rebalancing (part 3)

As contemplated, I've rounded out our examination of rebalancing with some random number generation...

The Test

Have Excel generate artificial returns over 500 weeks for each of 10 artificial stocks. This was accomplished by using the random number generator utility that allows one to select the statistical distribution (I selected a Normal distribution in keeping with Modern Portfolio Theory) and the associated parameters (mean, variance). I specified the random numbers be drawn from a Normal distribution with a mean annual return of 20% and an annualized standard deviation of 20%.

Observations

Under these conditions, I found that Rebalancing provided for a higher return than Buy&Hold only 4 out of 10 times (not very conclusive). However, the Rebalanced portfolio was always much less volatile than the Buy&Hold portfolio, which provided for a higher Sharpe Ratio every time. The Sharpe Ratio is basically just Return divided by Standard Deviation, which helps provide a feel for the 'significance' of the total Return over the investment period relative to how much that Return bounced around during the investment period (technical note: for simplicity I assumed the risk free rate = 0% when calculating the Sharpe Ratio).

[Caution: these are 'long-term' results (i.e. 500 weeks = approx. 10 years). In fact when I ran the test 10 times using an extreme annualized variance of 100%, rather than the more realistic 20%, I found the Rebalanced portfolio was actually MORE volatile than the Buy&Hold portfolio in 7 of the 10 trials. I think the reason is because under conditions of such extreme volatility, it takes longer than 10 years to begin to observe the 'long-term' result where a Rebalancing strategy can benefit from Reversion to the Mean.]

In terms of Alpha, the Rebalanced portfolio only outperformed 6 out of 10 times (again, not very conclusive) - I'm not sure why (a mystery for another day). However, in keeping with the theme of lower volatility, the Rebalanced portfolio had a Beta less than 1.0x every time (as calculated against the Buy&Hold portfolio).

Model Portfolio vs. Benchmarks

Just a quick update, the model portfolio has held up well against our benchmarks (VT, ACWI, FWWFX) and also the S&P 500 during the recent market correction. See charts above. For an explanation of how these results are calculated by Folioinvesting.com, see here.

Also, fyi, I placed trade orders today to rebalance the stocks within each of the Model Portfolio's sectors to restore the targeted equal weighting of each stock. The actual weights had drifted over time since the Model Portfolio was established 9/4/09. Amazingly, using Folio's platform, I was able to place the 665 trade orders within just a few minutes by simply entering orders to rebalance to equal weight for each of our sector portfolios. The orders should be executed Tuesday morning because the markets are closed Monday for Presidents Day.

Roth Conversions

We interrupt our series on rebalancing with a note that if you have an IRA account, this is the year to seriously consider converting it to a Roth IRA account. In a future post, I intend to demonstrate why this is advisable for folks under all but the most obscure of scenarios. In the meantime, here are three worthwhile links outlining much of the rationale and process.

Roth Conversion Mistakes to Avoid

7 Steps to a Roth IRA Conversion

10 Things You Need to Know About Roth IRA Conversions

Rebalancing (Part 2)

The second chart above summarizes the results of the rebalancing test conducted last week (the first chart conveys the Alpha and Sharpe Ratio, which although interesting to me and potentially others, are not really salient to this post). Essentially, the take-away is: "When there's no trend, rebalancing is your friend" (I'm a poet and didn't know it). In other words, when the stock market is range-bound (i.e. oscillating back and forth with no consistent direction) as illustrated in 'Phase I' of the chart, then the practice of selling your winners and buying your losers will outperform a buy&hold strategy. The reason is because 9 times out of 10, the stocks that perform the best when the market rises will perform the worst when the market falls. In fact, it's this tendency that is captured by the statistical metric, Beta.

However, when there is a strong trend as illustrated in 'Phase II' of the chart, then rebalancing will underperform the buy&hold strategy. That's because as the overall market continues to rise, the same stocks with higher betas continue to outperform. If you're consistently selling these stocks and reinvesting in the underperformers... you get the picture.

In 'Phase III' of the chart, you can see the sharp reversal in the overall market. Since the rebalanced portfolio contains less of the high beta stocks when this reversal occurs, its total value declines less than the buy&hold portfolio. So in the end, rebalancing ended up roughly equal to the buy&hold strategy with less volatility along the way. However, at the peak of the market, the rebalanced portfolio was approximately 15% lower than the buy&hold portfolio. You would have needed the emotional fortitude and conviction to stick with your rebalancing strategy for roughly 5 years while it underperformed the overall market from 2004 through 2009. Otherwise if at some point you abandoned the rebalancing strategy and let your high beta stocks become a larger component of your portfolio, then you would have experienced more of the subsequent market downturn and your portfolio would not have caught up to the buy&hold portfolio.

This all highlights one of the central tenants of investing: strategy matters, but unless you can accurately time the market, consistency matters more. So what's the conclusion? For me, I don't think it's worth rebalancing in my regular brokerage account that is subject to taxes on the gains because the tax costs would overwhelm the small benefit of rebalancing. However, for my IRA accounts that are not subject to taxes, when I'm eventually able to move them over to Folio Investing (zero trading commissions except for an annual fee of $290), I will rebalance on occasion in order to mitigate the portfolio volatility and perhaps eke out an incremental return advantage in the really long-run. But rather than rebalancing every week, I may choose to rebalance only when I expect the market to experience a reversal (thoughts on market-timing strategy to come in later posts).

Footnote 1: this insight as it relates to rebalancing being akin to market-timing isn't often mentioned in the typical investing books you might find at your local Barnes & Noble. It is however expounded upon in an excellent book called "The Intelligent Portfolio", which is based on insights from Bill Sharpe, who won a Nobel prize for his work on option pricing theory. If nothing else, you should spend the $20 on the book just for the included free 1-year subscription to Financial Engines, which is an excellent tool that conveys your probability of achieving an adequate retirement nest egg based on your financial plan, with the analysis based on Monte Carlo simulation (state of the art for financial planning).

Footnote 2: You may have noticed this portfolio of 10 stocks pretty much doubled in value over a time period when the overall market essentially went nowhere. That is primarily a quirk of choosing the 10 stocks now, rather than back in 2000, which reflects Survivorship Bias. For instance, if I were choosing 10 stocks back in 2000, I may have selected Lehman Bros. (which went bankrupt) rather than JP Morgan.

Rebalancing (Part 1)

Rebalancing is one of those things that always seemed to make intuitive sense to me although I've only recently developed a more in-depth appreciation for the consequences upon one's portfolio. Basically, rebalancing just means selling a portion of stocks that have done well and using the proceeds to buy more of the stocks that have lagged behind, in order to make the weighting of each stock in the portfolio more in line with your targets. If your portfolio is constructed to simply hold the same stocks as an index, then there is no rebalancing required because both the index weights and the weightings in your portfolio will drift together over time as certain stocks outperform others. However, if your target weights are fixed (e.g. equal weighting for each stock), then you will need to periodically rebalance lest your actual weightings diverge so much from the target weights that you become uncomfortable with the portfolio composition.

So what are those consequences of rebalancing? To answer that, it's helpful to create a model of what would have happened over the past 10 years if you had a 10-stock portfolio and rebalanced it every week back to equal weightings vs. if you had not rebalanced and simply let the weightings drift (i.e. Buy&Hold strategy). The three charts at right and bottom provide the results of this test. The stocks used in the model were taken from the list below of the largest companies in each economic sector.

Next week I'll discuss the results of this model and see if there are any insights to be had about rebalancing. We may also take a look at another model of the same thing, except rather than using the price histories of 10 real stocks, we'll construct a synthetic portfolio using the random number generator function in Excel, which will allow us to easily sensitize the results for varying levels of the artificial stocks' volatility, correlation, and returns. This way, we'll be able to see if rebalancing 'should' be beneficial in theory or if the model using real stock data is just a fluke based on the idiosyncrasies of those particular stocks.

Rebalancing (Prelude)

As a prelude to my thoughts on rebalancing, using the data at Google Finance, I made a list of all the economic sub-sectors and found the largest company in the world (based on market capitalization) for each sub-sector that trades on one of the U.S. stock exchanges. The result is the table shown at right. The companies highlighted in yellow are the largest in each sector.

A few observations:

1. Since I associate Retail with goods or 'stuff', it's interesting to remember it's a Service. Retailers don't make the stuff, they provide the service of getting the stuff from the factories to you.

2. Companies with headquarters located in the US comprise 69% of the total market cap for this list. However, US companies comprise only about 36% of the total market cap of all publicly traded companies in the world. Therefore, one can infer that US companies make up a disproportionate share of the world's largest companies.

3. The average Beta of these companies is 1.31 (calculated relative to the S&P 500). One might have assumed a list of the largest companies in the world would have more 'stability' than average, which would have been incorrect. The average Beta for the US companies on the list is 1.28, while the average Beta for non-US companies on the list is 1.37.

Program Note

Well it only took a month before I fell short of my new years resolution to do one blog entry per week. My habit is to write an entry on Saturday morning (my favorite time of the week) after sleeping late and then having my coffee and oatmeal. Last Saturday, an old college friend and I both took our two dogs and drove through the snowstorm to his brother's house to play with his two dogs. The six dogs had lot's of fun until someone dropped a beer can and my youngest pup (blood hound) ended up biting one of our host's dogs over it (awkward). My bad for letting my pup have a small sip of my beer on occasion. [Note: I recently learned beer isn't good for dogs so I don't share anymore, but alas he has already acquired the taste.]

Then I had a little too much fun with some neighbors/friends that night and Sunday ended up being a write-off as a result (my wife and 4-month old son were out of town visiting her parents and some friends for the weekend). However, I'm back in the saddle today and will just have to tweak the new years resolution to commit to keeping the blog one year plus one week in order to make up for the 'snow day' last week.

why the model portfolio is overweigted to small caps

Once one has chosen which stocks to include in a portfolio, there are many ways to decide how much of each to include relative to the whole (i.e. the weighting of each stock). The three most common methods are as follows:

1. Market Capitalization. Each company has a market value of it's total equity, which is simply the number of its shares multiplied by the price of each share. Most stock indexes are set up such that the portion of the index allocated to each stock corresponds to that company's market cap relative to the total market cap of all the companies in the index. If one wishes to construct a portfolio that will closely mimic the performance of the index (e.g. S&P 500), then the stock allocations in the portfolio will also have to be based on market cap weightings.

For instance, one first adds up the market cap of all the companies in the portfolio (say this total is $100 billion). Then, each individual company's market cap is divided by that total in order to calculate the portion of the portfolio that should be allocated to each individual stock. So if a particular stock's market cap is $5 billion, then 5% of the portfolio would be allocated to that stock ($5 billion / $100 billion = 5%).

Some benefits to this approach are that it's simple to compute and one's portfolio will not significantly under-perform the chosen stock index. A drawback is that the portfolio performance will be most heavily influenced by the performance of the few stocks with the largest market caps. For instance, the top 10 companies in the S&P 500 account for roughly 20% of the total market cap of all the companies in the S&P 500. So, even if a portfolio holds all 500 stocks in the S&P 500, but the portfolio is weighted according to market cap, then 20% of the portfolio's performance will depend on the performance of those 10 largest companies.

2. Equal Weighting. The simplest method - the weight of each stock equals 1 / (# of stocks in the portfolio). So if one's portfolio holds 100 stocks, then each stock is ascribed a 1% weighting in the portfolio. If 200 stocks, then each stock is ascribed 0.5% weighting, etc.

A benefit to this approach is that one's individual stock risk is reduced. No need to worry about waking up one morning to find that the largest company in your portfolio has been falsifying their accounting statements, is declaring bankruptcy, and your portfolio just lost a large part of it's total value. The drawback is that, in comparison to the index, one's portfolio will be more allocated to smaller companies (i.e. 'small caps'). Therefore there will be times when one's portfolio will outperform the index and there will be times when the portfolio will under-perform the index, the latter of which, because we're all evolved with a sensitivity to relative status, will cause one to feel like a failure and question one's own convictions with respect to investing strategy. Aside from the emotional distress (assuming one isn't mentally immunized against it), these feelings might cause permanent under-performance if one capitulates and switches the portfolio to market cap weightings just before the small cap stocks subsequently outperform large-caps because of (think: Wizard of Oz voice) Reversion to the Mean.

Just FYI, historically speaking, in the long-term, small caps have outperformed large caps, but I don't believe this will necessarily always be the case (it's probably just a historical quirk). Better bet is that over long periods of time, large and small caps will realize equal performance.

3. Mean-Variance Optimization. Theoretically, this method should provide for the best performance. Essentially it attempts to weight stocks in the portfolio according to (i) how the price movements of each stock correlate with price movements of the other stocks and (ii) the expected long-term appreciation of each stock, the net result of which should provide for maximum investment returns for any chosen level of stability in the portfolio value (i.e. how much the value of the portfolio bounces around and gives you heartburn). Unfortunately, this method doesn't outperform the simple method of Equal Weighting. Personally, I think that's because the calculated stock weightings according to Mean-Variance Optimization are extremely sensitive to the assumed volatility and expected return of each stock. Since it's impossible to predict the actual returns of each individual stock, it's a matter of garbage in, garbage out.

Side Note: I do think that on average, the volatility of individual stocks tends not to change drastically over time (at least relative to the volatility of stocks in general). So a variation of this mathematically oriented methodology can be useful if one wishes to simply minimize portfolio volatility, or even dial in a certain level of portfolio volatility.

Conclusion. The stocks in the model portfolio were initially weighted by first grouping the stocks into economic sectors. Each sector was allocated somewhat according to Market Capitalization. For instance, the Vanguard Total World Stock Index (ticker: VT, which is one of our benchmarks) has about 14% of its portfolio allocated to companies making consumer goods, so therefore I allocated roughly 14% of the hypothetical money in the model portfolio to consumer goods companies. Now, for various reasons the sector weightings of the model portfolio don't exactly match up to all the sector weightings of VT (mainly because I did not want to include oil companies or banks), but the point is that the sector weightings of the benchmark were indeed a consideration when establishing the sector weightings of the model portfolio.

Lastly, within each sector, the individual stocks were Equal Weighted because (i) I don't want to have a significant allocation to any individual stock and bear the idiosyncratic risk and (ii) I don't want to do the data gathering and mathematics associated with mean-variance optimization when it doesn't work anyway.

You may have noticed I said this is how the model portfolio was initially allocated. Over time, as certain stocks have outperformed others, the weights necessarily drift. Next week, perhaps we'll cover re-balancing and the pros and cons thereof. Then, since re-balancing will provide a nice segue to market-timing, I think we may switch gears from talking about what to buy/sell and begin talking about when to buy/sell, which (especially in the short-term) is a much more important determinant of portfolio performance.

the only investment guide you will ever need

Folks, I truly believe this is the best book you can buy on the subject of investing. There is a nice summary review here. Do yourself a favor and read the first 59 pages for free here. Then buy the paperback for about $10 at Amazon.

beta

Assuming one's portfolio is well diversified such that the unexpected misfortune of one company is generally offset by the unexpected good fortunes of another company, then the changes in overall portfolio value are generally related to the overall economy. This is because all companies' fortunes are somewhat related to the economy (in the short-run) and when the economy suffers, almost all the companies' business prospects face a headwind. Although when this happens, there will always be a few companies that perform well in spite of the economy, due to some random circumstance, but there is no way of knowing ahead of time which companies will do so.

Beta is simply a measure of how much the portfolio value correlates with the overall stock market, which in turn reflects market participants' expectations for the overall economy. For instance if your portfolio beta is 2.0 and the stock market appreciates 10% one month, then statistically speaking based on historical performance, your portfolio value is likely to increase 20%. If your portfolio beta is 0.5, then your portfolio value is likely to increase only 5%. So what? Well, if the economy is sucking wind and you're therefore at greater risk of financial distress in your own life (job loss, etc), that is the worst possible time for your portfolio (savings) to lose value. So, if one chooses to invest money in stocks that they can't stand to be without for at least 10 years (even during a stretch of unemployment) - which no competent financial professional would ever advise - one should at least avoid constructing a portfolio that will compound the problem by being overly sensitive to the economy.

Now, conventional financial theory asserts that if all investors view the world as outlined in the preceeding paragraph (i.e. all investors are rational), then they will be less attracted to stocks that are overly sensitive to the overall stock market / economy. This collective aversion to 'high beta' stocks, will cause those stocks to fetch lower prices, even if these more volatile companies' future business prospects are equal to the business prospects of 'low beta' stocks/companies. And if one pays less now for a high beta stock (vs a low beta stock) and that high beta stock nevertheless achieves average long-term profit growth equal to the low beta stock, then the investor will end up with a higher investment return (because in the long run, stock price appreciation tracks the company's profit growth).

Sounds great, right? If one can do without their savings for a long enough time period to ride out economic cycles, then one can collect a premium investment return by purchasing high beta stocks and constructing a portfolio with a high average beta! Trouble is, it doesn't work because investors are not 'rational' in the sense outlined above. Rather, people are more concerned with keeping up with the Joneses when the stock market is appreciating than they are with protecting themselves from a declining portfolio value when the economy inevitably falters (after all, it's not so bad to lose money so long as their friends are also losing money). Therefore, investors pay no heed to a stock's beta when deciding whether or not the stock price is attractive. If anything, an investor will pay more for a stock with a high beta, based on a presumption the overall stock market is going to rise and the high beta stock will therefore outperform (which of course it likely will in the short-run, if the investor's prognostication for the overall stock market proves correct). Wouldn't that same investor be fearful of underperformance if the overall stock market declines? Nah, Mr. or Ms. Investor wouldn't buy stocks at all if they thought the overall stock market was about to decline.

So where does that leave us? Why does our model portfolio contain stocks with low betas such that the average portfolio beta is only .79 (as measured against the S&P 500)? It gets back to 'addition by subtraction'. If investors are overpaying for high beta stocks, which represent companies whose profit growth will ON AVERAGE IN THE LONG RUN (the best caveat EVER!)not be any better than low beta stocks, then those high beta stocks will provide a lower long-term return (again, because long-term stock prices track profit growth). Obviously, we want to avoid owning stocks that will underperform due to this factor.

For more on the dynamic outlined above, I highly recommend a new book by Eric Falkenstein called Finding Alpha (which is where I learned about this - thank you, Mr. Falkenstein). I would say the book is a little advanced for anyone not previously acquainted with finance, but it's so full of knowledge and well written, I think anyone who has in fact had an introductory class in finance (or read up on basic finanical theory themselves) would be able to learn something from this book even without understanding each and every page. But I recommend first watching the free videos before deciding whether or not to spend money on the book.

technology holdings (links)

ASEI
NTCT
HPQ
MANH
CMTL
MANT
PCLN
DBD
ACS
ASIA
IBM
ORCL
IDCC
LLTC
GPRO
QCOM
MCHP
PEGA
VRSN
ROP
MFLX
XLNX
EPIQ
ADVS
TYL
EMC
AOS
JKHY
IACI
NJ
IDC
QSFT
MMS
INTU
MSFT
BBBB
QGEN
CHKP
BCSI
INFA
CTXS
PBI
SY
ALTR
ADI
MCRL
CAJ
AVX
MSCC
RX
IART
KYO
AME
EGOV
CACI
TMO
CSC
CERN
TSS
PKI
HMSY
DHR
IHS
SKIL
CSGS
GIB
HRS
MTSC
COMS
HITT
BEC
SYKE
BMC
MIL
TKLC
OTEX
CA
LXK
ARMH
DNEX
WBMD
GB
ILMN
SNPS
CSTR
HUB.B
LDR
AKAM
DOX
BIO
ALOG
TSM
NOVL
FELE
SYNA
CREE
SNDA
CPSI
COGT

services holdings (links)

NTT
KSS
STRA
WMT
OMC
CVS
COST
OCR
SPLS
PTNR
BJ
NFLX
ESRX
DCM
Q
SYY
TDS
CMCSA
CPRT
RHI
MHS
FTE
AAP
RSG
ROST
WAG
IRM
DV
APOL
PT
DWA
VZ
TNE
SWY
T
YHOO
AZO
MMC
ADP
PAYX
TEF
DEG
TI
HD
SHW
HEW
GME
CTL
ENL
ACN
PETM
FDO
TJX
HRB
DTV
WIN
LOW
BKC
SRCL
WPO
MCD
ESI
ORLY
WCN
PSA
MVL
CTAS
RBA
DLB
VOD
DLTR
FCN
PSO
KR
GPN
CHT
RUK
BSY
DRI
CHU
FTR
TSP
AMT
SKM
CECO
SJR
NTES
BCE
THI
TU
CHL
USM
HTCO
NLCI
CBZ
PFCB
TIVO
STAN
JTX
BWLD
BKS
PZZA
RCII
NCI
IGLD
BSI
RDK
PPD
PSMT
TUC
ATNI
NRCI
VLGEA
AAN
LTRE
CRI
DX
JACK
RECN
CBRL
PNRA
CAST
NATH
NSR
ROL
UTI
TRC
CLH
IWA
FRED
ANH
SVR
FRS
SPH
CHH
FAF
PTRY
CRN
TSCO
MNRO
FGP
ARDNA
FORR
HCSG
LABL
TXRH
SKT
EXPO
OHI
WMK
CRAI
RGC
CASY
PETS
TW
EXBD
ELRC
HURN
BKE
JCOM
USMO
DINE
LINC
SPTN
CATO
PFWD
SHEN
NAFC
CSS
GEOY
COCO
WTSLA
GOOD
HTX
JOBS
UMH
STON
CONN
BJRI
HOTT
MDS
CRMT

healthcare holdings (links)

KND
RHB
IVC
AMGN
PDLI
LHCG
PFE
LNCR
GILD
AMSG
BMY
BCR
HGR
CNC
GENZ
CELG
IPXL
NHC
MMSI
ALKS
MCK
SNY
MRK
DGX
ODSY
KCI
LPNT
FRX
BDX
MWIV
MYGN
CEPH
BRLI
NEOG
MGLN
BAX
ABT
BIIB
ZMH
CRXL
ENDP
NVO
STJ
LH
CAH
MDT
VAR
XRAY
CBST
THOR
MATK
EW
HAE
DVA
WMGI
AMMD
PSSI
CHE
SYK
SNN
HSIC
WPI
BLUD
PPDI
UHS
AGN
NVS
ABC
JNJ
TFX
CHTT
GSK
WST
TECH
STE
FMS
AZN
PRX
SHPGY
LMNX
HSP
RMD
CVD
BVF
ICUI
TEVA
PDCO
RSCR
CNMD
GTIV
VIVO
OMI
CYBX
WOOF
PRGO
SAB
AMED

transportation and utility holdings (links)

ODFL
JBHT
LSTR
SJI
CNL
PATR
WERN
KNX
CMS
EOC
FE
POM
PPL
NJR
EXPD
HTLD
UGI
SCG
PNY
WR
ETR
ATO
NU
GAS
UNS
SWX
FWRD
WGL
POR
PCG
FPL
UPS
HE
EIX
CHRW
TE
WTR
ALE
AVA
GXP
LFL
SO
NI
AGL
PNW
NWE
PGN
ED
D
IDA
TNP
NWN
NST
AEE
DTE
NVE
LNT
DPL
VVC
NFG
OGE
TCLP
CPL
SRE
CNP
EQT
AEP
DUK
DHT
AYE
MRTN
NRG
NAT
XEL
MWE
CPNO
WEC
RYAAY
PEG
BNI
ENI
UACL
OKS
NGG
KMP
CKH
TAC
TGP
ISH
OKE
TRP
DDMX
DCP
KSP
RJET

material and capital goods holdings (links)

CRH
BGG
HWK
SHLM
AIR
SXT
BMS
CCC
BECN
SON
MLM
CSL
NL
SQM
WIRE
ATK
SIAL
ATR
GVA
LLL
FLIR
MMM
ITW
LMT
CF
PLL
MATW
SYT
RTN
TNH
UTX
BCPC
SLGN
FAST
CUB
PTV
GWW
TIS
MLI
SMG
FMC
KMB
CCK
CMP
WSO
BVN
IFF
BLL
PX
ESLT
NEM
TRA
AZK
AGX
GOLD
AAON
VAL
WDFC
ORB
LII
STRL
SRDX
CCF
STST
SDTH
ACET
AEM
AVD
ASTE
HWKN
GFI
MKTAY
EGO

consumer goods holdings (links)

FDP
HSY
SAM
CALM
LKQX
DLM
WACLY
RGR
GIS
PEET
CHD
DMND
CAG
K
VCO
ECL
SJM
HANS
AIPC
SLE
PG
KO
TR
LANC
LNDC
VFC
GPC
CLX
UEIC
ADM
RAH
HNZ
KFT
CPB
UN
CBY
LNCE
WEYS
PEP
WWW
VGR
FIZZ
CL
DEO
UL
HRL
SAFM
RAI
CLC
SENEB
IRBT
FHCO
HQS
JBSS
MKC
ODC
DF
TAP
BF.B
USNA
FLO
COLM
UNFI
THS
RBI
NKE
LO
CVGW
CCU
MLR
JJSF
NPK
AKO.A

clean energy holdings (links)

Per a reader suggestion (thanks cousin), below is a listing of the model portfolio's clean energy holdings formatted to allow click-through to each company's summary on google finance. Same to follow later for the other sectors of the model portfolio.

ABAT
AMSC
ASTI
BCON
BEZ
BLDP
BMI
BWEN
CBAK
CCJ
CHK
CLNE
COMV
CPN
CPST
CSIQ
CVA
CZZ
ELON
ENER
ENOC
ENS
ESLR
FCEL
FSLR
FSYS
GU
HEV
HTM
IRF
ITRI
LDK
MXWL
OPTT
ORA
RZ
SOLR
SPIR
SPWRA
TSL
ULBI
USU
VLNC
WFR
ZOLT

Addition by Subtraction

I rarely feel highly confident that a particular sector of the economy is going to outperform the rest. More often, I have a view that a particular sector will underperform (call me a pessimist if you like). Therefore, my method of portfolio construction begins with selecting a large number of stocks that will both limit exposure to the travails of any individual company (i.e. idiosyncratic risk) while providing exposure to the various sectors of the global economy, with sector weightings in line with those of the global stock market. Then I simply cut out or reduce the weights of any particular sectors I feel will underperform. For example, in looking at the model portfolio holdings listed in the posts below, you may have noticed an absence of (think: Al Gore voice) banks and 'big oil' companies.

I see banks as leveraged plays on bond holdings and I don't think highly of the risk/return profile of bonds (100% potential loss with minimal potential gains in the context of an uncertain world: see Black Swan). As for Big Oil, my view isn't predicated on the price of oil per se (in fact I think the price of oil could easily rise very high, very fast), but is based on the idea that oil companies' costs (of extracting the oil) will rise even faster than their revenues (the price of oil) because over time it will require more and more energy/money to lift the same amount of hydrocarbons out of the ground per year and I don't see this dynamic being continually offset by improved extraction methods/technology.

Another phrase to describe this approach is 'enhanced indexing' - where the indexing is accomplished by first mimicking the sector weights of the overall market and the enhancement is accomplished by removing those sectors that are anticipated to underperform.

I think perhaps next week we might cover why our model portfolio is 'overweighted' to small cap stocks, rather than large caps, and why I favor that portfolio composition. Or perhaps we could cover a metric known as 'beta' and why the average beta of the stocks in our portfolio is purposefully low.