Inclination of Tech Stocks using Time Series Analysis and Prophecy of Returns using Recurrent Neural Network

The world of finance is undoubtedly volatile-the stock prices keep changing as each minute passes. This volatility can be understood and solved with modern technology i.e., the application of machine learning methods in amalgamation with financial analysis. First, a complete trend analysis is performed on the Closing Stock values of various MNC companies along with the total shares traded by them. Here, the issue of stock trading decision prediction is enunciated using Recursive Neural Networks (RNN) which incorporates Long-Short Term Memory (LSTM) algorithm. The keras library of python with the inclusion of layers is used. The usage of LSTM has proven to be one of the best methods of prediction as shown. The similarity between actual and predicted stocks is high and thus throws light on the success of the model. This can be a promising strategy for stock trading. The results thus obtained are depicted with the help of a few graphs that showcase the accuracy of the algorithm.

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