Stock Market Forecasting Using Metaheuristic LSTM Approach with Sentiment Analysis

Abstract

Machine learning, which differs from conventional algorithms and models in that it applies computer algorithms and statistical models in a systematic and all-encompassing manner, is utilized extensively in a variety of fields. Machine learning is mostly utilized in the realm of finance to analyze the trajectory of capital market prices. In this study, we employed conventional models and machine learning models for predicting linear and non-linear issues, respectively, to predict the time-series data of stocks with less risk. The LSTM (long short-term memory) neural network model is used to train and forecast stock price and stock price sub-correlation, and the proposed time series-based metaheuristic model is utilized to construct a prediction with risk assessment. The experiment findings demonstrate that: (1) Stock price and stock price correlation are accurately predicted by the MLSTM model; and (2) compared with the existing model for performance checking. Finally, we analyze the proposed model using a number of indicators.

As a result, our suggested solution offers less complicated assistance and method for risk analysis.