Gmail: https://www.visheshvats021@gmail.com
LinkedIn: https://www.linkedin.com/in/vatsvishesh/
GitHub: https://github.com/visheshvats
In this task, I tried to create a hybrid model for stock performance prediction using numerical analysis of historical stock prices, and sentimental analysis of news headlines.
Extract Sentiment Scores from given newspaper headlines data, with the help of nltk's SentimentIntensityAnalyzer
I have used stack LSTM (Long Short-Term Memory), to model the temporal effects of past events(both Textual, i.e the sentiment scores and Historical stock data) on Closing prices
Achieved Training loss: 0.001 and Test loss: 0.0018
Achieved RMSE on the Test data: 19.6623
Deep learning for stock prediction using numerical and textual information- Ryo Akita, Akira Yoshihara, Takashi Matsubara, Kuniaki Uehara
Historical stock prices: https://finance.yahoo.com/
Textual News Headlines: https://bit.ly/36fFPI6