Stock risk prediction method and apparatus

A risk forecasting and stock technology, applied in the field of information processing, can solve problems such as inaccurate stock forecasting and high risk

Inactive Publication Date: 2016-07-20
TSINGHUA UNIV
View PDF0 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a stock risk forecasting method and device to s

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 risk prediction method and apparatus
  • Stock risk prediction method and apparatus
  • Stock risk prediction method and apparatus

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] refer to figure 1 , showing a stock risk prediction method of the present application, specifically including:

[0043] Step 101: Obtain the stock market field required to be called by the local device.

[0044] The stock market field is an element associated with the stock market. The stock market field that needs to be invoked is set by the local device according to its own broadcast requirements. It can be pre-negotiated and defined by the front-end device and the local device. It is preset information. In the actual application of calling and playing stock market data, the stock market fields to be called can be given once before calling play for the first time, or can be given separately before calling play each time.

[0045] Among them, the stock market fields that need to be called include opening price, closing price, highest price, lowest price, trading volume, turnover, change amount, change rate, return on equity, price-to-book ratio, index smoothing averag...

Embodiment 2

[0062] refer to Figure 4 , showing a stock risk prediction method of the present application, specifically including:

[0063] Step 401: Obtain the stock market field required to be called by the local device.

[0064] Step 402: Predict the stock market field based on the hidden layer of the long-short-term memory recurrent neural network, and obtain the prediction result of the stock, wherein the hidden layer includes an input gate, a forget gate and an output gate.

[0065] Step 402 includes the following sub-steps:

[0066] Step 4021: The input layer of the LSTM recurrent neural network receives the stock market field, and the input layer maps the received stock market field to the hidden layer.

[0067] based on figure 1 The structural block diagram of the long-short-term memory recurrent neural network is shown, and the application shows the internal structure of the hidden layer, specifically as follows Figure 5 As shown, where the hidden layer includes multiple hi...

Embodiment 3

[0091] see Figure 7 , shows a structural block diagram of a stock risk forecasting device in Embodiment 3 of the present application, which may specifically include: an acquisition module 701, configured to acquire the stock market fields required to be called by the local device.

[0092] The prediction module 702 is used to predict the stock market field based on the hidden layer of the long-short-term memory recurrent neural network, and obtain the prediction result of the stock, wherein the prediction result includes the predicted value of the volatility of the stock market on the next trading day, so The long-short-term memory recurrent neural network also includes: an input layer and an output layer.

[0093] The judging module 703 is used for the prediction result is greater than the set threshold, and the stock prediction is risky.

[0094] Preferably, the stock market fields that need to be called include opening price, closing price, highest price, lowest price, tr...

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 application provides a stock risk prediction method and an apparatus. The method includes the following steps: acquiring stock fields required by a local device terminal to be invoked; based on a hidden layer in the long and short term memory circulation neural network, predicting the stock fields to obtain a prediction result of the stock. The prediction result includes a prediction value of a fluctuation ratio of a stock market on a trading day. The long and short term memory circulation neural network also includes an input layer and an output layer. If the prediction result is greater than a determined threshold value, there are risks in the stock. According to the invention, the method and apparatus address the problem of high risks caused by inaccurate stock prediction by using the ARCH model or the GARCH model.

Description

technical field [0001] The present application relates to the technical field of information processing, in particular to a stock risk prediction method and device. Background technique [0002] The stock market is a market where risks and benefits coexist, and the forecast research of the stock market is of great significance to the economic development and financial construction of our country. [0003] Stock investment has a very high risk, how to predict the downside risk of stocks, so as to formulate a reasonable investment portfolio strategy, has important practical significance. At present, the traditional stock forecasting method is to use the volatility of the stock to construct an autoregressive conditional heteroskedasticity model (Autoregressive conditional heteroskedasticity model, ARCH) or a generalized autoregressive conditional heteroscedasticity model (Generalized Autoregressive conditional heteroscedasticity, GARCH), and use this to construct an ARCH model ...

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): G06Q10/04G06Q40/04G06Q40/08
CPCG06Q10/04G06Q40/04G06Q40/08
Inventor 于洋王东刘荣
Owner TSINGHUA UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap