Stock Market Prediction using Numerical and Textual Analysis

Objective:

To create a hybrid model for stock price/performance prediction using numerical analysis of historical stock prices, and sentimental analysis of news headlines(of that particular day).

Approach:

• Extract Sentiment Scores from given newspaper headlines data, with the help of nltk's SentimentIntensityAnalyzer

• For this problem statement, I took inspiration from this awesome paper and decided to carry out Multivariate Time Series Forecasting using Keras' LSTM.

• I used 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 opening prices

• Achieved Training loss: 0.0479 and Validation loss: 0.0254

• Achieved RMSE on the Test data : 475.102

Data used to analyze and predict:

• Historical stock prices(SENSEX (S&P BSE SENSEX)) from https://finance.yahoo.com/

• Textual (News) data from https://bit.ly/36fFPI6

References:

Deep learning for stock prediction using numerical and textual information- Ryo Akita, Akira Yoshihara, Takashi Matsubara, Kuniaki Uehara