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Financial stock prediction method fusing clustering and ensemble learning

A technology that integrates learning and forecasting methods, applied in finance, forecasting, instruments, etc., to achieve reliable and accurate forecasting

Pending Publication Date: 2020-05-19
HUNAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Predicting future stock closing price movements based on historical stock price indices is a challenging problem

Method used

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  • Financial stock prediction method fusing clustering and ensemble learning
  • Financial stock prediction method fusing clustering and ensemble learning
  • Financial stock prediction method fusing clustering and ensemble learning

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Embodiment Construction

[0053] In the present invention, select 4 Chinese stocks: Shanghai Pudong Development Bank (SH: 600000), CITIC Securities (SH: 600030), ZTE (SZ: 000063), LeTV (SZ: 300104) as the total data set, predict the first Closing price for n days, where n ∈ {1,5,10,20,30}. For the financial stock prediction problem, the data set (the technical index of any stock) can be formalized as D={X,Y}, Y∈R, X is the total input sample, and Y is the label corresponding to the total input sample. X={X 1 ,X 2 ,...,X m-1 ,X m}, m is the number of samples. For predicting the closing price of the nth day in advance, any X i ,i∈[1,m-n], composed of 10 technical indicators shown in Table 1, can be expressed as X i ={X i0 ,X i1 ,X i2 ,X i3 ,X i4 ,X i5 ,X i6 ,X i7 ,X i8 ,X i9}. The closing price sequence of the day corresponding to the X sample is expressed as Y={Y 1 ,Y 2 ,...,Y m-1 ,Y m}. The corresponding advance forecast of the closing price of the stock on the nth day can be form...

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Abstract

The invention discloses a financial stock prediction method fusing clustering and ensemble learning. According to the method, a k-means clustering method is adopted to cluster a plurality of common technical indexes, so that a C-SVR-SVR (Clustering-SVR-SVR) prediction model and a C-SVR-RF (Clustering-SVR-RF) prediction model based on clustering can be provided; and then, a Bagging ensemble learning algorithm is adopted to propose a model E-SVR&RF (Ensemble-SVR&RF). And finally, the k-means clustering algorithm and the Bagging ensemble learning algorithm are combined, and a hybrid model C-E-SVR&RF (Clustering-Ensemble-SVR&RF) is provided. According to the invention, four Chinese stocks, namely SPD Bank (SH: 600000), CITIC Securities (SH: 600030), ZTE Corporation (SZ: 000063) and Le (SZ: 300104), are selected for experimental evaluation. Experimental results show that the C-SVR-SVR and the C-SVR-RF model of the k-means clustering algorithm are independently added, the prediction accuracyof the specific stock price can be improved, but the overall effect is not obvious. And the accuracy of stock price prediction can be improved by independently adding the ensemble learning algorithm.And a k-means clustering algorithm and an ensemble learning hybrid algorithm are fused, so that the stock price prediction accuracy can be further improved, and particularly, the prediction can be advanced by 20 and 30 days.

Description

technical field [0001] The invention relates to the field of stock forecasting, in particular to a financial stock forecasting method that integrates clustering and integrated learning. Background technique [0002] Predicting the future development direction of the stock market based on the development of the historical stock market is called stock forecasting. The stock price trend directly affects the economic interests of investors, and also affects and reflects the country's macroeconomic policies, so it has attracted widespread attention. The stock index, also known as the stock price index, is an indicator number compiled by a stock exchange or a financial service institution to indicate changes in the stock market. It is a comprehensive reflection of a country's economic situation. In recent years, the use of hybrid algorithms that integrate multiple machine learning models for financial stock forecasting has become a research hotspot. Predicting future stock closi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q40/04G06Q40/06G06K9/62
CPCG06Q10/04G06Q40/04G06Q40/06G06F18/23213
Inventor 许莹杨翠娟郑冰欣
Owner HUNAN UNIV
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