empirical_asset_pricing

In this project, we study using various machine learning models how the COVID pandemic affects share prices in tech stocks in the S&P 500.

Guide to our Github Repository

To run the code, please either clone the repository using Git:

git clone https://github.com/brandono7/empirical_asset_pricing.git 

or download the repository as a zip file and extract it to a location of your choice.

Note that due to large csv files in data, you will need to have git lfs installed in order to successfully clone the repository.

Prerequisites

Before running the project, make sure to install the following python libraries on your virtual environment via terminal / command prompt:

Required Libraries

pip install -r requirements.txt

Repository Structure

1. src folder: Contains the essential Jupyter Notebooks used for data cleaning, analysis, model building - random walk, linear models, random forest and neural networks and the Diebold Mariano Test. This folder also contains the $R^2$ results for each model and the actual and predicted risk_premiums for the dm test in CSV files.
2. data folder: Contains the datasets that were used for this project.

This project is an assignment from a NUS course - DSE4101: Capstone Project in Data Science and Economics I.

Authors: Brandon, Nicholas, Yuchen

Special Thanks to Prof Liu Chunchun for her guidance.

Last Updated: 21 Feb 2025