[R-sig-finance] Using R in equity research
Jim McLoughlin
jimmcloughlin at earthlink.net
Mon Jun 7 20:18:30 CEST 2004
Hi
> It seems to me that there are numerous tools available
> in R that can be put to good use in more mundane
> research tasks, things like determining sensitivities
> of earnings to economic growth and interest rates, or
> the sensitivity of price to sales ratios to
> profitability, leverage, and growth, or using ancova
> and time series data to estimate quarterly margins for
> a given company or industry. I've not yet seen a
> "cookbook" for accomplishing this kind of research.
I think the ultimate goal of this kind of research is still to explain
the cross section of returns, or to differentiate future winners from
losers. While you may be interested in earnings, price sales ratios,
etc, these are really intermediate results. Once you have these
numbers, the question is: now that have better earnings / sales
estimates, how should I use them? I would look at this as more of a
two step problem: 1) trying to obtain better forecasts of earnings,
etc; 2) using these forecasts to explain the cross section of returns
ala fama french, or to form portfolios of winners/losers.
There is a lot of research going along these lines, and it is one of
the reasons Accounting is such a hot field these days. Much of the
accounting research is concerned with using fine grained balance sheet
items as building blocks to better estimates of earnings, residual
income, return on assets, etc.
I don't have exact references handy (and a lot of what I've read are
working papers), but some of the names to check out are
Charles Lee (Cornell) -
http://www.johnson.cornell.edu/faculty/profiles/lee/
Stephen Penman (Columbia) - there are specific papers by Ou and Penman,
and Nassem and Penman
http://www0.gsb.columbia.edu/whoswho/full.cfm?id=55604
David Hirshleifer (Ohio State) -
http://fisher.osu.edu/fin/faculty/hirshleifer/
I think the Accounting literature is where you will find the gap
bridged between fundamental "CFA" style analysis and more rigorous
statistics.
cheers,
Jim M
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