scholarly journals Autoregressive Neural Network EURO STOXX 50 Forecasting Model Based on Principal Component Stock Selection

2021 ◽  
Vol 6 (2) ◽  
pp. 71-81
Author(s):  
Ahmad Abu Alrub ◽  
Tahir Abu Awwad ◽  
Emad Al-Saadi
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Stock Prices ◽  
Stock Price ◽  
Arima Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Model Based ◽  
Index Stock ◽  
Autoregressive Neural Network

Purpose: The given study looks into forecast accuracy of a traditional ARIMA model while comparing it to Autoregressive Neural Network (AR-NN) model for 984 trading days on EURO STOXX 50 Index. Methodology: A hybrid model is constructed by combining ARIMA model and feed-forward neural network model aiming to attain linear and non-linear price fluctuations. The study also incorporates the investigation of component stock prices of the index, that can be selected to improve the predictability of the hybrid model.  Findings:The reached ARIMA (1,1,3) model showed higher scores than AR-NN model however integrating selected exogenous stock prices from the index components gave much notable accuracy results. The selected exogenous stocks were extracted after conducting PCA and model scores were compared via MAPE and RMSE. Unique contribution to theory, practice and policy: The major contribution of this work is to provide the researcher and fnancial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the  outlines of proposed work with the aim to enhance the performance of existing techniques. Therefore, Empirical analysis is employed along with a hybrid model based on a feed-forward Neural Network. Lesser error is attained on the test set of Index stock price by comparing the performance of ARIMA and AR-NN while forecasting. Hence, The components of extracted Index stock price like exogenous features are added to make an influence from the AR-NN model. 

scholarly journals Modeling and Prediction of Stock Price with Convolutional Neural Network Based on Blockchain Interactive Information

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wei Zhang ◽  
Ke-xin Tao ◽  
Jun-feng Li ◽  
Yan-chun Zhu ◽  
Jing Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Contextual Information ◽  
Stock Price Prediction ◽  
Model Based ◽  
Effective Manner ◽  
Sentiment Computing

The interactive information in blockchain architecture establishes an effective communication channel between users and enterprises, enabling them to communicate in a comprehensive and effective manner. Therefore, taking blockchain interactive information as the research object, this paper explores how the intervention of official information on investors affects the stock price movement and then makes predictions on stock prices according to the emotional tendency of interactive information. With the contextual information fusion, a sentiment computing model based on a convolutional neural network is established to extract and quantify the emotional features of blockchain interactive information. Combined with investors’ emotional features, the stock price prediction model based on long short-term memory is proposed. The experiment results show that the accuracy of the model has been improved by incorporating the intervened emotional features, thereby proving that information clarification can have a positive effect on the stock price.

Stock Price Prediction Model Based on IWO Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1635-1640
Author(s):  
Hong Liu ◽  
Xiao Yan Lv
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Steepest Descent Method ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

In view of the deficiency of the standard back-propagation algorithm based on steepest descent method, a new kind of optimization strategy called invasive weed optimization (IWO) algorithm is introduced into the training process of feed-forward neural networks, and then a prediction model based on IWO feed-forward neural network (IWO-NN) is given. By the dynamic adjustment of standard deviation of the distribution of offspring individuals in IWO, the local convergence speed of networks is improved and the defect of trapping into a local optimum is reduced. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has better global astringency, robustness, and it is insensitive to initial values.

Stock Price Prediction Model Based on BA Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1646-1651 ◽  
Author(s):  
Xiao Yan Lv ◽  
Si Long Sun ◽  
Hong Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Frequency Tuning ◽  
Steepest Descent Method ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based ◽  
Intensive Exploitation

In view of the deficiency of the basic back-propagation (BP) algorithm based on steepest descent method. Bat algorithm (BA) in intelligent optimization is introduced into the training process of feed-forward neural networks, capturing the optimal solution of the objective function with a small population size and less number of iterations, and a prediction model based on BA feed-forward neural network (BA-NN) is given. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has advantages of frequency tuning and dynamic control of exploration and exploitation by automatic switching to intensive exploitation if necessary.

scholarly journals A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

2021 ◽  
Vol 35 (6) ◽  
pp. 483-488
Author(s):  
Asmaa Y. Fathi ◽  
Ihab A. El-Khodary ◽  
Muhammad Saafan
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Singular Spectrum Analysis ◽  
Predictive Performance ◽  
Backpropagation Neural Network ◽  
Singular Spectrum

The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.

Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility

2021 ◽  
pp. 115490
Author(s):  
Gabriel Trierweiler Ribeiro ◽  
André Alves Portela Santos ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Stock Price ◽  
Return Volatility ◽  
Model Based
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