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Archive for March, 2013

Update: Now with a log version.

Here’s the St Louis Fed’s FRED on Warren Buffett’s favored market measure, total market capitalization-to-GNP.

FRED Graph

According to the FRED data, the Q1 2000 TTM/GNP peak ratio was 158 percent, and the Q3 2007 TTM/GNP peak was 114 percent. The average for the full period – Q3 1949 to Q3 2012 – is 69 percent. The last time the market traded at a below-average ratio was Q1 2009.

Compare this to the Q3 2012 ratio – unfortunately the most recent point – at 100 percent. If we assume ~1 percent GNP growth in Q4 2012 and Q1 2013 (the long-run CAGR is about 1 percent per quarter), and the market has rallied around 10 percent, the ratio now stands at ~107 percent, which is around 40 percent over the long-run mean (since 1949). This level of overvaluation accords with the level of the Shiller PE and Tobin’s q at 40 and 44 percent respectively.

Here’s the log version, per commenter Jim’s request:

FRED Graph

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My piece on S&P 500 forward earnings estimates and the overvaluation of the market generated a number of heated emails and comments. I didn’t know that it was so controversial that the market is expensive. I’m not saying that the market can’t continue to go up (I’ve got no idea what the market is going to do). My point is that there are a variety of highly predictive, methodologically distinct measures of market-level valuation (I used the Shiller PE and Tobin’s q, but GNP or GDP-to-total market capitalization below work equally as well) that point to overvaluation.

The popular price-to-forward operating earnings measure does not point to overvaluation, but is flawed because forward operating earnings are systematically too optimistic. It’s simply not predictive, mostly because it fails to take into account the highly mean reverting nature of profit margins. Here’s John Hussman from a week ago in his piece Investment, Speculation, Valuation, and Tinker Bell (March 18, 2013):

From an investment standpoint, it’s important to recognize that virtually every assertion you hear that “stocks are reasonably valued” is an assertion that rests on the use of a single year of earnings as a proxy for the entire long-term stream of future corporate profitability.  This is usually based on Wall Street analyst estimates of year-ahead “forward operating earnings.” The difficulty here is that current profit margins are 70% above the long-term norm.

Most important, the level of corporate profits as a share of GDP is strongly and inversely correlated with the growth in corporate profits over the following 3-4 year period.

While I believe the Shiller PE and Tobin’s to be predictive, there are other measures of market valuation that perform comparably. Warren Buffett’s favored measure is “the market value of all publicly traded securities as a percentage of the country’s business–that is, as a percentage of GNP.” Here he is in a 2001 interview with Fortune’s Carol Loomis:

[T]he market value of all publicly traded securities as a percentage of the country’s business–that is, as a percentage of GNP… has certain limitations in telling you what you need to know. Still, it is probably the best single measure of where valuations stand at any given moment. And as you can see, nearly two years ago the ratio rose to an unprecedented level. That should have been a very strong warning signal.

A quick refresher: GDP is “the total market value of goods and services produced within the borders of a country.” GNP is “is the total market value of goods and services produced by the residents of a country, even if they’re living abroad. So if a U.S. resident earns money from an investment overseas, that value would be included in GNP (but not GDP).” While the distinction between the two is  important because American firms are increasing the amount of business they do internationally, the actual difference between GNP and GDP is minimal as this chart from the St Louis Fed demonstrates:

FRED Graph

GDP in Q4 2012 stood at $15,851.2 billion. GNP at Q3 2012 (the last data point available) stood at $16,054.2 billion. For our present purposes, one substitutes equally as well for the other.

For the market value of all publicly traded securities, we can use The Wilshire 5000 Total Market Index. The index stood Friday at $16,461.52 billion. The following chart updates in real time:

Chart

Here are the calculations:

  • The current ratio of total market capitalization to GNP is 16,461.52 / 16,054.2 or 103 percent.
  • The current ratio of total market capitalization to GDP is 16,461.52 / 15,851.2 or 104 percent.

You can undertake these calculations yourself, or you can go to Gurufocus, which has a series of handy charts demonstrating the relationship of GDP to Wilshire total market capitalization:

Chart 1. Total Market Cap and GDP

GDP WIlshire Total Market

Chart 1 demonstrates that total market capitalization has now exceeded GDP (note the other two auspicious peaks of total market capitalization over GDP in 1999 and 2007).

Chart 2. Ratio of Total Market Capitalization and GDP

Total Market Cap GDP Ratio

Chart 2 shows that the current ratio is well below the ratio achieved in the last two peaks (1999 and 2007), but well above the 1982 stock market low preceding the last secular bull market.

But, so what? Is the ratio of total market capitalization to GDP predictive?

In this week’s The Hook (March 25, 2013) Hussman discusses his use of market value of U.S. equities relative to GDP, which he says has a 90% correlation with subsequent 10-year total returns on the S&P 500:

Notably, the market value of U.S. equities relative to GDP – though not as elevated as at the 2000 bubble top – is not depressed by any means. On the contrary, since the 1940’s, the ratio of equity market value to GDP has demonstrated a 90% correlation with subsequent 10-year total returns on the S&P 500 (see Investment, Speculation, Valuation, and Tinker Bell), and the present level is associated with projected annual total returns on the S&P 500 of just over 3% annually.

Here’s Gurufocus’s comparison of predicted and actual returns assuming three different ratios (TMC/GDP = 40 percent, 80 percent, and 120 percent) at the terminal date:

Chart 3. Predicted and Actual Returns

Predicted and Actual Returns GDP Total Market Cap

Chart 3 shows the outcome of three terminal ratios of total market capitalization to GDP. Consider the likelihood of these three scenarios:

  1. A terminal ratio of 120 percent (equivalent to the 1999 to 2001 peak) leads to annualized nominal returns of 8.1 percent over the next 10 years.
  2. A terminal ratio of 80 percent (the long-run average) leads to annualized nominal returns of 3 percent over the next 10 years.
  3. A terminal ratio of 40 percent (approximating the 1982 low of 35 percent) leads to annualized nominal returns of -5 percent over the next 10 years.

For mine, 1 seems less likely than scenarios 2 or 3, with the long run mean (scenario 2) the most likely. For his part, Buffett opines:

For me, the message of that chart is this: If the percentage relationship falls to the 70% or 80% area, buying stocks is likely to work very well for you. If the ratio approaches 200%–as it did in 1999 and a part of 2000–you are playing with fire.

Gurufocus’s 80-percent-long-run-average calculation agrees with Hussman’s calculation of average annualized market return of 3%:

As of today, the Total Market Index is at $ 16461.5 billion, which is about 104.3% of the last reported GDP. The US stock market is positioned for an average annualized return of 3%, estimated from the historical valuations of the stock market. This includes the returns from the dividends, currently yielding at 2%.

Here’s Buffett again:

The tour we’ve taken through the last century proves that market irrationality of an extreme kind periodically erupts–and compellingly suggests that investors wanting to do well had better learn how to deal with the next outbreak. What’s needed is an antidote, and in my opinion that’s quantification. If you quantify, you won’t necessarily rise to brilliance, but neither will you sink into craziness.

On a macro basis, quantification doesn’t have to be complicated at all. Below is a chart, starting almost 80 years ago and really quite fundamental in what it says. The chart shows the market value of all publicly traded securities as a percentage of the country’s business–that is, as a percentage of GNP. The ratio has certain limitations in telling you what you need to know. Still, it is probably the best single measure of where valuations stand at any given moment. And as you can see, nearly two years ago the ratio rose to an unprecedented level. That should have been a very strong warning signal.

The current ratios of total market capitalization to GNP and GDP should be very strong warning signals. Further, that they imply similar returns to the Shiller PE and Tobin’s q, suggests that they are robust.

Order Quantitative Value from Wiley FinanceAmazon, or Barnes and Noble.

Click here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.

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Further to my point that if your valuation models use forward estimates rather than twelve-month trailing data, you’re doing it wrong, here are the results of our Quantitative Value backtest on the use of consensus Institutional Brokers’ Estimate System (I/B/E/S) earnings forecasts of EPS for the fiscal year (available 1982 through 2010) for individual stock selection:

We analyze the compound annual growth rates of each price ratio over the 1964 to 2011 period for market capitalization–weighted decile portfolios.

The forward earnings estimate is the worst performed metric by a wide margin. The performance of the forward earnings estimate is uniformly poor, earning a compound annual growth rate of just 8.63 percent on average and underperforming the Standard & Poor’s (S&P) 500 by almost 1 percent per year. Investors are wise to shy away from analyst forward earnings estimates when making investment decisions.

We focus our analysis on historical valuation metrics in Quantitative Value and leave the forward earnings estimates to the promoters on Wall Street.

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If your valuation models use forward estimates rather than twelve-month trailing data, you’re doing it wrong. Why? As we discussed in Quantitative Value, analysts are consistently too optimistic about the future, and so systematically overestimate forward earnings figures.

They are consistently, systematically, predictably ignorant of mean-reverting base rates. As we wrote in the book:

Exceptions to the long pattern of excessively optimistic forecasts are rare. Only in 1995 and 2004 to 2006, when strong economic growth generated earnings that caught up with earlier predictions, do forecasts actually hit the mark. When economic growth accelerates, the size of the forecast error declines; when economic growth slows, it increases.

This chart from JP Morgan Asset Management as of a week ago shows the chronic overestimation of operating earnings:

The chart comes via Zero Hedge, where they ask, “Is the market cheap?” My answer is not on the basis of the Shiller PE, which stands at 23.7 versus the long run arithmetic mean of 16.47 or around 40 percent overvalued. Neither is it cheap on the basis of Tobin’s q. Smither’s & Co. has it at 44 percent overvalued on the basis of q, and they note:

As at 12th March, 2013 with the S&P 500 at 1552 the overvaluation by the relevant measures was 57% for non-financials and 65% for quoted shares.

Although the overvaluation of the stock market is well short of the extremes reached at the year ends of 1929 and 1999, it has reached the other previous peaks of 1906, 1936 and 1968.

How about the single year P/E ratio as reported? The S&P 500 TTM P/E stands at 18 versus the long run mean of 15.49. But it’s cool because the “E” is growing, right? Err, no. The “E” peaked in February last year (see Standard & Poor’s current S&P 500 Earnings, go to “Download Index Data,” and select “Index Earnings”). The multiple will now have to expand just to keep the market where it is. You have to do these sort of acrobatics to get it going up:

Margins are now going to bounce free of the wreckage like those few lucky souls who remember to assume the brace position before the plane hits the ground, even though the as reported rolled over a year ago (I hope Denzel Washington is flying this plane).

So how is it cheap?

It’s at 14.5 on the basis of twelve-month forward operating earnings estimates versus a long run mean of 15.49. You gotta do what you gotta do to get the Muppets to buy.

Good luck with that.

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In the How to beat The Little Book That Beats The Market (Part 1 2, and 3) series of posts I showed how in Quantitative Value we tested Joel Greenblatt’s Magic Formula (outlined in The Little Book That (Still) Beats the Market) and found that it had consistently outperformed the market, and with lower relative risk than the market.

We sought to improve on it by creating a generic, academic alternative that we called “Quality and Price.” Quality and Price is the academic alternative to the Magic Formula because it draws its inspiration from academic research papers. We found the idea for the quality metric in an academic paper by Robert Novy-Marx called The Other Side of Value: Good Growth and the Gross Profitability Premium. Quality and Price substitutes for the Magic Formula’s ROIC a quality measure called gross profitability to total assets (GPA), defined as follows:

GPA = (Revenue − Cost of Goods Sold) / Total Assets

In Quality and Price, the higher a stock’s GPA, the higher the quality of the stock.

The price ratio, drawn from the early research into value investment by Eugene Fama and Ken French, is book value-to-market capitalization (BM), defined as follows:

BM = Book Value / Market Price

The Quality and Price strategy, like the Magic Formula, seeks to differentiate between stocks by equally weighting the quality and price metrics. Can we improve performance by seeking higher quality stocks in the value decile, rather than equal weighting the two factors?

In his paper The Quality Dimension of Value Investing, Novy-Marx considered this question. Novy-Marx’s rationale:

Value investors can also improve their performance by controlling for quality when investing in value stocks. Traditional value strategies formed on price signals alone tend to be short quality, because cheap firms are on average of lower quality than similar firms trading at higher prices. Because high quality firms on average outperform low quality firms, this quality deficit drags down the returns to traditional value strategies. The performance of value strategies can thus be significantly improved by explicitly controlling for quality when selecting stocks on the basis of price. Value strategies that buy (sell) cheap (expensive) firms from groups matched on the quality dimension significantly outperform value strategies formed solely on the basis of valuations.

His backtest method:

The value strategy that controls for quality is formed by first sorting the 500 largest financial firms each June into 10 groups of 50 on the basis of the quality signal. Within each of these deciles, which contain stocks of similar quality, the 15 with the highest value signals are assigned to the high portfolio, while the 15 with the lowest value signals are assigned to the low portfolio. This procedure ensures that the value and growth portfolios, which each hold 150 stocks, contain stocks of similar average quality.

Novy-Marx finds that the strategy “dramatically outperform[s]” portfolios formed on the basis of quality or value alone, but underperforms the Greenblatt-style joint strategy. From the paper:

The long/short strategy generated excess returns of 45 basis points per month, 50% higher than the 31 basis points per month generated by the unconditional quality strategy, despite running at lower volatility (10.4% as opposed to 12.2%). The long side outperformed the market by 32 basis points per month, 9 basis points per month more than the long-only strategy formed without regard for price. It managed this active return with a market tracking error volatility of only 5.9%, realizing an information ratio of 0.63, much higher than the information ratio of 0.42 realized on the tracking error of the unconditional long-only value strategy.

For comparison, Novy-Marx finds the Greenblatt-style joint 50/50 weighting generates higher returns:

The long/short strategy based on the joint quality and value signal generated excess returns of 61 basis points per month, twice that generated by the quality or value signals alone and a third higher than the market, despite running at a volatility of only 9.7%. The strategy realized a Sharpe ratio 0.75 over the sample, almost two and a half times that on the market over the same period, despite trading exclusively in the largest, most liquid stocks.

The long side outperformed the market by 35 basis points per month, with a tracking error volatility of only 5.7 percent, for a realized information ratio of 0.75. This information ratio is 15% higher than the 0.65 achieved running quality and value side by side. Just as importantly, it allows long-only investors to achieve a greater exposure to the high information ratio opportunities provided by quality and value. While the strategy’s 5.7% tracking error still provides a suboptimally small exposure to value and quality, this exposure is significantly larger than the long-only investor can obtain running quality alongside value.

And a pretty chart from the paper:

Novy-Marx 2.1

We tested the decile approach and the joint approach in Quantitative Value, substituting better performing value metrics and found different results. I’ll cover those next.

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