Comparisons of Stock Price Predictions Using Stacked RNN-LSTM
Interest patterns; Investments; Long Short-Term Memory (LSTM); Machine learning; Neural networks; RNN; Stock market
This paper seeks to identify how the RNN-LSTM can be used in predicting the rise and fall in stock markets thereby helping investors to understand stock prices. Therefore, by predicting the nature of the stock market, investors can use different machine learning techniques to understand the process of selecting the appropriate stock and enhance the return investments thereafter. Long Short-Term Memory (LSTM) is a deep learning technique that helps to analyze and predict the data with respect to the challenges, profits, investments and future performance of the stock markets. The research focuses on how neural networks can be employed to understand price changes, interest patterns and trades in the stock market sector.The datasets of companies such as IBM, Cisco, Microsoft, Tesla and GE were used to build the stacked RNN-LSTM model using timesteps of 7 and 14days. The two layered stacked RNN-LSTM models of the companies such as Microsoft and Tesla achieved their highest model accuracies after being trained over a span of one year whereas the other companies acquired their highest accuracies after being trained over a span of 4 to 5years which implies that the rate of change of economic factors affecting Microsoft and Tesla over a short span of time is high as compared to the other existing companies. 2021, Springer Nature Switzerland AG.
Communications in Computer and Information Science, Vol-1483, pp. 380-390.
Springer Science and Business Media Deutschland GmbH
Sequeira S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Banu P.K.N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India