Advertisements
Feeds:
Posts
Comments

Posts Tagged ‘F_SCORE’

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.]

Advertisements

Read Full Post »

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).

Read Full Post »

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.

Read Full Post »

%d bloggers like this: