The Senate climate bill, called the Clean Energy Jobs and American Power Act, just made its debut today. There are still pieces deliberately left out of the bill that will be subject to Senate debate, and of course, time will need to be taken to properly analyze the 800-page bill (hey, at least it’s shorter than Waxman-Markey’s 1300-pager). But here’s a first look–a rundown of its pros and cons, how it’s different from Read the full story on TreeHugger

Photo credit NASA via Discovery News

Of all the strange ideas I’ve heard, this tops the list. The amount of catalogued space debris within our planet’s orbit increased by nearly 50% since 2007, so DARPA wants to gather ideas for how to clear space junk from Earth’s orbit. James Hollopeter of GIT Satellite lends one idea – launch rockets full of water out to space. …Read the full story on TreeHugger

Tom Konrad, Ph.D., CFA

My Quick Clean
Energy Tracking Portfolio has produced unexpected out-performance. 
Is it because of high beta (β) in a rising market?

I recently asked why two portfolios which I had designed to track green
energy mutual funds ended
up out-performing them by a wide margin.  

This is the first of a short series of articles looking into possible
causes.  Could the portfolios be outperforming because the stocks they
contain rise more when the market rises (and fall more when the market falls)
than do the mutual funds they were designed to track?  In other words, are
they out-performing because of high beta (β) in a rising market?

A Beta Definition

From Wikipedia,

In finance,
the beta (β) of a stock
or portfolio
is a number describing the relation of its returns with that of the financial
market as a whole.^[1]

An asset with a beta of 0 means that its price is not at all correlated
with the market; that asset is independent. A positive beta means that the
asset generally follows the market. A negative beta shows that the asset
inversely follows the market; the asset generally decreases in value if the
market goes up and vice versa.^[2]

That’s a basic definition.  It’s worthwhile to note that β changes
for any stock or portfolio over time (usually slowly, but sometimes quickly in
times of market turmoil.)  Measured β will also vary depending on the
time increment used (are we interested in daily, weekly, monthly, or even hourly
changes of a stock with respect to the market,) and it will also vary depending
on which market index is used as a proxy for the market as a whole.  In my
recent article on hedging
using beta, I showed a graph with three measures of beta for my portfolio
against various market indexes.

Mathematically, if S is a stock (or portfolio) and M is the market index,
then

β = correlation(S,M) x
std.dev(S) / std.dev(M)

or

β = covariance(S,M)
/ variance(M).

Either formula can be calculated with standard spreadsheet functions.  I
gave an example using the first formula in my hedging
article, and a spreadsheet
using the latter formula is available here.  In both cases, the change in S will be β times the change in M, plus an error term
which is uncorrelated to the change in M.

Why High Beta Would Explain Out-Performance

I created my tracking portfolios at the end of February, which was,
co-incidentally, right before the stock market began its recent rise. 
From this graph,

you can see that both tracking portfolios have out-performed nearly every
possible benchmark.  In the first article in this series, I attributed the
difference between the two tracking portfolios to "winner-loser"
effects (hence the names of the portfolios.)  In theory, without these
effects, those portfolios should have produced returns approximately equal to
the average of the two.

Since the S&P 500 (my proxy for the market) gained 43% over the period in
question, while the mutual funds gained 56% on average, and the tracking
portfolios gained 80% on average, if β were the sole reason for the
out-performance, we would expect that the average mutual fund β would be
about 1.3 (=56%/43%) while the average tracking portfolio β would be about
1.9 (=80%/43%.)  There will be significant errors in these calculations
(recall the random error term and other caveats from the definition of β),
but we should at least expect that the β of the tracking portfolios is
higher than the β of the mutual funds.

In fact, I do not expect that the out-performance of the mutual funds over the
S&P500 will all be due to β.  In April, I argued that clean
energy in general was outperforming the market due to the greater political
support shown by the Obama administration compared to previous administrations,
something I dubbed the "Obama Effect."  If this is true, then
the β for the mutual fund portfolio will be less than 1.3, but β could
still explain the out-performance of the tracking portfolios if their β is
approximately 0.6 greater than the β for the mutual fund portfolio.

Calculating β

β for a portfolio is the weighted average of the β’s of the
portfolio’s components.  Furthermore, Yahoo! Finance provides some
pre-calculated β’s (from Capital IQ,
a  division of Standard & Poors), but not for all securities in my
portfolios.  Furthermore, I was unable to determine the market index used
to calculate the Yahoo! β’s.

I used the last 200 trading days of stock market data for the securities in
question, and the formula above to calculate β with respect to my market
proxy, the S&P 500, using an Excel spreadsheet.  Here are the results:

A Partial Explanation

The average β of the tracking portfolios is 0.3 more than the mutual
fund portfolio β, but we needed a difference of about twice that to explain
all the out-performance.  We also see the Obama effect here, which accounts
for the 13% out-performance of the mutual funds over the S&P 500.

My calculated β’s, plus the Obama effect on green stocks and funds, can
explain an average performance of the tracking portfolios of about 70% over the
time period in question, leaving about 10% of the out-performance
unexplained. 

As I hypothesized in the previous article, this out-performance could also
arise from the way I chose the stocks, which created a bias towards some Cleantech
sectors (mostly efficiency
stocks  and smart
grid stocks) when compared to the mutual funds.  It could also be the
mutual fund managers’ skill.  In either case, however, an out-performance
of 10% during seven months which have been as volatile as these last seven would
not be enough for me to reach any firm conclusions.  10% is small enough to
be a bias in my β calculations (recall that β depends on the choice of
index as well as the frequency of the data used, and it also changes over
time.)  10% could also be just luck.  

Since I won’t be able to show that any out-performance which remains is not
just luck, I see no need to continue this investigation.

An Inadvertent Discovery

It’s interesting to note that the β for the "Losers" is higher
than that for the "Winners."  In other words, I was wrong to
attribute the out-performance of the "Losers" portfolio to
winner-loser effects.  "Loser" out-performance is also explained
by β.  Why do the "Losers" have higher β than the
"Winners"?  Because the "Losers" were the
worst-performing of a group of stocks from 2/27/2006 to 2/27/2009, over which
period the S&P 500 fell 43%.  Since high-β stocks are likely 
to fall more than low-β stocks when the market as a whole is falling, my
"Losers" portfolio was biased towards high-β stocks.

None of this explains why the "Winners" portfolio also has higher
β than the mutual funds.  While the "Losers" were selected
with a high-β bias, the "Winners" were selected with a bias
towards low-β stocks.  We would therefore expect that the
"Winners" portfolio would have a β lower than the mutual funds
from which they were drawn.  

Here are some reasons that the mutual fund β’s are lower than the β’s
of their top holdings.

1. Mutual funds need to maintain a cash reserve in order to meet
   redemptions.  Cash has a β of 0, and so an allocation to cash will
   lower the funds’ β overall. For instance, the Calvert fund holds about
   4% cash.  Without the cash, the Calvert fund’s β would be 0.954
   rather than 0.916.  However, the funds would have to be holding about
   20% cash for this to be a full explanation.
2. The greater emphasis of my portfolios on energy efficiency and smart grid
   technologies does not account for the bias.  The average β
   for these companies in my portfolios was about the same as the portfolios as
   a whole.
3. The funds own a good number of diversified companies with exposure to
   green energy, but they do not own enough of many of these to put them in the
   top holdings of the funds.  One example would be Applied
   Materials (AMAT), a semiconductor firm with growing interests in
   solar.  Such firms will generally have lower β than pure-play
   firms, which is why I suggested readers invest
   in AMAT and nine
   other large companies with green energy exposure in the spring of 2008:
   I was concerned about a market decline (not that I had any clue how bad it
   would be) and low-β stocks are likely to fall less during declines.

Implication: More Bang For Your Buck

The discovery that the top holdings have higher β than the funds has some
implications for tracking portfolio creation.  In order to better match the
gains and losses of the mutual funds, we will need to invest less money. 
By holding some cash, β can be lowered.  Alternatively, by investing
the same amount, an investor can get more exposure to the positive
trends affecting green energy, such as peak oil and the advent of carbon
regulation.  

That’s why we’re here, isn’t it?

DISCLOSURE: Tom Konrad and/or his clients own AMAT, LXU, ELON, and
AMSC. The Guinness Atkinson Fund is an advertiser on his website, AltEnergyStocks.com

DISCLAIMER: The information and trades provided here and in the comments are for
informational purposes only and are not a solicitation to buy or sell any of
these securities. Investing involves substantial risk and you should evaluate
your own risk levels before you make any investment. Past results are not an
indication of future performance. Please take the time to read the full
disclaimer here.

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