Columbia-SEC-Pricing-NLP-ML
The project aims to build a stock pricing prediction KPI from SEC filings
1.Scrap and parse the text part of financial reports.
2.Do sentiment analysis using NLP.
3.Access stock price data.
4.Build machine learning models using sentiment data and stock price.
Contributors of the project are:
- Jieyu Yao <y.jieyu@columbia.edu>,
- Yue Chang <ulysses.cy@gmail.com>,
- Qing Ma <qm2124@columbia.edu>,
- Jun Guo <jg3555@columbia.edu>.
Update Feb. 23 2018
To execute the executor.py, which scrapes SEC files using information from data/edgar.csv and converts the 10-K/10-Q documents into sentiment vectors:
python executor.py
Thanks for the help from our professors Dr. Eugene Wu and Dr. Sambit Sahu.