towards your final module mark.

Word Limit: +/-10% either side of the word count (see above) is deemed to be acceptable. Any text that exceeds an additional 10% will not attract any marks. The relevant word count includes items such as cover page, executive summary, title page, table of contents, tables, figures, in-text citations and section headings, if used. The relevant word count excludes your list of references and any appendices at the end of your coursework submission. You should always include the word count (from Microsoft Word, not Turnitin), at the end of your coursework submission, before your list of references.

Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Module Code, Assignment Title, Word Count. This assignment will be marked anonymously, please ensure that your name does not appear on any part of your assignment.

References: You should use the Harvard style. to reference your assignment. The library provide guidance on how to reference in the Harvard style. and this is available from: http://library.soton.ac.uk/sash/referencing

Submission Deadline: Please note that the submission deadline for Southampton Business School is 16.00 for ALL assessments.

Turnitin Submission: The assignment MUST be submitted electronically via Turnitin, which is accessed via the individual module on Blackboard. Further guidance on submitting assignments is available on the Blackboard support pages.

It is important that you allow enough time prior to the submission deadline to ensure your submission is processed on time as all late submissions are subject to a late penalty. We would recommend you allow 30 minutes to upload your work and check the submission has been processed and is correct. Please make sure you submit to the correct assignment link.

Email submission receipts are not currently supported with Turnitin Feedback Studio LTI integrations, however following a submission, students are presented with a banner within their assignment dashboard that provides a link to download a submission receipt. You can also access your assignment dashboard at any time to download a copy of the submission receipt using the receipt icon. It is vital that you make a note of your Submission ID (Digital Receipt Number). This is a unique receipt number for your submission, and is proof of successful submission. You may be required to provide this number at a later date. We recommend that you take a screenshot of this page, or note the number down on a piece of paper.

The last submission prior to the deadline will be treated as the final submission and will be the copy that is assessed by the marker.

It is your responsibility to ensure that the version received by the deadline is the final version, resubmissions after the deadline will not be accepted in any circumstances.