• Add a comprehensive but concise description of your solution, in addition to the files and code itself. This should provide a comprehensive overview of your strategy, and would greatly help with judging!

• If you have any code and file submissions, add a publicly viewable link to the materials - GitHub, Google Drive, Google Colab, Dropbox could be good ways to do this

• You can also add any images and videos in the "Project Media" and "Video Demo" sections

• Also add the names, and emails of all teammates - this will make it easier for us to contact you if you are a finalist or winner!

Overview / Motivation

A description for the problem, and why you chose to investigate it. e.g. Based on the recent news, we explored whether neural networks could be used to predict the returns of Gamestop.

Data Cleaning and Modelling

A description of what you needed to do to the data , how you modelled it , and why e.g. We first calculated log-returns, given that prices are non-stationary. We then used neural networks as a model to forecast future log-returns, as we thought NNs might be able to learn complex non-linear relationships in the data.

Results and Evaluation

What did you find, was it expected, what would you do next / differently e.g. We found that neural networks had a poor out-of-sample performance on this task, but perhaps it was because we only used past log-returns as a feature. If we had more time, we would explore the use of further models, or adding more

Link to Google Colab Notebook (code)

Link to Report

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