New Reads In the Mail by Kahneman, Friedman, and Lewis

I was chatting with Antti Ilmanen the other day about some dividend papers and realized I had never read his recently published book Expected Returns .  Wow, big mistake.  While I am not finished yet (it is a loooong and dense 500 pages) it is a wonderful summary melding together historical asset returns with forecasting indicators and common sense.  A great read (with lots and lots of references for future reads in the source notes) for any practicioner in the financial markets.

Also in the mail or pre-ordered:

Thinking Fast and Slow - Daniel Kahneman

That Used to Be Us – Thomas Friedman

Boomerang, Travels in the New Third World – Michael Lewis

What Works on Wall St. (New edition) – O’Shaughnessy

Shiller CAPE and Inflation

As a follow up to my Shiller PE post from a couple weeks ago, here are some charts that simply look at the Shiller CAPE vs. 12 month trailing inflation.  As you can see, inflation of around 1% to 4% is rewarded with the highest multiples, but once you stay outside those bands watch out for some serious multiple contraction.

Charts inspired by the great book The Era of Uncertainty by Francois Trahan.  All data from the Shiller Dataset.

More reading here:

Shiller CAPE

Hussman models here

dShort and Butler Philbrick

Stock Market Valuation Models









13F Rebal Time

With all the 13Fs filed and published, AlphaClone and MarketFolly have been cranking away and should be fully updated.

Jay’s Hedge Fund Wisdom is out with a new issue.  (Here is a free past issue.)

Not surprising, but Icahn, Sprott, and Hayman clones all doing well YTD.  AlphaClone new funds added:

  1. Hawkins Capital
  2. Dialectic Capital Management
  3. Horseman Capital Management
  4. Steadfast Capital Management
  5. Peconic Partners
  6. Standard Pacific Capital
  7. Driehaus Capital Management
  8. Deerfield Management
  9. Centerbridge Partners
  10. Spinnaker Capital

All the Stocks and Bonds in the World…$212 Trillion

Nice graphic from McKinsey’s Report “Mapping Global Capital Markets” 2011 here:

Where the Black Swans Hide and the Ten Best Days Myth

It’s been a little slow with Cambria Quantitiative Research Monthly (more like quarterly), but here is the third issue (and the fourth should be close behind).  It happens to be a bit timely with the recent market action.  Hopefully it gives a little color on why volatility is exploding and some ideas on what to do (or not do) about it.

Let me know your thoughts!

As always, you can find my white papers for free on the SSRN.  Click to download.  (And if you can’t figure out how to download it you click “one-click download” and make sure any pop up blockers are disabled.)

Where the Black Swans Hide and the Ten Best Days Myth


Below we examine market outliers in financial markets. How much effect do these outliers have on long term performance? Can the investor prepare for these anomalies, or are they truly ‘black swans’ that cannot be managed? In this issue we examine numerous global financial markets on daily and monthly time frames. We find that these rare outliers have a massive impact on returns. However, these outliers tend to cluster and the majority of both good and bad outliers occur once markets have already been declining. We critique the “missing the 10-best-days” argument proffered by advocates of buy and hold investing, demonstrating that a significant majority of the 10 best days and the 10 worst days occur in declining markets. We continue to advocate that investors attempt to avoid declining markets where most of the volatility lies, and conclude that market timing and risk management is indeed possible, and beneficial to the investor. 


A Little Humor for Volatile Times

Gotta hand it to the NYPost…




Stock Market Valuation Models

However, valuation is not a timing tool, and the odds of a creature as fickle as the stock market obliging our “rational” analysis are in truth fairly low…While valuations are blunt tools on a cyclical time horizon, they have the unusual property of becoming progressively finer and finer instruments as the time horizon is lengthened. The following study examines ten important valuation tools from the only perspective that should really count in evaluating such tools: Their ability to forecast longer-term stock market returns.

- Doug Ramsey, The Leuthold Group

This is a great quote from the good folks at The Leuthold Group.  The take away is that most valuation models (whether in stock markets, currencies, or individual stocks) work well on longer time frames, but it is animal spirits that dominate in the short term.  Most of the longish valuation models tend to be in the same ballpark on valuation readings, whether it is Price to Cash Flow, Tobin’s Q, Dividend Yield, or other.

I’ve written quite a bit about fundamental valuation models including the Shiller CAPE and the  Hussman models here.  Another great post by dShort and Butler Philbrick is here.  (Note:  the Hussman/CAPE variant is projecting 10 year returns of 5% per annum at future PEs of 15…)

Here is a little experiment.   How about we take a look at only investing when the PE is below a certain range?  ie I want to be in the market only when the PE is < 15  (or 20, or 25 etc)…at all other times I’ll be sitting in the safety of cash.  Sounds reasonable, doesn’t it? Below are the results updating on a monthly update level from 1900-2010.

Uninspiring right?  The problem is of course the timeframe.  The CAPE works great on longer timeframes, and poorly on short ones.  The scatter plot on a monthly level of PE vs returns looks like a shotgun blast.  What if you looked at it on just a yearly level?  Below is the same chart updated once a year.


Similar.  By chopping off the highest valuations you get about the same returns but lower your volatility a bit with lower drawdowns (these #s are a little different since they are on a yearly basis, so the MaxDD will be understated).  This also isn’t really fair since we know all of the data ahead of time, so now we go and look at what happens if you invested in stocks when the PE was lower than average, but only using the rolling data up until that time (we use the 1881-1900 as the initial average).  Not bad, similar results without the datamining.


I would be interested in hearing unique ways in which investors utilize market valuation models in practice.

Regime Change Pt 2

I did a post a few months ago about an ongoing study we are working on that looks at various macro variables and how they affect asset classes.  While we are almost done, a lot of the findings are really cool and we hope to share once we can write them up.

We showed that at the time a steep yield curve and negative real interest rates were especially good for gold and REITs.  What about now, where do we stand?

Below is the same table with returns from what is currently negative real interest rates and a flatter yield curve.  Granted this is only two factors, but gold and small cap stocks look best, REITs look decent, and commodities look awful (using GSCI so that is mostly energy) with everything else as mixed.

We will be adding more variables as well as more indices so stay tuned…


Blame it on the CPUs

Great post from my friends at Bespoke showing that once again, nothing is new in investing…below is their post in its entirety since it is so spot on:

“When in doubt blame it on the computers.”

Nowadays, this seems to be the go to scapegoat for any market related problems.  Over the last few days, numerous reports have said it is the computers to blame for the whipsaw trading the market has seen in recent weeks.

The explanation sounds plausible, but it is not necessarily borne out by the facts.  Over the last 50 trading days, the average daily percentage move (up or down) in the DJIA has been 0.90%.  Relative to history, the current level is far from the extreme readings we saw during the Financial crisis when the average daily change rose to 3.71%.  Granted, the last few days have been extremely volatile, so if the recent trend continues, we will see the current 50-day average rise much higher.

One could still argue that computers were behind the big spike in volatility during the Financial Crisis, but what would explain the big spike in the 1930s, when the average daily change was also above 3%?  Last we checked, there were no computers back then.  While HFT and computer trading may be contributing to the recent surge in volatility, it isn’t solely to blame.  The reality is that when the market goes down, investors step to the sidelines, causing liquidity to dry up.  In illiquid markets, price volatility rises.

S&P Licenses More Managed Futures

One of the areas that I think is underserved in the ETF space is managed futures.  I would have thought by now that the Henry’s and Dunn’s of the world would have launched a fund, but so far it has been mostly indexes and higher cost mutual funds.  Interesting to see recent news that S&P has licensed another index, this time from Thayer Brook Partners.  Text below (hat tip: Jez L.)


S&P launches the S&P Systematic Global Macro Index (SGMI) London, August 9, 2011 – S&P Indices has launched the S&P Systematic Global Macro Index (SGMI), which aims to reflect price trends of highly liquid global futures, representing the general level of volatility taken by managers in the global macro and managed futures/Commodity Trading Advisor (CTA) space. The Index is diversified globally across 37 constituents, falling into the six most widely traded sectors– Commodities, Energy, Fixed Income, Foreign Exchange, Short Term Interest Rates and Equity Indices. Each constituent may be long, short or flat to indicate its trend.  The weighting scheme applies an even risk capital allocation across the index by sector
and again to each constituent within each sector so that no single sector or constituent drives the volatility of the index. The even risk capital allocation uses an index target volatility representative of the space, with available leverage of up to 300%, enabling the closest volatility match given a potentially low average correlation across the constituents. The sectors and constituents within the S&P SGMI are rebalanced monthly. Uniquely, the trend-following model used to determine the position of each constituent is flexible enough to allow a customized time-period for each constituent on a monthly basis, unlike models which are based on fixed time-periods. This means that if a longerterm trend is driving the market, the Index reflects that, but if a shorter-term trend becomes significant the Index picks that up, using an iterative process to test the stability of each trend. S&P Indices acquired the methodology underlying the S&P SGMI from Thayer Brook Partners LLP. The methodology was developed by Thayer Brook Partners LLP exclusively for S&P Indices. Jodie Gunzberg, Director of Commodities at S&P Indices said, “This methodical, rules based Index intends to measure the price trends and perform similar to that of the systematic global-macro space. Historically, this domain has had little correlation to traditional asset classes with relatively small drawdowns as compared with long-only equities or commodities.”  “Issues like high minimums and high fees have made it difficult for many investors to gain access to global macro and managed futures strategies. We envisage that new products based on this index will give investors the ability to invest in a long/short, comprehensive set of the main futures contracts. It’s liquid, tradable and it isn’t just based on commodities, but is well diversified across the six main asset classes in the futures markets.”

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