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Archive for November, 2009

The second method for boosting the performance of book value as a predictor of future investment returns is Joseph D. Piotroski’s elegant F_SCORE. Piotroski first discussed his F_SCORE in 2002 in Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. In the paper, Piotroski examines whether the application of a simple accounting-based fundamental analysis strategy to a broad portfolio of high book-to-market firms can improve the returns earned by an investor. Piotroski found that his method increased the mean return earned by a low price-to-book investor “by at least 7 1/2% annually” through the “selection of financially strong high BM firms.”

In addition, an investment strategy that buys expected winners and shorts expected losers generates a 23% annual return between 1976 and 1996, and the strategy appears to be robust across time and to controls for alternative investment strategies.

With a return of that magnitude, it’s well worth a deeper look.

Piotroski’s rationale

Piotroski uses “context-specific financial performance measures to differentiate strong and weak firms:”

Instead of examining the relationships between future returns and particular financial signals, I aggregate the information contained in an array of performance measures and form portfolios on the basis of a firm’s overall signal. By focusing on value firms, the benefits to financial statement analysis (1) are investigated in an environment where historical financial reports represent both the best and most relevant source of information about the firm’s financial condition and (2) are maximized through the selection of relevant financial measures given the underlying economic characteristics of these high BM firms.

F_SCORE

On the assumption that the “average high BM firm is financially distressed,” Piotroski chose nine fundamental signals to measure three areas of the firm’s financial condition: profitability, financial leverage/liquidity, and operating efficiency:

In this paper, I classify each firm’s signal realization as either “good” or “bad,” depending on the signal’s implication for future prices and profitability. An indicator variable for the signal is equal to one (zero) if the signal’s realization is good (bad). I define the aggregate signal measure, F_SCORE, as the sum of the nine binary signals. The aggregate signal is designed to measure the overall quality, or strength, of the firm’s financial position, and the decision to purchase is ultimately based on the strength of the aggregate signal.

F_SCORE component: Profitability

On the basis that “current profitability and cash flow realizations provide information about the firm’s ability to generate funds internally,” Piotroski uses four variables to measure these performance-related factors: ROA, CFO, [Delta]ROA, and ACCRUAL:

I define ROA and CFO as net income before extraordinary items and cash flow from operations, respectively, scaled by beginning of the year total assets. If the firm’s ROA (CFO) is positive, I define the indicator variable F_ROA (F_CFO) equal to one, zero otherwise. I define ROA as the current year’s ROA less the prior year’s ROA. If [Delta]ROA [is greater than] 0, the indicator variable F_[Delta]ROA equals one, zero otherwise.

I define the variable ACCRUAL as current year’s net income before extraordinary items less cash flow from operations, scaled by beginning of the year total assets. The indicator variable F_ ACCRUAL equals one if CFO [is greater than] ROA, zero otherwise.

F_SCORE component: Leverage, liquidity, and source of funds

For the reason that “most high BM firms are financially constrained” Piotroski assumes that an increase in leverage, a deterioration of liquidity, or the use of external financing is a bad signal about financial risk. Three of the nine financial signals are therefore designed to measure changes in capital structure and the firm’s ability to meet future debt service obligations: [Delta]LEVER, [Delta]LIQUID, and EQ_OFFER:

[Delta]LEVER seeks to capture changes in the firm’s long-term debt levels:

I measure [Delta]LEVER as the historical change in the ratio of total long-term debt to average total assets, and view an increase (decrease) in financial leverage as a negative (positive) signal. By raising external capital, a financially distressed firm is signaling its inability to generate sufficient internal funds (e.g., Myers and Majluf 1984, Miller and Rock 1985). In addition, an increase in long-term debt is likely to place additional constraints on the firm’s financial flexibility. I define the indicator variable F_LEVER to equal one (zero) if the firm’s leverage ratio fell (rose) in the year preceding portfolio formation.

[Delta]LIQUID seeks to measure the historical change in the firm’s current ratio between the current and prior year, where Piotroski defines the current ratio as the ratio of current assets to current liabilities at fiscal year-end:

I assume that an improvement in liquidity (i.e., [Delta]LIQUID [is greater than] 0) is a good signal about the firm’s ability to service current debt obligations. The indicator variable F_[Delta]LIQUID equals one if the firm’s liquidity improved, zero otherwise.

Piotroski argues that financially distressed firms raising external capital “could be signaling their inability to generate sufficient internal funds to service future obligations” and  the fact that these firms are willing to issue equity when their stock prices are depressed “highlights the poor financial condition facing these firms.” EQ_OFFER captures whether a firm has issued equity in the year preceding portfolio formation. It is set to one if the firm did not issue common equity in the year preceding portfolio formation, zero otherwise.

F_SCORE component: Operating efficiency

Piotroski’s two remaining signals seek to measure “changes in the efficiency of the firm’s operations:” [Delta]MARGIN and [Delta]TURN. Piotroski believes these ratios are important because they “reflect two key constructs underlying a decomposition of return on assets.”

Piotroski defines [Delta]MARGIN as the firm’s current gross margin ratio (gross margin scaled by total sales) less the prior year’s gross margin ratio:

An improvement in margins signifies a potential improvement in factor costs, a reduction in inventory costs, or a rise in the price of the firm’s product. The indicator variable F_[Delta]MARGIN equals one if [Delta]MARGIN is positive, zero otherwise.

Piotroski defines [Delta]TURN as the firm’s current year asset turnover ratio (total sales scaled by beginning of the year total assets) less the prior year’s asset turnover ratio:

An improvement in asset turnover signifies greater productivity from the asset base. Such an improvement can arise from more efficient operations (fewer assets generating the same levels of sales) or an increase in sales (which could also signify improved market conditions for the firm’s products). The indicator variable F_[Delta]TURN equals one if [Delta]TURN is positive, zero otherwise.

F_SCORE formula and interpretation

Piotroski defines F_SCORE as the sum of the individual binary signals, or

F_SCORE = F_ROA + F_[Delta]ROA + F_CFO + F_ ACCRUAL + F_[Delta]MARGIN + F_[Delta]TURN + F_[Delta]LEVER + F_[Delta]LIQUID + EQ_OFFER.

An F_SCORE ranges from a low of 0 to a high of 9, where a low (high) F_SCORE represents a firm with very few (mostly) good signals. To the extent current fundamentals predict future fundamentals, I expect F_SCORE to be positively associated with changes in future firm performance and stock returns. Piotroski’s investment strategy is to select firms with high F_SCORE signals.

Piotroski’s methodology

Piotroski identified firms with sufficient stock price and book value data on COMPUSTAT each year between 1976 and 1996. For each firm, he calculated the market value of equity and BM ratio at fiscal year-end. Each fiscal year (i.e., financial report year), he ranked all firms with sufficient data to identify book-to-market quintile and size tercile cutoffs and classified them into BM quintiles. Piotroski’s final sample size was 14,043 high BM firms across the 21 years.

He measured firm-specific returns as one-year (two-year) buy-and-hold returns earned from the beginning of the fifth month after the firm’s fiscal year-end through the earliest subsequent date: one year (two years) after return compounding began or the last day of CRSP traded returns. If a firm delisted, he assumed the delisting return is zero. He defined market-adjusted returns as the buy-and-hold return less the value-weighted market return over the corresponding time period.

Descriptive evidence of high book-to-market firms

Piotroski provides descriptive statistics about the financial characteristics of the high book-to-market portfolio of firms, as well as evidence on the long-run returns from such a portfolio. The average (median) firm in the highest book-to-market quintile of all firms has a mean (median) BM ratio of 2.444 (1.721) and an end-of-year market capitalization of 188.50(14.37)M dollars. Consistent with the evidence presented in Fama and French (1995), the portfolio of high BM firms consists of poor performing firms; the average (median) ROA realization is –0.0054 (0.0128), and the average and median firm saw declines in both ROA (–0.0096 and –0.0047, respectively) and gross margin (–0.0324 and –0.0034, respectively) over the last year. Finally, the average high BM firm saw an increase in leverage and a decrease in liquidity over the prior year.

High BM returns

The table below (Panel B of Table 1) extracted from the paper presents the one-year and two-year buy-and-hold returns for the complete portfolio of high BM firms, along with the percentage of firms in the portfolio with positive raw and market-adjusted returns over the respective investment period. Consistent with the findings in the Fama and French (1992) and Lakonishok, Shleifer, and Vishny (1994) studies, high BM firms earn positive market-adjusted returns in the one-year and two-year periods following portfolio formation:

Perhaps Piotroski’s most interesting finding is that, despite the strong mean performance of this portfolio, a majority of the firms (approximately 57%) earn negative market-adjusted returns over the one- and two-year windows. Piotroski concludes, therefore, that any strategy that can eliminate the left tail of the return distribution (i.e., the negative return observations) will greatly improve the portfolio’s mean return performance.

High BM and F_SCORE returns

Panel A of Table 3 below shows the one-year market-adjusted returns to the Piotroski F_SCORE strategy:

The table demonstrates the return difference between the portfolio of high F_SCORE firms and the complete portfolio of high BM firms. High F_SCORE firms earn a mean market-adjusted return of 0.134 versus 0.059 for the entire BM quintile. The return improvements also extend beyond the mean performance of the various portfolios. The results in the table shows that the 10th percentile, 25th percentile, median, 75th percentile, and 90th percentile returns of the high F_SCORE portfolio are significantly higher than the corresponding returns of both the low F_SCORE portfolio and the complete high BM quintile portfolio using bootstrap techniques. Similarly, the proportion of winners in the high F_SCORE portfolio, 50.0%, is significantly higher than the two benchmark portfolios (43.7% and 31.8%). Overall, it is clear that F_SCORE discriminates between eventual winners and losers.

Conclusion

Piotroski’s F_SCORE is clearly a very useful metric for high BM investors. Piotroski’s key insight is that, despite the strong mean performance of a high BM portfolio, a majority of the firms (approximately 57%) earn negative market-adjusted returns over the one- and two-year windows. The F_SCORE is designed to eliminate the left tail of the return distribution (i.e., the negative return observations). It succeeds in doing so, and the resulting returns to high BM and high F_SCORE portfolios are nothing short of stunning.

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Happy Thanksgiving, Folks

See you Monday.

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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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Price-to-book value is demonstrably useful as a predictor of future investment returns. As we discussed yesterday in Testing the performance of price-to-book value, various studies, including Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986), Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987), Josef Lakonishok, Andrei Shleifer, and Robert Vishny Contrarian Investment, Extrapolation and Risk (1994) and The Brandes Institute’s Value vs Glamour: A Global Phenomenon (2008) all conclude that lower price-to-book value stocks tend to outperform higher price-to-book value stocks, and at lower risk. Understanding this to be the case, the obvious question for me becomes, “Within the low price-to-book value universe, is there any way of further distinguishing likely stars from likely laggards and thereby further increasing returns?” The answer can be found in two studies: Joseph D. Piotroski’s seminal paper Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers (.pdf) and Lakonishok, Shleifer, and Vishny’s original Contrarian Investment, Extrapolation and Risk (1994).

I’ll be discussing both of these studies in some detail over the next two days, starting with LSV’s Two-Dimensional Classifications tomorrow.

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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.

Update

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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Aspen Exploration Corporation (OTC:ASPN) took the unusual step several days ago of making a second announcement regarding the payment of a cash dividend of $0.73 per share to stockholders of record on November 16, 2009. The company initially announced that the dividend would be payable to stockholders of record on November 16, 2009, with the dividend being paid on or about December 2, 2009. The second announcement provides that the Financial Industry Regulatory Authority (FINRA) has advised the company of the operation of Nasdaq Rule 11140(b)(2), which states:

In respect to cash dividends or distributions, stock dividends and/or splits, and the distribution of warrants, which are 25% or greater of the value of the subject security, the ex-dividend date shall be the first business day following the payable date.

This means that the dividend will trade with the stock until December 2, 2009, the payable date. Purchasers on December 3 or later will not be entitled to the dividend.

We’ve been following ASPN (see our ASPN post archive) because it’s trading at a discount to its $1.17 per share liquidation value and there are several potential catalysts in the stock, including a 13D filing from Tymothi O. Tombar, a plan to distribute substantially all of the net, after-tax proceeds from the completion of the Venoco sale to its stockholders ($5.3M), and the possibility that the company will dissolve. The stock is down 1.5% since we initiated the position to close yesterday at $0.983. This values the remaining stub of ASPN at $0.24 ($0.97 less $0.73) against a liquidating value I estimate at $0.44 ($1.17 less $0.73).

Here’s the text of the announcement [via Marketwire]:

DENVER, CO–(Marketwire – November 18, 2009) – Aspen Exploration Corporation (OTCBB: ASPN) viewed what appeared to be unusual market activity yesterday and today in light of its previous announcement of November 3, 2009. That announcement advised the public that a cash dividend of $0.73 per share will be payable to stockholders of record on November 16, 2009, with the dividend being paid on or about December 2, 2009. Notwithstanding the Board’s declaration of a record date, Aspen has been advised by the Financial Industry Regulatory Authority (FINRA) of the application of Nasdaq Rule 11140(b)(2) which states: “In respect to cash dividends or distributions, stock dividends and/or splits, and the distribution of warrants, which are 25% or greater of the value of the subject security, the ex-dividend date shall be the first business day following the payable date.” Persons needing further information or interpretation should consult with their broker-dealer or legal advisors.

[Full Disclosure:  I do not have a holding in ASPN. 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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Soapstone Networks Inc (NASDAQ:SOAP) has released its 10Q for the period ended September 30, 2009.

We first looked at SOAP on February 2nd (see Greenbackd’s post archive here) because it was trading well below its net cash value. An activist investor, Mithras Capital, had disclosed an 8.7% holding and called on the company to liquidate. After some urging on Mithras Capital’s part, management acceded to the request and announced a liquidation. SOAP stockholders approved the liquidation of the company on July 28 and received a special dividend of $3.75 per share the next day. Based on our $2.50 purchase price, the $3.75 per share special dividend returned our initial capital plus 50%. At yesterday’s close, the $0.65 stub represents a total return to date of 76%. Management originally estimated the final distribution to be between $0.25 and $0.75 per share, which means the stub is presently trading at a 30% premium to the $0.50 midpoint of the distribution range.

On September 9, in our guest blogger series, Wes Gray and Andy Kern took a look at the SOAP stub as a stand alone investment. Gray and Kern argued that there was plenty of value left in the stub:

e. Total Return Possible

Low Estimate: .39 first distribution (Q2 2010), .06 second distribution (Q4 2010)

=>-4.57%

Expectation: .70 first distribution (Q1 2010), .06 second distribution (Q4 2010)

=>59.01%

High Estimate: .82 first distribution (Q4 2009), .15 second distribution (Q4 2010)

=>102.98%

Expected Return:

P(Low)=.25

P(Estimate)=.50

P(High)=.25

ð .25*-.0457+.50*.5901+.25*1.0298=54.11% expected return by Q4 2010.

At its $0.65 close yesterday, the stub is up 20.4% since that post.

The sale of the company’s non-cash assets including its “principal intellectual property assets,” the value of which we were speculating about on August 11, yielded cash consideration of approximately $2.2M. SOAP does not expect to receive any additional material consideration for the few remaining non-cash assets left in its possession.

The value proposition updated

According to the most recent 10Q, which was prepared on a liquidation basis, SOAP has around $11.4M in net assets. This includes total liabilities of around $5.6, of which $5.5M is a reserve for liquidation costs. Here is an extract from the 10Q:

With 15.2M shares on issue, and assuming SOAP spends the full $5.5M reserve for liquidation costs, SOAP looks likely to yield $0.75 per share, the upper end of management’s estimated range and a 15% return from here. If there are any savings in the $5.5M reserve, SOAP could pay out substantially more.

Conclusion

Given that the stock is trading at a 15% discount to what now appears to be the low end of the likely final distribution, I’m going to maintain Greenbackd’s position in SOAP.

[Full Disclosure: I do not have holding in SOAP. 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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One of my favorite macro indicators is the long-term Dow:gold ratio. Rolph Winkler of Reuters blog Contingent Capital did the heavy lifting last week to produce a chart of the Dow Jones Industrial Average priced in gold per ounce since 1900:

The Dow:gold ratio is not everyone’s cup of tea. Paul Kedorosky likens it to measuring yo-yos in meerkats, but says it’s “semi-useful.” I agree. Several semi-useful observations that can be made from the chart include:

  1. Gold has outperformed the DJIA from the late 1990s to the present. In the late 1990s the Dow was more expensive in gold than it had ever been in the preceding 100 years.
  2. In 2009, the gold trade is getting long in the tooth. Most of the really big gains in gold have already been made. It’s no longer obviously cheap relative to equities, however
  3. …it’s probably not over yet. The Dow:gold ratio has traditionally bottomed at a point significantly lower than we have seen this time around. This might suggest that it still has a ways to fall before it reaches the nadir. For the bottom to come in, either gold has to go up, equities have to come down, or some combination of both has to occur. My guess is the latter, however, this is not the only view out there. For example, in the Buttonwood’s notebook column of the Economist, Buttonwood asks, “Is gold the next bubble?

WHAT are the preconditions for a bubble? Perhaps there are four: easy credit conditions, a significant trend-breaking event, the lack of plausible valuation measures and an appealing story.

Gold fulfils most of these conditions. One can argue about the credit conditions; lending is still weak but crucially interest rates are low. That helps given that gold has no yield; in effect, the opportunity cost of holding gold has disappeared. The event that changed minds was the credit crunch, which caused a partial loss of faith in banks. Gold has no valuation issues (no yield or earnings); since people hold it as a store of value, it can be worth whatever they want it to be worth. And it has a plausible backstory; spendthrift governments are monetising their deficits like the Weimar Republic before them.

…whereas one can say, based on historic valuation measures, that Wall Street is currently 40% overvalued, one can make no such bold statement on gold.The next stage of a bubble would be broad-based public interest.

One thing clear to me from the chart is that buying equities from the late 1990s to the present was like running up the down escalator. It was fun, but it wasn’t the easiest way to get to the top. Standing still on the up escalator was an easier ride. This was the point of my Buffett on gold post last week. The change in the Dow:gold ratio for the period 1964 to 1979 makes it clear why Buffett was bested by gold over that period. The change in the ratio for the period from the early 1980s through to the late 1990s, combined with Buffett’s otherworldly ability to identify undervalued equities, also explains the lollapalooza gains made by Berkshire Hathaway during that period. It might also suggest that at some stage in the near future equities will again be the up escalator, but not quite yet, for the reasons below.

In an inflationary environment a business must keep increasing the price of its goods or services just to keep its margins static, and any reinvestment in plant and machinery must be undertaken at increasingly higher prices. If it can’t increase its prices or it doesn’t earn enough to keep up with its maintenance capital expenditure, then it will shrink and risks falling behind any competitor that can. In other words, it has to run up the down escalator, and if it can’t run faster than the escalator, then it’s going backwards. Businesses with no pricing power and low returns-on-equity will therefore suffer in an inflationary environment. While it is true that a business with pricing power and high return-on-equity is better able to protect itself somewhat from inflation, it is not true that inflation is good for this business either. Since I (and, I suspect, most investors) can’t prospectively pick one from the other, perhaps stepping onto the up escalator in such times is not such a bad idea. All gold does is sit there, yes, but it can’t be printed, so it tends to appreciate against the dollar as the dollar is debauched.

Has the dollar been debauched? The Austrian economist in me thinks so. Einhorn, John Paulson, Rogers and Buffett’s commentary on US fiscal and monetary policy can’t all be wrong. Keeping interest rates too low for too long and printing too much money – what Buffett describes as “Greenback emissions” – will result in inflation measurable in the CPI in the not too distant future. (As an aside, I think there is inflation now, but because it’s not running through the CPI yet it doesn’t exist according to the orthodox view, which also happens to be the one in power, and on both sides of politics, for that matter).

What can we deduce from the foregoing? If gold does as it has done in past cycles, it should do well for the foreseeable future. That has to be tempered by the fact that the gold price has run a long way, both in dollar terms and in comparison to equities (as measured against the DJIA). Gold could have a big reversal – in the mid-1970s the DJIA rallied significantly against gold before sinking to its long-term bottom – before it continues onto historical highs. In this regard, Jim Rogers’ recent commentary is instructive [via The Globe and Mail]:

Jim Rogers: I don’t ever like to buy something making all time highs however I’m not selling my gold. Gold is going to go much higher in the course of the bull market. Doesn’t mean it can’t go down 20 per cent next year but during the course of the bull market it is going to go much higher it is certainly not a bubble yet.

Jim you are typically a contrarian investor. If everyone is buying, shouldn’t you be selling?

Jim Rogers : Yes, I should be selling at the top, but I don’t think this is the top. Gold, if you adjust it for its old highs, adjust it for inflation back in 1980, gold should be over $2000 an ounce right now. In my view, in this bull market in commodities gold will make all new highs adjust for inflation.

When will gold hit 2k?

Jim Rogers: I wish I was that smart. You should watch TheStreet.com. They know everything.

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The Economist has an article, High-speed slide, which discusses a recent study by Grant Thornton about the disappearance of the initial public offering (IPO) market in the U.S., and, in particular, the death of the small IPO. In the October 2009 study, Market structure is causing the IPO crisis, authors David Weild and Edward Kim argue that the recent paucity of U.S. IPOs is a result of the “market structure” failing the IPO, rather than a cyclical downturn. That may seem unlikely at first blush, but the data are compelling:

The first six months of 2009 represents the worst IPO market in 40 years. Given that the size of the U.S. economy, in real GDP terms, is over 3x what it was 40 years ago, this is a remarkable and frightening state of affairs. Only 12 companies went public in the United States in the first half of 2009, and only eight of them were U.S. companies. The trend that disfavors small IPOs and small companies has continued. The median IPO in the first half of 2009 was $135 million in size. This contrasts to 20 years ago when it was common for Wall Street to do $10 million IPOs and have them succeed.

The implication, say the authors of the study, is that the market is closed to most small companies:

From 1991 to 1997 nearly 80% of the IPOs were smaller than $50 million. By 2000 the number of sub-$50 million IPOs had declined to only 20% of the market. The market for underwritten IPOs, given its current structure, is closed to 80% of the companies that need it.

From page 8 the study, a graph showing the decline in the number of small IPOs (<$50M) relative to large IPOs ($50M+):

The Economist points out that the “50 or so” new companies to list this year is just “one-seventh of the level needed to offset the average annual loss of listed companies in recent years.”

The study discusses a number of possible causes of the decline, including the introduction of low-cost brokerages and new regulations and legislation. From page 4 of the study, a graph showing the decline in the number of IPOs and the timing of regulatory changes:

The Economist focuses on the impact of low-cost brokerages:

An accidental victim of this technological revolution, the report says, was the ecosystem that helped bring small firms to market and then nourished them once there. “It’s a bargain-basement market today,” says David Weild, a co-author of the report. “You get what you pay for, and that’s nothing but trade execution.”

The “high-frequency” traders who have come to dominate stockmarkets with their computer-driven strategies pay less attention to small firms, preferring to jump in and out of larger, more liquid shares. Institutional investors, wary of being stuck in an illiquid part of the market, are increasingly following them.

Another factor is the near-evaporation of research on small firms, which has been undone by the rise of passive index investing and by rules that banned the use of investment-banking revenues to subsidise analysts. With less funding to go around, analysts are increasingly concentrating on large, frequently traded shares, says Larry Tabb of TABB Group, a consultancy. The centre of gravity in research has shifted to “buy-side” firms, like hedge funds, which do not generally disseminate their work.

The authors of the study point to other regulatory and legislative acts, including the “order precedence rule,” commonly known as the “Manning Rule” after a legal case against Charles Schwab, the Gramm-Leach-Bliley Act, which saw the end of the Glass-Steagall Act of 1933 and formally allowed the combination of commercial banks, securities firms and insurance companies, Regulation Fair Disclosure, which devalued stock research, and the Global Settlement ruling, which has made research coverage tougher for issuers to secure. Sarbannes-Oxley was simply the final nail in the coffin.

The authors suggest two changes to reinvigorate the market, neither of which I find particularly palatable. They are the establishment of a new market segment without automated trade execution but with fixed trading commissions used to fund research and looser rules governing institutional investment in pre-IPO companies. These are band-aids that won’t get to the root cause of the problem. If the regulatory and legislative changes backfired on the U.S. IPO market, as the authors claim, perhaps winding back some that legislation would help it. The list of regulation and legislation detailed in the appendix of the study, stretching over the last three pages, is enough to choke a donkey. SarbOx has not received its fair share of the blame, possibly because the market was already an ex-market by 2002, the year Sarbannes-Oxley was enacted. The authors write that SarbOx was “a bit of a red herring” because “[online] brokerage and decimalization were significantly more damaging to the IPO market.” That’s all well and good, but it’s also plain that – bubble years aside – the smaller end of the market has further declined since the enactment of SarbOx. SarbOx has created an enormous regulatory and compliance burden on listed companies, and the corollary is not true: Fraud is as endemic as ever, and people still lose money to sheisters. The additional SarbOx regulatory burden cannot do anything other than reduce the number of companies for which being public is a worthwhile exercise. Smaller companies will incur proportionately higher costs in meeting the burden than their larger brethren and that means the additional regulatory burden will only ever be observable at the margin – the smaller end of the market. If we wish to see the IPO market back in health, we need to reduce the regulatory burden on all companies.

Update

The net effect of the decline in listings is striking (via BusinessWire):

The number of U.S. listed companies has fallen by more than 22 percent since 1991, or 53 percent when calculating in inflation-adjusted GDP growth. In contrast, exchanges in Asia are adding new listings faster than GDP growth rates.

According to the study, 360 new listings per year — a number not approached since 2000 — are required by the United States simply to replace the number of listed companies that are lost every year. Moreover, 520 new listings per year are needed to grow the U.S. listed markets roughly in line with GDP growth. In reality, the U.S. has averaged fewer than 166 IPOs per year since 2001, with only 54 in 2008.

Hat tip Jules.

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The Official Activist Investing Blog has published its list of activist investments for October:

Ticker Company Investor
ADPT Adaptec Inc. Steel Partners
ARGL.OB Argyle Security, Inc. Mezzanine Management
ASCMA Ascent Media GAMCO Investors
ASPM Aspect Medical Systems Covidien plc
BARE Bare Escentuals Sandler Capital Management
BASI Bioanalytical Systems Inc Thomas Harenburg
BLDR Builders FirstSource Inc. Stadium Capital Management
BLUD Immucor Inc. VA Partners
COHM.PK Coachmen Industries Inc. GAMCO Investors
DVD Dover Motorsports Marathon Partners
FFHS First Franklin Corp Lenox Wealth Management, Inc
FMMH.OB Fremont Michigan Insuracorp Inc. Steak & Shake Co
GMXR GMX Resources Centennial Energy Partners
GRNB Green Bankshares, Inc. Scott Niswonger
HPOL Harris Interactive Inc. Mill Road Capital
IMMR Immersion Corp Ramius Capital
IPCS iPCS, Inc. Greywolf Capital Management
ITP Intertape Polymer Group Inc. KSA Capital Management
KANA.OB Kana Software KVO Capital Management
LDIS Leadis Technology Inc Dialectic Capital Management
LM Legg Mason Inc. Nelson Peltz
MEG Media General Inc. GAMCO Investors
MGYR Magyar Bancorp Inc. Financial Edge Fund
MYE Myers Industries, Inc. GAMCO Investors
OPTV OpenTV Corp Kudelski SA
PLFE Presidential Life Corp Herbert Kurz
RSG Republic Services inc Cascade Investment
RUBO Rubios Restaurant Alex Meruelo
SURG Synergetics USA Inc Steven Becker
TBTC.OB Table Trac Inc. Doucet Asset Management
TGY Tremisis Energy Acquisition Corp Bulldog Investors
TMEN.PK Thermoenergy Corp Quercus Trust
TRMA Trico Marine Kistefos AS
VII Vicon Industries Inc. Anita Zucker
VXGN.OB VaxGen Inc. Steven Bronson
XOHO.OB XO Holdings Inc. Carl Icahn
YORW The York Water Co. GAMCO Investors

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