Feeds:
Posts
Comments

Greenbackd Readers: Save $1750 off 2010 Value Investing Congress West

December 8, 2009 by Tobias Carlisle

Greenbackd is a proud sponsor of the 2010 5th Annual Value Investing Congress West and I’ve been able to secure a special discount for you to attend.

Though the Congress is more than 6 months away, over 40% of the seats have already been reserved. Register by midnight next Tuesday, December 15, 2009 with discount code P10GB1 and you’ll save $1,750 off the regular price of admission.

Every year hundreds of people from around the world converge at this not-to-be-missed event to network with other savvy, sophisticated investors and learn from some of world’s most successful money managers. At the upcoming event, all-star investors will share their thoughts on today’s tumultuous markets and present their best, actionable investment ideas. Just one idea could earn you outstanding returns.

Here are Jon Heller’s notes on Lloyd Khaner of Khaner Capital’s talk at this year’s event:

The Key to Turnarounds

First time presenter at the Value Investing Congress, Khaner, who has compounded 445.4% since 1991 (versus 295.2% for the S&P 500), looks for the following attributes in potential investments:

  • Unique management
  • Strong decision making ability
  • Avoid value traps
  • Debt/Equity less than 70%
  • Avoid dying industries
  • Franchise companies with manageable debt

Khaner is a big believer in the concept of “CEO family trees,”placing value on those that have been trained or worked under other successful CEO’s.

Khaner listed the signs of a successful turnaround, including:

  • Cutting unprofitable sales
  • Cutting headcount
  • New senior managers
  • Fix customer relationships
  • CEO sets plan within 3 months
  • Gross Margin up
  • SG&A down
  • Focus on Return on Capital
  • Restructure Debt-Push out maturities.

One of Khaner’s favorite ideas is Starbucks (SBUX):

  • Slowing new store openings
  • Improving service
  • Expects positive comps fiscal 2010
  • ROIC growth 100-200 bps next 3+ years
  • FCF $500-$750 million 3+ years

See a slide show of the last Value Investing Congress in New York.

Don’t miss your opportunity to learn from these financial luminaries. The insights you gain could guide your investment decisions for years to come. Remember, you must register by midnight December 15, 2009 with discount code P10GB1 to take advantage of this special offer and SAVE $1,750 off the regular price to attend. Avoid disappointment – reserve your seat today.

Posted in About, Value Investment | Tagged | Leave a Comment »

Piotroski’s F_SCORE in practice: Highway Holdings Limited (NASDAQ:HIHO)

December 7, 2009 by Tobias Carlisle

Highway Holdings Limited (NASDAQ:HIHO) is presently the only stock listed on the American Association of Individual Investors website with a Piotroski F_SCORE of 9, the highest possible Piotroski F_SCORE. An F_SCORE of 9 indicates that HIHO is “financially strong” in Piotroski’s framework. An F_SCORE of 8 indicates that it has failed on one dimension, and so on. Piotroski’s F_SCORE probably works better in the aggregate than in the case of a single company simply because the binary signal of each component is insufficiently granular to provide much information. Ideally, I’d identify 30 high BM companies scoring 9 on the Piotroski F_SCORE and construct a portfolio from them. The only problem with the practical implementation of that strategy is there aren’t 30 high BM companies scoring 9 on the Piotroski F_SCORE, so we’re stuck with HIHO as the only representative of Piotroski’s F_SCORE in practice. For that reason, this test is imperfect, but that does not mean it is not useful. It analyses more dimensions that I typically do, so perhaps it will work fine for a single company.

HIHO closed Friday at $1.73, giving it a market capitalization of $6.5M. Book value is $11.4M, or $3.03 per share, which means that HIHO is trading at approximately 57% of book value (a P/B of 0.57 or a BM of 1.75). I estimate the liquidation value to be around $5.2M or $1.39 per share, which means that HIHO is trading at a premium to its liquidation value and is not, therefore, a liquidation play. I regard the $1.39 per share liquidation value as the downside in this instance, and the $3.03 per share book value as the upside.

About HIHO

HIHO is a foreign issuer based in Hong Kong. From the most recent 6K dated November 10, 2009:

Highway Holdings produces a wide variety of high-quality products for blue chip original equipment manufacturers — from simple parts and components to sub-assemblies. It also manufactures finished products, such as LED lights, radio chimes and other electronic products. Highway Holdings operates three manufacturing facilities in the People’s Republic of China.

The value proposition: Piotroski’s F_SCORE

The objective of Piotroski’s F_SCORE is to identify “financially strong high BM firms.” It does so by summing the following 9 binary signals (for a more full explanation, see my post on Piotroski’s F_SCORE):

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.

The components are categorized as follows:

  • F_ROA, F_[Delta]ROA, F_CFO, and F_ACCRUAL measure profitability
  • F_[Delta]MARGIN and F_[Delta]TURN measure operating efficiency
  • F_[Delta]LEVER, F_[Delta]LIQUID, and EQ_OFFER measure leverage, liquidity, and source of funds

The following are based on HIHO’s March 31 year end accounts set out in its 20F dated June 22, 2009. Here’s how HIHO achieves its F_SCORE of 9:

  1. F_ROA:  Net income before extraordinary items scaled by beginning of the year total assets (1 if positive, 0 otherwise). HIHO’s net income for 2009 was $0.8M/$17.8M = 0.04, which is positive so F_ROA is 1.
  2. F_CFO: Cash flow from operations scaled by beginning of the year total assets (1 if positive, 0 otherwise). HIHO’s cash flow from operations for 2009 was $2.0M/$17.8M = 0.11, which is positive so F_CFO is 1.
  3. F_[Delta]ROA: Current year’s ROA less the prior year’s ROA (1 if positive, 0 otherwise). HIHO’s ROA for 2009 was 0.04, and its ROA for 2008 was -0.09, and 0.04 less -0.09 = 0.13, which is positive so F_ROA is 1.
  4. F_ACCRUAL: Current year’s net income before extraordinary items less cash flow from operations, scaled by beginning of the year total assets (1 if CFO is greater than ROA, 0 otherwise). HIHO’s net income for 2009 was $0.8M/$17.8M less cash flow from operations of $2.0M/$17.8M. CFO > ROA so F_ACCRUAL is 1.
  5. F_[Delta]LEVER: The change in the ratio of total long-term debt to average total assets year-on-year (1 if decrease, 0 if otherwise). HIHO’s long-term debt ratio in 2009 was $0.6M/average($17.8M and $20.5M) = 0.03 and in 2008 was $0.8M/average($22.4M and $20.5M) = 0.04, which is a decrease year-on-year so F_[Delta]LEVER is 1.
  6. F_[Delta]LIQUID: The change in the current ratio between the current and prior year (1 if increase, 0 if otherwise). HIHO’s current ratio in 2009 was $14.9M/$5.9M = 2.53 and in 2008 was $16.8M/$9.2M = 1.83, which means it was increasing year-on-year and so F_[Delta]LIQUID is 1.
  7. EQ_OFFER: 1 if the firm did not issue common equity in the year preceding portfolio formation, 0 otherwise.  HIHO reduced its common equity on issue in 2009 by 99,000 shares, so EQ_OFFER is 1.
  8. F_[Delta]MARGIN: The current gross margin ratio (gross margin scaled by total sales) less the prior year’s gross margin ratio (1 if positive, 0 otherwise). HIHO’s gross margin ratio in 2009 was $6.7M/$33.7M = 0.2 and in 2008 was $5.1M/$33.2M = 0.15. 0.2 less 0.15 = 0.05, which is positive, so F_[Delta]MARGIN is 1.
  9. F_[Delta]TURN: The current year asset turnover ratio (total sales scaled by beginning of the year total assets) less the prior year’s asset turnover ratio (1 if positive, 0 otherwise). HIHO’s 2009 year asset turnover ratio was $33.7M/$17.8M = 1.9  and in 2008 was $33.2M/$20.5M = 1.6. 1.9 less 1.6 = 0.3, which is positive, so F_[Delta]TURN is 1.

HIHO has a perfect 9 on the Piotroski F_SCORE.

The value proposition: Liquidation value

Here is the liquidation value analysis based on its most recent quarterly financial statement to September 30, 2009 (the “Book Value” column shows the assets as they are carried in the financial statements, and the “Liquidating Value” column shows our estimate of the value of the assets in a liquidation):

Conclusion

With a book value of $11.4M against a market capitalization of $6.5M, HIHO has a book-to-market ratio of 1.75 and is therefore a high BM stock. This makes HIHO an ideal candidate for the application of the Piotroski F_SCORE, which seeks to use “context-specific financial performance measures to differentiate strong and weak firms” within the universe of high BM stocks. HIHO scores a perfect 9 on the Piotroski F_SCORE, which indicates that it is a “strong firm” within that framework. As a check on the downside, I estimate the liquidation value to be around $5.2M or $1.39 per share. For these reasons, HIHO looks like a reasonable bet to me, so I’m adding it to the Special Situations portfolio.

HIHO closed Friday at $1.73.

The S&P500 Index closed Friday at 1,105.98.

[Full Disclosure: I do not have a holding in HIHO. 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.]

Posted in Highway Holdings Limited (NASDAQ:HIHO), Special Situation, Stocks | Tagged , , , | 2 Comments »

Piotroski’s F_SCORE in practice

December 4, 2009 by Tobias Carlisle

It seems to me counterintuitive that book value should be useful as a value metric. Book value, after all, is a historical accounting measure of a company’s balance sheet. It has nothing to do with “intrinsic value,” which is the measure conceived by John Burr Williams in his 1938 treatise The Theory of Investment Value. Warren Buffett is a well-known proponent of “intrinsic value.” In his 1992 letter to shareholders he provided the following explication of the concept:

In The Theory of Investment Value, written over 50 years ago, John Burr Williams set forth the equation for value, which we condense here: The value of any stock, bond or business today is determined by the cash inflows and outflows – discounted at an appropriate interest rate – that can be expected to occur during the remaining life of the asset. Note that the formula is the same for stocks as for bonds. Even so, there is an important, and difficult to deal with, difference between the two: A bond has a coupon and maturity date that define future cash flows; but in the case of equities, the investment analyst must himself estimate the future “coupons.” Furthermore, the quality of management affects the bond coupon only rarely – chiefly when management is so inept or dishonest that payment of interest is suspended. In contrast, the ability of management can dramatically affect the equity “coupons.”

The investment shown by the discounted-flows-of-cash calculation to be the cheapest is the one that the investor should purchase – irrespective of whether the business grows or doesn’t, displays volatility or smoothness in its earnings, or carries a high price or low in relation to its current earnings and book value. Moreover, though the value equation has usually shown equities to be cheaper than bonds, that result is not inevitable: When bonds are calculated to be the more attractive investment, they should be bought.

Buffett’s explanation draws a sharp distinction between intrinsic value and book value – “The investment shown by the discounted-flows-of-cash calculation to be the cheapest is the one that the investor should purchase – irrespective of whether the business…carries a high price or low in relation to its…book value.” While Buffett’s statement may be true, that does not mean that book value is useless as a value metric. Far from it. As the various studies we have discussed recently demonstrate – 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) – low price-to-book value stocks outperform higher priced stocks and the market in general. Why might that be so?

In Contrarian Investment, Extrapolation and Risk, Lakonishok, Shleifer, and Vishny frame their findings in the context of contrarianism, what I like to call the Ricky Roma style of investing:

I subscribe to the law of contrary public opinion. If everyone thinks one thing, then I say, “Bet the other way.”

The problem, as I see it, with low price-to-book investment is that the strategy always flashes a buy signal. When the market is getting very toppy, you can still find the cheapest decile, quintile, quartile, or whatever on a price-to-book basis to buy. That decile might not recede as much as the market in general, but I’d bet odds on that it will still recede. That might not be a problem if, in the aggregate, the price-to-book strategy is able to generate satisfactory long-term returns. A better strategy, however, would remove the opportunities to trade as the market gets expensive, forcing you to sit in cash. I believe this is why Piotroski’s F_SCORE and Graham’s Net Current Asset Value strategies perform so well. When the market gets expensive, those opportunities disappear.

The American Association of Individual Investors website offers various value and growth screens to its membership. Piotroski’s F_SCORE is one such screen. The AAII reports that, of the 56 screens it offers, the only screen that had positive results in 2008 was Piotroski’s F_SCORE (via Forbes):

Believe it or not, the five stocks that AAII bought using Piotroski’s strategy in 2008 gained 32.6% on average through the end of the year. The median performance for all of the AAII strategies last year? -41.7%.

It’s clearly an austere screening criteria if it only lets a handful of stocks get through when the market is high, and in this respect very similar to Graham’s Net Current Asset Value strategy. Right now it has one stock on its screen. That stock? Tune in next week for the full analysis. (I’m starting to sound like The Motley Fool).

Posted in About, Greenbackd, Net Current Asset Value, Stocks, Value Investment | Tagged , , , | 10 Comments »

Carl Icahn’s activist investing master class at Yale

December 3, 2009 by Tobias Carlisle

Carl Icahn recently gave a guest lecture to Professor Robert Shiller’s Yale Financial Markets class.

In the lecture, Icahn talks about how he started out in finance and evolved into a shareholder activist. He trots out a few of his old saws: the biggest challenge facing corporate America is weak management and today’s CEOs, with exceptions, might not be the most capable of leading global companies. He also discusses the economy and slaps down an undergrad Yalie who has the temerity to have him repeat an answer, which is fun to watch. There are a few gems, including this one:

I was borrowing money and bought all these convertibles and I thought I was a genius and Jack Dreyfus said, you’re going to lose all your money. I had made a few bucks playing poker and that’s how I started with about eight, ten thousand dollars and I made all this money by borrowing at 90%. I would go out and I was making a lot more in two weeks than my father made in two years. My father said, well you know, put the money away. I said, no Dad, I’m really going to make a fortune here. So, I went out–I remember once–and bought a Galaxy convertible. It was a beautiful car. I had a beautiful girlfriend; she was a model–it was just pretty nice.

What happened? The crash came in 1962. I was wiped out in one day; I didn’t even have the poker winnings left. I tell you, I can’t recall if the car left first or the girl left first, but it was pretty close–maybe the same day actually. After that, I learned you have to learn something and I became an expert in options.

Here’s Icahn on his investment strategy:

What I do today still is pretty much the same idea. You buy stocks in a company that is cheap and you look at the asset value of the companies that you buy the stocks in and it becomes a little more complex. Basically, you look for the reason that they’re really cheap and the major reason is often–and usually–very poor management. In a sense, it’s like an arbitrage. You go in; you buy a lot of stock in a company; and you then try to make changes at the company. Today, if you read the newspapers tomorrow, you’ll read–we’re trying to do the same thing at Motorola and if you bother to read The Wall Street Journal tomorrow–or maybe The Times, I don’t know–you’ll see a little bit of what we’re trying to do there. We’re trying to get them to change the structure of the company. We think the board is a very poor board there and we’re trying to change what happens.

And, finally, Icahn responding to a question about activism:

Student: Hi, Mr. Icahn. One major criticism that one CEO against corporate activist that they think activists don’t think long-term interest of the corporation; they just want to get money and get out. How do you answer to that?

Icahn: I would just say that the facts don’t bear that out as far as I’m concerned. I mean, if you–I own quite a few companies. Any company we got control of I put literally hundreds of millions of dollars into them. I mean, I bought a company in 1985–a rail car company–we put hundreds of millions; we still have the fleet. I bought casinos and energy companies and over the years kept them; sold them now, but that’s after ten years. So, any company that we’ve been able to get control of I actually kept. Because getting control is a great thing. If you really believe that management’s not doing well, you can go and clean them up and put a good guy in, So, we–I know they criticize you like that, but that’s part of the propaganda machine; but it’s just not the facts.

Student: A related question is that, what do you do when your activist spirit is not appreciated, as in the case of Motorola when you asked for a seat on the board but just get declined? What’s your next step?

Icahn: Alright, you have patience and now it’s a year later and we’ll see what happens now. Motorola is a good example of what I’m talking about. People don’t like it; they don’t like the cell phone business, but I really think that that business, if you look at Motorola and study it, you’re buying that whole business for nothing. It’s not reflected in the stock price, but they have to do it. As I said publicly, take that business out of Motorola; spin it off and give it to the shareholders. I think, then, you’ve got a real good value. What I’m saying is, nobody likes it now, but hopefully I’m correct on that. I really think by being an activist and putting pressure on that board that has done nothing, really–I think eventually that will happen, hopefully.

Hat tip Mark.

Posted in About, Activist Investors, Carl Icahn, Stocks, Value Investment | Tagged , , | 4 Comments »

Goldman Sachs says P/B dead-as-dead; Special sits and event-driven strategies the new black

December 2, 2009 by Tobias Carlisle

The phenomenal Zero Hedge has an article, Goldman Claims Momentum And Value Quant Strategies Now Overcrowded, Future Returns Negligible, discussing Goldman Sachs head of quantitative resources Robert Litterman’s view that  “strategies such as those which focus on price rises in cheaply-valued stocks…[have] become very crowded” since August 2007 and therefore unprofitable. The strategy to which Litterman refers is “HML” or “High Book-to-Price Minus Low Book-to-Price,” which is particularly interesting given our recent consideration of the merits of price-to-book value as an investment strategy and the various methods discussed in the academic literature for improving returns from a low P/B strategy. Litterman argues that only special situations and event-driven strategies that focus on mergers or restructuring provide opportunities for profit:

What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.

In a follow-up article, More On The Futility Of Groupthink Quant Strategies, And Why Momos Are Guaranteed To Lose Money Over Time, Zero Hedge provides a link to a Goldman Sachs Asset Management presentation, Maybe it really is different this time (.pdf via Zero Hedge), from the June 2009 Nomura Quantitative Investment Strategies Conference. The presentation supports Litterman’s view on the underperformance of HML since August 2007. Here’s the US:

Here’s a slide showing the ‘overcrowding” to which Litterman refers:

And its effect on the relative performance of large capitalization value to the full universe:

The returns get really ugly when transaction costs are factored into the equation:

A factor decay graph showing the decline in legacy portfolios relative to current portfolios, lower means and faster decay indicating crowding:

Goldman says that there are two possible responses to the underperformance, and characterizes each as either a “sticker” or an “adapter.” The distinction, according to Zero Hedge, is as follows:

The Stickers believe this is part of the normal volatility of such strategies

• Long-term perspective: results for HML (High Book-to-Price Minus Low Book-to-Price) and WML (Winners Minus Losers) not outside historical experience

• Investors who stick to their process will end up amply rewarded

The Adapters believe that quant crowding has fundamentally changed the nature of these factors

• Likely to be more volatile and offer lower returns going forward

• Need to adapt your process if you want to add value consistently in the future

In Contrarian Investment, Extrapolation, and Risk, Josef Lakonishok, Andrei Shleifer, and Robert Vishny argued 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. Value strategies “exploit the suboptimal behavior of the typical investor” by behaving in a contrarian manner: selling stocks with high past growth as well as high expected future growth and buying stocks with low past growth and as well as low expected future growth. It makes sense that crowding would reduce the returns to a contrarian strategy. Lending further credence to Litterman and Goldman’s argument is the fact that the underperformance seems to be most pronounced in the large capitalization universe (see the “A closer look – value” slide) where the larger investors must fish. If you’re not forced by the size of your portfolio to invest in that universe it certainly makes sense to invest where contrarian returns are still available. Special situations like liquidations and event-driven investments like activist campaigns offer a place to hide if (and when) the market resumes the long bear.

My firm Acquirers Funds® helps you put the acquirer’s multiple into action. Click here to learn more about our deep value strategy.

Posted in About, Greenbackd, Liquidation Value, Stocks, Value Investment | Tagged , , | 4 Comments »

Greenbackd Portfolio Q4 2009 Performance and Update

December 1, 2009 by Tobias Carlisle

November 30, 2009 marked the end of Greenbackd’s fourth quarter and first year, and so it’s time again to report on the performance of the Greenbackd Portfolio and the positions in the portfolio, and outline the future direction of Greenbackd.com.

Fourth quarter 2009 performance of the Greenbackd Portfolio

The fourth quarter was another satisfactory quarter for the Greenbackd Portfolio, up 14.3% on an absolute basis, which was 9.8% higher than the return on the S&P500 return over the same period. A large positive return for the period is great, but my celebration is tempered once again by the fact that the broader market also had a pretty solid quarter, up 7.4%. The total return for Greenbackd’s first year (assuming equal weighting in all quarters) is 136.8% against a return on the S&P500 of 34.2%, or an outperformance of 102.6% over the return in the S&P500.

It is still too early to determine how well Greenbackd’s strategy of investing in undervalued asset situations with a catalyst is performing, but I believe Greenbackd is heading in the right direction. Set out below is a list of all the stocks in the Greenbackd Portfolio and the absolute and relative performance of each from the close of the last trading day of the third quarter, September 1, 2009, to the close on the last trading day in the fourth quarter, November 30, 2009:

*Note the returns for SOAP and NSTR include special dividends paid. See below for further detail.

You may have noticed something odd about my presentation of performance. The S&P500 index rose by 7.4% in the fourth quarter (from 1020.62 to 1,095.63). Greenbackd’s +14.3% performance might suggest an outperformance over the S&P500 index of 6.9%, while I report outperformance of 9.8%. I calculate Greenbackd’s performance on a slightly different basis, recording the level of the S&P500 Index on the day each stock is added to the portfolio and then comparing the performance of each stock against the index for the same holding period. The Total Relative performance, therefore, is the average performance of each stock against the performance of the S&P500 index for the same periods. As we discussed above, the holding period for Greenbackd’s positions has been too short to provide any meaningful information about the likely performance of the strategy over the long term (2 to 5 years), but I believe that the strategy should outperform the market by a small margin.

Update on the holdings in the Greenbackd Portfolio

There are currently ten stocks in the Greenbackd Portfolio:

  1. TSRI (added November 12, 2009 @ $2.10)
  2. CNVR (added November 11, 2009 @ $0.221)
  3. NYER (added November 3, 2009 @ $1.75)
  4. ASPN (added October 1, 2009 @ $0.985)
  5. KDUS (added September 29, 2009 @ $1.51)
  6. COSN (added August 6, 2009 @ $1.75)
  7. FORD (added July 20, 2009 @ $1.44)
  8. DRAD (added March 9, 2009 @ $0.88)
  9. SOAP (added February 2, 2009 @ $2.50. Initial $3.75 dividend paid July 30)
  10. NSTR (added January 16, 2009 @ $1.91. Initial $2.06 dividend paid July 15)

Greenbackd’s investment philosophy and process

I started Greenbackd in an effort to extend my understanding of asset-based valuation described by Benjamin Graham in the 1934 Edition of Security Analysis. (You can see a summary of Graham’s approach here). Through some great discussion with Greenbackd’s readers, many of who work in the fund management industry as experienced analysts or even managing members of hedge funds, and by incorporating the observations of Marty Whitman (see Marty Whitman’s adjustments to Graham’s net net formula here) and Seth Klarman (the Seth Klarman series starts here), I have refined Greenbackd’s process. I believe that the analyses are now pretty robust and that has manifest itself in satisfactory performance.

Tweedy Browne provides compelling evidence for the asset-based valuation approach. In conjunction with a reader of Greenbackd I have now conducted my own study into the performance of sub-liquidation value stocks over the last 25 years. The paper has been submitted to a practitioner journal and will also appear on Greenbackd in the future.

The future of Greenbackd.com

Greenbackd is a labor of love. I try to create new content every weekday, and to get the stock analyses up just after midnight Eastern Standard Time, so that they’re available before the markets open the following day. Most of the stocks that are currently trading at a premium to the price at which I originally identified them traded for a period at a discount to the price at which I identified them. This means that there are plenty of opportunities to trade on the ideas (not that I suggest you do that without reading the disclosures and doing your own research). If you find the ideas here compelling and you get some value from them, you can support my efforts by making a donation via PayPal.

If you’re looking for net nets in the meantime, here are two good screens:

  1. GuruFocus has a Graham net net screen, with some great functionality ($249 per year)
  2. Graham Investor NCAV screen (Free)

I look forward to bringing you the best undervalued asset situations I can dig up in the next quarter and the next year.

Posted in About, Activist Investors, Greenbackd, Liquidation Value, Net Current Asset Value, Stocks | Tagged , | 3 Comments »

Piotroski’s F_SCORE

November 30, 2009 by Tobias Carlisle

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.

Posted in About, Stocks, Value Investment | Tagged , , , , | 4 Comments »

Happy Thanksgiving, Folks

November 26, 2009 by Tobias Carlisle

See you Monday.

Posted in About, Greenbackd | Tagged | Leave a Comment »

Contrarian Investment, Extrapolation, and Risk: LSV’s Two-Dimensional Classifications

November 25, 2009 by Tobias Carlisle

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.

Posted in About, Stocks, Value Investment | Tagged , | 32 Comments »

Juicing P/B: Piotroski F_SCORE and LSV’s Two-Dimensional Classifications

November 24, 2009 by Tobias Carlisle

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.

Posted in About, Stocks, Value Investment | Tagged | 1 Comment »

« Newer Posts - Older Posts »