Deep Learning-Based Stock Price Prediction Using LSTM Model  

作　　者：Jiayi Mao Zhiyong Wang 

机构地区：[1]Chengdu Shude High School,Chengdu 610400,China [2]University of Electronic Science and Technology of China,Chengdu 610400,China

出　　处：《Proceedings of Business and Economic Studies》2024年第5期176-185,共10页商业经济研究（百图）

摘　　要：The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.

关 键 词：Autoregressive integrated moving average(ARIMA)model Long Short-Term Memory(LSTM)network Forecasting Stock market 

分 类 号：F42[经济管理—产业经济]