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rCharts + factorAnalytics | french/fama & frazzini/pedersen
Raw
Readme.md

factor data from

Betting against Beta factors used in Frazzini A and L.H. Pedersen (2013), “Betting Against Beta"
Fama and French, 1993
"Common Risk Factors in the Returns on Stocks and Bonds"
Journal of Financial Economics
Raw
code.R
#use monthly factor data from Betting Against Beta paper
#This file contains monthly return of the Betting against Beta factors used in Frazzini A and L.H. Pedersen (2013), “Betting Against Beta"
#Copyright ©2013 Andrea Frazzini and Lasse Heje Pedersen
#combine factor data from Fama/French
#See Fama and French, 1993
#"Common Risk Factors in the Returns on Stocks and Bonds"
#Journal of Financial Economics
require(gdata)
require(PerformanceAnalytics)
require(factorAnalytics)
require(quantmod)
require(rCharts)
#read spreadsheet
babFactors <- read.xls(
"http://www.econ.yale.edu/~af227/data/BAB%20factors%20-%20Frazzini%20and%20Pedersen.xlsx"
,pattern = "caldt"
,blank.lines.skip = T
,stringsAsFactors = F
)
#convert spreadsheet data to R xts
#remove % with gsub, make numeric, and divide by 100
babFactors.xts <- as.xts(
do.call(cbind,
lapply(
babFactors[,-1]
,function(x){
df<-data.frame(as.numeric(
gsub(
x=x
,pattern="%"
,replacement=""
)
)/100)
colnames(df) <- colnames(x)
return(df)
}
)
) #date is first column; will use in order.by
,order.by = as.Date(paste0(babFactors[,1],"-01"),format="%Y%m-%d")
)
#now read Fama/French Factor data
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors.zip"
my.tempfile<-paste(tempdir(),"\\F-F_Research_Data_Factors.zip",sep="")
my.usefile<-paste(tempdir(),"\\F-F_Research_Data_Factors.txt",sep="")
download.file(my.url, my.tempfile, method="auto",
quiet = FALSE, mode = "wb",cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_factors <- read.table(file=my.usefile,
header = TRUE, sep = "",
as.is = TRUE,
skip = 3, nrows=1052)
#get dates ready for xts index
datestoformat <- rownames(french_factors)
datestoformat <- paste(substr(datestoformat,1,4),
substr(datestoformat,5,7),"01",sep="-")
#get xts for analysis
french_factors.xts <- as.xts(french_factors,
order.by=as.Date(datestoformat))
french_factors.xts <- french_factors.xts/100
factorsUS <- na.omit(merge(french_factors.xts,babFactors.xts[,1]))
colnames(factorsUS)[5] <- "BAB"
#not necessary but grab DJIA from FRED for sanity check
djia <- to.monthly(getSymbols("DJIA",src="FRED",auto.assign=F))[,4]
colnames(djia) <- "DowJonesIndu"
index(djia) <- as.Date(index(djia))
returnsFactors <- na.omit(
merge(
ROC(djia,n=1,type="discrete")
,factorsUS
)
)
betasRolling <- rollapply(
returnsFactors[,-1]
, width = 36 #3 year or 36 month rolling
, by.column=FALSE
, by=1
, FUN = function(x){
fit.time <- fitTimeSeriesFactorModel(
assets.names=colnames(x[,1]),
factors.names=colnames(x[,-c(1,4)]),
data=x,
fit.method="OLS"
)
return(fit.time$beta)
}
)
colnames(betasRolling) <- colnames(returnsFactors)[-c(1,2,5)]
require(reshape2)
betasRolling.melt <- melt(data.frame(index(betasRolling),betasRolling),id.vars=1)
colnames(betasRolling.melt) <- c("date", "factor", "beta")
nBeta <- nPlot(
beta ~ date,
group = "factor",
data = na.omit(betasRolling.melt),
type = "multiBarChart", #lineChart #stackedAreaChart, #bar, area don't work with negative
height = 400,
width = 700
)
#nBeta$chart(stacked = TRUE, useInteractiveGuideline=TRUE)
nBeta$xAxis(tickFormat =
"#!function(d) {return d3.time.format('%Y')(new Date(d * 24 * 60 * 60 * 1000));}!#"
)
nBeta$yAxis(tickFormat =
"#!function(d) {return d3.format('0.2f')(d);}!#"
)
#nBeta
nBeta$params$type = "lineChart"
nBeta
#chart.Correlation(returnsFactors)
Raw
index.html
<!doctype HTML>
<meta charset = 'utf-8'>
<html>
<head>
<link rel='stylesheet' href="http://netdna.bootstrapcdn.com/bootswatch/2.3.1/cosmo/bootstrap.min.css">
<link rel="stylesheet" href="http://netdna.bootstrapcdn.com/twitter-bootstrap/2.3.1/css/bootstrap-responsive.min.css" >
<link rel='stylesheet' href="http://getbootstrap.com/2.3.2/assets/js/google-code-prettify/prettify.css">
<link rel='stylesheet' href="http://aozora.github.io/bootplus/assets/css/docs.css">
<link rel='stylesheet' href='http://cdnjs.cloudflare.com/ajax/libs/nvd3/1.1.15-beta/nv.d3.css'>
<script src='http://ajax.googleapis.com/ajax/libs/jquery/1.8.2/jquery.min.js' type='text/javascript'></script>
<script src='http://d3js.org/d3.v3.min.js' type='text/javascript'></script>
<script src='http://cdnjs.cloudflare.com/ajax/libs/nvd3/1.1.15-beta/nv.d3.js' type='text/javascript'></script>
<style>
.rChart {
display: block
margin: auto auto;
width: 600px;
height: 400px;
}
/*
body {
margin-top: 60px;
}
*/
</style>
</head>
<body>
<div class='container'>
<h3>Factors | 3 Year Rolling Beta</h3>
<div class='row'>
<div class='span12'>
<div class="bs-docs-example">
<div id='chart270050c22c48' class='rChart nvd3'>
</div>
<br/>
<pre><code class='r'>#use monthly factor data from Betting Against Beta paper
#This file contains monthly return of the Betting against Beta factors used in Frazzini A and L.H. Pedersen (2013), “Betting Against Beta&quot;
#Copyright ©2013 Andrea Frazzini and Lasse Heje Pedersen
#combine factor data from Fama/French
#See Fama and French, 1993
#&quot;Common Risk Factors in the Returns on Stocks and Bonds&quot;
#Journal of Financial Economics
require(gdata)
require(PerformanceAnalytics)
require(factorAnalytics)
require(quantmod)
require(rCharts)
#read spreadsheet
babFactors &lt;- read.xls(
&quot;http://www.econ.yale.edu/~af227/data/BAB%20factors%20-%20Frazzini%20and%20Pedersen.xlsx&quot;
,pattern = &quot;caldt&quot;
,blank.lines.skip = T
,stringsAsFactors = F
)
#convert spreadsheet data to R xts
#remove % with gsub, make numeric, and divide by 100
babFactors.xts &lt;- as.xts(
do.call(cbind,
lapply(
babFactors[,-1]
,function(x){
df&lt;-data.frame(as.numeric(
gsub(
x=x
,pattern=&quot;%&quot;
,replacement=&quot;&quot;
)
)/100)
colnames(df) &lt;- colnames(x)
return(df)
}
)
) #date is first column; will use in order.by
,order.by = as.Date(paste0(babFactors[,1],&quot;-01&quot;),format=&quot;%Y%m-%d&quot;)
)
#now read Fama/French Factor data
my.url=&quot;http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors.zip&quot;
my.tempfile&lt;-paste(tempdir(),&quot;\\F-F_Research_Data_Factors.zip&quot;,sep=&quot;&quot;)
my.usefile&lt;-paste(tempdir(),&quot;\\F-F_Research_Data_Factors.txt&quot;,sep=&quot;&quot;)
download.file(my.url, my.tempfile, method=&quot;auto&quot;,
quiet = FALSE, mode = &quot;wb&quot;,cacheOK = TRUE)
unzip(my.tempfile,exdir=tempdir(),junkpath=TRUE)
#read space delimited text file extracted from zip
french_factors &lt;- read.table(file=my.usefile,
header = TRUE, sep = &quot;&quot;,
as.is = TRUE,
skip = 3, nrows=1052)
#get dates ready for xts index
datestoformat &lt;- rownames(french_factors)
datestoformat &lt;- paste(substr(datestoformat,1,4),
substr(datestoformat,5,7),&quot;01&quot;,sep=&quot;-&quot;)
#get xts for analysis
french_factors.xts &lt;- as.xts(french_factors,
order.by=as.Date(datestoformat))
french_factors.xts &lt;- french_factors.xts/100
factorsUS &lt;- na.omit(merge(french_factors.xts,babFactors.xts[,1]))
colnames(factorsUS)[5] &lt;- &quot;BAB&quot;
#not necessary but grab DJIA from FRED for sanity check
djia &lt;- to.monthly(getSymbols(&quot;DJIA&quot;,src=&quot;FRED&quot;,auto.assign=F))[,4]
colnames(djia) &lt;- &quot;DowJonesIndu&quot;
index(djia) &lt;- as.Date(index(djia))
returnsFactors &lt;- na.omit(
merge(
ROC(djia,n=1,type=&quot;discrete&quot;)
,factorsUS
)
)
betasRolling &lt;- rollapply(
returnsFactors[,-1]
, width = 36 #3 year or 36 month rolling
, by.column=FALSE
, by=1
, FUN = function(x){
fit.time &lt;- fitTimeSeriesFactorModel(
assets.names=colnames(x[,1]),
factors.names=colnames(x[,-c(1,4)]),
data=x,
fit.method=&quot;OLS&quot;
)
return(fit.time$beta)
}
)
colnames(betasRolling) &lt;- colnames(returnsFactors)[-c(1,2,5)]
require(reshape2)
betasRolling.melt &lt;- melt(data.frame(index(betasRolling),betasRolling),id.vars=1)
colnames(betasRolling.melt) &lt;- c(&quot;date&quot;, &quot;factor&quot;, &quot;beta&quot;)
nBeta &lt;- nPlot(
beta ~ date,
group = &quot;factor&quot;,
data = na.omit(betasRolling.melt),
type = &quot;multiBarChart&quot;, #lineChart #stackedAreaChart, #bar, area don't work with negative
height = 400,
width = 700
)
#nBeta$chart(stacked = TRUE, useInteractiveGuideline=TRUE)
nBeta$xAxis(tickFormat =
&quot;#!function(d) {return d3.time.format('%Y')(new Date(d * 24 * 60 * 60 * 1000));}!#&quot;
)
nBeta$yAxis(tickFormat =
&quot;#!function(d) {return d3.format('0.2f')(d);}!#&quot;
)
#nBeta
nBeta$params$type = &quot;lineChart&quot;
nBeta
#chart.Correlation(returnsFactors)
</code></pre>
</div>
</div>
</div>
</div>
<script type='text/javascript'>
$(document).ready(function(){
drawchart270050c22c48()
});
function drawchart270050c22c48(){
var opts = {
"dom": "chart270050c22c48",
"width": 600,
"height": 400,
"x": "date",
"y": "beta",
"group": "factor",
"type": "lineChart",
"id": "chart270050c22c48"
},
data = [
{
"date": -13820,
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"beta": -0.1302441547612
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{
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"beta": 0.3177938142463
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"beta": 0.2768042501376
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if(!(opts.type==="pieChart" || opts.type==="sparklinePlus" || opts.type==="bulletChart")) {
var data = d3.nest()
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//instead of main would think a better default is opts.x
return opts.group === undefined ? opts.y : d[opts.group];
})
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}
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data.map(function(d, i){
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nv.addGraph(function() {
var chart = nv.models[opts.type]()
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.height(opts.height)
if (opts.type != "bulletChart"){
chart
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.y(function(d) { return d[opts.y] })
}
chart.xAxis
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chart.yAxis
.tickFormat(function(d) {return d3.format('0.2f')(d);})
d3.select("#" + opts.id)
.append('svg')
.datum(data)
.transition().duration(500)
.call(chart);
nv.utils.windowResize(chart.update);
return chart;
});
};
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src='https://google-code-prettify.googlecode.com/svn-history/r232/trunk/src/lang-r.js'>
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for (var i=0; i < pres.length; ++i) {
pres[i].className = "prettyprint linenums";
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