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Before this gets overblown into some needless controversy - I am explaining
what this spreadsheet is all about right here.
The reasons this spreadsheet was not uploaded to RT/Metastock before were
- simply because I thought nobody here would be interested, hence it was
shared with a few close friends before the public release @ Microsoft
MoneyCentral.
- I felt kinda embarrassed tooting my own horn.
It seems to me that the latter was a needless concern. Grandma phrases like
God helps those who help themselves etc etc come to mind.
Here is how this started:
Jon Markman, an author (ex LA Times, now Microsoft, and responsible for a
bestseller entitled Online Investing) wrote an article publishing some of
his research on stock-specific seasonality.
This research was astounding to me - in its simplicity, and verifiability.
Here is that article:
http://moneycentral.msn.com/articles/invest/models/4804.asp
Independently, and unrelated to the above, I have observed and traded
seasonal patterns successfully in the energy derivatives complex. My work
has been visual converted to Excel, clumsy, but gratifying since it has been
profitable.
I got to know him, and we started exchanging ideas. One thing led to the
other, I did the index research, put it on a user-friendly spreadsheet, and
shared it with Jon. He got excited, and wrote it up in an article at their
website.
This started out as a discovery. It is aquiring a life of its own - perhaps
the way of the world in internet time - for that has since resulted in about
200 emails in the past 3 weeks from strangers seeking investment advice, a
book offer from someone in London, and assorted other flora and fauna.
Now.
Do I care it gets mass-distributed? Heck, no. This wouldn't be in public
domain otherwise.
The spreadsheet focusses on indices that have liquid, tradeable options
because that sphere is where I trade the funds I manage and that is where I
can find direct measurable portfolio impact.
However, that work has now expanded to include Fidelity Select Sector Funds,
and I am happy to share that work here if anybody is interested. Fund data
is currently inaccurately adjusted for dividends/distributions and being
cross-checked - but the trends exist and the trends are tradeable and I
intend to trade them with real money come Jan 2000.
The following is the text of what was sent to Jon before he wrote it up in
his article - which is at this url.
http://moneycentral.msn.com/articles/invest/models/4950.asp
Microsoft owns all the copyrights for all urls referenced in this space.
Finally, and once again - I mean no disrespect towards Mark the person - my
previous email was towards the act - which looked and felt like plagarism in
the crude way the email landed in my face - I believe I asked for basic
decency out of sheer indignation.
====
The spreadsheet as explained:
Sector picking (through Sector Mutual Funds or their tracking sector
stocks) – beat S&P 500 Indexing by a factor of 10 to 1 since 1994 using NO
LEVERAGE. The results are remarkably better if individual stocks are chosen.
Methodology:
PRQs were tested across 150 companies in different industry sectors, just to
establish the validity of the concept.
Research then extended to 31 industry sector indices, for as far back as
sector index data could be found.
This spreadsheet is the result of the industry sector research. It provides
a "Heat Map" for sector rotation that seems to evolve in the market.
The Heat Map is set about as follows:
Each column has data for a single index across the 12 months.
Each row has data for a single month, across the page index.
Green cells are "Best Months to Buy these sectors"
Red cells are "Worst Months to Buy these sectors; conversely, best months to
Short them".
Blue cells are Best of Breed for the month, across all sectors researched so
far.
A summary of the best 3 sector returns for each month is listed in the
initial 3 gray columns, and the Benchmark index is S&P 500.
To use this to its desired initial screening potential or even as a
standalone strategy, the reader would:
Buy the best performing index for Dec at the Close on the last trading day
of Nov.
Rotate into the next month in similar fashion on the last trading day in
Dec.
Assuming no leverage or transaction costs, the trader outperforms SnP 500
Indexing by the average factor listed in Row 21. (An outperform reading of 1
equals the benchmark, of 0 under-performs, and of 10 beats the benchmark 10
to 1).
The trader would further research the index components’ chart patterns and
PRQs for individual stock selection. This component list (and where given,
weight within the index) is hyperlinked with the Index name in Row 2. Just
have an internet connection handy.
The work-in-progress is as follows:
I am PRQ'ing sector components, and then doing the Heat Map for each stock
in that sector at Month Level.
The Month level will then be imploded to explore the best weeks in the month
to hold that stock, or its options. Given that this data captures an entire
month while options expire on the third Friday of each month, there will be
some fine-tuning. But the core concept seems to be ready to be set in
stone – the findings are really exciting:
Some Findings:
A/ Forest and Paper Products is the best performing sector in April of every
year. FPP appreciates better than SPX by a factor of 10. Better than
Internets, Semis, Banks, you name it. This is consistent with Jon's original
PRQ article and his experience with Dow 30 component Cyclicals.
B/ Semiconductors have a tendency to outperform the rest of the world and
their own annual buy-and-hold results in the January, April, and July of
each year – notably, these are the first months in every quarter.
C/ Networking stocks are best bought in April and held through July. This
beats the SPX by a factor of 9 to 1 during the same period.
D/ While September is billed to be the worst month for the market (true),
the sectors studied actually end up losing the most of their value in
August. This leads me to believe that the September under-performance
catches up with the not-too-favored stocks that make up the general index,
while the real damage in the market's YTD faves happens in August.
E/ Contrary to current popular belief, Oil prices, Oil sector stocks and
Airline stocks can, and do rally together - the spreadsheet will show that
these sectors simultaneously outperform the rest of the world (including
Internet stocks) in March of each year.
F/ Airlines again outperform the universe in Oct and Nov each year, but Oil
sector stocks curiously decline in this period.
G/ While on Oil: This is the classic Anti-January effect sector. The sector
plunges >5% on average every year, (the worst performer of the universe)
making it a prime candidate for shorting during the month.
H/ The best January Effect halo exists over Brokers, Semiconductors, and
Internets. While other sectors do find investor enthusiasm, it appears that
following the crowd while ignoring these 3 sectors obligate you to
mediocrity, for you give up >4 to 1 returns in a generally strong market.
I/ There is something to be said about being defensive. Drug related
sectors, for example, typically outperform the benchmark during the market’s
weak period in Aug-Sep. But within the drug sector, the more momentum driven
Biotech sector is better owned than the Mercks of the world.
Next steps for me:
Sector-Component Level seasonality
Week-of-month seasonality at Sector and Component Level.
Trading Instrument choice and Trading Plan finalization.
Since I now have a general heat map, I will start with sectors that enjoy
their best seasonality in Dec and Jan. If you are interested, I will send
these spreadsheets over.
Weaknesses/Shortcomings noticed so far:
a. Data inconsistency: Index measurements start from different dates in
history, a little skew may exist.
b Multiple index participation: Lots of stocks appear in multiple index
standings.
c. Unknown reasons for some stock exclusions: For eg, the SnP Bank Index
(BIX) does not have Citibank as a component. Go figure.
d. Bad data on MSN/CSI database. Can't use it unless manually washed.
e. From a trader's perspective, given today's ATR, a lot of these numbers
seem miniscule - a whole month's performance can be reached within a week or
less.
Comments welcome.
Gitanshu
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