Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Stock price prediction method and system based on artificial intelligence

A technology of price forecasting and artificial intelligence, applied in the field of artificial intelligence, can solve problems such as one-sidedness of consideration factors, and achieve the effect of accurate stock prices

Inactive Publication Date: 2018-05-11
宏谷信息科技(珠海)有限公司 +1
View PDF0 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The technical problem to be solved by the present invention is to provide an artificial intelligence-based stock price forecasting method and system to solve the one-sided problem of existing stock forecasting considerations

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Stock price prediction method and system based on artificial intelligence
  • Stock price prediction method and system based on artificial intelligence
  • Stock price prediction method and system based on artificial intelligence

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] This embodiment provides a stock price prediction method based on artificial intelligence, such as figure 1 shown, including steps:

[0069] S11: Obtain the stock price characteristics and stock news characteristics of the trading day within the preset time;

[0070] S12: Input the stock price feature and stock news feature into the bidirectional recurrent neural network model for training;

[0071] S13: Input the mixed feature vector output by the bidirectional cyclic neural network model into the multi-layer perceptron for classification training;

[0072] S14: Predict the stock price of the next trading day according to the output of the multi-layer perceptron.

[0073] This embodiment provides a dual-cycle neural network model that combines stock price features and stock news features. The stock prediction model uses a dual-cycle neural network with intuitive considerations. First, the stock price has a certain time-series correlation in the short term. Second, t...

Embodiment 2

[0124] This embodiment provides a stock price prediction system based on artificial intelligence, such as figure 2 shown, including:

[0125] The feature module 21 is used to obtain the stock price features and stock news features of the trading day within the preset time;

[0126] The first training module 22 is used to input the two-way recurrent neural network model with stock price features and stock news features for training;

[0127] The second training module 23 is used to input the mixed feature vector output by the bidirectional cyclic neural network model into the multi-layer perceptron for classification training;

[0128] The prediction module 24 is used to predict the stock price of the next trading day according to the output of the multi-layer perceptron.

[0129] This embodiment provides a dual-cycle neural network model that combines stock price features and stock news features. The stock prediction model uses a dual-cycle neural network with intuitive co...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a stock price prediction method and system based on artificial intelligence, so as to solve the problem that considerations of the existing stock prediction are one-sided. Themethod includes the steps of acquiring stock price features and stock news features on a trading day within a preset time; inputting the stock price features and the stock news features into a bidirectional recurrent neural network model for training; inputting a mixed feature vector output by the bidirectional recurrent neural network model to a multi-layer perceptron for classification training;and predicting the stock price on a next trading day based on the output of the multi-layer perceptron. According to the invention, on the basis of a framework of a bidirectional recurrent network, the price features and the news features are combined and the acquired data information is fully utilized to more accurately predict the stock price.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based stock price prediction method and system. Background technique [0002] Stock price forecasting refers to the use of historical price information and stock-related market information to predict the rise and fall of a stock or its price in the future. In recent years, deep learning methods have made many advances in the field of natural language processing. Deep learning methods are also gradually applied to the field of stock forecasting. [0003] In 1965, Fama proposed the efficient market hypothesis, which believed that the stock market is an "effective information" market, and the stock price fully reflects the events that have occurred, as well as the impact of those events that have not yet occurred but the market expects to occur on the stock price. This assumption provides a basis for the subsequent stock forecasting work....

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04G06Q30/02G06Q40/04
CPCG06Q10/04G06Q30/0206G06Q40/04
Inventor 张潇
Owner 宏谷信息科技(珠海)有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com