Gotta hand it to the NYPost…
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.
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…
CLICK TO ENLARGE
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.
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.”
I was chatting with Professor Tetlock earlier and he passed along this interesting opportunity. It looks like fun plus you can make up to $600.
To join, go to http://www.goodjudgment.info
We are writing with an unusual but, we hope, intriguing request. We are in the process of recruiting knowledgeable people to participate in a quite unprecedented study of forecasting sponsored by the Intelligence Advanced Research Projects Activity (“IARPA”) and focused on a wide range of political, economic and military trends around the globe. The goal of this unclassified project is to explore the effectiveness of techniques such as prediction markets, probability elicitation, training, incentives and aggregation that the research literature suggests offer some hope of helping forecasters see further and more reliably into the future.
To join, go to http://www.goodjudgment.info. You do not need to read anymore of this message, but, if you are curious, further details follow.
There are several teams recruiting participants. Ours builds on Phil Tetlock’s work described in his book Expert Political Judgment; his co-PIs are Barb Mellers and Don Moore, with an advisory board that includes Daniel Kahneman, Robert Jervis, Scott Armstrong, Michael Mauboussin, Carl Spetzler and Justin Wolfers. It involves a multi-disciplinary effort to understand how people use the knowledge they have to develop expectations about the future and what sorts of processes and strategies lead to success.
We need to recruit as many as 2500 people who have a serious interest in and knowledge about world affairs, politics, and global economic matters and are interested in testing their own forecasting and reasoning skills. So please consider visiting the project web-site at http://www.goodjudgment.info. You will find what you need to register there and more information about the project.
We are committed to maintaining high standards for admission to this special program. And we would greatly welcome your participation if you are so inclined (please be advised that the minimum time commitment would be several hours in passing training exercises, grappling with forecasting problems, and updating your forecasting response to new evidence throughout the year).
The primary motivation for participating should be Socratic: self-knowledge. But we can also offer a token honorarium of $150 to each participant for completing each year in the forecasting tournaments. And, although all participants will be given key anonymity, the winners of the forecasting tournaments will know who they are and will be free to go public if they so wish.
Of course, we understand if you yourself do not have the time to engage in an exercise of this sort. But we would be very grateful if you are willing to pass this request on to colleagues and readers of your work who might be interested in participating in this program and who would be likely to qualify for admission.
We can promise the following: this will be an intellectually stimulating experience (indeed, should you be bored, you should drop out); participants will have the opportunity to work with state-of-the-art techniques (training and incentive systems) designed to augment accuracy; and participants will receive feedback on how well calibrated (among other things) their subjective probability judgments are in relation to others for various categories of problems.
In short, we think it will be fun. If we were not running it, we would volunteer ourselves.
Many thanks for considering this, and for passing this invitation on to others who might also be interested.
Philip Tetlock, Psychology and the Wharton School, University of Pennsylvania
Barb Mellers, Psychology and the Wharton School, University of Pennsylvania
Don Moore, The Haas School, University of California, Berkeley
With the news that Apple is now the largest company by market cap in the US hitting the wire, analysts should take a look at this older post of ours summarizing Arnott’s research.
(We did some older posts on the subject of the largest company by market cap overall. The original study was featured in the book “Mosaic: Perspectives on Investing” by Pabrai.)
From Arnott’s letter:
“We find the leader in any sector underperforms the average stock in its own sector by 3.5% in the next year … and the next year … and the next year. As Table 1 shows, the damage doesn’t really slow down for at least a decade, as the top dog in each sector lags its own sector by 3.3% per year for the next decade!
From these results, one might conclude that an investor could do rather well by investing in the Russell 1000, minus its 12 sector leaders. Better still, perhaps we should exclude all of the companies that have been sector leaders any time in the past decade because the performance drag for the top dogs tends to persist for a decade or more. These stocks typically comprise about one-fourth of the Russell 1000! If these stocks suffer a 300–400 bps shortfall in most years, one could outperform the index by nearly 100 bps per annum merely by leaving the top dogs out, cancelling the corrosive influence of competitors, populists, and pundits.”
Now, Arnott runs billions on indexes that are not market cap weighted, but the arguement is certainly persuasive. He also co-wrote the very good book The Fundamental Index: A Better Way to Invest.
Below are the 9 sector SPDRs and their top holding in each:
“The Commanding General is well aware the forecasts are no good. However, he needs them for planning purposes.”
- Kenneth Arrow, Nobel Laureate Economist…recalling the response he and colleagues received during the Second World War when they demonstrated that the military’s long-term weather forecasts were useless. (via Future Babble)
Virtually every day there are pundits and gurus on the airwaves, internet, and print making predictions. At the beginning of 2011 a few of these gurus made some pronouncements as to the future returns of the US stock market. Laszlo Biryini contended that the S&P 500 (currently trading at 1300 as of this writing – sorry, wrote this about a week ago) would rise to 2854 by 2013, or a 120% gain from current levels. Robert Prechter, on the other hand, said he thought the Dow would decline 90% by 2017, which would imply that the S&P 500 trades down to around 130.
So there you have it, opposing gurus who believe that stocks will either rise or decline by 30% annualized over the next number of years. (To complicate the matter even further, you have Shiller estimating the S&P 500 to gain about 10% total by 2020 which splits the two gurus in half.)
Interestingly enough, if you combine the current S&P 500 level (1300, PE of 23) with the lowest (5) and highest historical values (45) for the Shiller cyclically adjusted price earnings ratio (CAPE) you get to values similar to the forecasts at both ends (300 and 2600). I think the most interesting but unlikely forecast is all three being correct over various timeframes!
The only difference between the S&P500 at 300 (an 80% decline) and the S&P500 at 2600 (a near double) is opinion, namely, what you think those underlying stocks are worth. Now, we could certainly go on and on making well-thought out arguments as to why either value is justified (low/high interest rates, profit margins, productivity, mean reversion, discounted cash flows, etc.), but at the end of the day it is simple human beliefs on the value of stocks that drive their short term price levels. As the late, great Kurt Vonnegut opined in his book Galapagos, circa 1985:
“The thing was, though: When James Wait got there, a worldwide financial crisis, a sudden revision of human opinions as to the value of money and stocks and bonds and mortgages and so on, bits of paper, had ruined the tourist business not only in Ecuador, but practically everywhere…Ecuador, after all, like the Galapagos Islands, was mostly lava and ash, and so could not begin to feed its nine million people. It was bankrupt, and so could no longer buy food from countries with plenty of topsoil, so the seaport of Guayaquil was idle, and the people were beginning to starve to death…Neighboring Peru and Columbia were bankrupt, too…Mexico and Chile and Brazil and Argentina were likewise bankrupt – and Indonesia and the Philippines and Pakistan and India and Thailand and and Italy and Ireland and Belgium and Turkey. Whole nations were suddenly in the same situation as the San Mateo, unable to buy with their paper money and coins, or their written promises to pay later, even the barest essentials. ..They were suddenly saying to people with nothing but paper representations of wealth, “Wake up, you idiots! Whatever made you think paper was so valuable?”
The financial crisis was simply the latest in a series of murderous twentieth century catastrophes which had originated entirely in human brains. From the violence people were doing to themselves and each other, and to all other living things, for that matter, a visitor from another planet might have assumed that the environment had gone haywire, and that people were in such a frenzy because Nature was about to kill them all.
But the planet a million years ago was as moist and nourishing as it is today – and unique, in that respect, in the entire Milky Way. All that had changed was people’s opinion of the place.”
How does an investment manager reconcile all of the various prognostications he hears on a daily basis?
Simple – ignore them.
Now I am not recommending to completely ignore the basis behind the arguments, as many new approaches and research projects have been originated by ideas presented in print and on TV. But in general, one should ignore the forecasts of so called experts as they are likely to be about as accurate as a monkey throwing darts against a wall or a coin flip. There is enormous amount of research to back up the inability of experts to make solid predictions.
One such researcher on expert predictions is Philip Tetlock, a professor of management at the Wharton School at UPenn . He started tracking experts and their forecasts and predictions a quarter century ago, and he has compiled over 300 professionals and academics that have made over 80,000 forecasts. (Here is Tetlock’s home page as well as a sample book chapter. Daniel Drezner has two excellent posts on the book, here and here, and a review from The New Yorker.)
He examined both the outcomes of their predictions as well as their processes – i.e. how they reacted to being wrong and how they dealt with contrary evidence. In general they offered no benefit over a random prediction, and ironically enough, the more famous the expert, the less accurate the predictions were. The experts with the least confidence made the best predictions.
“Isaiah Berlin borrowed from a Greek poet, “The fox knows many things, but the hedgehog knows one big thing”? The better forecasters were like Berlin’s foxes: self-critical, eclectic thinkers who were willing to update their beliefs when faced with contrary evidence, were doubtful of grand schemes and were rather modest about their predictive ability. The less successful forecasters were like hedgehogs: They tended to have one big, beautiful idea that they loved to stretch, sometimes to the breaking point. They tended to be articulate and very persuasive as to why their idea explained everything. The media often love hedgehogs. “
BECOMING A BETTER INVESTOR
The characteristics enabling one to appear on TV and become a famous pundit are not the same as the characteristics of being a successful trader or money manager. Here is a passage from Future Babble on how to be a successful pundit, as illustrated by the charismatic overpopulation doomsdayer Paul Ehrlich:
“Be articulate, enthusiastic, and authoritative. Be likable. See things through a single analytical lens and craft an explanatory story that is simple, clear, conclusive, and compelling. Do not doubt yourself. Do not acknowledge mistakes. And never, ever say, “I don’t know.”
People unsure about the future want to hear from confident experts who tell a good story, and Paul Ehrlich was among the very best. The fact that his predictions were mostly wrong didn’t change that in the slightest.”
Now notice the difference in thinking with one of the greatest hedge fund managers ever, George Soros, “I think that my conceptual framework, which basically emphasizes the importance of misconceptions, makes me extremely critical of my own decisions.” I know that I am bound to be wrong, and therefore more likely to correct my own mistakes.”
Most of the greatest traders and money managers I know think in terms of all sorts of possibilities and probabilities of various scenarios.
Likewise, this follows in line with the old Maynard Keynes expression, “When the facts change I change my mind. What do you do sir?”
Indeed, the title of one of my favorite investment books is “Being Right or Making Money” by Ned Davis. The title alone summarizes almost everything an investor needs to know about investing – do you care more about being correct, or do you care more about increasing your wealth?
Being Wrong: Adventures in the Margin of Error by Kathryn Schultz
Mistakes Were Made (But Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts by Carol Tavris and Elliot Aronson
How We Know What Isn’t So: The Fallibility of Human Reason in Everyday Life by Cornell psychologist Thomas Gilovich
Expert Political Judgment: How Good Is It? How Can We Know? By Philip Tetlock
I wrote a month ago that the endowments and real money funds would face a high hurdle this past year (ending June 30th), and it looks like at least the initial numbers are pretty good.
Ivy allocation from book: 24.27%
Bloomberg: ” Endowments and foundations gained an average of 20 percent in the year ended June 30, their best performance in 14 years, according to consultant Wilshire Associates Inc.”
This seems to be the question many people are asking in today’s markets. Most commentators and media focus only on equities (even though the bond market is bigger than the stock market). While bonds have had a fantastic run (the long bond is up about 14% YTD), equities are down around 5% (although with current vol that # could be anywhere by the time this gets published).
Many investors are also nervous and wringing their hands about the day to day volatility. While most of my systems and approaches are on a much longer timeframe (weeks to months), it is interesting to see how markets have responded to similar down days such as yesterday. I looked at all -5% down days in the US back to the 1920s as well as all -5% days in Japan to the 1950s.
Below is a table of the average, median, max, and min summaries for T +1 (ie today), T + 2 (Monday), T+ 3 (Tuesday), and the 7-day and 14-day total returns. While you can see that there is a little bit of outperformance for buying after these down days (which represent about 1% of all days as mentioned in my last post), there is such wide variability (plus or minus 20% in two weeks) that it is impossible to forecast with conviction what may happen in the ensuing days even though that short term outperformance annualizes to about 30-50%. That is some solid alpha, but you are taking on the risk of a much worse outcome as well. (We’ve replicated this for 15 countries with fairly similar results.)
As always, have a plan and be prepared going in to every market situation while realizing all of the possible outcomes, both good and bad as well as the strengths and weaknesses of any approach. Especially so that you are not asking yourself, “What do I do now?”