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The last part of the coursework is more open-ended. You should extend your analysis by both applying familiar techniques in a new way and exploring an approach that we have not covered

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DSM120 Financial Data Modelling Coursework Assignment Questions | SUSS

Midterm Coursework assignment: April to September 2025 Study Session

The coursework is worth 100 marks, weighted at 50% of the final mark.

  • Please note you are permitted to upload your coursework in the final submission area as many times as you like before the deadline. You will receive a similarity/originality score, which represents what the Turnitin system identifies as work similar to another source. The originality score can take over 24 hours to generate, especially at busy times, e.g. submission deadline.
  • If you upload the wrong version of your coursework, you can resubmit updated
    versions of your coursework. You need to click on the ‘submit paper’ button again and submit your new version before the deadline.
  • In doing so, this will delete the previous version that you submitted, and your new updated version will replace it. Therefore, your Turnitin similarity score should not be affected. If there is a change in your Turnitin similarity score, it will be due to any changes you may have made to your coursework.
  • Please note, when the due date is reached, the version you have submitted last will be considered as your final submission, and it will be the version that is marked.
  • Once the due date has passed, it will not be possible for you to upload a different version of your assessment. Therefore, you must ensure you have submitted the correct version of your assessment, which you wish to be marked, by the due date.

Coursework introduction

The intention of the module is for you to follow along with each of the topics, testing all the methods and ideas on your portfolio of at least half a dozen time series.

The coursework mostly stems from this activity. Make sure that two of the time series in your portfolio include high-frequency financial data of your choice from the available high-frequency data sets on the module page on VLE. Each high-frequency data set contains minute-by-minute data for 3 years. You may not consider the full 3-year range of the high-frequency time series for the analysis; instead, you may select part of the range for your analysis.

Submission requirements

Students are required to submit the following:

  • A Jupyter notebook, containing the relevant code and any additional supporting material. Please make sure the work is reproducible when the
    The marketing team runs it locally.
  • A report (ideally HTML, generated from Jupyter Notebook) detailing the methodology, analysis, and findings.

Note on the datasets: If the datasets used are available publicly (or accessible on the VLE), you do not need to upload them in your submission. Please use and provide a direct link to the dataset within your code.

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Background

The coursework is designed to directly support the learning outcomes of the module by providing you with a hands-on approach to analysing real-world time-based data, particularly in financial contexts.

Through the portfolio-based assessment, you will apply key time series analysis techniques. A major focus of the coursework is on predicting future values of time series, requiring you to engage with regression models, Monte Carlo simulations, and autoregressive methods. In addition, the open-ended component of the coursework aligns with the module’s broader objective of equipping you with the ability to independently investigate, analyse, and make predictions on complex time-based data using both traditional and modern computational techniques.

The Coursework assignment

Part 1

At the end of each of the first five topics, you will find a section outlining what you should include in your portfolio at that stage. Your submission should provide a structured account of your engagement with each topic, demonstrating both implementation and critical analysis. This should include:

  • A summary of your approach, describing the steps you have taken and the reasoning behind your choices.
  • Results presented clearly, using graphs, error estimates, or any other appropriate method to communicate findings effectively.
  • The software and code used ensure reproducibility and clarity in implementation.
  • A reflection on your findings, discussing observations about the datasets and methods explored.
  • A critical evaluation of the models applied, assessing their limitations and the insights gained from your analysis.

[60 Marks]

Part 2

In this section, you will extend your analysis by conducting ACF, PACF, and AR(q) predictions at two different frequencies of the selected high-frequency dataset. For example, you may extract data at intervals of 5 minutes and 10 minutes and perform comparative analysis.

Use Monte Carlo methods in conjunction with AR(q) to simulate 25 potential future scenarios for each frequency for both 5-minute and 10-minute data intervals. Employ kernel density estimation (KDE) to estimate the probability distributions of these future scenarios, providing insights into forecast uncertainty.

Compare and evaluate forecasting performance across both frequencies,
considering model effectiveness at different time resolutions. Explore how alternative forecasting approaches, parameter refinements, or extended comparisons can provide deeper insights.

[20 marks]

Part 3

The last part of the coursework is more open-ended. You should extend your analysis by both applying familiar techniques in a new way and exploring an approach that we have not covered in detail during the lectures. To achieve full marks, you should engage in both aspects of this exploration.

You may wish to consider that the topics that we think will be covered are STL decomposition, ARIMA, Vector Auto Regression, ARCH, GARCH, and Kalman filter. Some possibilities for things not covered are deep learning, recurrent neural networks, and technical trading analyses.

[20 marks]

How this Coursework will be Graded

The marks in this coursework are allocated as follows:

Part 1

There are 60 marks available for completing tasks outlined at the bottom of each of the first five topics. You do not need to perform any additional implementation, and it is perfectly acceptable to use stats models or other libraries. For each of those five topics:

  • Up to 4 marks for producing the results of the main algorithms on selected data. Full marks will be awarded for well-structured implementation with appropriate visualisation.
  • Up to 4 marks for reflecting on how well the method works under different conditions, with a clear discussion of its strengths and limitations.
  • Up to 4 marks for exploring beyond the standard implementation, such as comparing multiple approaches, experimenting with different parameters, or applying additional evaluation metrics.

Part 2

There are 20 marks available for completing this part:

  • Up to 8 marks for conducting ACF, PACF, and AR(q) analysis at two different frequencies, demonstrating correct implementation and a well-reasoned discussion of the results.
  • Up to 8 marks for applying Monte Carlo methods in conjunction with AR(q), ensuring well-structured simulations, appropriate visualisations, and insightful interpretation, including Kernel Density Estimation (KDE) to assess probability distributions of future scenarios.
  • Up to 4 marks for extending the analysis beyond standard implementation, such as comparing different forecasting approaches, evaluating performance variations across time resolutions, or refining parameter selection.

Part 3

The remaining 20 marks are more open-ended and more attuned to your creativity and backgrounds. There are several ways to obtain these marks, and it is up to you to put them together.

  • Up to 10 marks for finding something different and conducting the kind of
    analysis we have been doing on the topics. You can do two of these.
  • Up to 10 marks for investigating an approach that was not covered in detail in the lectures. Your submission should include both the implementation and an evaluation of its results.

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References

Alongside the course materials, for essential reading on time series forecasting and analysis, please refer to:

  • Rob J. Hyndman & George Athanasopoulos (2021), Forecasting: Principles and Practice (Online Book) – A widely used, frequently updated resource
    covering key forecasting concepts.
  • Franses, van Dijk, & Opschoor, Time Series Models for Business and Economic Forecasting – Focuses on the role of time series in economic
    modeling.
  • James Douglas Hamilton (1994), Time Series Analysis – A comprehensive,
    mathematically rigorous reference for advanced time series methods.

A full list of reading materials is available on the VLE page