CN104240125A - 一种机构和个人股评准确度评级方法 - Google Patents

一种机构和个人股评准确度评级方法 Download PDF

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CN104240125A
CN104240125A CN201410501844.4A CN201410501844A CN104240125A CN 104240125 A CN104240125 A CN 104240125A CN 201410501844 A CN201410501844 A CN 201410501844A CN 104240125 A CN104240125 A CN 104240125A
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王志恒
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Abstract

一种机构和个人股评准确度评级方法,所属技术领域为信用评级。根据机构或个人股评综合准确度P,将机构或个人评级分为5星级。机构或个人股评综合准确度P是该机构或个人对所有做出股评股票的年度EPS准确度和股票价格准确度的算术平均值。设定机构或个人对某一股票预测的年度EPS或股票价格符合正态分布N(μ,σ2),机构或个人对某一股票预测的年度EPS或股票价格预测值为X时,其对某一股票预测的年度EPS或股票价格准确度为正态分布函数N(μ,σ2)曲线横轴上(μ-|μ-X|)至(μ+|μ-X|)范围之外的曲线面积占总面积的比例。该方法可用于对证券公司和个人发布股评的准确度进行预测和评级。

Description

一种机构和个人股评准确度评级方法
技术领域
本发明所属技术领域为信用评级。
背景技术
目前,一些机构的股票评论员和个人股票评论员经常对个股的前景作出评价,给出年度EPS(每股盈余)预测和目标价位,并给出“推荐”、“买入”、“中性”等评级。如何评定这些推测的准确度,现在还没有统一的办法。
评级方法使用比较多的是信用评级。信用评级方法有不同的分类,如定性分析法与定量分析法、主观评级方法与客观评级法、模糊数学评级法与财务比率分析法、要素分析法与综合分析法、静态评级法与动态评级法、预测分析法与违约率模型法等等,同时还有各行业的评级方法。
本发明借鉴信用评级一些方法的优点,提出一种机构和个人股评准确度评级方法。
发明内容
一种机构和个人股评准确度评级方法,其特征在于:根据机构或个人股评综合准确度P,将机构或个人评级分为5星级,其中综合准确度为0-10%的,评级为半星级,用半颗五角星表示;综合准确度为10-20%(不含10%)的,评级为1星级,用1颗五角星表示;综合准确度为20-30%(不含20%)的,评级为1星半级,用1颗半五角星表示;综合准确度为30-40%(不含30%)的,评级为2星级,用2颗五角星表示;综合准确度为40-50%(不含40%)的,评级为2星半级,用2颗半五角星表示;综合准确度为50-60%(不含50%)的,评级为3星级,用3颗五角星表示;综合准确度为60-70%(不含60%)的,评级为3星半级,用3颗半五角星表示;综合准确度为70-80%(不含70%)的,评级为4星级,用4颗五角星表示;综合准确度为80-90%(不含80%)的,评级为4星半级,用4颗半五角星表示;综合准确度为90-100%(不含90%)的,评级为5星级,用5颗五角星表示。
机构或个人股评综合准确度P是该机构或个人对所有做出股评股票的年度EPS准确度和股票价格准确度的算术平均值。公式如下:
其中P是机构或个人股评综合准确度,PAi是机构或个人对第i只股票股评的年度EPS准确度,PBj是机构或个人对第j只股票股评的年度股票价格准确度,n是机构或个人做出年度EPS股评股票的个数,m是机构或个人做出股票价格股评股票的个数。
年度EPS是上市公司公布的本年度EPS,年度股票价格是该股票本年度最后一个交易日的价格。
设定机构或个人对某一股票预测的年度EPS或股票价格符合正态分布N(μ,σ2),其中期望值μ为股票实际年度EPS或年度股票价格,σ2是所有机构和个人对某一股票预测的年度EPS或股票价格的方差。机构或个人对某一股票预测的年度EPS或股票价格预测值为X时,其对某一股票预测的年度EPS或股票价格准确度为正态分布函数N(μ,σ2)曲线横轴上(μ-|μ-X|)至(μ+|μ-X|)范围之外的曲线面积占总面积的比例。
具体实施方式
计算机构或个人对某一股票预测的年度EPS准确度时,设其年度EPS预测值为X,先找出所有机构和个人对该股票预测的年度EPS值xi,以该股票实际年度EPS为期望值μ,计算每一预测的年度EPS值与期望值μ之差的平方之和,除以预测的年度EPS值个数n减1,再开根号,得到标准差σ,即:
 
经过u=(X-μ)/σ变换求得u值,对u值取绝对值后,查标准正态分布表得到Φ(|u|),则机构或个人对该股票预测的年度EPS准确度PA为1减去Φ(|u|)所得值的2倍,即:
计算机构或个人对某一股票预测的股票价格准确度时,设其股票价格预测值为X,先找出所有机构和个人对该股票预测的股票价格xj,以该股票实际股票价格为期望值μ,计算每一股票价格预测值与期望值μ之差的平方之和,除以股票价格预测值个数m减1,再开根号,得到标准差σ,即:
经过u=(X-μ)/σ变换求得u值,对u值取绝对值后,查标准正态分布表得到Φ(|u|),则机构或个人对该股票预测的股票价格准确度PB为1减去Φ(|u|)所得值的2倍,即:
将某一机构或个人对所有做出股评股票的年度EPS准确度和股票价格准确度求算术平均值,得到该机构或个人股评综合准确度。
根据某一机构或个人股评综合准确度确定该机构或个人的星级。
该发明可应用于对股评机构和个人股评准确度的预测。

Claims (5)

1.一种机构和个人股评准确度评级方法,其特征在于:根据机构或个人股评综合准确度P,将机构或个人评级分为5星级,其中综合准确度为0-10%的,评级为半星级,用半颗五角星表示;综合准确度为10-20%(不含10%)的,评级为1星级,用1颗五角星表示;综合准确度为20-30%(不含20%)的,评级为1星半级,用1颗半五角星表示;综合准确度为30-40%(不含30%)的,评级为2星级,用2颗五角星表示;综合准确度为40-50%(不含40%)的,评级为2星半级,用2颗半五角星表示;综合准确度为50-60%(不含50%)的,评级为3星级,用3颗五角星表示;综合准确度为60-70%(不含60%)的,评级为3星半级,用3颗半五角星表示;综合准确度为70-80%(不含70%)的,评级为4星级,用4颗五角星表示;综合准确度为80-90%(不含80%)的,评级为4星半级,用4颗半五角星表示;综合准确度为90-100%(不含90%)的,评级为5星级,用5颗五角星表示。
2.根据权利要求1所述的综合准确度,机构或个人股评综合准确度P是该机构或个人对所有做出股评股票的年度EPS准确度和股票价格准确度的算术平均值。
3.根据权利要求1所述的综合准确度,设定机构或个人对某一股票预测的年度EPS或股票价格符合正态分布N(μ,σ2),机构或个人对某一股票预测的年度EPS或股票价格预测值为X时,其对某一股票预测的年度EPS或股票价格准确度为正态分布函数N(μ,σ2)曲线横轴上(μ-|μ-X|)至(μ+|μ-X|)范围之外的曲线面积占总面积的比例。
4.根据权利要求2所述的年度EPS准确度,计算机构或个人对某一股票预测的年度EPS准确度时,设其年度EPS预测值为X,先找出所有机构和个人对该股票预测的年度EPS值xi,以该股票实际年度EPS为期望值μ,计算每一预测的年度EPS值与期望值μ之差的平方之和,除以预测的年度EPS值个数n减1,再开根号,得到标准差σ,经过u=(X-μ)/σ变换求得u值,对u值取绝对值后,查标准正态分布表得到Φ(|u|),则机构或个人对该股票预测的年度EPS准确度PA为1减去Φ(|u|)所得值的2倍。
5.根据权利要求2所述的股票价格准确度,计算机构或个人对某一股票预测的股票价格准确度时,设其股票价格预测值为X,先找出所有机构和个人对该股票预测的股票价格xj,以该股票实际股票价格为期望值μ,计算每一股票价格预测值与期望值μ之差的平方之和,除以股票价格预测值个数m减1,再开根号,得到标准差σ,经过u=(X-μ)/σ变换求得u值,对u值取绝对值后,查标准正态分布表得到Φ(|u|),则机构或个人对该股票预测的股票价格准确度PB为1减去Φ(|u|)所得值的2倍。
CN201410501844.4A 2014-09-27 2014-09-27 一种机构和个人股评准确度评级方法 Pending CN104240125A (zh)

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Cited By (2)

* Cited by examiner, † Cited by third party
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CN108885623A (zh) * 2016-09-02 2018-11-23 浙江核新同花顺网络信息股份有限公司 基于知识图谱的语意分析系统及方法
CN109299252A (zh) * 2018-08-17 2019-02-01 北京奇虎科技有限公司 基于机器学习的股票评论的观点极性分类方法和装置

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108885623A (zh) * 2016-09-02 2018-11-23 浙江核新同花顺网络信息股份有限公司 基于知识图谱的语意分析系统及方法
CN108885623B (zh) * 2016-09-02 2022-05-10 浙江核新同花顺网络信息股份有限公司 基于知识图谱的语意分析系统及方法
US11593671B2 (en) 2016-09-02 2023-02-28 Hithink Financial Services Inc. Systems and methods for semantic analysis based on knowledge graph
US12141713B2 (en) 2016-09-02 2024-11-12 Hithink Financial Services Inc. Systems and methods for semantic analysis based on knowledge graph
CN109299252A (zh) * 2018-08-17 2019-02-01 北京奇虎科技有限公司 基于机器学习的股票评论的观点极性分类方法和装置

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