Valuation is important

I’ve often touted the decision-making process over end results as a way of evaluating positions and managers a priori. While I’ve tried and tested various factors, some have remained robust and predictive across markets, countries and asset classes. Guess what, they all revolve around value. Tweedy Browne tested some of these factors a while back and updated their research last year. I encourage everyone to read this, even if you don’t agree nor implement the factors (Tweedy apparently should have implemented their own research judging by their performance). For the full report, titled “What Has Worked In Investing” click here.

What Has Worked in Investing is an attempt to share with you our knowledge of
historically successful investment characteristics and approaches. Included in this booklet
are descriptions of over 50 studies, approximately half of which relate to non-U.S. stocks.
Our choice of studies has not been selective; we merely included most of the major studies
we have seen through the years. Interestingly, geography had no influence on the basic
conclusion that stocks possessing the characteristics described in this booklet provided the
best returns over long periods of time. While this conclusion comes as no surprise to us, it
does provide empirical evidence that Benjamin Graham’s principles of investing, first
described in 1934 in his book, Security Analysis, continue to serve investors well. A
knowledge of the recurring and often interrelated patterns of investment success over long
periods has not only enhanced our investment process, but has also provided long-term
perspective and, occasionally, patience and perseverance. We hope this knowledge will also
serve you well.

WHAT HAS WORKED IN INVESTING
1. Low Price in Relation to Asset Value Stocks
2. Low Price in Relation to Earnings Stocks
3. A Significant Pattern of Purchases by One or More Insiders
4. A Significant Decline in a Stock’s Price
5. Small Market Capitalization

…
Dr. Josef Lakonishok (University of Illinois), Dr. Robert W. Vishny (University of Chicago)
and Dr. Andrei Shleifer (Harvard University) presented a paper funded by the National
Bureau of Economic Research entitled, “Contrarian Investment, Extrapolation and Risk,”
May 1993, which examined investment returns from all companies listed on the New York
Stock Exchange (NYSE) and American Stock Exchange (AMEX) in relation to ratios of
price-to-book value, price-to-earnings and price-to-cash flow between 1968 and 1990. In
their abstract, the authors state, “This paper provides evidence that value strategies yield
higher returns because these strategies exploit the mistakes of the typical investor and not
because these strategies are fundamentally riskier.”

A subsequent paper, interestingly co-authored by Burton G. Malkiel, the Princeton
Professor and author of A Random Walk Down Wall Street, which argues against the
efficacy of actively managed investment strategies in favor of index funds, investigated
whether the predictable return advantages associated with contrarian strategies set forth in
previous empirical studies was persistent and exploitable by investment managers. In this
study published in The Journal of Economics and Statistics (May 1997) entitled, “The
Predictability of Stock Returns: A Cross-Sectional Simulation,” Zsuzsanna Fluck (New York
University), Burton G. Malkiel (Princeton) and Richard E. Quandt (Princeton) examined
the performance of 1,000 large-company stocks ranked by price/earnings ratios and priceto-
book value ratios from 1979 through 1995, and confirmed the findings of the previous
Lakonishok, Shleifer and Vishny study, “Contrarian Investment, Extrapolation and Risk,”
finding that,

The papers by Fluck, Malkiel and Quandt, and by Lakonishok, Shliefer and Vishny, together with similar studies described in the “Assets Bought Cheap” and “Earnings Bought Cheap” sections of What Has Worked In Investing demonstrate that, at the extreme, investors overvalue and undervalue individual stocks, and that the best returns come from buying stocks at the extreme end of the value spectrum.

VIX all over the news

Birinyi Associates came out with a report describing the VIX as a coincident indicator rather than a leading indicator.

Speculation that equity returns will be positive after the volatility gauge decreases and negative when it climbs has little basis in fact, Birinyi said. The VIX provides a summary of historical price swings and tends to move in lockstep with equities instead of forecasting their direction, the firm found.

Immediately, traders and commentators all over chimed in, but here‘s a quick summary of (some of) the responses.

Why is anyone so surprised? If it was such a good indicator, it would be easy to use and incorporate into a trading program, but it’s not. For those following at home, we’re long the VXX (VIX ETF). It’s not supposed to provide a hedge, nor are we viewing it as predicting the market. It just looks to be undervalued based on its long term averages, after having gone through a massive down move. We were early getting in, but are still holding on. If you want to express a negative view on the market, there are better ways that are more efficient, more direct, etc.

For each investment or trade, we continue to recommend that you decide WHY you are getting into the trade BEFORE placing it. What view are you expressing? The VIX is just another asset that the market sometimes underprices and that shows mean reverting tendencies.

Uncertainty and risk

How do we assign probabilities and expected returns to fat tail events? Often, when people can’t estimate the impact of certain events, the tendency is to guess wish it away, ignore it, or guess it doesn’t exist. Yet, if asked what is the GDP of the US, should you guess “Zero” because you don’t know the answer? Of course not. And so, policy-makers and investors (for our purposes) are often called on to make decisions based on incomplete information. Sometimes, the return profile can be estimated; at other times, we have to make decisions without even knowing the shape of the distribution.

In a recent paper David Farber, a law professor at University of California, explored different tools we can use, or at least that can prompt some healthy debate. While some of the issues he discusses are not directly financial (like global warming or terrorist threats), they are related and the ideas discussed are very relevant. I’ve included his conclusion from the paper.

Our society faces serious problems, many of which would be difficult to manage under the best of circumstances. Addressing these problems is all the more difficult because they often involve threatened harms whose dimensions are only understood imperfectly. In particular, we are often unable to quantify the probability of harm with any confidence. It is sometimes tempting to ignore such hazards as speculative. That is clearly the wrong response. Just because you do not know exactly how big a number is, there is no reason to assume it to be zero. (Q: What is the GDP of China? If you don’t know, should you guess zero?)

A better response would be to use some variety of the precautionary principle, which at least keeps the threatened harm on the agenda and counsels caution in dealing with it. But the precautionary principle, or even the catastrophic risk precautionary principle advocated by Sunstein, falls well short of providing concrete guidance. This Article has explored developments in economic theory that may provide more clarity in dealing with unquantifiable uncertainties.

As we have seen, such uncertainties can be associated with fat tailed distributions – either because we know the distribution and the expected risk or its variance turn out to be infinite, or because the nature of the distribution prevents us from setting key parameters accurately enough to determine the expected risk or variance. There is some reason to think that, because of internal feedbacks, both climate change and financial crashes may have such characteristics.

In other situations, we may simply have no good idea of how to assign probabilities in the first place or of what the probability distribution might look like. In those situations, we need to think about a variety of possible scenarios. Examples include estimates of the medium-run risks and benefits of nanotechnology, or the long-term risks of nuclear waste disposal. Ambiguity theory helps address these situations, and the most easily applied models advise assessing decisions based on a combination of the best-case and worst-case scenarios. This leads to the α-precautionary principle, which weighs the best and worst potential outcomes in assessing a course of action.

One lesson of this investigation into non-quantifiable hazards is that uncertainty is not unitary but plural. Former Secretary of Defense Donald Rumsfeld famously distinguished between known knowns, known unknowns and unknown unknowns, with the latter being the most worrisome. The “known knowns” correspond not merely to certainties but to probability distributions that are well understood – risks, rather than uncertainties, in the lexicon of this article.

But the known unknowns fall into several categories. Some are known in the sense that we have a grasp on the shape of the probability distribution but not key parameters – and in some cases, the key parameters turn out to be difficult to determine or even unknowable. There are also risks that we know are unknowable, such as the evolution of human society over future millennia. These known unknowables merge into the Rumsfeld’s category of unknown unknowns.

Yet, in some cases we may have a handle on even the unknown unknowns. For instance, we may not have a good understanding of which particular future shocks might affect a system. Nevertheless, understanding the feedbacks in the system and the statistical distribution of outcomes might enable us to understand how the system will respond even to unknown shocks. Uncertainty, then, is a multidimensional concept.

It would certainly be nice if economics were to provide a foolproof way of making decisions under conditions of uncertainty, given the importance of those decisions for society. The prospects for such a development are themselves – there is no other way to put this – highly uncertain, and they are all the more so because uncertainty takes so many forms. Certainly, no such methodology now exists, and one might well question whether it is even possible. But an analytical tool need not be decisive to still be useful.

Complicated but potentially catastrophic problems like global climate change or financial crashes will always present difficult choices, once we at least get to the point of acknowledging that these threats are real and must be dealt with. There is no easy recipe for divining the right solution to problems whose parameters involve so much uncertainty. We cannot afford to ignore perils simply because their probability is uncertain, nor can we safely proceed on the basis of speculative numerical estimates. But we can gain some much-needed clarity with the tools discussed in this article.

For the full article, click here.

Deadly discount rate

We’re reading more and more about research being done in low to negative real interest rate environments. For example, what discount rate should we use for valuations? All the PE deals being struck, what WACC are they using? Who’s financing it at a fixed rate? For that matter, how are the banks figuring out their spreads over LIBOR?

We often go back to the same set of books and principles, and I’m reminded of Ed Easterling’s book Unexpected Returns. We are moving from a period of price stability to instability. It doesn’t actually matter for valuations whether we move towards inflation or deflation – both will be bad for stocks. That being said, I’m heartened by the articles coming out that hopefully provide a reality check. Today, for example, in the Asia Times, David Goldman highlights why a low interest rate environment makes today’s valuations look exceedingly expensive.This article is a great start to trying to measure the sensitivity of stocks to changes in the interest rate – AT THESE RATES. That’s they key. These are still not normal times! Goldman describes a (crude, but effective for order of magnitude) model to measure equity duration (interest-rate sensitivity).

The recovery of the S&P 500 since its March 2009 lows reflects an anemic level of earnings as well as a very low discount rate. A rise in the short-term interest rate (in reality, in the whole yield curve) could take a very big bite out of equity prices. I don’t quite believe that a 2% risk free rate implies a drop in the S&P by half — this is a numerical example rather than a realistic model — but it does highlight the sensitivity to watch out for.

To read the full article (which you should), click here.

Naysayers will tell you a lot of things about imminent recoveries, low relative P/E’s, or forward P/E expectations. I’m still wary of valuations based on unrealistic circumstances and inputs.

Sentiment Indicators

I’ve never been able to model these accurately and robustly, but the idea is intriguing for a contrarian (like I try to be). The question is what indicators to use, what time frame should you look at, how robust is the data, are you measuring coincident indicators/correlation/causation, how do you quantify the predictive value, etc. ETF’s have certainly given us some insight, as does watching the VIX. I was recently sent the following articles about using the Rydex levered bull vs. levered bear ETF’s. The first article looks at daily moves and the second at weekly moves. I have not tested these indicators myself and cannot attest to their validity, but it is something that is worth looking into:

http://www.zerohedge.com/article/rydex-market-timers-amazing

http://thetechnicaltakedotcom.blogspot.com/2010/01/rydex-market-timers-long-term-view.html

These articles seem to be looking at assets, not prices, but I assume you could look at prices as well. Just some food for thought for the modelers out there.

Placing speculative limits is BAD – now if only the Fed will heed it’s own research

In a recent paper published by the New York Fed, Erkko Etula shows that speculators help stabilize commodity markets. To quote:

Taken together, my results highlight the importance of speculative capital for the stability of commodity markets. In this way, the paper not only contributes to the broader literature on limits of arbitrage pioneered by Shleifer and Vishny (1997), but also shows that recent arguments in favor of speculative
trading restrictions have been starkly misguided.

Another interesting outgrowth of this research is that Etula is able to model some of the volatility of commodities based on the flow of funds report. For those trading in the options arena, especially those using quant based approaches, this might point to an interesting factor to test further. For the full report click here.

IMPORTANT NOTICE: Inverse, Leveraged and Inverse-Leveraged Exchange Traded Funds are no longer available for new or additional purchases at UBS

Effective July 27, 2009, UBS is suspending the offering of Inverse, Leveraged and Inverse-Leveraged Exchange Traded Funds (ETFs). You will no longer be able to make new or additional purchases and will only be able to liquidate current positions through UBS at this time. Any attempt to execute a trade of such ETFs will be rejected.

Please contact your Financial Advisor with questions.

Toxic Equity Trading Order Flow on Wall Street

This is a must read for all traders, investors, regulatory officials, and anyone involved in the markets. Themis Trading LLC has blown off the cover of program trading to highlight how algorithmic based programs make money. Everything from automatic bid-spread systems, pinging (searching for hidden liquidity), and liquidity rebates are discussed. These systems are driving volume and volatility and driving false readings and higher costs for the rest of the investing community.

Arnuk and Saluzzi provide two solutions:

1. Orders must be valid for 1 second (which is huge when talking about millisecond moves), and
2. Curb program trading if markets move more than 2% limits.

I have another solutions, which is really simple: hold positions longer! The markets will go where they intend to go anyway, which Arnuk and Saluzzi confirm, however, trading without the advantages of high speed super-computers just costs more because of these algo strategies. Solution: don’t try to compete in a losing game. Hold positions longer and play the time arbitrage. You might pay an extra penny for execution, but if you’re planning on holding the position for longer than a year or even two, then that penny shouldn’t impact your long term IRR.

Toxic Equity Trading Order Flow on Wall Street

Profits Diluted 4% by U.S. Share Sales, Dividend Cuts

June 8 (Bloomberg) — American common equity is increasing for the first time in five years, threatening to dilute corporate profits as companies sell a record amount of stock and cut dividends the most since 1938.

Wells Fargo & Co., ProLogis and more than 150 other companies raised $82.2 billion this quarter, beating the record pace at the height of the technology bubble in 2000, according to data compiled by Bloomberg. The combination of adding shares and restricting dividends will reduce annual equity returns as much as 4.1 percent, the data show.

“The math is inescapable,” said Alan Gayle, the Richmond, Virginia-based director of asset allocation at Ridgeworth Investments, which manages $60 billion. “You’ve got weak earnings, the share price goes down and then, ‘What? They want to raise equity?’ Clearly that isn’t a good thing.”

http://www.bloomberg.com/apps/news?pid=20601087&sid=aCYuw3iBBUmM

Interesting Trade Idea

How about this reversion to the mean trade…

1. Break down the S&P 500 into industry subsectors (http://www2.standardandpoors.com/spf/pdf/index/GICS_500_Scorecard.pdf).
2. Look at the subsectors that have had the most significant changes in weighting over the past 2 years.
3. Go long the subsector that has lost the most ground. If you’re looking to balance it out, then short the subsector that has gained the most ground.
4. Hold for one year, the redo. So, you’re always looking at rolling 2 year changes.

I’d be interested to see if it keeps you out of a lot of trouble. Anyone out there testing this stuff?

Samberg’s Pequot Capital to Close

Sobering.

http://online.wsj.com/public/resources/documents/PequotLetter052709.pdf

Goldman to pay $60 mln in subprime settlement

By the time the dust settles I would expect GS to be neck deep in litigation.

NEW YORK (MarketWatch) — Massachusetts Attorney General Martha Coakley on Monday said her office has reached a $60 million settlement with Goldman Sachs Group Inc. related to the investment bank’s role in securitizing subprime mortgage loans.

http://www.marketwatch.com/news/story/Goldman-pay-60-mln-subprime/story.aspx?guid=%7BD6BD5788%2D41D5%2D49F2%2DA78E%2D0CD9711B0186%7D

Low frequency fundamental value equity strategies…

What’s the deal? I just got off the phone with another strategy builder from one of the big shops looking for a new home. That’s one last week, one this week…Are funds abandoning the low frequency strategies en masse? Is there more time arbitrage for those who can hold on to positions (on both sides) and ignore the noise? Worth following.

Are stocks really less volatile in the long term?

In a recent paper, Professors Stambaugh and Pastor argue that stocks exhibit higher volatility over long horizons. “Evidence of lower long-horizon variance is cited in support of higher equity allocations for long-run investors (e.g, Siegel, 2008) as well as the increasingly popular “life-cycle” mutual funds that allocate less to equity as investors grow older (e.g., Gordon and Stockton, 2006, Greer, 2004, and Viceira, 2008).” The implications touch pension investors or any investors determining allocation based on time horizon (i.e. the vast majority).

For me, one of the most interesting aspect of this paper is that time does not necessarily increase predictability. Assuming that equities are not more volatile over the long term, as the paper suggests, but instead are equally volatile. That would support Mandlebrot’s fractal modeling. Even if volatility is higher, that is, we see the same pattern, but with a certain multiple, using fractals, we should still be able to run simulations and “build” hypothetical returns. Despite our recent foray into genetic algorithms, I find myself continually going back to the work of Mandlebrot…talk about confirmatory bias. Here’s the abstract:

Conventional wisdom views stocks as less volatile over long horizons than over short horizons due to mean reversion induced by return predictability. In contrast, we find stocks are substantially more volatile over long horizons from an investor’s perspective. This perspective recognizes that parameters are uncertain, even with two centuries of data, and that observable predictors imperfectly deliver the conditional expected return. We decompose return variance into five components, which include mean reversion and various uncertainties faced by the investor. Although mean reversion makes a strong negative contribution to long-horizon variance, it is more than offset by the other components. Using a predictive system, we estimate annualized 30-year variance to be nearly 1.5 times the 1-year variance. (Pastor, Lubos and Stambaugh, Robert F.,Are Stocks Really Less Volatile in the Long Run?(February 17, 2009). Available at SSRN: http://ssrn.com/abstract=1136847)

Also, check out this interview with Stambaugh and Jeremy Siegel at Wharton. http://knowledge.wharton.upenn.edu/article.cfm?articleid=2229

Genetic Algorithms cont’d…

It’s interesting how many new inquiries and papers we have been seeing on building these types of algorithms. Here’s another one: Hybrid Evolutionary Techniques for FX Arbitrage Prediction by Tristan Fletcher.

Abstract:     
This paper discusses the need for a missing value technique to fill in gaps in time series representing foreign exchange (FX) prices and assist in the observation of potential arbitrage opportunities. It highlights the requirement for prediction methods to establish the persistence of these opportunities (latency). Naieve missing value and prediction techniques are investigated and then compared with Kalman Filtration, Ensemble Kalman Filtration, Regression and Neural Network techniques. A technique not known to be applied in this domain before, namely NeuroEvolution using Augmented Topologies (NEAT), is then examined in order to asses its ability in filling in missing values and the prediction of arbitrage opportunities in comparison to these other more established techniques. Hybrid functions, incorporating the most successful of the techniques, are constructed in order to ascertain whether combinations of techniques are more successful than their constituents. Data from for various data providers for three markets is used taken over periods representing different levels of market activity (liquidity).

Citation info: Fletcher, Tristan,Hybrid Evolutionary Techniques for FX Arbitrage Prediction(August 31, 2007). Available at SSRN: http://ssrn.com/abstract=1323607