AllMight2099/Stock-Market-Prediction-using-Numerical-and-Textual-Analysis

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 paper: https://www.researchgate.net/publication/306925671_Deep_learning_for_stock_prediction_using_numerical_and_textual_information and decided to carry out Multivariate Time Series Forecasting using Keras' LSTM.

• I used Long Short-Term Memory (LSTM), 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