指数基金300495 溢价识别(ah股溢价指数基金)

指数基金 溢价辨认。这种 能够协助出资者更好地了解指数基金的特色，然后挑选合适自己的指数基金。下面咱们就来看看指数基金的分类。一般来说，指能够协助出资者更好地了解指数基金的特色，然后挑选合适自己的指数基金。下面咱们就来看看指数基金的分类。

发表于河南

一、ERP是什么：

1.股权危险溢价ERP(equity risk premium)又名股债利差，指商场出资组合或具有商场均匀危险的股票收益率与无危险收益率的差额。差额越大，未来权益类出资潜在收益偏高，合适超配股票。差额越小，未来权益类出资潜在收益偏低，适合下降股票仓位。咱们用股权危险溢价目标来反响出资者的危险偏好，能够作为商场危险偏好目标。

2.公式：股权危险溢价=1/PE-r 注: PE用上证指数的前史TTM市盈率替代，1/PE代表股票商场全体潜在的盈余收益率；r代表商场无危险利率，用盈余收益率与十年期国债收益率比值(上证)替代。

3.定论：前史上4 个底部股权危险溢价状况来看，商场见底时，商场危险偏好高位滞涨，且商场处在低位(股权危险溢价处在高位)。当商场见底后，股票商场处于底部震动，而商场危险偏好也在高位震动。当商场打破底部区间震动上行时，危险偏好也会快速下行。二、股债比是什么：1.股债比是指商场出资组合或具有商场均匀危险的股票收益率与无危险收益率的比值。咱们用股债比目标来反响出资者的危险偏好，能够作为商场危险偏好目标。2.公式：股债比=1/PE:r

三、股债性价比:该模型由美联储FED提出，也叫FED模型或股债危险溢价率。咱们用指数市盈率的倒数(即EPS/Price)来代表持有股市的预期收益率，用十年期国债收益率代表持有债券的预期收益率。二者差值大于0代表股市预期收益更好，反之亦然。该差值与其长时间均值的违背度。代表当时股市性价比的凹凸程度。正向违背越多则股市越有价值，应该高配，反之亦然

四，股息率溢价

股票安全性:咱们股票指数的股息率来代表持有股市的预期收益率，用十年期国债收益率代表持有债券的预期收益率。二者差值大于0代表股市安全性更好，反之亦然。该差值与其长时间均值的违背度，代表当时股市安全性的凹凸程度。正向违背越多则股市安全性越高，能够高配，反之亦然。

怎么自己画图

股债比值与上证指数走势绘图过程

参阅网站：.woniu500/z k/erp.html

1.用电脑在浏览器里翻开以下地址：

echarts.apache.org/examples/zh/editor.html?c=line-simple

2清空左面输入框里边全部内容，

3.用记事本翻开“02股债比值与上证指数走势图txt”文件，然后按”Ctrl+A”键选中“02股债比值与上证指数走势图txt”里边全部内容，接着按”Ctrl+C”键仿制“02股债比值与上证指数走势图以下一切代码，里边全部内容，终究切换到过程2中清空的输入框，并按”Ctrl+V”键将仿制的内容仿制到过程2中清空的输入框里边，并按”运转按”钮,显现见下图：

option = {

tooltip: {

trigger: 'axis',

axisPointer: {

type: 'cross',

crossStyle: {

color: '#999'

}

}

},

toolbox: {

feature: {

dataView: {show: true, readOnly: false},

magicType: {show: true, type: ['line', 'bar']},

restore: {show: true},

saveAsImage: {show: true}

}

},

legend: {

data: ['股债比', '上证指数']

},

xAxis: [

{

type: 'category',

data: ['200201', '200202', '200203', '200204', '200205', '200206', '200207', '200208', '200209', '200210', '200211', '200212', '200301', '200302', '200303', '200304', '200305', '200306', '200307', '200308', '200309', '200310', '200311', '200312', '200401', '200402', '200403', '200404', '200405', '200406', '200407', '200408', '200409', '200410', '200411', '200412', '200501', '200502', '200503', '200504', '200505', '200506', '200507', '200508', '200509', '200510', '200511', '200512', '200601', '200602', '200603', '200604', '200605', '200606', '200607', '200608', '200609', '200610', '200611', '200612', '200701', '200702', '200703', '200704', '200705', '200706', '200707', '200708', '200709', '200710', '200711', '200712', '200801', '200802', '200803', '200804', '200805', '200806', '200807', '200808', '200809', '200810', '200811', '200812', '200901', '200902', '200903', '200904', '200905', '200906', '200907', '200908', '200909', '200910', '200911', '200912', '201001', '201002', '201003', '201004', '201005', '201006', '201007', '201008', '201009', '201010', '201011', '201012', '201101', '201102', '201103', '201104', '201105', '201106', '201107', '201108', '201109', '201110', '201111', '201112', '201201', '201202', '201203', '201204', '201205', '201206', '201207', '201208', '201209', '201210', '201211', '201212', '201301', '201302', '201303', '201304', '201305', '201306', '201307', '201308', '201309', '201310', '201311', '201312', '201401', '201402', '201403', '201404', '201405', '201406', '201407', '201408', '201409', '201410', '201411', '201412', '201501', '201502', '201503', '201504', '201505', '201506', '201507', '201508', '201509', '201510', '201511', '201512', '201601', '201602', '201603', '201604', '201605', '201606', '201607', '201608', '201609', '201610', '201611', '201612', '201701', '201702', '201703', '201704', '201705', '201706', '201707', '201708', '201709', '201710', '201711', '201712', '201801', '201802', '201803', '201804', '201805', '201806', '201807', '201808', '201809', '201810', '201811', '201812', '201901', '201902', '201903', '201904', '201905', '201906', '201907', '201908', '201909', '201910', '201911', '201912', '202001', '202002', '202003', '202004', '202005', '202006', '202007', '202008'],

axisPointer: {

type: 'shadow'

}

}

],

yAxis: [

{

type: 'value',

name: '%',

},

{

type: 'value',

name: '上证指数',

}

],

series: [

{

name: '股债比',

type: 'line',

// stack: '总量1',

data: ['0.94', '0.93', '0.94', '1.01', '1.09', '0.87', '0.82', '0.77', '0.81', '0.86', '0.79', '0.91', '0.86', '0.85', '0.90', '0.92', '0.93', '0.91', '0.95', '1.01', '0.90', '0.93', '0.78', '0.71', '0.69', '0.66', '0.61', '0.58', '0.78', '0.86', '0.82', '0.75', '0.73', '0.82', '0.73', '0.87', '0.89', '0.83', '1.04', '1.07', '1.71', '1.71', '1.84', '1.66', '1.84', '2.08', '1.92', '1.89', '1.92', '1.90', '1.91', '1.69', '1.66', '1.56', '1.53', '1.52', '1.54', '1.48', '1.28', '0.99', '0.85', '0.80', '0.66', '0.52', '0.57', '0.52', '0.45', '0.39', '0.35', '0.31', '0.41', '0.38', '0.48', '0.49', '0.63', '0.58', '0.92', '1.07', '1.07', '1.29', '1.42', '2.28', '2.17', '2.42', '1.99', '1.85', '1.60', '1.55', '1.43', '1.19', '0.95', '1.23', '1.18', '1.04', '1.00', '0.95', '1.06', '1.10', '1.04', '1.17', '1.54', '1.65', '1.53', '1.56', '1.51', '1.20', '1.17', '1.19', '1.15', '1.13', '1.12', '1.13', '1.60', '1.56', '1.53', '1.58', '1.82', '1.78', '1.95', '2.17', '2.09', '1.90', '2.06', '1.92', '2.36', '2.53', '2.69', '2.67', '2.57', '2.50', '2.64', '2.28', '2.14', '2.16', '2.32', '2.45', '2.46', '2.80', '2.62', '2.29', '2.24', '2.16', '2.0', '2.0', '2.1', '2.10', '2.08', '2.18', '2.52', '2.51', '2.22', '2.21', '2.19', '2.26', '2.16', '1.73', '1.79', '1.79', '1.44', '1.32', '1.27', '1.33', '1.60', '1.90', '2.05', '1.96', '1.93', '2.01', '2.56', '2.60', '2.33', '2.34', '2.34', '2.44', '2.44', '2.36', '2.43', '2.32', '2.05', '2.09', '1.83', '1.80', '1.81', '1.73', '1.68', '1.65', '1.58', '1.53', '1.54', '1.40', '1.42', '1.42', '1.33', '1.43', '1.50', '1.59', '1.82', '2.04', '2.01', '2.06', '1.96', '2.19', '2.30', '2.48', '2.49', '2.13', '2.10', '1.91', '2.28', '2.24', '2.28', '2.39', '2.30', '2.18', '2.28', '2.16', '2.28', '2.52', '2.82', '2.77', '2.8', '2.45', '2.17', '2.03']

},

{

name: '上证指数',

type: 'line',

// stack: '总量2',

yAxisIndex: 1,

data: ['1491.67', '1524.70', '1603.91', '1667.75', '1515.73', '1732.76', '1651.59', '1666.62', '1581.62', '1507.50', '1434.18', '1357.65', '1499.82', '1511.93', '1510.58', '1521.44', '1576.26', '1486.02', '1476.74', '1421.98', '1367.16', '1348.30', '1397.23', '1497.04', '1590.73', '1675.07', '1741.62', '1595.59', '1555.91', '1399.16', '1386.20', '1342.06', '1396.70', '1320.54', '1340.77', '1266.50', '1191.82', '1306.00', '1181.24', '1159.15', '1060.74', '1080.94', '1083.03', '1162.80', '1155.61', '1092.82', '1099.26', '1161.06', '1258.05', '1299.03', '1298.30', '1440.22', '1641.30', '1672.21', '1612.73', '1658.64', '1752.42', '1837.99', '2099.29', '2675.47', '2786.34', '2881.07', '3183.98', '3841.27', '4109.65', '3820.70', '4471.03', '5218.83', '5552.30', '5954.77', '4871.78', '5261.56', '4383.39', '4348.54', '3472.71', '3693.11', '3433.35', '2736.10', '2775.72', '2397.37', '2293.78', '1728.79', '1871.16', '1820.81', '1990.66', '2082.85', '2373.21', '2477.57', '2632.93', '2959.36', '3412.06', '2667.75', '2779.43', '2995.85', '3195.30', '3277.14', '2989.29', '3051.94', '3109.11', '2870.61', '2592.15', '2398.37', '2637.50', '2638.80', '2655.66', '2978.84', '2820.18', '2808.08', '2790.69', '2905.05', '2928.11', '2911.51', '2743.47', '2762.08', '2701.73', '2567.34', '2359.22', '2468.25', '2333.41', '2199.42', '2292.61', '2428.49', '2262.79', '2396.32', '2372.32', '2225.43', '2103.63', '2047.52', '2086.17', '2068.88', '1980.12', '2269.13', '2385.42', '2365.59', '2236.62', '2177.91', '2300.6', '1979.21', '1993.80', '2098.38', '2174.67', '2141.61', '2220.50', '2115.98', '2033.08', '2056.30', '2033.31', '2026.36', '2039.21', '2048.33', '2201.56', '2217.2', '2363.87', '2420.18', '2682.83', '3234.68', '3210.36', '3310.30', '3747.90', '4441.65', '4611.74', '4277.22', '3663.73', '3205.99', '3052.78', '3382.56', '3445.40', '3539.18', '2737.6', '2687.98', '3003.92', '2938.32', '2916.62', '2929.61', '2979.34', '3085.49', '3004.70', '3100.49', '3250.03', '3103.64', '3159.17', '3241.73', '3222.51', '3154.66', '3105.54', '3192.43', '3273.03', '3360.81', '3348.94', '3393.34', '3317.19', '3307.17', '3462.08', '3259.41', '3168.90', '3082.23', '3095.47', '2847.42', '2876.4', '2725.25', '2821.35', '2602.78', '2588.19', '2493.9', '2584.57', '2940.95', '3090.76', '3078.34', '2898.70', '2978.88', '2932.51', '2886.24', '2905.19', '2929.06', '2871.98', '3050.12', '2976.53', '2880.3', '2750.3', '2860.08', '2852.35', '2984.67', '3310.01', '3395.68']

}

]

};

4.终究绘图成果见下图：

5.简略介绍一下代码所对应绘图的方位：

6.弥补：自己怎样填充数据呢？我拿填充9月22日的数据剧烈阐明

A.用浏览器器翻开上证证券交易所官网：

.sse/market/stockdata/overview/day/

找到以下图片均匀市盈率数据，成果显现是PE：15.52

B.用浏览器翻开查询十年期国债收益率的网站：

cn.investing/rates-bonds/china-10-year-bond-yield

查到22日数据是3.115，那么22日的股债比=100/15.52/3.115=2.07

C.用浏览器翻开检查指数点数的网站

查到22日的点数是：3274.30

quote.eastmoney/zs000001.html

这个数字，在哪里都能够找到，就不多说了

D.用记事本翻开“02股债比值与上证指数走势图txt”文件，在以下三个区域别离加上数据，加完后按“Ctrl+S”保存，保存后按“Ctrl+C”仿制记事本里边内容到过程2-3里边的输入框里，并按履行后，显现如下图：

留意：1.我的网站.woniu500是每个月的月底生成数据，所以我们也能够用每个月月底的终究一天作为当月数据，2.加数据时记得把你的输入法切换到英文，加数据格式是：逗号+单引号，单引号里边放数据。

不明白的话，私聊我吧

其实很简略，便是看看这个阶段，投股票合算仍是债券合算？