To create a hybrid model for stock price performance prediction using numerical analysis of historical stock prices, and sentimental analysis of news headlines.
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Extract Sentiment Scores from given news headlines dataset, with the help of nltk's SentimentIntensityAnalyzer.
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Use Multivariate Time Series Forecasting using the LSTM (Long Short-Term Memory) model in Keras and Tensorflow. LSTM analyses the features from both sentiment scores and numerical Historical stock data to predicit opening stock prices.
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Achieved Training loss: 0.05 and Validation loss: 0.02
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Achieved RMSE on test data : 511
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Historical stock prices(SENSEX (S&P BSE SENSEX)) from https://finance.yahoo.com/
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Textual (News) data from https://bit.ly/36fFPI6
Deep learning for stock prediction using numerical and textual information- Ryo Akita, Akira Yoshihara, Takashi Matsubara, Kuniaki Uehara