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Stock prediction system integrating volatility

A forecasting system and volatility technology, applied in forecasting, finance, data processing applications, etc., can solve problems such as difficult choices for investors, unsatisfactory model forecasting accuracy, etc., to solve the problem of unsatisfactory forecasting accuracy and good investment advice. Effect

Pending Publication Date: 2022-07-29
HARBIN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to propose a stock forecasting system that integrates volatility, in order to solve the problem that investors are difficult to choose and the accuracy of other model predictions is not ideal

Method used

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  • Stock prediction system integrating volatility
  • Stock prediction system integrating volatility
  • Stock prediction system integrating volatility

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Embodiment Construction

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the present invention Among the embodiments in the above, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] like Figure 1-Figure 2 As shown, an embodiment of the volatility-integrated stock forecasting system described in this embodiment includes the following steps.

[0037] Step 1: Acquire the basic data of the stock, and obtain the relevant news text information of the stock.

[0038] Through the tushare module in python, the basic data of the stock is obtained, including: closing price, opening price, trading v...

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Abstract

The invention discloses a stock prediction system fusing volatility. The basic information of the stock is obtained through a push module in the python language, wherein the basic information comprises the opening price, the closing price, the trading volume and the like. The method comprises a generalized autoregressive conditional heterovariance (GARCH)-based model and a long short-term memory neural network (LSTM)-based model. The method is based on a generalized autoregressive conditional heterovariance (GARCH) model, is used for predicting the stock volatility, and predicts the volatility characteristics of the stock by taking basic information of the stock as input. On the basis of a long short-term memory (LSTM) neural network model, the fluctuation characteristics of the stock and basic information of the stock are used as the input of the model, and the LSTM neural network model is utilized to predict the rise and fall of the stock.

Description

technical field [0001] The invention designs a stock prediction system integrating volatility, which belongs to the field of artificial intelligence deep learning. Background technique [0002] Computers and related technologies continue to develop in the process of production and life with increasing data volume. Different industries have begun to rely on data mining, machine learning and other methods to carry out industry innovations in their industries and promote the scientific development of the industry. Stock market forecasting is considered to be a challenging direction in financial time series research, and the purpose of data analysis is to predict future stock price rising or falling trends. The Efficient Market Hypothesis is one of the representative theories of the stock market volatility logic. This theory holds that the stock market is an efficient market, and the stock price trend can timely, accurately and fully reflect all valuable information, including t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/04G06N3/04
CPCG06Q10/04G06Q40/04G06N3/048G06N3/044
Inventor 高兴华张玉王青
Owner HARBIN UNIV OF SCI & TECH
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