Feeds:
Posts
Comments

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.

About these ads

Read Full Post »

Happy Thanksgiving, Folks

See you Monday.

Read Full Post »

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.

Read Full Post »

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.

Read Full Post »

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.]

Read Full Post »

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.]

Read Full Post »

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.]

Read Full Post »

Older Posts »

Follow

Get every new post delivered to your Inbox.

Join 5,388 other followers

%d bloggers like this: