scholarly journals Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

2016 ◽  
Author(s):  
Xin Sun ◽  
Ziqi Tang ◽  
Ziwei Guo
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Data Cleaning ◽  
Model Analysis ◽  
Wavelet Neural Network ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Cleaning Technology

scholarly journals RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1676-1682 ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Stock Price ◽  
Future Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Non Linear

Recent years have seen the wide use of Time series forecasting (TSF) for predicting the future price stock, modeling and analyzing of finance time series helps in guiding the trades and investors decision. Moreover considering the stock as the dynamic environment, it is pronounced as the non-linearity of time series which affects the stock price forecast immediately. Hence, in this research work we propose intelligent TSF model, which helps in forecasting the early prediction of stock prices. The proposed stock price forecasting model employed both short-term (i.e. recent behavior fluctuation) using log bilinear (LBL) model and long-term (i.e., historical) behavior using recurrent neural network (RNN) based LSTM (long short term memory )model. Subsequently, this model is mainly helpful for the home brokers since they do not possess enough knowledge about the stock market. Proposed RNNLBL hybrid model shows the satisfying forecasting performance, these results in overall profit for the investors and trades. Furthermore, proposed model possesses a promising forecasting in case of the non-linear time series since the pattern of non-linear pattern are highly improbable to capture through these state-of-art stock price forecasting models.

Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting

2011 ◽  
Vol 3 (1) ◽  
pp. 45-68 ◽  
Author(s):  
Rashedur M. Rahman ◽  
Ruppa K. Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Price Forecasting ◽  
Insurance Company ◽  
Memory Architecture ◽  
Backpropagation Algorithm ◽  
Network Algorithms ◽  
Training Time ◽  
Stock Price Forecasting ◽  
Neural Network Algorithm

The neural network is popular and used in many areas within the financial field, such as credit authorization screenings, regularities in security price movements, simulations of market behaviour, and so forth. In this research, the authors use a neural network technique for stock price forecasting of Great West Life, an insurance company based in Winnipeg, Canada. The Backpropagation algorithm is a popular algorithm to train a neural network. However, one drawback of traditional Backpropagation algorithm is that it takes a substantial amount of training time. To expedite the training process, the authors design and develop different parallel and multithreaded neural network algorithms. The authors implement parallel neural network algorithms on both shared memory architecture using OpenMP and distributed memory architecture using MPI and analyze the performance of those algorithms. They also compare the results with traditional auto-regression model to establish accuracy.

scholarly journals Short term stock price forecasting with application of neural network

2021 ◽  
Author(s):  
Jiayin Liu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Price ◽  
Principal Component ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Price Forecast ◽  
Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

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