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The results show that the prediction error value only partially reflects the model accuracy of the stock price prediction, and cannot reflect the change in the direction of the model predicted stock price.
We show the effectiveness of our method by conducting experiments on real market data.
Prediction of stock price and stock price movement patterns has always been a critical area of research.
In this approach, the open values of the NIFTY 50 index are predicted on a time horizon of one week, and once a week is over, the actual index values are included in the training set before the model is trained again, and the forecasts for the next week are made.
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.
In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models.
Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems.
We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data.
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input.