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 network error in test data show t...
A multilayer perceptron (MLP) neural network model with the Back-Propagation algorithm was applied to predict the Saudi Arabia stock market prices 13. Their proposed model results from their simulation demonstrated the viability of the proposed model in predicting Saudi Arabia stock markets. A trading...
International Joint Conference on Neural Network May 2017311被引用 9笔记PDF 引用 收藏 摘要原文 Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on...
theory foundations and good mapping capabilities, and is suitable for various application areas of neural network [16]. Nevertheless, there are still some problems for WNN to predict stock prices. In this paper, our research will focus on reducing input dimensions and optimizing the structure for ...
A DNN-based prediction model is designed based on the PSR method and a long- and short-term memory networks (LSTMs) for DL and used to predict stock prices. The proposed and some other prediction models are used to predict multiple stock indices for different periods. A comparison of the ...
there are many ways to predict the movement of share price. The main goal of this article is to predict stock price indices using artificial neural network (ANN) and train it with some new metaheuristic algorithms such as social spider optimization (SSO) and bat algorithm (BA). We used some...
technicalandeconomicalindexesandthetimingforwhentobuyandsellstocks.Thegoalistopredictthebest timetobuyandsellforonemonthinthefuture. TOPIXisanweightedaverageofmarketpricesofallstockslistedon the First Section of the Tokyo Stock Exchange. It is weighted by the number of stocks issued for each company. It...
Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental resu
corpus) as feature extractor, historical stock market data are utilised to generate an embedding matrix and fused with established neural network architectures, such as Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to predict stock market ...
to predict the closing price of shares listed on the Johannesburg Stock Exchange (JSE) and attempt to capture complex inter-share dependencies. The results show that ST-GNN architectures, specifically Graph WaveNet, produce superior performance relative to an LSTM and are potentially capable of captur...