good link

No post this weekend. Had to work a full day at my day job.

I did however follow a great link provided by Falkenblog. Has to do with low volatility stocks providing returns in line with overall equities (contradicting Modern Portfolio Theory), so that your return per unit of heartburn is maximized. Not only heartburn, but 'risk' insofar as (i) big drawdowns to your account have the potential to cause panic and lead you to sell at the worst possible time or (ii) you may have an unexpected use for that money you previously thought was 'long-term' and end up needing to sell at relatively low prices.

Quote for the Week: "He that cleaves to wealth had better cast it away than allow his heart to be poisoned by it: but he who does not cleave to wealth, and possessing riches, uses them rightly, will be a blessing unto his fellows." - Siddhartha Gautama Buddha (c. 563 BC - 483 BC).

saving philosophy major; market timing minor

I won't be doing a full post this week with analysis since I was traveling for work all this past week and need to catch up on emails today and the chores corresponding thereto. What free time there was this weekend was spent indulging my new interest in backpacking, which entailed spending hours and untoward amounts of money at R.E.I. accumulating the gear I intend to use on at least a couple trips to the Shenandoah National Park this spring/summer. Funny how it's so easy to justify spending when it can be classified as an 'investment' that will produce years of enjoyment. Any remaining hesitancy can be easily obliterated by imagining future trips with my sons (the youngest of which is only 6 months of age). All I can hope is that by spending the money, I'll feel obligated to actually go out and put the gear to use (sort of like a gym membership initiation fee).

Separately, in regards to the 'market timing' posts of late, I should mention that these quantitative rule-based strategies aren't really market timing in the purest sense of the word, at least not to my way of thinking. I think pure market timing entails moving in and out of the market based on esoteric gut-level decision making. In other words, it's based on an intuitive synthesis of whatever quantitative and/or qualitative information happens to be available at the time and is therefore not conducive to testing against historical data. Alternatively, I see the rule-based strategies as data-driven decision making, which because they're systematic, are conducive to testing. Now, perhaps it's possible that someone has an ability to practice pure market timing, but even if so, there's no point in writing about it because the reader can never know whether or not the ability truly exists because it can't be verified by a test. Rather, any prudent reader would have to fall back to Occam's razor and assume the writer is full of BS and is simply trying to either enhance their own bank account balance or their social status.

Now, having provided the above disclaimer, I may from time to time write about a personal decision to enter or exit the market based on whether or not I think it's headed up or down. I'm not sure why I might do this, other than this blog might one day be read by my kids when I'm gone and it might be nice and/or helpful from them to read in relation to their own future investing endeavors. Or perhaps, having a written record of my trials and errors might help me refine a strategy quicker than I otherwise would. In any event, I'll try to practice my own market timing rarely and only then with thoughtful reasoning based on the three fundamental drivers of market prices, which are (i) long-term valuation metrics, (ii) intermediate-term economic growth, and (iii) short-term market sentiment. Still, there is no way of knowing ex-ante whether my decisions will do me more harm than good over the long run.

To a large extent, do to the huge uncertainty, market timing is an insignificant factor in long-term investing success. Much more important in accumulating a nest egg sizable enough to maintain one's standard of living throughout retirement is the discipline to save money, which in turn is a lot like diet and exercise. My friend across the street who is a financial advisor conveyed this analogy to me as follows: "What diet and exercise regimen is best? The one you can stick to." It doesn't matter if you follow Adkins, South beach, Weight Watchers, or Jenny Craig because it ultimately comes down to the simple fact of calories in and calories out. In terms of saving money, it ultimately comes down to finding some way of mastering your desires. You will never succeed in denying yourself something you want. Your only hope is to change what you want. Obviously, this is an ideal state of mind and, based on my expenditures this weekend, one I've yet to reach.

Quote for the Week: "Life's necessities are cheap and easily obtainable. Those who crave luxury typically have to spend considerable time and energy to attain it; those who eschew luxury can devote this same time and energy to other, more worthwhile undertakings." - Lucius Annaeus Seneca (c. 4 BC-AD 65), Roman Stoic philosopher.

market timing (part 5.5)

I thought it worth elaborating on last week's post regarding rule-based trading according to moving-average momentum. In particular, if that strategy provides greatly reduced volatility with somewhat less reduced returns, then that begs the question of whether or not there is a way to generate only slightly reduced volatility with non-reduced returns? In other words, buy&hold levels of returns with less volatility than the buy&hold portfolio. [By the way, if I ever find a formula like this, or better yet, one with greater returns than the buy&hold portfolio and less volatility - I may try making a living off it before I disclose it in this space.]

As a straightforward non-creative attempt at providing an answer, I've tweaked last week's analysis as follows: rather than going to cash when the moving-averages are trending down, see what happens if you short the market at those times. The results of this test are shown in the chart above.

As expected, this strategy produces the same volatility as the buy&hold portfolio. The reason is because, every day, long or short the market, your portfolio will bounce around one way or another in proportion to how the market moves. Also expected, the Beta of this strategy is in the ballpark of zero. To understand the reason, simply imagine being long the market 50% of the time and short the market 50% of the time. When you're long, your Beta is 1.0; when you're short, your Beta is -1.0. Mathematically, 50%*1.0 + 50%*(-1.0) = 0.

However, the returns from this strategy don't beat the buy&hold portfolio. They don't even do well enough to provide a superior Alpha in comparison to last week's strategy of going to cash, rather than shorting the market. I can't really provide a full explanation for why this is, except to say two things: 1) transaction costs are doubled because not only do you have to buy and sell, you also have to sell and buy (to short), and 2) the market is very (although maybe not perfectly) efficient, which causes a high degree of randomness.

Overall, I have to conclude this buy&short strategy is inferior to last week's buy&sell strategy because you have higher volatility and (slightly) lower returns.

Side note: check out 'Black Monday' (and the days following in Oct-'87) in the chart above. Wow.

Technical Notes:

1. One reader commented last week that the 6% returns for the buy&hold portfolio looked low. In other words, everyone tends to think stocks provide 10% returns in the long run. A few reasons for the discrepancy: first, the returns shown in each decade are geometric, rather than a straight average of the ten years; second, the returns in each decade exclude the results of the first 200 days in order to first calculate a 200-day moving-average prior to beginning the analysis; third, these returns are for the Dow Jones Industrial Average (historical dividend adjusted pricing provided by yahoo finance), which may vary from what an outfit like Ibbotsson may deem to be the 'stock market'.

2. It's interesting how the Beta can be near zero and yet the trading portfolio appears to somewhat follow the buy&hold portfolio when viewed on a 10-year chart. This is something to keep in mind when considering any statistic that's calculated based short term data (e.g. daily). Many paradoxes in finance (life?) can be resolved by rigorous attention to the time frame under discussion. In many cases, the small deviations from the short-term statistic (be it Beta, an average, or whatever) accumulate in one direction over the long-term. For instance, people like to point out that when the U.S. market declines, foreign stocks tend to decline as well, thereby nullifying the diversification benefit. However, when they say this, they are mainly thinking in terms of the short-run (i.e. days), when the diversification benefit is a actually a long-term phenomenon. Foreign stocks are less correlated with U.S. stocks in the long run mainly due to long run factors like demographics, political regimes, etc.

Quote of the Week: "Not needing wealth is more valuable than wealth itself." - Epictetus (AD 55–AD 135) Greek Stoic Philosopher.

market timing (part 5)

This week I decided to test a slightly different momentum strategy as follows:

1. If the price exceeds both the 50-day moving average ("MA") price and the 200-day MA price, then buy.

2. If the price is less than both the 50-day MA price and the 200-day MA price, then sell.

3. Otherwise, hold (e.g. price exceeds 50-day MA, but is less than the 200-day MA).

Same as last week, I tested this strategy against Dow Jones Index prices going back to 1930. For each decade, I waited 200 days (in order to calculate a 200-day MA) and then bought into the market. From there, all buy/sell decisions were driven by the aforementioned rules.

The chart above illustrates the results. Again the trading portfolio was less volatile than the buy&hold portfolio. Again, the average Alpha was approximately 2% (annualized). Again, the strategy performed well during the 1930s, when you would have needed it the most.

However, this strategy produced a more consistent Alpha, the standard deviation of which was only 3%, so the average Alpha of 2.3% divided by the standard deviation of roughly 3.0% was about 0.76. Although I still can't say this is statistically significant, it's better than the 0.53 result from last week's trading strategy.

The only decade in which this strategy didn't work well was the 1990s, when pretty much everything simply marched upward. And in my opinion, not doing as well as the overall market in the good times, isn't as awful as doing worse than the overall market in the bad times.

If you study the chart in detail, take note of the 2000s. What's interesting here is although the Alpha was technically 0%, that's basically just a quirk of both the Trading Portfolio and the Buy&Hold Portfolio having produced 0% returns. You'll notice the standard deviation of the Trading Portfolio's annual returns during this decade was only 11%, which is much less stomach churning than the Buy&Hold Portfolio's 25%. In my book, having the same returns (even 0%) with much less volatility is a win. Think about if you lost a job with corresponding health insurance and faced some unexpected medical bills - all of a sudden, that savings you thought wouldn't be needed for at least 10 years is the subject of urgent demand. Would you rather face the prospect of pulling your money out of a Buy&Hold Portfolio or the more stable Trading Portfolio?

Quote for the Week: As in nature, emotions abhor a vacuum. If we progress in vanquishing negative emotions such as wishing for certain things to be different and instead spend more time enjoying certain other things as they are, then we will find we are experiencing a degree of tranquility that our life previously lacked. We will then naturally become more susceptible to joy. - Paraphrasing of "A Guide to the Good Life: The Ancient Art of Stoic Joy", page 123.

market timing (part 4)

As contemplated last week, I've tested our optimized trailing stop-loss and trailing go-purchase parameters against some out-of-sample data to evaluate if this strategy has any relevance or if the positive results using S&P data from the 2000s is simply a quirk of randomness. The chart above conveys the results using Dow Jones Index data from 1930-2000.

Again, as with most strategies that entail being out of the market some portion of time, the volatility of the trading portfolio is less than that of the buy&hold portfolio. As is typical, this lower volatility is accompanied by lower returns. To determine if the returns are sufficient given the reduced volatility, we scale down the buy&hold returns according to the lower Beta of the trading portfolio. Then we compare these 'adjusted' buy&hold returns to the trading portfolio returns, to see if the trading strategy added any excess return or 'Alpha'.

In short, I think the results are minimal / inconclusive. You can see the average Alpha of the trading strategy over the decades is roughly 2% per year, which although nothing to sneeze at, is a small amount when compared to the approximately 4% standard deviation of that same Alpha . In other words, the Alpha doesn't appear highly statistically significant and one could reasonably conclude the Alpha is actually 0% and the obtained result of 2% is simply a fluke.

However, I still found this to be a worthwhile / interesting exercise. For one, I think investors should always position themselves to withstand the worst (i.e. don't bet more than you can afford to lose). In this case, although worse fates can always occur, the 1930s were a tough time by any standard. Imagine nearing or having just entered retirement and then realizing a negative 5% annualized return over the next decade. Talk about something that will force a re-prioritization of your life. In this context, I think it noteworthy how well the more conservative trading strategy of trailing stop-losses and trailing go-purchases outperformed the riskier buy&hold strategy. I mean, if a strategy is going to come through for you with flying colors when you need it the most, then it warrants some consideration regardless of its average performance. This brings to mind some words of wisdom often quoted by a friend and financial advisor who when mentioning the inadequacies of averages, says something to the effect of, "The average depth of Lake Michigan is only four feet, but I wouldn't want to walk across it".

Quote for the Week: "The more pleasures a man captures, the more masters he will have to serve." - Lucius Annaeus Seneca (c. 4 BC-AD 65), Roman Stoic philosopher.

market timing (part 3)

Market timing rules that rely on quantitative data (stock prices, economic data, etc) to generate a buy/sell decision can generally be classified as momentum strategies or reversion to the mean strategies. The premise of momentum strategies is essentially that whatever is increasing will build on itself in some fashion and continue going up (at least in the short-term). One of the simplest momentum trading rules is a stop-loss, whereby if the price of the stock drops below a certain level, the rule is to sell it at that point rather than continue riding it down. By the same token, one can create a rule whereby if the price of the stock increases above a certain level, the stock is purchased at that point in hopes of riding it upward.

The Test

To test the efficacy of this sort of strategy, I set up a back-test using historical price data for SPY, which is a stock that tracks the S&P 500 index. The rules I used were:

1. If the price of SPY drops to a level that is eight standard deviations (calculated on a daily basis) lower than its most recent highest price, then a stop-loss is triggered and the stock is sold.

2. Then, if the price of SPY increases to a level that is seven standard deviations higher than its most recent lowest price, a 'go-purchase' order is triggered and the stock is bought.

Results

The results of this test are shown in the charts above. Just as with the timing strategy based on retail sales data (a couple posts below), this strategy entails being out of the market a substantial amount of time (37% of the time in this case), which causes the trading portfolio value to be less volatile than the buy&hold portfolio value. As a result, the Beta of the trading portfolio is only 0.3 as calculated against the buy&hold portfolio. However, the return of the trading portfolio is 2.9% (annualized) vs. -3.4% for the buy&hold portfolio, which implies a trading portfolio Alpha of 4.0%.

Next Steps

You may wonder how I came up with the parameters for the test (eight standard deviations, etc). The answer is that I optimized the parameters to provide for the maximum Alpha based on this data set. The resulting Alpha for differing stop-loss and go-purchase rules are shown above in the sensitivity chart. Next week, I'll run this test again using price data from the past year to see how our optimized parameters perform against out of sample data. If the stock prices are truly random, it's not likely that our optimized parameters will result in any meaningful Alpha (but we'll see). I'll also run this test against price data for a different stock as another way to see if our results are at all robust.

Technical Notes

1. The test includes transaction costs of 0.20% for each trade.

2. The trading portfolio earns 0% interest during those times it holds all cash.

Quote for the Week: "No man is crushed by misfortune unless he has first been deceived by prosperity." - Lucius Annaeus Seneca (c. 4 BC-AD 65), Roman Stoic philosopher.

Market Timing (part 2)

Since I'm on vacation with the family this weekend, I thought I'd simply point you to some of the most worthwhile articles I've seen on the subject of long-term market cycles.

First off is a presentation written in 2005 by my first boss and Investmentor. The upshot is that in the long run (10-20 yrs), market cycles are driven by valuation. To position yourself best, non-traditional diversification is key.

Second is a presentation by Contrarian Edge that seconds the notion of valuation being the driver of long-term market cycles, but rather than diversification as a way to cope with this reality, the main focus is market timing based on your own intrinsic view of value.

The last article is a recent post by Crossing Wall Street that essentially makes the point that when evaluating market valuations, one should account for inflation/interest rates and therefore the market may not be as expensive now as many perceive. However, I would simply add that inflation/interest rates are more likely to rise from these current levels than fall, and therefore the conclusion would be the same which is that the market will face tough headwinds for years to come.

market timing (part 1)

I've been exploring simple quantitative market timing rules occasionally during the past year and the most promising I've found is related to retail sales. The chart above illustrates the S&P 500 return (adjusted for dividends as reported by yahoo finance) against retail sales as reported by the U.S. Census Bureau (payroll figures included as well for good measure). As you can see by looking at the blue circles, the last two major market peaks were foretold by a top in year-over-year retail sales. However, if you look all the way back to 1994, which is as far back as this economic data series is available electronically, you will notice there were some tops in retail sales where the S&P did NOT subsequently enter a downtrend. Just goes to show that you have to maintain your skepticism in regards to market timing rules insofar as you may discover one that is helpful, but its not likely to be fullproof.

So, would trading based on retail sales be helpful? To answer this question, I set up a back-test as follows: If the average of the trailing-2-month ("T2M") Y/Y retail sales growth figures are greater than the average trailing-12-month ("TTM") Y/Y retail sales growth figures, then buy the S&P 500. If not, then sit out of the market (and earn 0% for purposes of this test). The second chart above shows the results of this trading strategy vs. a buy&hold strategy. As you can see, the Trading Portfolio spends a significant amount of time 'out of the market' and is therefore substantially less volatile than the Buy&Hold Portfolio. Although the Trading Portfolio generates a lower total return, the dramatically reduced volatility provides for some Alpha (i.e. excess return in relation to its Beta) and a superior Sharpe Ratio. Just for kicks, I also 'tortured the data' and ran the test since 1998, thus excluding those early time periods when the trading rule wasn't very effective, the results of which are included in the table above. One day I might hand crank the test going back to 1953 when retail sales were first reported, just to gain a longer-term perspective, but of course there is no time for that sort of manual exercise today.

One thing I find interesting about these sort of quantitative timing strategies, is how they produce risk/return profiles so different from the underlying asset class, which perhaps could represent an opportunity for further diversification beyond the traditional stocks/bonds/cash mixtures.

The main thing to take away from this analysis is that in the intermediate term (2-5 years), the stock market follows macroeconomic fundamentals. Knowing that simple fact is useful for maintaining perspective and keeping an even keel whilst the daily headlines and market pundits tempt you to trade, trade, trade (usually to your detriment). Just as a side pontification, which I might elaborate upon at some point in the future, I think the market is mainly swayed by sentiment in the short term and valuation in the long term (10-20 years).

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
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ROP
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XLNX
EPIQ
ADVS
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EMC
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JKHY
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NJ
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QSFT
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SY
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MCRL
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TMO
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OTEX
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LDR
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DOX
BIO
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COGT

services holdings (links)

NTT
KSS
STRA
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OMC
CVS
COST
OCR
SPLS
PTNR
BJ
NFLX
ESRX
DCM
Q
SYY
TDS
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FTE
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IRM
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PT
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VZ
TNE
SWY
T
YHOO
AZO
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ADP
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TEF
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TI
HD
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CTL
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FTR
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TU
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