| Summary: | Stock prediction has been a popular area of research. It is challenging due to the
dynamic, chaotic, and non-stationary nature of data. However, significant
advancements in the field of machine learning, has encouraged the usage of these
advanced techniques in the application of stock price prediction.
This project focuses on the New York Stock Exchange Composite (NYSE) Index for
stock Opening Price and Stock Movement (Direction) forecasting. NYSE index is
downloaded from Yahoo! Finance. It leverages Technical Indicators as well as market
Sentiment Analysis to facilitate the prediction of stock index. Technical Indicators are
obtained via feature engineering of the stock index. Sentiment Analysis is obtained via
data pre-processing of extracted Twitter Tweets to which VADER is applied. Further,
Recursive Feature Addition (RFA) algorithm is implemented to identify impactful
Technical Indicators and discard insignificant Technical Indicators.
The pre-processed features of the data are fed into the proposed models – LSTM
(Long Short-Term Memory), PCA-LSTM (Principal Component Analysis-Long Short-Term Memory) and CNN-LSTM (Convolutional Neural Network-Long-Short
Term Memory). The model performances are evaluated and compared with one
another as well as with benchmark models, namely, ARIMA (Autoregressive
Integrated Moving Average) and SVR (Support Vector Regression).
The results indicate that incorporation of technical indicators, market sentiment
analysis score, PCA in the case of LSTM as well as applying RFA algorithm improve
model performance in terms of RMSE, MAE, Accuracy and F1 Score. Further, the
proposed models exceed benchmark model performance in terms of Accuracy and F1
Score and overall perform well in terms RMSE and MSE metrics.
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