Real Estate Fun(damentals)

Excellent post over at calculated risk regarding a personal interest of mine: single family home vacancy.

Money quote: "It is easy to see why many housing economists view the current “low” level of housing production as a plus for the overall health of the housing market, even before allowing for the current high number of residential mortgage loans either seriously delinquent or in some stage of foreclosure. It is also easy to understand why competent housing analysts believe that any government policies designed to encourage additional construction of housing units would be “dumber than dishwater,” and that the only government policies designed to encourage increased “AD&C” activity would NOT be acquisition, development, and construction lending, but instead “acquisition, destruction, and or conversion” of existing vacant housing units."

Trend Analysis (home prices)

It's interesting to me that the Case Shiller Home Price Index (adjusted for inflation) does not exhibit mean reversion characteristics like the stock market (R^2 < 1%). The first chart above shows the index since 1890. Just like last week, the black line is the line of best fit with exponential growth equation y=b*m^x. Once again, the blue line simply represents where the black line would have ended up on the right-hand side if it were calculated each year based solely on data available through that year. The second chart (scatter) just shows there is no statistically notable mean reversion. What I see is a data series that typically does not vary greatly from the trend line; except for the extraordinary bubble of the 2000s. It appears prices have returned to 'normal', but of course prices could potentially 'over correct' and decline ~20% below the trend line as they were for much of the 1920s and 1930s. In case you were wondering, home prices have only exceeded inflation by 0.22% annually since 1890. Separately, look at how wild inflation used to be in the olden days.

Trend Analysis (part 2)

I wanted to follow up this morning to show how the trend analysis described below is superior to the naive method used by some financial planners whereby they simply calculate the historical annual return for the market and extrapolate that return into the future. What I found is actually a better method than the one described in the post below.

The chart above shows cumulative annual returns since 1880 vs. returns over the subsequent ten years. The line of best fit is y = -8.7698*x+0.1407, where y = annualized returns over the subsequent ten years and x = the annualized returns calculated from 1880 up to the date of calculation. So, since annualized returns since 1880 have equaled 1.7% (inflation adjusted), projected annual returns over the next ten years are -1.0%. So, naively extrapolating 1.7% real returns into the future would be an inferior assumption as it does not account for the mean reverting nature of the market.

The really shocking thing is that this simple regression has R^2=46%, which is incredibly high for a single factor model in relation to a system as random as the stock market.

It's even more shocking when comparing returns over the prior 20 years (rather than since 1880) vs. the subsequent ten years, which is shown in the second chart above. Here the R^2 is 59%. The market close as of 2/25/11 resulted in annualized returns over the prior 20 years of 3.96% (inflation adjusted). Based on this regression with standard error of 3.58%, real annualized returns over the next ten years are projected to be 1.7% with a 50% confidence interval ranging from -0.8% to 4.1%. The 90% confidence interval is -4.2% to 7.6%.

Personally, given the significantly higher R^2 for this regression, I'm inclined to favor this method over the one in the post below that's based on the close/trend metric.

Trend Analysis

I was viewing a site on my blog roll (http://www.multpl.com/) the other day and thought it might be worthwhile to run some trend analysis on the inflation adjusted monthly S&P 500 data going back to 1880, which you can download there. The charts above reveal some interesting findings.

In the first chart, viewed over the past 130 years on a logarithmic scale, the growth of the S&P 500 appears relatively stable around the black trend line. The black trend line is a least-squares regression based on the exponential growth equation y=b*m^x, where x=month, m=coefficient that provides the best fit, and b=constant that provides the best fit. For example, Feb-2011 is the 1,563rd month of the data series, so x=1,563. The best-fit coefficient (m) is 1.001428. The best-fit constant (b) is 95.34. So the result (y) = 95.34*1.001428^1,563 = 886. So 886 is where the black trend line ends up at the right hand side of the chart.

The black trend line of course was regressed using all the data from 1880 to 2011. However, if one was to have traded based on this metric at any point in the past, then of course they would have only had data up until that date. Therefore, the blue line shows where the black trend line would have ended up if calculated each month in the past. For example, in Jan-1900, the black trend line based solely on data from 1880-1900, would have ended up at 165, which is what is shown by the blue line.

Now, if someone had calculated the black trend line in Jan-1900 and derived an end result of 165, they also would have seen the S&P 500 actually was 170 then, so the close(170) / trend(165) equaled 1.03. In other words, at that time, based solely on trend analysis, the market seemed to be at a normal level.

The second chart above shows a scatter comparison of this close/trend metric vs. returns achieved by the S&P 500 over the subsequent year. As you can see, there is a wide dispersion with lots of noise and very little signal. This jives with the stock market being a mostly unpredictable system.

The third chart shows a scatter comparison of the close/trend metric vs. annualized returns over the subsequent five years. There is a bit tighter relationship, but still mostly noise.

The fourth chart shows annualized returns over the subsequent ten years. Here the relationship is noticeably tighter, but the R^2 is still only 11%.

The fifth and last chart is interesting. It shows returns over the subsequent ten years, but the data is just since 1950. So each month the trend line is calculated, it's based on at least 70 years of data (1880-1950) and at the end is based on 120 years of data (1880-2000). Here the relationship is even tighter, with R^2=37%. In some fields, 37% isn't very impressive, but when speaking of a system as random as the stock market, it indicates a relatively strong signal.

So the line of best fit drawn through the last scatter chart has an equation of y = -0.061x+0.126, where x=close/trend. This means, if close/trend = 1.0, then the expected annualized return over the subsequent ten years (y) is 6.5% (inflation adjusted). If close/trend = 2.0, then the expected return would be 0.4%.

Where do we stand now? Well, as of 2/25/11, the S&P 500 closed at 1,320 and the black trend line was at 886, so close/trend was 1.49. Therefore, solely based on this trend analysis, the projected annualized return over the next ten years is 3.5% (inflation adjusted). The standard error of the regression is 4.45%, so the 90% confidence interval is 3.5% +/- 1.65*4.45%, which provides a range of -3.8% to 10.9%. Not very helpful, right? That's the nature of the stock market. All we can say is that when the market is priced substantially above trend, there is a tendency for subsequent returns to be sub par. For grins, the 50% confidence interval for annualized returns over than next ten years is a range of 0.5% to 6.5% (inflation adjusted).

small cap stock beta

Tonight, as I was giving the 'night time' bottle to our 15-month old, I finally figured out something I've been pondering off and on for a while. I wondered, why would a disciple of Modern Portfolio Theory (MPT) expect small cap stocks to provide out-sized returns solely because they are more risky? Actually, I really wondered why they would perceive small caps as more risky?

Having been exposed to arbitrage pricing methods enough to be dangerous, I thought perhaps a collection of small cap stocks should be equivalent to one large cap stock. Or vice versa, one large company is perhaps equivalent to a collection of small companies. But then I remembered the math related to beta calculations and how say 10 small cap stocks, each with betas around 2.0, if put together would create a portfolio that still has a beta of 2.0. The reason is because the beta statistic itself already accounts for the cancelling out of individual stock idiosyncrasies, thereby providing a measure of a stock's underlying covariance with the overall market, net of any 'noise'.

So then I thought, of course that's why MPT followers would expect small caps to provide out-performance (because of greater risk as measured by beta). But I wondered, what is it about being small that makes small companies qualitatively more volatile? It can't be they are too small to have diversified revenue streams, otherwise as implied above, this volatility would go away when many small caps are held together in one portfolio. Rather, it must be something to do with each individual revenue stream (i.e. business) itself being volatile/risky.

But then, why would small companies be predisposed to having volatile revenues? I thinks it's simply by default. In other words, if a company finds a stable revenue stream, they tend not to stay small for very long. Because once you find a stable revenue stream, you no longer have to be quite so nimble to survive, and you can begin to pursue efficiency at the expense of flexibility. You start hiring some MBAs to standardize processes, acquire competitors to transfer best practices, badda bing badda bang, you get BIG.

new year

Not that I was ever a prolific writer, but now that my one year of blogging has been accomplished (last year's resolution), there will be fewer, more sporadic posts going forward. Perhaps a wrap up, bare bones summary of my investing philosophy as it stands at this point in my life, but otherwise probably just random stuff I think my friends may find useful. Too bad my other resolutions weren't achieved (ahem,exercise,cough).

By the way, as previously mentioned was my intention, I've moved my real brokerage accounts over to folioinvesting.com and will henceforth occasionally share those actual results (e.g. the chart above). So far so good. Once I submitted the account transfer requests online, my retirement funds were moved from my employer (a large bank) over to folio in less than a week. Then, in about 60 seconds, I submitted orders for each of my IRA, Roth IRA, and Taxable Accounts to purchase the 100 stocks in my model portfolio and it was done the next day with $0 commissions (but you have to pay a $290 annual fee to get all you can trade for free). Next step may be to get the wifey to transfer hers and then see how well the platform really works for someone who wants to easily mimic someone else's model portfolio.

new addition to the blog roll

For us Gen X-ers who are somewhat interested in keeping tabs on those Millennials, you could do worse than read this guy. Check it out - http://leighdrogen.com/

bonds vs. bond funds

You may hear sometimes that purchasing a ladder of bonds is better than purchasing a bond fund. A ladder of bonds just means you buy say 5-10 different bonds with various maturity dates. When each bond matures (assuming the issuer doesn't default), you get paid the principal amount of that particular bond. If you plan on spending the payments received upon each bond's maturity then you have a 'non-rolling ladder' bond portfolio. If you plan to reinvest the maturity payment into another bond, then you have a 'rolling ladder' bond portfolio. Since bond funds reinvest the maturity payments received from their maturing bonds, a passively managed bond fund is largely equivalent to a rolling ladder. Except a bond fund provides better diversification and lower transaction costs than you can achieve by constructing your own rolling ladder. But to realize the lower costs of a fund, you have to pick one that is passively managed with a low expense ratio.

Although comparing an individual bond or a non-rolling ladder to a bond fund is like comparing apples to oranges, folks sometimes think a non-rolling ladder provides more certainty and/or less risk because one can ostensibly predict the future payments to be received. However, this perceived attribute isn't real when you account for opportunity costs.

Additional resources:
https://advisors.vanguard.com/iwe/pdf/ICRTBF.pdf

bonds

I'm not a bond guy, but if I were allocating part of my portfolio to bonds, this is how I'd do it. Whatever you do, DO NOT allocate more than 2-3% of your portfolio to a single bond. Doesn't matter if it's rated investment grade. You can buy it investment grade and the next week it can be non-investment grade. Trust me, I know from personal experience.

recent reading

In the past month, I've read a few well written books about investing, all of which advocate value investing:

Bull's Eye Investing - Not only an interesting read, but the author was writing in 2004 and got a lot of things right about the subsequent six years.

The Little Book that Still Beats the Market - Starts off really hokey, but the second half makes a compelling case for buying quality stocks with cheap prices based on an objective formula that adjusts for varying tax rates and debt loads of companies, so you won't miss some good buys just because they have depressed earnings.

The Little Book of Sideways Markets - Explains why the overall stock market will continue to tread water with high volatility over the next decade and recommends a means of outperforming in such an environment. I thought this book might have been banal, but it actually makes a lot of smart, nuanced observations and is well written.

buying foreign small caps; decreasing exposure to Yen and China

I'm generally of the opinion that it makes sense to avoid mutual funds and exchange traded funds with their embedded management fees that act as a drag on a portfolio's returns. I feel this way due to the advent of platforms such as folioinvesting.com where individual investors can construct a portfolio containing a large number of stocks without incurring commissions for each trade. However, with foreign stocks, pretty much the only ones that trade on U.S. exchanges are large-cap companies, many of which happen to be energy companies and banks. Since I wish to obtain exposure to other sectors, I've purchased the following exchange traded funds geared to foreign small caps (within the model portfolio - 1% allocation to each): BRF, SCIF, and DGS.

To make room, I've sold the three Japanese stocks - WACLY, NTT, and DCM - which is in keeping with concerns about the future direction of the Yen. Basically, I'm trading out of a country with poor demographics and high debt and into countries with with favorable demographics and low debt, which I believe will provide for some long-term currency appreciation to go along with the investment returns from economic growth.

Separately, I sold the two Chinese stocks - HNP and SNDA. I don't have a rigorous reason; I'm just worried about the Chinese economy being unbalanced and the potential turbulence in Chinese stocks that would likely result from a recession over there. As replacements, I bought VE and TM (1% allocation to each).

Wait, didn't I just say I was worried about the Yen? Yes, but actually TM should benefit from a declining Yen as 70% of its revenues are derived from outside Japan and 58% of its expenses are incurred inside Japan. Furthermore, the stock is cheap based on price/book, price/cash flow, and price/sales, all of which is in keeping with my re-born value investing fetish.

happy holidays

coleman 100 portfolio

The charts above are created by morningstar.com as a feature of their premium membership offering. As you can see, the 100 stocks provide for a well diversified portfolio by economic sector, market cap, and geography. In fact, I would go so far as to say this equal-weighted porfolio is more diversified than the S&P500, which is market-cap weighted.

The average beta of this portfolio is 0.69x, which means in any short run time period, it should zig and zag with a magnitude about equal to 69% of the S&P500. In the long run, as the small short-run deviations accumulate, I'm expecting the portfolio to out-perform the S&P500 and also out-perform my true benchmark, which is the Vanguard Global Stock ETF (ticker: VT).

The portfolio is tilted towards stocks with valuation ratios (price/earnings, etc) lower than the S&P500 and growth prospects (earnings growth projections) greater than the S&P500. The benefit of investing in companies with decent growth prospects, rather than solely low valuation ratios is that it helps one avoid companies that are cheap for a reason that have increased risk of bankruptcy. It's a nod to the value investors' philosophy of buying companies with decent business prospects at a cheap price.

Lastly, the first chart above is a back-test showing how this portfolio would have performed against the S&P500 over the past five years. This is only an indication and isn't definitive because some of the 100 stocks haven't been around five years, which is indicated by the dotted line for the first portion of the time period.

Note: In order to view the chart above showing the 100 stock holdings, or any of the other charts, click the image twice for a larger version.

portfolio overhaul

It has now been exactly 15 months since I established the model portfolio 9/4/09. There have been changes along the way, as I tried my hand at a little market timing from June-November with a market-neutral portfolio. Although the hedge used was the ETF that moves inversely to the S&P500, which left the portfolio somewhat exposed to foreign currency movements vis-a-vis the dollar and small cap stocks vis-a-vis large cap stocks. The portfolio has also evolved as I've continually pruned it from roughly 700 stocks down to 169 stocks. This was mainly done by eliminating all but the lowest beta stocks. This week I've culled the portfolio down to 100 stocks by eliminating some of the pricier names, thereby increasing the tilt towards value stocks.

My intention is to hold these 100 stocks throughout 2011. Furthermore, I'm going to fund my account at folioinvesting.com and henceforth report the results from my real portfolio, rather than a model (paper) portfolio.

The next post will provide a summary of the 100 stocks, but before getting to that, I've posted for the record the results of the model portfolio over the past 15 months (see charts above). Nothing stellar, but not bad. Essentially, total return was in line with the overall market, but the pathway of getting there was less stomach-churning than the overall market, so if you had been invested like the model, there was less chance you would panic and sell out at the bottom. I mention this because most investors fall short of matching the overall market for this very reason, which is why in some ways, I think the volatility of one's portfolio is an important determinant of realized returns.

value investing

I've been listening to the siren song of the value investing philosophy lately. It started with attending a lunch sponsored by the local CFA chapter where the speaker was Pat Dorsey from Morningstar. Then I bought his book and read it. Now I've signed up for the premium membership tools (stock screeners, research reports, etc) over at morningstar.com.

Sometimes I wonder if the main near-term benefit of writing this blog and managing a model portfolio is that it absorbs my free time devoted to the investing hobby such that I end up leaving my own portfolio alone without a bunch of excessive trading.

Anyhow, I wondered whether or not low beta stocks may provide superior returns in part because they overlap with morningstar's notion of economic moats. In short, there seems to be a little connection, but it's not overwhelming. In the chart above, you can see how the 169 stocks in the model portfolio are rated by morningstar in terms of having economic moats (not all 169 stocks are covered). It appears as though the wide moat stocks do indeed exhibit less volatility as measured by standard deviation of returns (beta isn't yet available as a field to screen). Also, it appears the wide moat stocks in the model portfolio are considered by morningstar to trade at a discount to their assessment of fair value. However, this could potentially be caused by morningstar calculating higher fair values for stocks they deem to have wide moats.

At the risk of being rash, I decided to go ahead and tweak the model portfolio just a bit to provide a slight value tilt. I sold nine stocks with no or narrow moats that were deemed to be overvalued 1.5x or more and I bought nine stocks with wide moats that were deemed to be 0.86x or less of fair value (which puts them in top quartile of morningstar's database).

Sales: CHD, EW, BJ, ORLY, ILMN, INFA, PNY, RYAAY, LFL
Purchases: EXC, GE, MDT, NVS, RHHBY, ZMH, WU, AMAT, CSCO

esoteric rambling hand waver (yours truly)

I'm on the verge of transitioning the model portfolio back to being net long stocks for two somewhat related reasons. Most importantly, when adjusting for last year's cash-for-clunkers program (i.e. excluding auto sales), retail sales growth seems to have stabilized with a slight up trend.

Secondly, I just think the forces of inflation will eventually overwhelm deflation. I know there is plenty of slack in the labor market right now which is generally where higher inflation expectations originate, but the problem is that much of this slack is comprised of folks tied to the housing/finance industry who don't have the skills to immediately switch to more productive sectors of the economy. So I think in those more productive sectors, we actually could see some wage inflation. Meanwhile, in housing/finance, the blood has been let and folks left with jobs probably won't see a steady drop in their nominal incomes.

Then of course you have the 'currency wars' whereby sovereign states with inverted age demographic pyramids (e.g. Japan, E.U.) or even just age columns (e.g. U.S.) are pursuing Quantitative Easing in order to monetize their high levels of external debt. Of course the party line is they are only trying to ward off deflation, but call me cynical, I don't think the general public has the stomach for another Paul Volker to come in and disinflate when banks start lending again (which they are now by the way) thereby increasing credit, which I believe is the largest driver of the total money supply.

So who wins with inflation in the long run? If you have a mortgage loan or large amount of other debt, you might break even because that gets repaid with less valuable dollars, but you still have to cope with higher costs throughout the rest of your budget (food, energy, consumer goods, healthcare). Who loses with inflation? Anyone without a mortgage, especially retirees who are trying to live off their savings. Ultimately, if it really gets out of hand and transitions to hyperinflation, everyone loses because exchanging goods and services for currency could become undesirable, which is the basis for the specialization of labor that underlies modern society. I realize that last point could qualify for tin-foil hat club membership, but I do believe most things sound crazy until they don't.

As an aside, I suspect the reason bonds and stocks have rallied together of late is due to the strong bid from the FED underlying bond prices. As institutions understandably sell treasuries at incredibly low yields to the FED, they redeploy the proceeds into riskier assets like corporate bonds. The sellers of the corporate bonds redeploy their proceeds into preferred equity and the sellers of preferred equity redeploy into common equity. Thus, the FED can increase asset prices across the entire risk spectrum. Now there is a seller for every buyer in each of these asset classes, but as the prices get bid up, you have more companies raising capital via bond issuances or stock offerings. If the companies invest that money to create new productive assets (machines, software, etc), then it will drive economic growth, at least in nominal terms before adjusting for inflation. In real terms, since the world's population growth is declining, we don't need as much real growth in production of goods and services. But of course real growth would still be desirable insofar as it would necessarily increase material wealth per capita. I think that is a good thing for everyone out there making less than say ~$70m per year, but above that level, I subscribe to the view that material wealth doesn't correlate with happiness.

So, back to where I started, the only question is when I increase exposure to stocks and how much. I've been thinking there would be a pull-back coming this week on news of Republican gains in congress and the putative quantitative easing announcement. You know, the old "buy the rumor, sell the news" bit. Unfortunately, that seems to be a popular bit of advice, so if there are enough market participants out there of this persuasion, we may actually see the market rise again this week.

Conclusion: I'm going to sell the 1/3 allocation to the ETF that moves inversely with the S&P on Monday and sit with the proceeds in cash until next week when I may redeploy the cash into the low beta stock holdings.

time to short Yen?

According to this persuasive article, it may be time to short Yen, which can be done by purchasing the Exchange Traded Fund that moves 2x the inverse of the Yen; ticker YCS.

model portfolio 10/10/10

Although I decided to go 'market neutral' back in June based on weakened retail sales data, the instrument I used to hedge the stock exposure is the ETF that is short the S&P 500 (ticker: SH). Since the model portfolio's long positions are roughly 1/3 foreign stocks, I essentially hedged out the stock exposure while leaving some foreign currency exposure. Sometimes, it's better to be lucky than good (in the short run). So while the market has been fairly volatile and the model portfolio has been fairly stable (providing for a superior risk-adjusted profile), the model portfolio has only maintained its return lead over the indexes due to the declining dollar alluded to above.

real estate (part 4)

I wondered what it might look like if one were to trade the top-25 cities based on momentum. Again this is based on OFHEO data, which pertains to single-family residential.

The Test:
1. Rank the 25 cities based on price appreciation over the prior three quarters.
2. Invest 10% of the portfolio into each of the top-10 ranked cities.
3. Whenever a city falls out of the top-10, sell the investment (with 6% transaction cost) and buy whatever city has moved up to the top-10. These trades are modeled using a two-quarter lag, so as not to incorporate any hindsight bias. Two quarters is probably the minimum amount of time necessary to receive the price appreciation data from the government pertaining to the prior quarter and then make the trades.

Results:
1. With transaction costs, the momentum trading strategy results pretty much match the results achieved by simply buying and holding all 25 cities. The benchmark of buying and holding all 25 cities is without any re-balancing and the associated transaction costs.
2. Without transaction costs, the momentum trading strategy is far superior, generating much higher returns with approximately the same volatility.

Conclusion:
As with many quantitative strategies, transaction costs become the limiting factor. However, the insight is useful if one is deploying new money and would have to incur the transaction costs in any event.

One day I'd like to see how closely the OFHEO data tracks the appreciation of apartment properties and other commercial property types. Since those larger transaction sizes typically have much lower transaction costs (say 1.5%), I'd like to see if this would be a viable investment strategy, or at least a helpful augmentation.

great minds

think alike as crossingwallstreet makes the same point articulated in this space not two weeks ago.