Event-driven approach for stock return forecast

Project Description

This project uses Machine Learning to analysis the impacts of various events to company's stock price and use it to predict future price changes.

34 different types of pre-classified events, from Departure of Directors to Failure to Make a Required Distribution, have been considered.


Confusion metrics:

TP: 637 FP: 183
FN: 333 TN: 142


To run the model, run python3 main.py


Prepare dataset in datasets.py Prepare the corresponding model in models.py

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