Also, firefly algorithm is used to [redict price index in next week. The results of research show that combining neural networks and firefly optimization algorithm has better performance than neural network to predict the price index. In addition, acceptable value of error-sequre means for ...
IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how one can use neural networks to predict stock prices. It is built with the goal of allowing beginners to understand the fundamentals of how neural network models are built and go through the entire ...
In this research, we study the problem of Chinese stock market forecasting using traditional Neural Network methods, including Deep Feedforward Network, Convolution Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM) and we have also integrate with the Bi-direction techn...
Therefore, accurate prediction of stock prices is a difficult task in the finance field [1], [2]. In the existing stock price prediction methods, time series [3], gray [4], prosperity [5] and other methods are usually used. Since White first used the neural network to predict the ...
Two learning algorithms including Linear Regression and Neural Network Standard Back Propagation (SBP) were tested and compared. The models were trained from two years of historical data from January 2006 to December 2007 in order to predict the major stock prices indexes in the United States, ...
Khodayar, FAsian Network for Scientific InformationJournal of Applied SciencesTEHRANI R,KHODAYAR F. Optimization of the Artificial Neural Networks Using Ant Colony Algorithm to Predict theVariation of Stock Price Index [ J]. Journal of Applied Science, 2010, 10(3) : 221-225....
Furthermore, Bildirici and Ersin (2014) [26] also combined Markov switching ARMA-GARCH model with neural networks to predict exchange rates and stock returns. They used neural network approach to predict the parameter of ARMA and GARCH models, which is very similar with our work. Recently, ...
According to research, the accuracy of neural networks in making price predictions for stocks differs. Some models predict the correct stock prices 50 to 60% of the time. Still, others have posited that a 10% improvement in efficiency is all an investor can ask for from a neural network....
we can build a network, learn a certain scale of stock data, and obtain a model that can predict stock market trends more accurately through network training, which greatly helps us to master the trend of stocks. This project uses LSTM (Long Short-Term Memory Network) to successfully predict...
Due to their ability to model complex relationships and understand underlying factors of the data, these models are a good tool to forecast stock prices. Recent advances in the field are the usage of Long Short Term Memory (LSTM) models and Recurrent Neural Network for forecasting. ...