Prediction of Stock Market Using LSTM-RNN Model

Each expanding and flourishing financial system relies on the stock market, and each money invested in the business seeks to maximize revenue while minimizing related hazards. Because of its intrinsic interplay, non-linearity, and complexity, predicting stock collective qualities has invariably been both appealing and complicated for investors. Consequently, various research on stock market predictive models utilizing technical or fundamental assessment have been performed utilizing different soft-computing methods and techniques. It has never been convenient to buy shares in a portfolio of resources; the unusually volatile economic stock does not enable convenient designs to predict prospective investment value systems with greater accuracy. Machine learning (ML), which involves teaching machines to execute activities that would ordinarily necessitate human intelligence, is presently the dominant pattern in science investigation. This research study examines a novel method for predicting stock market prices by using recurrent neural networks (RNN), specifically the LSTM-Long-Short Term Memory. The main objective of this investigation is to determine the impact of the prediction using ML technique can forecast events.