Skip to content

Model news data in short, medium and long term for stock price trend prediction

License

Notifications You must be signed in to change notification settings

gyanesh-m/Event-embedding-stock-prediction

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Event-embedding-stock-prediction

News titles and content were collected for top 10 companies from Nifty50, Nifty50 midcap and Nifty50 smallcap indices according to their market capitalisation ,from the following websites:

  • economictimes.indiatimes.com
  • reuters.com
  • ndtv.com
  • thehindubusinessline.com
  • moneycontrol.com
  • thehindu.com

However, only news titles were used for this experiment.The train-test split was 80-20. This model achieved the following accuracy for different indices during a 50 epoch training session:

Model for Accuracy
Nifty50 53%
Nifty50 midcap 62.29%
Nifty50 smallcap 62.92%

The summary of the news data collected from 1st Jan 2011 to 30th September 2017 is as follows:

Nifty50 midcap cos. No. of news Nifty50 smallcap cos. No. of news Nifty 50 cos. No. of news
indraprastha gas 329 indiabulls real estate 150 larsen & toubro 303
federal bank 626 south indian bank 194 infosys 6751
tata chemicals 331 indiabulls ventures 16 hdfc bank 1399
voltas 335 escorts 290 reliance industries 10563
page industries 56 future consumer 377 itc 2713
divi's laboratories 337 care ratings 187 icici bank 3066
mahindra & mahindra financial services 4997 equitas holdings 132 housing development finance corporation 3290
l&t finance holdings 338 dcb bank 273 maruti suzuki india 1708
bharat forge 351 can fin homes 108 tata consultancy services 321
tvs motor company 520 sterlite technologies 761 kotak mahindra bank 891
Total 8220 Total 2488 Total 31005

Setup

The dataset can be downloaded from here.

The saved models can also be downloaded from here.

Prerequisites

Following dependencies are required for this project:

* en-vectors-web-lg>=2.0.0
* h5py>=2.7.1
* Keras>=2.1.1
* numpy>=1.13.3
* pandas>=0.21.0
* pickleshare>=0.7.4
* spacy>=2.0.2
* tensorflow>=1.4.0
* tensorflow-tensorboard>=0.4.0rc3

File Structure

USEFUL_FILES
  -> vector-generation.py-Uses pre trained model from spacy to generate the vectors of news title.
  -> pre-processing.py-Rearranges the news data into three subgroups of short , medium and long 
                       term time period for each of the indices. It also aligns stock price date 
		 	with news date.
  -> model.py-Contains the architecture of the model used for training.

Note

This model utilises the news data scraped from this repo.

Acknowledgement

This project was inspired from this paper

Releases

No releases published

Packages

No packages published

Languages