Posts Tagged ‘Asset Value’

In a post in late November last year, Testing the performance of price-to-book value, I set up a hypothetical equally-weighted portfolio of the cheapest price-to-book stocks with a positive P/E ratio discovered using the Google Screener, which I called the “Greenbackd Contrarian Value Portfolio“. The portfolio has been operating for a little over 4 months, so I thought I’d check in and see how it’s going.

Here is the Tickerspy portfolio tracker for the Greenbackd Contrarian Value Portfolio showing how each individual stock is performing:

(Click to enlarge)

And the chart showing the performance of the portfolio against the S&P500:

[Full Disclosure:  No positions. This is neither a recommendation to buy or sell any securities. All information provided believed to be reliable and presented for information purposes only. Do your own research before investing in any security.]

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This week we’ve been examining the various studies that have considered book value as a predictor of future investment returns, and methods for “juicing” or improving its performance. Josef Lakonishok, Andrei Shleifer, and Robert Vishny’s landmark 1994 study Contrarian Investment, Extrapolation, and Risk examined book value in the context of a larger investigation into the performance of value stocks relative to glamour stocks in the United States. Book value was one of four one-variable metrics used to classify a stock as “value” or “glamour” (the others were cash flow, earnings and 5-year average growth rate of sales). Lakonishok, Shleifer, and Vishny (LSV) argue that value strategies produce superior returns because most investors don’t fully appreciate the phenomenon of mean reversion, which leads them to extrapolate past performance too far into the future. To exploit the flaw in intuitive forecasts – you know how I love a counter-intuitive strategy – they argue that contrarian investors should sell stocks with high past growth as well as high expected future growth and buy stocks with low past growth and as well as low expected future growth. In practice, this means adding to each of the four one-variable value metrics a second dimension to further tune the selection process. The result is LSV’s Two-Dimensional Classification.

Contrarian Investment, Extrapolation, and Risk

In Contrarian Investment, Extrapolation, and Risk LSV define “value strategies” as “buying stocks that have low prices relative to earnings, dividends, book assets, or other measures of fundamental value.” They argue that, while there is some agreement that value strategies produce higher returns, the interpretation of why they do so is more controversial. The paper is a response to Fama and French’s 1992 paper, The Cross-Section of Expected Stock Returns, which argued that value strategies produce abnormal returns only because they are fundamentally riskier. LSV seek to demonstrate that value strategies yield higher returns because these strategies “exploit the suboptimal behavior of the typical investor” and “not because these strategies are fundamentally riskier.”  (LSV’s research was updated this year by The Brandes Institute, who extended LSV’s research through to June 2008, creating a 40-year comparison of the relative performance of value and glamour stocks.)

LSV test two potential explanations for the outperformance of value stocks over glamour stocks:

  1. LSV’s contrarian model, which argues that value strategies produce superior returns because investors extrapolate past performance too far into the future.
  2. Fama and French’s contention that value stocks are fundamentally riskier than glamour stocks. This second potential explanation is outside the scope of this post, but is dealt with in some detail in the paper. I encourage you to read it if you’re interested in the efficient markets debate.

LSV test the contention that value strategies produce superior returns because investors extrapolate past performance too far into the future by examining simple one-variable classifications of glamour and value stocks. Glamour stocks are those that “have performed well in the past,” and “are expected by the market to perform well in the future.” Value stocks are those that “have performed poorly in the past and are expected to continue to perform poorly.” The stocks are classified on the basis of one of four variables: book-to-market (B/M, the inverse of price-to-book), cash flow-to-price (C/P), earnings-to-price (E/P), and 5-year average growth rate of sales (GS). LSV examine 2,700 firms on the NYSE and AMEX between 1968 and 1989. At the end of each April, they rank each stock on the basis of the variable tested (B/M, C/P etc) and then divide the stocks into deciles. Each decile is treated as a portfolio and held for 5 years. LSV track the performance of each decile portfolio in each of the 5 years and present the results as follows (Rt is the average return in year t over the 5 years after formation, CR5 is the compounded 5-year return assuming annual rebalancing. SAAR is the average annual size-adjusted return computed over the 5 years after formation. The Glamour portfolio is the decile portfolio containing stocks ranked lowest on B/M, C/P, or E/P, or highest of GS and vice versa for the Value portfolio):

As the four panels make clear, value outperforms glamour in rank order and regardless of the simple one-variable classification chosen. LSV attribute the outperformance to the failure of investors to formulate their predictions of the future without a “full appreciation of mean reversion.”

That is, individuals tend to base their expectations on past data for the individual case they are considering without properly weighting data on what psychologists call the “base rate,” or the class average. Kahneman and Tversky (1982, p. 417) explain:

One of the basic principles of statistical prediction, which is also one of the least intuitive, is that extremeness of predictions must be moderated by considerations of predictability… Predictions are allowed to match impressions only in the case of perfect predictability. In intermediate situations, which are of course the most common, the prediction should be regressive; that is, it should fall between the class average and the value that best represents one’s impression of the case at hand. The lower the predictability the closer the prediction should be to the class average. Intuitive predictions are typically nonregressive: people often make extreme prediction on the basis of information whose reliability and predictive validity are known to be low.

Anatomy of a Contrarian Strategy: LSV’s Two-Dimensional Classification

According to LSV, to exploit this flaw of intuitive forecasts, contrarian investors should sell stocks with high past growth as well as high expected future growth and buy stocks with low past growth and as well as low expected future growth.

Prices of these stocks are likely to reflect the failure of investors to impose mean reversion on growth forecasts.

LSV test the Two-Dimensional Classifications in a similar manner to the one-variable classifications above. At the end of each April between 1968 and 1989, 9 portfolios of stocks are formed. The stocks are independently sorted into ascending order in 3 groups (rather than deciles, for the obvious reason – 9 annual portfolios is easier to track than 100): 1. the bottom 30%, 2. the middle 40%, and 3. the top 30% based on each of two variables. The sorts are for 5 pairs of variables: C/P and GS, B/M and GS, E/P and GS, E/P and  B/M and B/M and C/P. Depending on the two variables used for classification, the Value portfolio either refers to the portfolio containing stocks ranked in the top group (3.) on both variables from among C/P, E/P, or B/M, or else the portfolio containing stocks ranking in the top group on one of those variables and in the bottom group (1.) on GS and vice versa for Glamour. (For the purposes of this post, I’m including only those examining B/M as one of the variables. The others are, however, well worth considering. Value determined on the basis C/P or E/P combined with GS produced slightly higher cumulative returns averaged across all firms for the period of the study. Interestingly, this phenomenon reversed in large stocks, with B/M-based strategies producing slightly higher cumulative returns in large stocks.):

These tables demonstrate that, within the set of firms whose B/M ratios are the highest (in other words, the lowest price-to-book value), further sorting on the basis of another value variable – whether it be C/P, E/P or low GS – can enhance returns. This is LSV’s Two-Dimensional Classification. LSV conclude that value strategies based jointly on past performance and expected future performance produce higher returns than “more ad hoc strategies such as that based exclusively on the B/M ratio.” The strategy is quite useful. It can be applied to large stocks, which means that it can be used to implement trading strategies for larger and institutional investors, and will continue to generate superior returns.

Next we examine Joseph D. Piotroski’s F_SCORE as a means for juicing P/B.

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This site is dedicated to undervalued asset situations, but I haven’t yet spent much time on undervalued asset situations other than liquidations and Graham net current asset value stocks. Two areas worthy of further study are low price-to-book value stocks and low price-to-tangible book value stocks. I’ve found that it is difficult to impossible to find any research examining the performance of stocks selected on the basis of price-to-tangible book value. That may be because book value alone can explain most of the performance and removing goodwill and intangibles from the calculation adds very little. Tangible book value is of interest to me because I assume it more closely describes the likely value of a company in liquidation than book value does. That assumption may be wrong. Some intangibles have value in liquidation, although it’s always difficult to collect on the goodwill. If anyone knows of any study explicitly examining the performance of stocks selected on the basis of price-to-tangible book value, please shoot me an email at greenbackd at gmail or leave a comment in this post.

Book value has received plenty of attention from researchers in academia and industry, starting with Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986) and  Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987). In Value vs Glamour: A Global Phenomenon, The Brandes Institute updated the landmark 1994 study by Josef Lakonishok, Andrei Shleifer, and Robert Vishny Contrarian Investment, Extrapolation and Risk. All of these studies looked at the performance of stocks selected on the basis of price-to-book value (among other value metrics). The findings are uniform: lower price-to-book value stocks tend to outperform higher price-to-book value stocks, and at lower risk. On the strength of the findings in these various studies I’ve decided to run a handful of real-time tests to see how a portfolio constructed of the cheapest stocks determined on a price-to-book value basis performs against the market.

Constructing a 30-stock portfolio

The Ibbotson, LSV and Brandes Institute studies created decile portfolios and Thaler and DeBont created quintile portfolios. I propose to informally test the P/B method at the extreme, taking the cheapest 30 stocks in the Google Finance screener (I use the Google Finance screener because it’s publicly available and easily replicable) and creating an equally weighted portfolio. Here is the list of stocks generated as at the November 20, 2009 close:

Symbol Market cap Price to book Last price P/E ratio Book value/share
TOPS 33.97M 0.11 1.15 0.92 9.77
CEP 76.78M 0.15 3.38 4.76 23.23
SVLF 28.99M 0.17 0.76 2.48 5.09
BXG 77.47M 0.23 2.38 33.81 12.24
SGMA 13.46M 0.29 3.52 14.12 11.88
KRG 190.16M 0.3 3.02 28.97 10.3
BDR 5.88M 0.31 0.95 9.99 3.14
FREE 34.30M 0.31 1.62 1.61 5.71
IOT 22.51M 0.32 5.4 3.75 16.98
WPCS 20.69M 0.35 2.98 16.61 8.53
SSY 8.71M 0.35 1.83 14 5.19
CUO 19.18M 0.36 12 7.81 32.93
ONAV 73.53M 0.37 3.84 6.83 10.87
SBLK 202.11M 0.37 3.46 1.93 9.59
CHMP 17.68M 0.38 1.77 6.64 5.19
XFN 17.64M 0.39 0.96 5.22 2.34
HTX 966.12M 0.39 3.01 29.92 7.68
KV.A 178.31M 0.4 3.57 2.18 9.27
ULTR 144.94M 0.4 4.91 4.75 12.6
MDTH 145.35M 0.42 7.4 30.63 18.86
HAST 42.70M 0.43 4.42 23.92 10.45
TBSI 255.08M 0.44 8.53 4.67 20.01
GASS 129.40M 0.44 5.8 6.51 14.25
CONN 145.52M 0.44 6.48 6.88 14.89
BBEP 596.46M 0.44 11.3 3.39 25.7
CBR 230.18M 0.45 3.31 11.57 7.53
PRGN 222.19M 0.48 5.15 2.29 11.37
EROC 352.29M 0.48 4.59 1.25 9.76
JTX 119.45M 0.5 4.15 6.55 8.47
INOC 23.93M 0.5 1.94 5.68 3.77

For the sake of comparison the S&P500 closed Friday at 1,091.38.

Perhaps one of the most striking findings in the various studies discussed above was made by DeBondt and Thaler. They examined the earnings pattern of the cheapest companies (ranked on the basis of price-to-book) to the most expensive companies. They found that the earnings of the cheaper companies grew faster than the earnings of the more expensive companies over the period of the study. DeBondt and Thaler attribute the earnings outperformanceof the cheaper companies to the phenomenon of “mean reversion,” which Tweedy Browne describe as the observation that “significant declines in earnings are followed by significant earnings increases, and that significant earnings increases are followed by slower rates of increase or declines.” I’m interested to see whether this phenomenon will be observable in the 30 company portfolio listed above.

It seems counterintuitive that a portfolio constructed using a single, simple metric (in this case, price-to-book) should outperform the market. The fact that the various studies discussed above have reached uniform conclusions leads me to believe that this phenomenon is real. The companies listed above are a diverse group in terms of market capitalization, earnings, debt loads and businesses/industries. The only factor uniting the stocks in the list above is that they are the cheapest 30 stocks in the Google Finance screener on the basis of price-to-book value. I look forward to seeing how they perform against the market, represented by the S&P500 index.


Here’s the Tickerspy portfolio tracker for the Greenbackd Contrarian Value Portfolio.

[Full Disclosure:  No positions. This is neither a recommendation to buy or sell any securities. All information provided believed to be reliable and presented for information purposes only. Do your own research before investing in any security.]

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In a new paper Value vs Glamour: A Global Phenomenon (via SSRN)  The Brandes Institute updates the landmark 1994 study by Josef Lakonishok, Andrei Shleifer, and Robert Vishny investigating the performance of value stocks relative to that of glamour securities in the United States over a 26-year period. Lakonishok, Shleifer, and Vishny found that value stocks tended to outperform glamour stocks by wide margins, but their earlier research did not include the glamour-driven markets of the late 1990s and early 2000s. The paper asks, “What effect might this period have on their conclusions?” To answer that question, The Brandes Institute updated the research through to June 2008, examining the comparative performance of value and glamour over a 40-year period, and extending the scope of the initial study to include non-U.S. markets, to determine whether the value premium is evident worldwide.

The research focuses on our favorite indicator, price-to-book value, but also includes price-to-cash flow, price-to-earnings, sales growth over the preceding five years and combinations of the foregoing. Here is The Brandes Institute’s discussion on price-to-book:

Lakonishok, Shleifer, and Vishny on price-to-book

The Brandes Institute  hewed closely to Lakonishok, Shleifer, and Vishny’s methods, described on page 3 of the paper:

First, the sample of companies as of April 30, 1968 was divided into deciles based on one of the criteria above. Second, the aggregate performance of each decile was tracked for each of the next five years on each April 30. Finally, the first and second steps were repeated for each April 30 from 1969 to 1989.

We start with the price-to-book criterion as an example. First, all stocks traded on the NYSE and AMEX as of April 30, 1968 were sorted into deciles based on their price-to-book ratios on that date. Stocks with the highers P/B ratios were grouped in decile 1. For each consecutive decile, P/B ratios decreased; this cuilminated in stocks with the lowest P/B values forming decile 10.

In essence, this process created 10 separate portfolios, each with an inception date of April 30, 1968. The lower deciles, which consisted of higher-P/B stocks, represented glamour portfolios. In contrast, the higher deciles – those filled with lower-P/B stocks – represented value portfolios.

From there, annual performance of deciles 1 through 10 was tracked over the subsequent five years. Additionally, new 10-decile sets were constructed based on the combined NYSE/AMEX sample as of April 30, 1969, and every subsequent April 30 through 1989. For each of these new sets, decile-by-decile performance was recorded for the five yeras after the inception date. After completing this process, the researchers had created 22 sets of P/B deciles, and tracked five years of decile-by-decile performance for each one. Next, [Lakonishok, Shleifer, and Vishny] averaged the performance data across these 22 decile-sets to compare value and glamour.

As the chart below indicates, [Lakonishok, Shleifer, and Vishny] found that performance for glamour stocks was outpaced by performance for their value counterparts. For instance, 5-year returns for decile 1 – those stocks with the highest P/B ratios – averaged an annualized 9.3%, while returns for the low-P/B decile 10 averaged 19.8%. These annualized figures are equivalent to cumulative rates of return of 56.0% and 146.2%, respectively.

Value Glamour 1

[Lakonishok, Shleifer, and Vishny] repeated this analysis for deciles based on price-to-cash flow, price-to-earnings, and sales growth. The trio found that, for each of these value/glamour criteria, value stocks outperformed glamour stocks by wide margins. Additionally, value bested glamour in experiments with groups sorted by select pairings of P/B, P/CF, P/E, and sales growth.

The Brandes Institute update

The Brandes Institute sought to extend and update Lakonishok, Shleifer, and Vishny’s findings. They replicated the results of the Lakonishok, Shleifer, and Vishny study to validate their methodology. When they were satisfied that there was sufficient parity between their results and Lakonishok, Shleifer, and Vishny’s findings “to validate our methodology as a functional approximation of the [Lakonishok, Shleifer, and Vishny] framework,” they adjusted the sample in three ways: First, they included stocks listed on the NASDAQ domiciled in the US. Second, they excluded the smalles 50% of all companies in the sample. Finally, they divided the remaining companies into small capitalization (70% of the group by number) and large capitalization (30% of the group by number):

To expand upon [Lakonishok, Shleifer, and Vishny’s] findings we begin with our adjusted sample, which now includes data through 2008. Specifically, we added decile-sets formed on April 30, 1990 through April 30, 2003 and incorporated their performance into our analysis. This increased our sample size from 22 sets of deciles to 36. In addition, the end of the period covered by our performance calculations extended from April 30, 1994 to April 30, 2008.

Exhibit 3 compares average annualized performance for U.S. stocks from the 1968 to 2008 period for deciles based on price-to-book. Returns for deciles across the spectrum changed only slightly in the extended time frame from our replicated [Lakonishok, Shleifer, and Vishny’s] results. Most notably, the overall pattern of substantial value stock outperformance persisted. During the 1968 to 2008 period, performance for decile 1 glamour stocks averaged an annualized 6.9% vs. an average of 16.2% for the value stocks in decile 10. Respective cumulative performance equaled 39.6% and 111.9%.

Value Glamour 2

Set out below is the comparison of large cap and small cap performance:

Value Glamour 3The paper concludes that the value premium persists for the world’s developed markets in aggregate, and on an individual coutry basis. We believe it is more compelling evidence for value based investment, and, in particular, asset based value investment.

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In an August post, Applying value principles at a country level, we discussed The growth illusion, an article appearing in a Buttonwood’s notebook column of The Economist. In that article, Buttonwood argued that valuation, rather than economic growth, determined investment returns at a country or market level. Buttonwood highlighted research undertaken by Elroy Dimson, Paul Marsh and Mike Staunton from the London Business School, which suggested that chasing growth economies is akin to chasing growth stocks, and generates similarly disappointing results. Buttonwood concluded that higher valuations – determined on an earnings, rather than asset basis – led to lower returns:

What does work? Over the long run (but not the short), it is valuation; the higher the starting price-earnings ratio when you buy a market, the lower the return over the next 10 years. That is why buying shares back in 1999 and 2000 has provided to be such a bad deal.

It raised an interesting question for us: Can relative price-to-asset values be used to determine which countries are likely to provide the best investment returns? It took some time, but we’ve tracked down some research that answers the question.

In Fundamental Determinants of International Equity Returns: A Perspective on Conditional Asset Pricing (9.17MB .pdf) Journal of Banking and Finance 21, (1997): 1625-1665. (P42), Campbell Harvey and Wayne Ferson examined, among other things, the relationship between price-to-book value and future returns from a global asset pricing perspective. Harvey and Ferson found that “the price-to-book value ratio has cross-sectional explanatory power at the country level,” although they believe that its use is mainly in determining “global stock market risk exposure.”

An earlier – and slightly more readable – study by Leila Heckman, John J . Mullin and Holly Sze, Valuation ratios and cross-country equity allocation, The Journal of Investing, Summer 1996, Vol. 5, No. 2: pp. 54-63 DOI: 10.3905/joi.5.2.54, also examined the link between equity returns at a market level and valuation measures. Heckman et al found that, despite the substantial accounting differences across countries, price-to-book measures are useful for predicting the “cross-sectional variation of national index returns.”

The results are perhaps unsurprising given the various studies demonstrating the relationship between valuation determined on a price-to-earnings basis and country level returns. We believe they are useful nonetheless given the ease with which one can invest in many global markets and our own predisposition for assets over earnings valuations.

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