Stock Price Prediction using LSTM

Abstract

The prediction of predict the long-term value of stocks is somewhat challenging due to the complexity of the task. One of the most common factors that prevent investors from accurately assessing stock prices is the relationship between the market and the company's fundamentals. In this paper, we propose an algorithm that uses machine learning techniques to predict stock prices. The goal of proposed work is to analyze the effectiveness of LSTM models in forecasting stock prices. Data on a particular company's historical stock price is preprocessed and then trained to produce an LSTM model. It is then subjected to a test set to evaluate its accuracy and other aspects. As compare to SVR, RNN and other models, the LSTM model was able to capture the stock price trends and patterns in a way that is more accurate than traditional forecasting techniques. Since the stock market is volatile, making informed decisions can be challenging for investors when relying solely on SVR's predicted prices. The findings of the study suggest that the model can be a useful tool for financial analysts and investors. The preferred method is to employ a computer-based algorithm, as it will only advise you based on facts and figures, and it won't take into account biases or emotions.