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ICM316 Programming for Fintech

1. What is the purpose of this assessment?

The following table shows which of the module learning outcomes are being assessed in this assignment. Use this table to help you see the connection between this assessment and your learning on the module.

Module Learning Outcomes being assessed

Carry out a practical project that involve Python applications in Finance

Conduct a web crawling and deal with big data

Conduct textual analyses

Apply econometric models to test a financial theory / hypothesis

Interpret and critically discuss the empirical results in light of prior finance literature

2. What is the task for this assessment?

Task (attach an assignment brief if required)

The purpose of this project is to conduct an empirical investigation using Python coding skills. The skills involved in this project include (i) web crawling; (ii) managing and querying from a big dataset; (iii) textual analyses; (iv) an econometric model; and (v) critically discussing of related finance literature.

Details of the project are as follows:

(1) Using Python codes to download and store all filings of at least 200 companies in 2022 from the U.S Securities and Exchange Commission Electronic Data Gathering, Analysis and Retrieval system (EDGAR) to a local computer in an efficient method.

(2) Using Python codes to download daily stock prices, trading volume and annual financial data of at least the companies in (1) for the period from 1st January 2022 to 31st December 2022. Note: you are NOT required to download all annual financial data for the companies. You can decide which annual financial data to be included e.g. firm total assets, total debts etc.

(3) Using Python codes to create a Structural Query Language (SQL) database for the downloaded data, including the filings, financial data and stock data.

(4) Using Python codes to select a sample of at least 100 firms and query their 10-Q filings from the database. Note: You are NOT required to query all the firms that filed 10-Q forms in 2022.

(5) Conduct textual analyses of the sampled firms’ 10-Q filings.

(6) Produce a summary statistics of these companies’ stocks characteristics, e.g., returns, trading volume, liquidity, etc. during positive/negative filing dates.

(7) Conduct an econometric procedure to examine the relationship between any of the stock characteristics and tone in 10-Q filings.

3. What is required of me in this assessment?

Guidelines/details of how to prepare your submission

Your submission should comprise:

• A report with clear structure (at least introduction, main body, conclusion) and tables and figures. Reference list with all sources used. The report file should be Microsoft Word format.

• A code file with codes for Anaconda Jupyter Notebook Python 3.6+.

The report should NOT include Python codes.

The submission should be uploaded to [Blackboard> ICM316> Assessment].

All members of a given group will receive the same mark, and so it is up to you to determine the allocation of work within the group. Each group leader should upload your submission to Blackboard with the group number, group members’ names and student numbers on the cover page.

Three key pieces of advice based on the feedback given to the previous cohort who completed this assignment

Start working on the group project right away.

Nominate a group leader, organise the group work and have regular meetings to discuss progress.

The group leader should report immediately to the course convenor if any member of the group is not cooperating/contributing.

It is essential to get your codes work in Anaconda Jupyter Notebook.

In addition, the readability of your codes is important. Using comments (#) is recommended.

Furthermore, you should be able to interpret outcomes of your codes and empirical results.

It does not matter whether your findings are statistically significant or not, you should provide a correct and appropriate interpretation of your findings.

Ensure that you dedicate sufficient time to understanding your findings and their implications and communicate this clearly in your report.

For a high mark, you are expected to go beyond the references provided under ‘Resources’ below to show that you have read widely for this project and can link your findings to those of existing studies.

For Group Work Only

Elements of Group Working:

☐ Classroom Briefing by Module Convenor

☒ Regular Meetings of All Team Members

☐ Record and Keep Evidence of Meetings (agenda/minutes)

☒ Record Attendance and Member Contributions

☐ Team Reflection Document

☐ Submit Peer Assessment Required

Formatting Guidelines

Microsoft Word

Word limit/guidance and penalty applied

2,000 words, excluding tables/figures, references.

Referencing Style

Harvard

Guidance on Academic Misconduct (including using Turnitin practice area)

The work you produce must be your own or that of members of your group if it is a group assessment. It must not have been submitted as part of other assessments, at this or another institution.

You should ensure that the work you produce adheres to the University’s statement on academic integrity and to the regulations regarding academic misconduct (such as plagiarism and cheating).

You can find information about this at:

http://www.reading.ac.uk/internal/exams/Policies/exa-misconduct.aspx

4. The Marking Scheme (Marking criteria/rubric)

Please refer to the marking criteria rubric at the end of this document.

5. What resources might I use to get started?

Python 3.6+.

Microsoft Word can be used to prepare the report and to format tables / figures.

Lecture and seminar notes.

Loughran-McDonald dictionary for textual analyses could be found here . A simplified version of the dictionary is provided at [Blackboard> ICM316>Assessment>Dictionary]. You are free to use other dictionary(ies).

Students are expected to read relevant literature which can be accessed via the Library Resources, including but not limited to:

- Loughran & McDonald (2011) When is liability not a liability? Textual analysis, Dictionaries, and 10-Ks. Journal of Finance, 66,35-65.

- Jiang et al. (2019) Manager sentiment and stock returns. Journal of Financial Economics, 132, 126-149.

6. Guidance regarding the use of AI tools

The use of AI tools:

☐ Is prohibited for this assignment

☒ Is permitted for this assignment, provided that their use is properly acknowledged, in accordance with university guidelines

Note: the MISuse of Generative AI tools, including the failure to appropriately acknowledge the use of such tools, is considered Academic Misconduct and carries sanctions, as detailed in the Assessment Handbook.

Academic misconduct guidelines:

https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/9-academic-integrity-and-academic-misconduct-final.pdf (see section 9.2 a) i – Plagiarism

Annex 1 related to the use of AI tools:

https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/9a-gait-aiam.pdf