Thai VI

Second screen: Eliminate all companies with an F score below 5.[2]

[2] The F-Score (the Piotroski F-score) is a measure of the financial strength of a company.

The F-score is the sum of the scores for each of nine tests.

Each test scores one for a pass and zero for a fail.

The tests are:
i) net profit is positive;
ii) operating cash flow is positive;
iii) net profit ÷ total assets at beginning of year, minus the same number for the previous year is positive;
iv) operating cash flow is greater than net profit;
v) long term debt ÷ by average assets has not increased;
vi) the Current ratio has increased (the change is more than zero, so even a negligible increase passes the test);
vii) no raising of ordinary (common) equity over the previous year: this test is passed if the company did not issue any ordinary shares; viii) gross margin has improved over the previous year; and
ix) asset turnover has increased.

The Global 100 is the most extensive data-driven corporate sustainability assessment in existence, and inclusion is limited to a select group of the top 100 large-cap companies in the world. Launched in 2005, the annual Global 100 is announced each year during the World Economic Forum in Davos. The Global 100 methodology for evaluating companies' sustainability or "clean capitalism" performance was cited as a leading global practice (by SustainAbility in their Rate the Raters research program, Phase 4).

ผมต้องการลงทุนใน บริษัทที่กำลังชนะ บริษัทที่ชนะแล้วและจะชนะต่อไปอีกนาน พูดแบบวิชาการหน่อยก็คือ ผมชอบบริษัทที่มีการตลาดดีเยี่ยมและเหนือกว่าคู่แข่งมาก...
เห็นการผลิตหรือการให้บริการที่ดีเยี่ยมของบริษัท

Key Statistics for DAI
Current P/E Ratio (ttm) 9.9052
Estimated P/E(12/2013) 10.4934
Relative P/E vs. DAX 0.6353

Key Statistics for BMW
Current P/E Ratio (ttm) 9.5409
Estimated P/E(12/2013) 9.4247
Relative P/E vs. DAX 0.6119

Does the F-Score work?

Empirical analysis to test out the strategy in the UK market seems to be very limited. However, Piotroski’s research in the US does suggest that this type of fundamental analysis can be an effective value filter.

By investing in the top performers, he showed that, over a 20-year test period from 1976 through 1996 that “the mean return earned by a high book-to-market investor can be increased by at least 7.5% annually”. Furthermore, he found that buying the top stocks in the market and shorting those that got the worst scores would have resulted in 23% annualized gains, more than double the S&P 500 broad market index return. Piotroski also found that weak stocks, scoring two points or less, were five times more likely to either go bankrupt or delist due to financial problems.

More recently, the American Association of Individual Investors revealed that the F Score was the only one of its 56 screening methodologies that had positive results in 2008 (up 32.6% on average across 5 stocks, versus -41.7% for all of the AAII strategies over the same period).

Joseph D. Piotroski
is an American professor who specializes in accounting and financial reporting issues. As of October 2010 he serves as associate professor of accounting at the Stanford University Graduate School of Business. Prior to that, he taught at the University of Chicago Graduate School of Business from July 1999 to June 2007. [1]

Piotroski is known in the investing world for an influential 2000 paper he wrote while at the University of Chicago, entitled Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. In the piece, Piotroski laid out a way to buy and short stocks using several accounting-based criteria. His back-testing showed that the method would have produced returns well above the broader market averages over a two-decade period.

The next section of this paper reviews the prior literature on both “value”
investing and financial statement analysis and defines the nine financial signals
that I use to discriminate between firms.

I chose nine fundamental signals to measure three areas of the firm’s financial
condition:
profitability,
financial leverage/liquidity,
and operating efficiency.

The signals used are easy to interpret and implement, and they have broad appeal
as summary performance statistics. 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 mea-
sure, 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 posi-
tion, and the decision to purchase is ultimately based on the strength of the
aggregate signal.