Stock medium and long term trend prediction method and system based on Bayes classifier

A Bayesian classifier and trend forecasting technology, applied in instrumentation, finance, character and pattern recognition, etc., can solve problems such as reducing the accuracy of medium and long-term forecasting, failing to show detailed changes in stock trends, and accumulating errors.

Active Publication Date: 2015-07-01
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0004] At present, the medium and long-term forecasting methods of existing stocks mainly include: using the forecast results of the previous step to iteratively and recursively obtain the subsequent medium and long-term forecast (reference example: Yang Yiwen, Lin Yupei. Fuzzy time series modeling and stock market multi-step Forecasting [J]. Computer Engineering and Application, 2014, (5): 252-256.), but there is a cumulative error in this method, and the cumulative error increases with the growth of the forecast step; using the moving window algorithm in the modeling sequence The forecasting model is recursively updated by deleting some old data and incorporating some new data (reference example: Jian Qingming, Zeng Hu

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  • Stock medium and long term trend prediction method and system based on Bayes classifier
  • Stock medium and long term trend prediction method and system based on Bayes classifier
  • Stock medium and long term trend prediction method and system based on Bayes classifier

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Embodiment Construction

[0073] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0074] In a specific implementation, the current trading day is set as 0 time, and the following is an example of predicting the stock trend in the next 32 trading days, that is, predicting the future 0-2, 2-4, 4-8, 8-16, 16 - Average stock price for 32 trading days.

[0075] Such as figure 1 As shown, a medium and long-term stock trend prediction method based on Bayesian classifier includes the following steps:

[0076] Step 1: Select stock data for a period of time, and determine the starting point of the learning interval, the starting point of the confidence judgment interval, the starting point of the prediction interval and the interval length d j .

[0077]Take the 1032 daily opening prices of a stock befo...

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Abstract

The invention relates to a stock medium and long term trend prediction method based on a Bayes classifier. The method comprises the steps of selecting stock data, and determining all starting points and an interval length dj; dividing a compartment, and calculating the interval slope of historical data; learning the interval slope of the historical data and predicting confidence coefficient judgment compartments, so as to obtain the average price of stocks of a plurality of trading days by taking the confidence coefficient judgment compartment as the starting points; calculating confidence, and comparing the confidence and a preset threshold value; predicting a future compartment slope, and converting the future compartment slope to obtain the average price of the stock of the plurality of trading days by taking the prediction interval starting points as the starting points; normalizing the ups and downs of the average price of the stock of the plurality of trading days by taking the prediction interval starting points as the starting points; and building a stock tank. According to the method provided by the invention, the accumulative errors can be prevented, the trend change of the stocks in the prediction intervals can be displayed, the fluctuating change tendency of a stock market can be caught better, and the transaction exposure can be effectively estimated.

Description

technical field [0001] The present invention relates to a data processing system or method specially suitable for administration, commerce, finance, management, supervision or forecasting purposes, especially a medium and long-term stock trend forecasting method and system based on a Bayesian classifier. Background technique [0002] The stock market is an important place for the optimal allocation of capital resources, and mastering its changing laws is not only a dream for investors, but also has important practical significance for the research and management of the macro national economy. Since the factors affecting the stock price include internal factors of the enterprise, economic factors, system factors, people's psychological factors, etc., and the degree and mode of influence of various factors are different, it is very difficult to accurately predict the stock price. [0003] Time series analysis has become an indispensable part of stock market research both theor...

Claims

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Application Information

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IPC IPC(8): G06Q40/04G06K9/62
Inventor 金学波聂春雪施彦
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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