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Tabloid journalism using R !

Alpert, William 
Hey list !

Esteemed list-member Pat Burns helped the stock market tabloid Barron's
do a cover story last week that tested the returns to the stock picks
that Jim Cramer makes on his CNBC show Mad Money.  I think the story is
accessible without subscription at our website www.barrons.com, but I'll
send copies to anyone who cares and can't find it.  Since today's
Saturday, it's now the past week's edition.

We used R to test various investment strategies, such as buying the
morning after Cramer's 6 pm show, buying on the second day after the
show, etc. One reason we kept testing new strategies is that Cramer and
CNBC kept shifting their claim about when Cramer advises his audience to
Buy, as we showed them how each successive strategy lagged the market.
They also made weasely arguments that you should only count certain of
his recommendations -- as if viewers would know that his fingers are
crossed some of the times that he tells you to Buy or Sell.  As it
happened, the segment of his show that he argued most shrilly for us to
deselect (his Lightning Round recommendations to phone callers) was the
only segment with statistically significant good excess returns -- in
this case, on the Sell recommendations. His prepared Sell
recommendations went up.

We did find that you might make an interesting 20-day return by shorting
his Buys the morning after his shows, with offsetting S&P500 positions.

We downloaded our stock histories from Edgar Online's I-Metrix service,
using some Excel macros cooked up by Edgar Online analyst Elias-John
Kies and my neighbor Madison Mcgaffin, who's a rising sophomore at
Tufts. My story credits to them got cut in the editing.

Pat bootstrapped our 95% confidence intervals. We also did some event
study style analyses, incorporating each stock's average return over its
prior 250 days. These suggest that Cramer is a momentum guy who probably
rips his ideas from the headlines.

Very few statistical details made it into the final story. Nor did my
appreciative credit to Tim Hesterberg, of Insightful in Seattle, who
gave us some good advice but who bears no responsibility for any
woebegone errors.

Hey Pat ! Feel free to correct any misreporting done here by this
misanthropic layman reporter.

Bill Alpert
Barron's

-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of
r-sig-finance-request at stat.math.ethz.ch
Sent: Saturday, August 25, 2007 6:00 AM
To: r-sig-finance at stat.math.ethz.ch
Subject: R-SIG-Finance Digest, Vol 39, Issue 23


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Today's Topics:

   1. Re: Bloomberg (Dan Davison)
   2. ARIMA(0,1,0)+c results estimate incorrect drift (Nathan Bryant)
   3. Re: Bloomberg (Davy Cielen)


----------------------------------------------------------------------

Message: 1
Date: Fri, 24 Aug 2007 12:11:24 +0100
From: Dan Davison <davison at stats.ox.ac.uk>
Subject: Re: [R-SIG-Finance] Bloomberg
To: Thomas Steiner <finbref.2006 at gmail.com>
Cc: r-sig-finance at stat.math.ethz.ch
Message-ID: <20070824111124.GD19118 at stats.ox.ac.uk>
Content-Type: text/plain; charset=us-ascii
On Thu, Aug 23, 2007 at 10:38:00PM +0200, Thomas Steiner wrote:
This approach does give a good pure R solution doesn't it? e.g.
something like

gradient.under.graph <- function(n=100, y0=0, mu=-0.7, sd=1,
nrects=1000) {
    y <- y0 + cumsum(rnorm(n, mean=mu, sd=sd))
    plot(NA, xlim=c(1,n), ylim=range(y), bty="n")
    col <- colorRampPalette(colors=c("dark blue","light blue"))(nrects)
    incr <- (max(y) - min(y)) / nrects
    rect(0, seq(min(y), max(y)-incr, length=nrects), n, seq(min(y)+incr,
max(y), length=nrects), col=col, border=NA)
    polygon(x=c(1,1:n,n), y=c(max(y), y, max(y)), col="white",
border=NULL) }

is not slow.

Dan
------------------------------

Message: 2
Date: Fri, 24 Aug 2007 11:12:09 -0400
From: Nathan Bryant <nbryant at optonline.net>
Subject: [R-SIG-Finance] ARIMA(0,1,0)+c results estimate incorrect
	drift
To: r-sig-finance at stat.math.ethz.ch
Message-ID: <46CEF549.2060107 at optonline.net>
Content-Type: text/plain; charset=UTF-8; format=flowed


Hi,

I seem to have found a problem. Integrated ARIMA with drift uses a
linear regression on xreg and the results don't look numerically stable.
Forecasts are inconsistent with an equivalent arima(0,0,0) on
differenced data and with rwf. Maybe someone knows if there is a
rational explanation?

Try this,

library(forecast)
set.seed(3)
rand <- rnorm(1000, mean=.2)
mean(rand)
fit1 <- arima(diffinv(rand), order=c(0,1,0), include.drift=T)
coef(fit1)
fit2 <- arima(rand, order=c(0,0,0))
coef(fit2)



Without seed you may have to repeat the experiment a few times to see
the nuisance, seed is included to show an example of the problem:
[1] 0.2063965
drift
0.2073960
intercept
0.2063965




Nathan Bryant



------------------------------

Message: 3
Date: Fri, 24 Aug 2007 23:36:35 +0200
From: Davy Cielen <dcielen at vub.ac.be>
Subject: Re: [R-SIG-Finance] Bloomberg
To: Dan Davison <davison at stats.ox.ac.uk>
Cc: r-sig-finance at stat.math.ethz.ch
Message-ID: <1187991395.15359.10.camel at davy-desktop>
Content-Type: text/plain; charset="us-ascii"

It looks very nice and is fast indeed,
I tried to build a gradient with 100.000 intervals,

it was build in no time.

Can this type of solution be converted to an overal gradient independent
of the direction and then be added to some library?  

Great work Tom and Dan,

Davy

Thread (3 messages)

Alpert, William Tabloid journalism using R ! Aug 25 Dirk Eddelbuettel Tabloid journalism using R ! Aug 25 Gabor Grothendieck Tabloid journalism using R ! Aug 25