Analysis of financial market trend based on autoregressive conditional heteroscedastic model and BP neural network prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 5845-5857
Author(s):  
Xin Zhang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
High Frequency ◽  
Summation Method ◽  
High Frequency Data ◽  
Frequency Data ◽  
Closing Price ◽  
Neural Network Prediction ◽  
Network Prediction

High-frequency data such as stock prices are aggregated into low-frequency monthly data for modeling. However, the summation method only applies to high frequency data in the form of flow, and the summation method reduces the sample size. Based on this, this paper uses the mixing model to construct the financial status index, which can model the data of different frequencies and compensate for the defects of the same frequency data modeling to some extent. Moreover, based on principal component analysis and text mining technology, this paper constructs two kinds of sentiment indexes, and studies the influence and prediction of two sentiment indexes on the closing price of stock market. In addition, in the empirical analysis, this paper establishes the GARCH model and BP neural network prediction model and predicts the closing price. Finally, this paper compares the pros and cons of predictive models and sentiment indices. The research shows that the BP neural network model established by using the lag variable of the Web text sentiment index as the input layer variable is more reliable and can be widely used in the stock market.

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.

scholarly journals A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

2019 ◽  
pp. 477-484
Author(s):  
Yahui Chen ◽  
Zhan Wen ◽  
Qi Li ◽  
Yuwen Pan ◽  
Xia Zu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Quantitative Data ◽  
Prediction Models ◽  
Investor Behavior ◽  
Prediction Approach ◽  
The Impact

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

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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