Part 1: The Underrated Piotroski Stock Picking Method
I’ve discovered a real gem: Piotroski’s F-score. It’s a 9-point score that tests a given stock using 9 fundamental measures supposed to indicate financial health. Stocks can be classified into the 9 score groups from 1 to 9 and, although the effect is strongest at both extremes of the scale, low-scoring stocks can be expected to decline in value, while high-scoring ones can be expected to increase in market value.
This post is the first of a series about the Piotroski stock-picking methodology and its real-life application:
- Part 1: The Underrated Piotroski Stock-Picking Methodology
- Part 2: Ranking Stocks Found via the Piotroski Filter
- Part 3: Anatomy of a Piotroski Portfolio: Buying and Selling
- Part 4: My current Piotroski results
Why is the Piotroski stock-picking method ‘underrated’?
Because it takes some time to implement and explain, and most people do not like that. Similarly to the superior return of Value Averaging VS Dollar Cost Averaging, the latter of which produces absolutely no statistical improvement over random investing, people seem to stick with what is easiest to understand rather than with what actually works. I believe rational judgment is crucial in every subject, and that applies to investing as well. The reader who is not willing to read actual scientific papers / good books on investment may as well give up right away and avoid losing a lot of money in a field he does not have the patient to work with adequately. Click here for the full paper by Joseph D. Piotroski in PDF.
The Piotroski screening methodology
Some online stock screeners offer automatic Piotroski calculation, so they can be used to simplify the process. Personally, I used screener.co for my research, which also allowed me to focus on European stocks only (because I prefer keeping my investments in euros for now). I didn’t short low-scoring stocks, as Piotroski suggest in the paper, but rather concentrated on investing in high-scoring ones at or above a score of 8, which widens the final group a bit and makes sure that not too many companies get filtered out because of one single factor. Investing in low-scoring stocks could however be a great way to make money when the market is at a peak and you suppose that a bull market is coming to a close. Backtests on other websites have shown that choosing only stocks with a score of 9 increases return dramatically, so if you can, concentrate on those stocks, but in my case the German market didn’t offer enough of those stocks so I loosened my criteria a bit.
I also only considered stock with a price-to-book value of 5 or lower and at least a bit of trading activity (otherwise I wouldn’t be able to buy the stock when I intend to). Many of the stocks found by this method are unpopular and little traded, which is precisely why they have such potential: they haven’t been discovered yet. That is, in my mind, one of the main reasons why people stick with ineffective strategies like dollar cost averaging, even though they have been proven to be of little use by many studies. People just prefer simple and popular strategies instead of less well-known ones that actually work. This may be described as a kind of inertia of the masses, if you will.
The first backtest I did, over the past year, using Google Finance’s portfolio tool, showed an increase of value of about 43% for a portfolio of 10 EU stocks, of which 9 were German. During that same period, the German Index, DAX, only increased in value by about 22%. So in the last year, the strategy would have offered twice the return of the index with a diversified portfolio of 10 stocks! That is incredible.
After a month of sticking to the strategy, these results hold true. I plan on keeping these 10 stocks for 1 year before selling. Right now I’ve earned a 1.5% increase while the DAX is down 3% over the past month. It’s just one month of course, but if the future goes into the same general direction, this is very promising. Many people never beat their local index, not even for one month.
Note: Obviously using Google Finance’s portfolio tool to check how a portfolio would have fared over the last 12 months if bought exactly a year ago is not a scientific backtest. Given my limited resources and the questionable value of backtesting, I used it simply as an emotional crutch to reassure myself a bit. This does not constitute scientifically sound reasoning, though, even if a positive return over the past year seems, to me, preferable to a negative one.
I have since learned that it is of very little use to check on your portfolio every day, not least thanks to Nassim Taleb’s excellent book, Fooled by Randomness, in which the author explains among other things that the signal-to-noise ratio changes over time. If you check stock prices every 10 minutes, you are getting basically 99% noise, whereas longer intervals of checking up on your investment will increase the signal and reduce the noise. That is important because, as the author correctly explains, psychologically, it hurts you more to lose 100 € than it pleases you to gain 100 €. So if, over a week, you lose money on 2 days and earn money on 3, with a positive net result, you may still feel bad. To avoid this, I will now check on my stocks only once at the end of each week from now on. It feels a lot better, and the results are often better, too, so this emotional strategy here definitely works better than daily checking for me.