Recent studies have improved stock price forecasting with the emerging deep learning models. Despite advancements in deep learning, stock price prediction faces significant challenges. Existing studies predominantly focus on forecasting future prices, with limited attention to nowcasting, which predicts current...
Further, a deep learning model namely along short term memory (LSTM) neural network is utilized to forecast the stock priceof one country by using the price of other countries that have a correlation and causalrelationship with the target stock market. PC matrix shows that there exis...
Chinese Stock Market Forecast 本项目使用深度学习和自然语言处理方法对沪深两市的A股价格涨跌进行预测,系统架构如下图所示。 Our Project is aimed at predicting the price trend of individual stocks using deep learning and natural language processing, the system architecture is shown below. 数据获取(Data Col...
The forecasting of the stock exchange asking price has been affected by a number of monetary and nonmanetary indexes that might be used as a warning rule for investors. Expecting the future trend of the stock market is a critical issue in investment sect
However, many scholars doubt the financial ratios do not consistently outperform the historical average benchmark forecast out of sample3. In addition other researchers started with the price trend itself, using technical indicators and found that technical indicators were efficient in predicting the ...
Designing robust and accurate predictive models for stock price prediction has been an active area of research for a long time. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. ...
Forecasting stock market indexes is an important issue for market participants, because even a small improvement in forecast accuracy may lead to better trading decisions than those of other participants. Rising interest in deep learning has led to its application in stock market forecasting. However,...
LSTM is a deep RNN method that can capture the temporary features of time series input and solve the unknown long-term reliance problems of complex financial time series data. We adopt the LSTM model to forecast future price changing directions of the stock market index. By using the ...
this article seeks to investigate whether LSTM can be applied to the stock price forecast. This paper compares the pros and cons of LSTM in time series prediction by comparing RNNs with LSTM. In this paper, the daily data of the Shanghai Composite Index and the Dow Jones Index is taken ...
deep learning methods. The primary problem of their work is overfitting. The research problem of predicting Bitcoin price trend has some similarities with stock market price prediction. Hidden features and noises embedded in the price data are threats of this work. The authors treated the research ...