Optimization of Stock Prediction Methods based on Long Short-Term Memory Model

Abstract

These days, with the fast-paced development of the economy, a growing number of individuals have started making stock market investments, aiming to earn more money with wise decisions. However, a high level of risk has become inevitable because of the high level of stock price volatility, which is impacted by several variables. As a result, forecasting stocks has emerged as a critical component of financial research. One of the fundamental stock prediction models, Long Short-Term Memory (LSTM), is introduced in this article. With the consideration of the increasing level of requirement of the stock prediction, this dissertation also discusses the different ways of optimization of the LSTM models from hybridization, genetic algorithm, attention mechanism to the variant LSTM. Although the optimized models have been experimented with to show better performance in stock prediction, there are still several limitations waiting to be improved in the future. This dissertation has shed light on the analysis and comparison of stock prediction models based on the LSTM, with paramount significance to the future development of stock prediction.