Component Tactical Asset Allocation: Part 3

This is the final of three posts on Tactical Asset Allocation. Part 1. Part 2.

The subject of this post is the theory of the market process and tactical asset allocation and why I believe that the former implies the latter will be a more successful strategy than buying and holding index funds.

The fundamental implication of the Efficient Markets hypothesis is that if information will be quickly incorporated in security prices. Mathematically this implies that stocks follow a random walk with jumps when new is absorbed by the market. Based on this assumption, the ideal investment strategy under the Capital Asset Pricing Model is to hold the market portfolio for the long-term (I know I’m ignoring Treynor-Black).

Beyond the typical objections to EMH, there are two main insights from the market process school that leads me to reject the EMH and I believe they are the most important observations of the school.

First, there are significant government interventions that make the market remarkably inefficient. The most important is the business cycle and monetary problems that in the 20^th century has almost always been caused by central banking. The standard Mises-Hayek theory of the business cycle is that by pushing the interest rates lower than the natural rate of interest, a central bank encourages investment beyond what would happen in the free market. Eventually the investment works its way into a boom for the producers of consumers’ goods and prices begin to increase. In order to stop the boom (to prevent runaway inflation), the central bank must raise rates. This action ends the boom and the process to correct the malinvestments made in the boom period. Since capital intensive industries are more exposed to interest rates, they typically feel more pain than consumers’ goods industries.

Moreover, governments intervene in other ways that are frequently not understood well enough by investors or they don’t always understand the implications. Economics is not a terribly difficult discipline and figuring out the implications of dumb government policies isn’t that difficult. If the government subsidizes ethanol production, farmers will use less land for the production of the typical agricultural products and those prices will have to rise. If there is a war in the Middle East, it is likely that oil (unless they have refineries in the country in question) and defense stocks will typically increase in value. Investors react in the short-term, but over the course of 6 months to a year or longer, these plays are still profitable. I’m not sure whether it is the uncertainty of these situations or that investors systemically do not think the government is as bad as it is (possible given the state of business school economics courses, also it is very difficult to quantitatively test), but investors do not react strongly enough.

Second, the market process school emphasizes the role of the entrepreneur in moving the market to equilibrium prices. EMH doesn’t care about why prices behave in certain ways, it merely attempts to model them. However, what is seen as a random walk are actually deliberate actions taken by entrepreneurs engaging in speculation and arbitrage. Entrepreneurs who have greater foresight will outperform those who don’t. Furthermore, the investing, particularly derivatives, are referred to as zero-sum games. The profits are zero-sum; however, ex ante, all of these trades are positive sum in terms of utility. These trades show ex post profits or losses depending on the skill in forecasting of the entrepreneur.

Finally, as Hayek notes, there’s no such thing as perfect knowledge, as is assumed by the actors in CAPM. Knowledge is dispersed throughout society and the purpose of the market is to organize that knowledge. Market prices reflect the knowledge of all market participants.

Combined these three factors make tactical asset allocation an attractive prospect. Since there are cycles that can be observed by students of the Austrian School, it makes little sense to buy and hold equities when there are lengthy periods of time where you can lose a significant sum of money. Also, not only is there a purpose to being an entrepreneur which is ignored in the EMH, but paying attention to what happens to prices can reveal knowledge about other market participants’ knowledge and opinions in the market. Michael Covel notes in his book about trend following that trend followers tend not to be in the business of predicting trends, they have imperfect knowledge and as a group they have no opinions on the market. However, if the market is going up, they would be more than happy to buy and vice versa to sell. The logic is essentially the same for TAA. Mr. Faber isn’t providing a service in predicting the market, he’s trying to improve on the buy and hold passive strategy by staying out of the market when market participants have a negative outlook. Nothing wrong with that.

Component Tactical Asset Allocation: Part 2

This is the continuation to the previous Component TAA: Part 1 post.

First I will present the results with a single momentum strategy comparing the AA, TAA, and Momentum TAA strategies with 0 leverage and with 2-1 leverage. Then, I will present alternate momentum strategies using different js and ks, but investing in a constant number of ETFs followed by a constant j and k with a different % of ETFs available. I will conclude with work in progress to improve it further. I might add an additional post describing why the economist in me prefers Tactical Asset Allocation as an investment philosophy to the Efficient Markets Hypothesis and the Capital Asset Pricing Model.

Before I begin, I should note that the Domestic ETFs I invest in a separated into two groups, sector and style. Whichever one I can invest in earlier (sector), I will use that return and then later, I average the two's returns. I might get a better return without doing this, but that particular market is so broad that I wanted to investigate the combined effect. Not only do some sectors out perform, but sometimes value outperforms growth and large-cap outperforms small-cap. I wanted to be able to include this relationship as well, I'm just not sure how much stronger this effect is compared to the sectors. I also had a longer list of sectors that I cut down on prior to running these returns, so the ranking since I updated the data can actually choose from more sectors and gets better returns. I'm only reporting a sector basket with the 9 Spider select ETFs.

The first table represents a unlevered comparison of the AA, TAA, and Momentum TAA (with j=4 and k=2 investing in the ETFs ranking in the top 25%) with equal asset allocation between the 5 asset classes and the Risk Parity weights discussed in the previous part. The TAA beats the AA which is the conclusion reached by Faber. However, the Sharpe Ratio (@ 6% for all) increases with a more normal kurtosis (3=normal, greater than 3 indicates fat tails) by using the risk parity weights. There are similar results comparing the TAA and Momentum TAA, however it seems like the Kurosis for the TAA portfolio is relatively constant. This makes sense since the data is cut off prior to 1998 and excludes some of the larger price movements.


The next table is the same strategies and comparison as above, but with 100% leverage. It is more for general interest than comparison. The method used in the paper by Panagora was to use the Risk Parity weight and then lever the portfolio to a desired return (such as the S&P500's average return) so that variance would be minimized. In this case, the return on the TAA without Risk Parity Weights would be greater than the standard deviation on something like the S&P500. You could use roughly 20% leverage to increase the return of the TAA Risk Parity to roughly the return on the normal TAA (this same argument works to target the standard deviation as well). However, the Sharpe ratio in this case would be less than if you had not used leveraged. The return is the same, but the variance is actually greater (the same holds true for AA, TAA, and Momentum TAA). So even without the large 2-1 leverage reported below, if you measure your investment success by your Sharpe ratio, then it won't make sense to use leverage. However, relatively speaking, the risk parity weights outperform the equal weighted portfolio. If you're an investor seeking to maximize profit or would be willing to accept more risk in exchange, then you should use the risk parity instead of the equal weight portfolios.



The third table presents the CAGR, Standard Deviation, and Sharpe Ratio comparing different momentum strategies. Recall from the previous article that j represents the number of periods to look back to and k represents the number of periods to hold (since k can be greater than 1, then even if you hold 6 ETFs when k=1, it will be variable for k>1). Using more complete data, it is clear that the Sharpe Ratios increase as j comes to 3 or 4 and declines after that. However, there is no clear trend on what happens with k. It usually increases to 2 and declines after that, but it is not consistent. If at all possible, I would prefer a larger k to a smaller k since it guarantees that I will have less turnover.



Finally, the last table shows the returns with j=4 and k=2, but investing in a different percentage of the ETFs that have sufficient return histories. The trend in this case is clear, return increases as you increase the percentage until it tops out between 25 and 33.3%. However, these returns are all gross and the others could relatively increase if transactions costs are included. Furthermore, I would suspect the tax consequences are greater. Instead of picking the best sectors, at 75% you're getting out of the worst. For portfolios with less than half a million dollars, there might be too many ETFs to be able to use the 75% or 50% to make it worth it. However, I should also note that the benefit of the original TAA model is that each position can be approximated with futures contracts which could possibly reduce costs and provide an easier method to use leverage.



To conclude, gross returns and gross Sharpe ratios are greater using the Momentum TAA with risk parity portfolios. However, there are still additional ways that it could be improved. This strategy can be considered a component in a larger overall strategy. For example, Mr. Faber discusses alternative strategies such as mean reversion and following hedge fund managers that produce significant returns. I think that there are strategies in options, distressed debt, value investing, macro investing, mean reversion, and statistical arbitrage (or investing in hedge funds that specialize in stat, risk, or convertible arb) that can add to this return while not being correlated with the TAA or Momentum TAA. Unfortunately, with the exception of mean reversion, these strategies are either not quantitative (macro, distressed debt, value) or are difficult to backtest (options - competence, and arbitrage are arbitraged away).

Next, there are additional beta factors that can be considered or thought about in different ways. For example, a recent paper indicates that the returns for currency managers are largely Beta. Those returns could be an additional asset class that could be added with little correlation to the others. Also, there is evidence that investing in commodities based on their term structure (buy most backwardated positive roll-return commodities, short most negative roll-return contango commodities). These two strategies, combined with mean reversion of the five assets used in TAA and the Momentum TAA risk parity weights, could be particularly strong and they could be included in a broader portfolio using the risk parity weights.

Finally, I recently discussed a probit model I use to forecast recessions. I am considering linking that model (and augh converting it to Matlab) to this program so that I choose margin based on the probability of a recession. The returns of this strategy outperform the S&P500 during the bad times, but they still underperform compared to the remainder of the period. I'm going to consider increasing leverage when the probability estimates are low and cut off leverage when the probability begins to increase. I believe this can improve returns.

edit: There was a slight discrepancy with the interest rate data in the original results that has been corrected.

On to Part 3.

Component Tactical Asset Allocation: Part 1

Mebane Faber published an article in the Journal of Wealth Management in the Spring of 2007 called a Quantitative Approach to Tactical Asset Allocation. The thrust of the paper is that if you invest in U.S. stocks, foreign stocks, bonds, commodities, and REITs when they are above their respective 200 day moving averages and invest in commercial paper otherwise, you can achieve returns similar to equity investments with significantly lower volatility. Mr. Faber has graciously provided the monthly returns from the strategy as well as much more information on his website, World Beta. Based on the data on the website (more up to date than the original paper), the timing model returned 12% since 1972 with a standard deviation of 6.43% (.93 Sharpe) compared to an 11.5% return on the buy and hold asset allocation strategy (20% in each asset mentioned above) with a 9.78% standard deviation (.56 Sharpe). I programmed his strategy into Matlab using the same data and found similar results (slightly different due to the vagaries of Matlab rounding and computing returns statistics based on monthly data instead of yearly data).

Lately I have been interested in how to improve on this concept. First, I would like to discuss two additions I made and I will make an additional post to discuss the results of what I tested.

On his blog, Mr. Faber compares different methods that readers have requested to improve the returns (that he doesn't use). The first is to enter long positions above the 200 day MA and short positions below while the second is to enter each position "all in," equally weighted for each buy signal, no positions in cash . Each of these methods fails to improve the Sharpe ratio. I expect the L/S portfolio fails due to the fact on that most of the top 50 best and worst days are when the market is below the 200 day moving average. It is possible to profit by shorting the worst, but you can get burned on the best.

I believe the "all in" portfolio fails by ignoring the correlations between the assets (and would require more re-balancing costs than the traditional TAA). In order to test this, I followed a white paper by Panagora Research which describes the Risk Parity Portfolio. Their concept is to adjust the weights of a portfolio so that the amount you can risk on each position is equal. The traditional method to do this is to estimate the Value at Risk for a portfolio and break it into the component parts for each security. This method takes into account the correlations among each asset and the Beta. As a technical concern, I waited a year to create the Risk Parity weights (but used the initial 20% allocation during that period to make comparisons to Faber's paper) and brute forced the first weights using the covariance matrix and existed at the time of investment decisions and only changed the weights if the Component Value at Risk of an individual asset went outside predefined bounds. The weights stay relatively constant over time, but I could have created tighter bounds where they would change more often. As of the time of writing, bonds would have 34.8% weight, REITs 15.9%, Commodities 19.3%, Domestic Stocks 15.1%, Foreign Stocks 14.9%. In other words, REITs and Stocks would have their shares reduced and bonds would increase their weights in order to take into account the fact that they are more strongly correlated with each other than Commodities and Bonds and have higher variances. Based on the research provided by Panagora, I expected a slightly lower return, but a substantially reduced standard deviation.

The other method I used was investigating the j-k Momentum strategy proposed by Jegadeesh and Titman. In this paper, J and T investigate ranking stocks based on their returns from j periods ago and holding them for k periods forward. They used this model to show that stocks have a momentum factor like a size or value factor that helps determine their future returns. Within the context of the TAA model, I chose to test this strategy by choosing a proportion of the ETFs for an asset class and then applying the j, k methodology to a proportion of the ETFs with returns. I waited until a certain proportion of the total ETFs (in each classand that I considered representative of the asset class) began to trade to start the momentum strategy for that asset class. I will only report (and compare) the returns since the earliest strategy began to take effect (Select Spiders began trading in December of 1998, but it requires j months before the strategy can work). Since they have uneven start times, I used the returns from the normal TAA strategy when the Momentum strategy cannot work. I should emphasize that I am not using only a Momentum strategy on ETFs, but investing in a j-k Momentum strategy based on a TAA model. Within each Momentum category, the ETFs are equally weighted and I don't think it makes sense to use Risk Parity Portfolios in this context.

Furthermore, if I am not mistaken, Mr. Faber uses a method similar to this in actual practice, however, he does not report his results using this method. The most obvious reason is that he created his model in Excel which is substantially more cumbersome the model gets more complex. Also, ETFs have a short history that may not be indicative of the 35 years of returns where the TAA model has shown considerable strength. There's also no doubt that using ETFs in this strategy would require more trading costs and more taxes (unless in a tax-free account). Even if this strategy is not successful (it is), it is at least interesting to investigate and note the return characteristics for different levels of j and k.

The next post will compare the Equal Allocation (no TAA) model to the TAA and their Risk Parity equivalent portfolios, it will compare the TAA models with the Momentum TAA models (equal allocation and risk weighted), and some discussion about future additions I plan on testing.

On to Part 2.

Probit Models and Crisis

In a typical linear regression model, you would regress something like yt=b1+b2*xt+e (t is a subscript and e represents error). Many are aware of this concept. However, there are many assumptions that must be made in order to get the math to be correct in OLS. Due to these assumptions, such as constant variance and normality of residual errors, if you yt dependent variable consists of only 0 and 1 or is truncated by any other means, then you will have misspecified your model. Given some series xt and b1 and b2, it is possible to predict yt much larger than 1 or smaller than 0. Two questions arise, how do you correct for this and why does it matter?

The main method to correct for truncated variables is to use a probit or logit model. The concept behind probit and logit is essentially the same except that they use different means to make a correction. In a probit model, instead of estimating the equation above that is yt=b1+b2*xt, you would estimate yt=phi(c1+c2*xt) where phi is the cumulative normal distribution. Since the cumulative normal distribution is bound by 0 to 1, then any values that are chosen within the parentheses can only predict values of y between 0 and 1. The logit model is similar, but instead of weighing by the normal distribution, it uses a calculation based on e^x and natural logs.
And why does it matter? These models are meant to estimate the likelihood that events will take place or not (this is the 0 and 1). The benefit of the normal distribution is that you're actually estimating the probability that a particular event will take place given your independent variables. I can think of two uses of an econometric model, to explain the past and explain the future. Econometric models have their uses in the former, but many times drastically fail in the latter. It is possible that the models are misspecified or that fundamental relationships are not merely absent, but are immeasurable. Furthermore, the models may not be stable which can cause forecasting errors to be too large to be useful. However, I find probit models to be a useful alternative to typical forecasting when events can be categorized as binary.

My primary use of the probit models follows in the footsteps of Wright (06-07) by examining the NBER dataset of business cycle dates. Wright uses a binary variable equal to 1 if there would be a recession within the next x months and 0 otherwise. Originally in my research, I attempted multiple lags forward such as 6,12,16 months ahead, but I eventually focused in on the 12 month as a base to compare alternative models. I continued to investigate additional variables and have settled on a fair group of variables that outperforms Wright's variables while maintaining and important economic significance of each. I continued to use the Fed Funds and the spread of long-term treasuries over the effective fed funds rate. I added the spread of the Fed Funds relative to BAA corporate securities adding a factor that includes the markets tolerance for risky debt (TED isn't as strong as this one) as well as measure of volatility of the S&P500 (% of days in last quarter with a change greater than or less than 1.25%). Finally, I added the most important component which is the money supply measure. Following Paul Kasriel (originally testing Mish's MPrime), I use the long-term change in real monetary base (bank reserves +currency divided by CPI). Positive values of this indicator represent expansionary monetary policy while negative shows that the Fed is tightening the money supply. As Mr. Kasriel notes, this normally happens to curb an expansion and combined with an inverted yield curve represents a particularly powerful indicator of a recession.

Over the 40 years tested (had to exclude the recent ones since no NBER dating exists for the current financial crisis), these variables in a probit model correctly classify 91% of months as whether they are in a recession or not (defined by above 50%). It's recent track record is that the estimated probability of a recession in the next 12 months briefly went above 75% back in 2006 and then came up above 75% again in June of 2007. Nevertheless, I would use a figure above 50% to definitely eliminate any leverage in U.S. equities and possibly cut down size (and an increasing trend in general as a sign to cut down) , above 75% means "Sell Mortimer!" The high values in June 2007 were driven by real monetary base values that were historically low. In previous episodes it barely crosses into negative values, but it has stayed essentially flat since then.

I have been mulling over the implications of this model for several months. In particular, the model estimates the probability of a recession, but the probabilities increase during all financial crises. For example, in times of large volatility or greater credit risk (flight to safety) or future rate cuts, this number increases. I wondered whether it would be appropriate to test times of financial crisis and recession using this model (how to measure, but at least my false positive in 1998 would go away) . However, even if this financial crisis is not called a recession, I wouldn't say this model failed because it was predicting one. A serious financial crisis occurred and a significant stock market correction. Using this model to avoid those situations is much more important than the classification.

Indications and the Visa IPO

Visa IPOed yesterday at $59.50 after the initial investors paid $44.00 for their initial holdings. While there is a significant amount of coverage following the IPO, there has been little discussion on how to trade in IPO in general. This was the first major IPO that I've had the luxury to trade, but I have followed many in the past and traders at work spent all week talking about the major initial public offerings in the past five years and how they traded them.

An IPO is the primary offering of equity in a company that will be listed on an exchange. The company sells shares in its company in order to raise capital for future expansion. The difficult part of the process is determining the number of shares to offer and the price at which they should be sold. It is the job of investment bankers to determine the interest in the shares and get a sense of how many should be offered and at what price. They also perform due diligence on the company to determine an estimate the company's value. The difficult part is that the company does not trade actively so not only is the valuation subjective, but there is almost no objective way to confirm if the valuation is correct or not until the offering itself. Several methods are used to estimate the value. The IBankers could use a comparable multiples approach which values the company similar to the market valuation of other companies in the same industry. A discounted cash flow model will estimate the value based on forecasts of future earnings. Finally, the IBankers can compare recent IPOs to the present one; if marketwatch.com's IPO does well, you would expect thestreet.com's IPO to do well also.

For the trader who does not have access to the Investment Banker with the original distribution of shares, there is an opportunity to take advantage of a relative difference in the Banker's valuation and the market's valuation. Philosophically, value is only subjective and the value of a company can be considered only subjective. The market price of shares and the market value of a company is determined by the subjective opinions of the marginal investors (who happen to be the largest market participants like institutions and hedge funds). So the question is, how does the trader determine when the marginal beliefs are out of line with valuation provided by the investment bankers?

Before stocks open, they give an indication of interest if there is a significant discontinuity. This indication is in the form of a bid and offer prices based on opening orders already submitted. If there is going to be a gap up or down on an IPO, it will be shown on the indications prior to when the stock opens. Major IPOs typically are underpriced by at least 30-50% according to the traders I work with. To determine if there is going to be a large jump in the IPO price, listen (or watch) for the indications to keep hitting the offer. On the Nymex IPO, the offer kept getting hit many points above the IPO price. This lead traders to put in more orders to bid the price up further. When Nymex opened, it jumped something like 30 points (from the opening price, but significantly more from the IPO price) until the offer was no longer hit by the bidders (the price got too steep and buying interest no longer kept up with the selling interest). A market order at that time would have faced at least a point of slippage, but a substantial gain nonetheless with no down ticks going up 30 points. In other words, each 100 shares earned 3000 dollars.

On the Visa IPO, I looked for indications to drastically increase similar to what I had heard about the Nymex IPO. At 9:45, there was a bid/ask indication of 52.50 to 57.50, up from the IPO price of 44. I considered this a good sign and then saw the next indication of 55-60 at 9:52. I expected another indication at roughly 10. When this indication did not come, I reduced my size and changed my orders to market OPG (at the open). After a significantly longer gap where I was hoping for another indication, the stock eventually opened at 59.50. I considered this a particularly weak opening and exited my position almost instantaneously at the opening price. Visa hovered briefly around the figure and a small number of shares ended up getting bid in the 60s, but over the next hour, the stock went down to 55 (a possible buy point since the bid indication was at 55, but it easily could have sunk further).

Visa rallied later in the day, but the quick profits to be made on IPOs are getting the price the IBankers offer and taking advantage of the mispricing. Even a 400 million float wasn't enough to result in some mispricing, but there was almost no jump after the open which you would have known had you paid careful attention to what the indications were telling.

Minimum Variance Portfolios

I am interested in the cross-section of stock returns, why some companies with particular characteristics outperform others. According to the conventional Capital Asset Pricing Model, stock returns have a linear relationship with market risk. The model predicts that companies with high sensitivities (Beta) to the market will tend to outperform the market in the future. In practice, many of the greatest long-term investors choose stocks (whether they know it or not) that fluctuate less than the average stock (and have a lower Beta).

Falkenstein Financial Data’s blog, Falkenblog, describes a possible arbitrage of going long stocks with small Betas and shorting stocks with big Betas. The aim is to profit if CAPM is wrong. Since many of the stocks in their universe are small that would be difficult to short, they came up with an alternative on their website. It is called a Minimum Variance Portfolio. In simplest terms, I would say that it is just the portfolio of the stocks with the smallest Betas, but the actual algorithm is a little more complicated. The algorithm chooses the weights of the stocks in order to minimize the variance after taking into account the correlations of each stock (so you can’t take all low Beta stocks that are highly correlated). The minimum variance portfolios have greater returns with lower volatility (though they may under-perform the market during boom times as conservative investors tend to do) than their base indices.

Looking on their website, I noticed that several of the top 50 holdings of the S&P500 minimum variance portfolio contain holdings of Warren Buffett and Berkshire Hathaway. Anheuser-Busch, Coca Cola, Johnson and Johnson, Proctor and Gamble, United Parcel Service, and United Health Group are the overlapping holdings. Almost all of the companies on the list are well-established, well-run companies.

I was curious if the reason for the out-performance of this group was due to any remaining factors that may not have been presented on their website. I performed a multiple regression analysis of the returns of the S&P500 and its accompanying minimum variance portfolio against six technical and fundamental factors available on Kenneth French’s website. The six are the equity risk, size, value, momentum, short-term reversal, and long-term reversal premiums. The equity risk premium is similar to Beta from above, but it is relative to the entire market and not just the S&P500 (so we can assume it won’t equal 1). Size (SMB) and value (HML) premiums are from the Fama-French literature and reflect the risk of small companies and companies with low Price/Book ratios. Momentum is based on the relative performance of portfolios formed based on 2-12 month returns. Short and long term reversal portfolios are similar to momentum except instead of assuming higher momentum leads to higher returns, they assume mean-reversion. All things considered, I wanted to throw as many factors as I had data on out there to see if anything stuck.

The regressions are both highly significant in their F tests. The coefficients and intercepts are all significant at the 1% level except the coefficient on long-term reversal for the S&P500. What I found most interesting is that this model almost perfectly explains the S&P500 with an adjusted R squared of 98.8%, but the adjusted R squared for the minimum variance portfolio is only 60%. In other words, this model does not describe minimum variance portfolios and there could be some other factor out there that might help explain the returns. All things considered, the reversal factors add very little to the analysis. While the S&P500 returns are mostly determined by the market risk premium, less than 50% of the variance of the returns of the minimum variance portfolio is described by the market risk premium alone while the remaining factors add about 15%.

Comparing the coefficients of the full model, I noticed that the minimum variance portfolio has a lower coefficient on the size factor indicating that it holds larger companies. However, these companies are more exposed to growth than value. The momentum effect is virtually identical, but there is a strong long-term reversal tendency of the minimum variance portfolio, but smaller (and negative) short-term reversal relative to the S&P500.

If I have free time, I might create price to cash flow and dividend yield factors from Professor French’s data and combine it with some of the other portfolios on the Falkenstein website. It is likely that the minimum variance portfolios really do have some quality (such as idiosyncratic volatility) that is difficult to model. Nevertheless, I am quite convinced that MV Portfolios will have more promise than building portfolios with CAPM (just buy the market) or Fama-French (buy small-cap value, hold 20-50 years). CAPM is too lazy for the entrepreneur in me and it is hard to buy small-caps as an institutional investor without moving the markets. Building a MVP could be the first step in a screen and then one could shorten that list to 15-25 names based on fundamental information and industry trends. However, it seems like there could be something else that is explaining their returns that is worth investigating.

Agriculture Prices

For those who have been watching, CPI core and headline came in flat today.

                 Dec. Jan. Feb.
                 2007 2008 2008
                          
Energy............ 1.7   .7  -.5
Food..............    .1   .7   .4
All items less                                                       
 food and energy     .2   .3   .0

I was under the impression that if agriculture prices increased .4% and energy decreased .5%, then a play today would be long DBA (the ag etf) and short USO (the oil etf). Unfortunately, the market seems to have paid more attention to the fact that CPI overall is negative. As of roughly 3:30, DBA is down 2.3% from the open, but USO is only down .15%. In other words, it would have been a horrible trade. On the other hand, DBA has ripped about 2 dollars since a double bottom around 1:30. Crude had a very intense downtick around 2:14, but besides that, it has trended down. It didn't trade this, but for future reference, it might make sense to wait until any craziness in the market dies down before putting on a trade like this.

More on inflation

I admire Paul Kasriel's forecasting abilities. He has a recent research report (HT: Calculated Risk) where he describes a scenario where inflation can increase. He is more scared of inflation than I am. His scenario is that foreign countries will drop the dollar instead of defending their pegs ending their purchases (to defend). The current defending involves purchasing dollars by printing home country currency which leads to inflation (that will inevitably need to be reined in). Furthermore, these countries have acquired massive dollar holdings and eliminating the buying pressure could further depress the dollar and result in inflation at home.

I think these are all good points (though that were just as true in 2005 as they are now), but he misses one main point. The massive dollar holdings abroad represent significant assets on the balance sheets of central banks. If they drop the dollar, they know it will cause a drop in the value of their assets which will have a competing affect on the value of central bank reserves. I think each central bank is worried that another (major) central bank will unpeg their currency first, not that different than Mutually Assured Destruction. If inflation gets out of control in one of these countries where they have nothing else left to do, then Mr. Kasriel's scenario is a real possibility. However, I would consider this an exogenous shock to react to if it occurs and not something forecastable (unless seen a mile away in import prices or trends in inflation numbers of those countries) than I would actively bet money on.

In other words, I'm still more worried about the monetary base contracting in the U.S. and leading the recession here than I am about Mr. Kasriel's scenario.

Thoughts on inflation

"Inflation is always and everywhere a monetary phenomenon." - tons of Economists

With equity markets dropping today and a record increase in Non-Farm Payrolls, I thought I would begin a discussion on the determinants of inflation and what it means when there is a recession and what it means for asset allocation.

Inflation really only makes sense when you refer to it as the debasement of a currency. Simply put, governments and central banks print too much money in order to reduce interest rates. Consumers borrow more and drive up the prices of existing goods. The market process contribution to this reasoning is that the changes do not happen instantaneously, but happen over time. This causes real dislocations in the economy which helps bring about the business cycle.

Market process theorists, monetarists, and others have used money supply data to guide them to central banking policies towards the creation of money. I really don't have the answers as to which one to use or not not use. For example, I don't know whether Travelers Checks or TAF should be included. I prefer to stick with monetary base adjusted for inflation (so that I can compare it between periods). Monetary base is cash plus reserves, it's simple and easy to use. The long-term change in this statistic is a good indicator to reflect monetary policy. When it is increasing, it means the Fed is creating money faster than the money is being debased.

This graph is courtesy of Paul Kasriel. Periods of increasing real monetary base are associated with boom times and periods of decreasing RMB typically coincide with recessions or other economic crises (not necessarily a recession defined by NBER). Currently facing a period of declining RMB, we could have expected one or the other. The question to ask is, what now?

To understand the current inflation, it should be clear that agriculture and energy prices are a large component in headline CPI. For example, the energy CPI index was .7% in January compared to .3% for the core number. Looking at the CPI chart below, core commodities, however, are actually relatively flat compared to the last boom. Core commodities (excludes food and energy commodities) also dropped significantly during the last recession as core commodities do. The reason is that as economic and industrial activity slows, metals such as zinc, copper, and aluminum are in less demand. If there is to be a recession, or any further slowing of economic activity, these core commodities will likely enter a bear market over the course of the slowdown.

Several market commentators are still worried about the possibility of inflation despite the fact that RMB is most decreasing. This is mostly due to a long-run bull run in commodities driven by agriculture and energy prices and a weaker dollar. Indeed, if we are entering a recession, industrial minerals should be beat down further than they are.

Above are the graphs for the past two years worth of DBA and DBB (the agricultural and base minerals etfs, respectively). DBA (and GLD and USO) have all been on a run lately. Some of this run is certainly based on fundamental factors (ethanol, the collapse of the financial markets, and market's determined belief in peak oil). However, I would caution anyone looking to put on new positions in these commodity classes to hedge your risk by shorting a value of DBB equal to the whole value of those positions. If the market thinks inflation will be coming down, industrial minerals will decline faster than the others since it fundamentally should do that. The spread will certainly widen though it might make sense to wait to put on the long side of the spread until there is a pullback or a naked position on the short side until the recent trend shows signs of ending. You don't have to think that inflation is going to decrease (which it usually does in recessions, but not always), but just that industrial minerals will decrease more than the rest of commodities.

Warren Buffett's Derivative Positions

Berkshire Hathaway recently released its chairman's letter to the shareholders. Chairman Warren Buffett, opposed to many of the absurd and overlevered uses of derivatives, has used derivative positions over several years to take large positions. The most recent letter describes their current contracts which are divided among writing credit default swaps and puts on equity indices and leftover currency positions. Since Buffett has written the credit default swaps and sold the puts, he is able to hold all of the premium upfront and has eliminated his counterparty risk (which can be significant since they are all long-term contracts) and he is able to invest that money with returns greater than the likely implied interest rates in the contracts (either buying companies or pieces of companies). If he has an untimely death, I would hope that his successor can allocate capital as efficiently as Buffett can.

Credit Default Swaps
Berkshire-Hathaway (BRK) holds 54 contracts on specific bonds in high-yield indices. These contracts expire between 2009 and 2013 and BRK has received 3.2 billion, with a liability of .47 billion (and a max of 4.2). Professor Ed Altman at NYU has pointed out that there were practically no defaults in 2007 compared to historic averages (source: his Bankruptcy class). In fact, he estimates that the high-yield bonds will go to 4.64% (ht: FinanceProfessor), significantly higher than it is now. Typically when the economy sours, there are waves of defaults. These contracts are a very good investment given the historic lows and the financial trouble that will be coming.

Selling Puts
40 put contracts were written by BRK on the S&P500 and three other foreign indices. These contracts will last 15 to 20 years and were struck at the market. BRK has received premiums of 4.5 billion with a liability of 4.6 billion. However, these are European puts which means that they can only be exercised at expiration, 15-20 years from now. With inflation at 3% over the next 15-20 years, I see almost no possibility that Buffett will have to pay out any money on these contracts. In that time period, he (and his successor) will be able to invest that money generating large profits for shareholders. However, I do see these markets coming down over the next year or two which will require large liabilities and could temporarily hurt earnings. It's such a good investment (when not levered, which I doubt Buffett would do) in the long-term that they will never actually have to pay out any money. I would ignore any profits or losses they generate in the next five years. These won't be real losses by any stretch.

Currency Swaps
BRK held a currency swap in the Brazilian real in 2007 purchased in 2002 and euro-denominated Amazon.com bonds purchased at something like 57% of par during the internet crash. As the dollar fell, the Brazilian real and the euro have increased in value which have yielded very strong profits for Buffett. In fact, almost half of the profit on his Amazon bonds was due to appreciation of the euro and the bonds are now valued at more than par. However, Buffett is no longer exposing himself to bets on the weak dollar as he has in years past. This seems like a smart play. Real monetary base has been essentially flat or negative for the past year now and most inflation that will be coming will be due more to higher commodity prices (which will come down if the overall economy slows and affects all countries) than due to too much real printing of dollars. Core inflation will likely go lower throughout the year which will give some strength back to the dollar. I would wait to put on any positions shorting the dollar until real monetary base starts cranking up and then I would only do it after looking at the relative monetary policies of the opposite country.

Buffett discusses many other interesting topics in his letter than deserve attention (his analysis of accounting assumptions is always written better than anyone else), but I will confine myself with these three topics.