Magic Formula Investing

Just following up from my post about Joel Greenblatt’s book You Can Be A Stock Market Genius, I have discovered another of his books – The Little Book That Beats The Market.

The book touts a “value-oriented” approach that looks for bargain stocks whose share price is cheap relative to the company’s profitability. His version is a “magic formula” that ranks stocks on the basis of two variables—the earnings yield and the business’s return on capital.

The book has an associated website which (after you register) will let you rank stocks based on the criteria suggested in the book. Being earnings yield and return on captial. I have registered and the site looks to be an excellent free resource.


12 Responses to Magic Formula Investing

  1. Falafulu Fisi says:

    I would definitely advise against anyone about rushing to endorse Joel Greenblatt’s view on beating the financial market. The following is an editorial review of his book from Amazon:

    But he argues that because these areas are not overstudied by the analysts, possible market inefficiencies can be exploited.

    I have to say, that this is a bullshit layman’s statement. The field of mathematical finance has accelerated in its development over the last 20 years where the discipline has adopted advanced algorithms similar or the same as used in theoretical nuclear physics. This means that anyone who is well-versed in theoretical physics, could easily devise trading systems to explore the anomalies in the financial markets by using advance algorithms to gain reasonable profits. Mr. Greenblatt’s assertion that these areas are not over-studied by the analyst, shows that he is probably out of his depth, this is irrelevant of his experience. Experience in financial markets and depth of knowledge in financial markets are completely different domains, however some analysts are good at both. One , just have to read the following Herald’s article about what I am talking about, and that is the analysts have definitely over-studdied the financial market models, in which is directly in contrast to what Mr. Greenblatt’s claim in his book.

    Humans made redundant as super-trader does the sums

    At the moment, I am developing an application that can do the capability that is described in the article above. I’ve never worked in the financial market industry before, but since I don’t intend to work in this industry in the future my background is irrelevant, since my main focus is software product development and not to become an investment adviser. Also in the article, it clearly stated that financial institutions are seeking expertise from other disciplines such as Astro-Physics, Statistics, Mathematics, Computing, etc, and these institutions don’t indicate that they would be keen on hiring MBA expertise for such development. In about 2015, stock-brokers would be out of a job, since according to the article, Algorithmic Trading will become the main platform of trading in the financial markets. In such time, the war to beat the market lies in who’s developing the most accurate state-of-the-art mathematical-algorithm.

    I want to emphasize how Joel has limited knowledge about recent advances in the field of financial mathematics, even though he might be a highly regarded expert in the industry, is a publication that I frequently read. I regularly read this The Journal of Computational Finance (JCF) in order for me to gain what are the latest algorithms out there that have been published in peer review economic/finance literatures. There are tons of different journals for finance/economic peer review publications available today, but I only looked at a few in order to gain knowledge about recent advances in financial mathematics algorithms. Example, the following published algorithm which appeared in JCF in 2005, was implemented by the author for Credit Sussie First Boston (CSFB):

    Numerical pricing of discrete barrier and lookback options via Laplace transforms

    I know this because I had made contact with the author of that paper, Prof. Steven Kow of Columbia University for some questions regarding the derivation of his algorithm, because I am going to implement it. Steven then told me , that he has implemented that algorithm for CSFB, but I am welcome to ask him any question for clarification regarding his published algorithm. See, this is the sort of thing that Joel Greenblatt has no clue about how advance analysts in their analysis of the financial market, this validates my point about industry experts who have no expertise in financial mathematics setting themselves as the all round expert. This is a misconception, where people think that industry expertise is the same as analytical expert knowledge. The example I have given above, is not the only one. There have been numerous authors (peer review publications) that I had come into contact in the last year or so regarding their published algorithms, and most say, the published algorithms have been implemented (software) for some financial institutions.

    Finally, I think that Joel Greenblatt’s is too simplistic that the user just might want to flip a coin and see what is the projected outcome in the financial market might be.

  2. bertfresno says:

    Just came across this article which I thought was a good reply to Falafulu Fisi’s comment about investing and mathematical models.

    A quote from Matthew Rothman:

    “Wednesday is the type of day people will remember in quant-land for a very long time,” said Mr. Rothman, a University of Chicago Ph.D. who ran a quantitative fund before joining Lehman Brothers. “Events that models only predicted would happen once in 10,000 years happened every day for three days.”

    And this quote from this article in the NY Times.

    Renaissance Institutional Equities Fund, a $26 billion-plus hedge fund managed by mathematician James Simons, is down “in the order of 7 percent” for the year through Aug. 8, according to a letter the fund sent to investors Thursday.

    Renaissance, one of the largest “quantitative” hedge funds, told investors that it has “not had good luck during these last few days.” It said it has been “caught in what appears to be a large wave of de-leveraging on the part of quantitative long-short hedge funds.”

  3. Falafulu Fisi says:


    Models are by no means certainty, but when someone works in a complex dynamical system such as a market analyst, his/her only hope is quantitative analysis. Time travel is something impossible, it means, no one travels to the future to see what happens then come back to the present day to devise his/her investment plan. How, one could theoretically see the future? The answer is Quantitative analysis. If quantitative analysis, is not a requirement, then an investment house or company might as well hire a psychic to do forecasting. There are formal theoretical framework for doing mathematical forecasting, so one either adopts it or not. The domain of forecasting is based on historical data. No historical data, then model is impossible, since it is analogous to someone who has amnesia, which none of the past history exist. It means that such a person finds it hard to operate in life, since everything he has to re-learn or guess. A person who remembers the past would operate more efficiently than one who has no memory of the past.

    Financial mathematic is no different from any other emerging technology in life and that is , things or models are getting better as time progresses. When computers were first built about 60 years ago, one used to occupy a warehouse. Today, anyone can carry a laptop with a better computing power compared to one from say, 1950.

    Financial mathematic is a huge field, that covers Economics, Statistics, Mathematics, Complex System, Feedback Control Theory, Digital Signal Processing, Fluid Dynamics & Statistical Physics, Quantum Physics, and many more ,etc… Dr. Rothman might be running one type of models from say, from Capital Asset Pricing Model or Arbitrage Pricing Theory for example. He has no knowledge about models in Econo-Physics for example, where models have started to appear in the literature, that can deal with such situation, which is called emerging behavior in Complex System theory, ie, a behavior that just emerges, that seemed to come from no-where, however things in nature just don’t show effects that have no causes. All things have causes, but in emergent behavior it tends to be masked by the systems normal behavior. This type of behavior is known as points of unstability or breakpoints in complex dynamical systems theory. Market crash is one such example, that is you’re caught by surprise. Econo-Physicists at OCCF (Oxford Center for Computational Finance) have published a number of papers in this area of market mini-game, which covers emergent behavior. Other researchers have picked up on the topic and have extended the idea where their work has been published. Here is more info on minority games from the Econo-Physics websites. Another example of emergent behavior was the collapsed of the Tacoma bridge in the early 1940s. This was caused by the wind frequency matching the natural frequency of the bridge, therefore the bridge kept absorbing the wind energy and built up rapidly in a short time, upto a point that the systems reached its breakpoint, therefore it collapsed. This behavior came from nowhere, but emerges in a very short period of time.

    To the best of my knowledge is that there are lots of traditional economists who are unaware of the advances in Econo-Physics, because researches in Econo-Physics are being published in Physics journals rather than economic/finance journals, so I wouldn’t be surprised if Dr. Rothman is has been using the traditional economics modeling and not aware of recent advances from Econo-Physics that such emergence behavior could be modeled , of course there is no such thing as 100% correct in modeling. In traditional economics theory, complex dynamical systems theory has not yet penetrated to become a mainstream. This domain is still the curiosity of physicists, mathematicians, computer scientists, etc…, and I am not surprised at all that Dr. Rothman of Lehman Brothers was caught by surprise. Here is an article about a similar situation.

    Market calms before storm

    As I have stated that Mathematical finance is a huge field, where one person is expertise in one domain, however that doesn’t mean that he/she becomes expertise in other unconventional fields as econo-physics. Models are not God, and of course they have weaknesses, but technology is not static. As time progresses, technology improves and Mathematical finance is no different.

  4. […] I have posted before about Magic Formula investing, with the help of Digital Look I have prepared a table of magic formula type shares in […]

  5. Not that I’m impressed a lot, but this is a lot more than I expected for when I found a link on Delicious telling that the info here is quite decent. Thanks.

  6. Jane Goody says:

    After reading through this article, I feel that I need more information on the topic. Can you share some resources please?

  7. Wayne Weist says:

    What a fantastic website!  Far too many people attempt to trade instead of first finding a proper education in forex.  This is likely why 98% of traders give up.  It just doesn’t make any sense to risk your money on something you know so little about.

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  10. Elliot Berg says:

    Very good news the EURO shows level of potency once more:-)

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