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Thursday, April 1, 2010

Yes, Naked Strategies Can Adjust to Changing Market Conditions!

Static models are used frequently in medicine. For example, the methods that apply to diagnosing the flu do not change from week to week or month to month.

In cardiology, radiology, and psychology, static models are used to make diagnoses and recommend treatments for patients with all types of symptoms. In The Naked Portfolio Manager, I gave examples in each of these disciplines where statistical prediction models proved more reliable than the judgment-based methods of clinicians in the field. For example, the Goldman Chest Pain Decision Aide model proved more reliable at making better triage decisions than the emergency room doctors and cardiologists at Cooke County Hospital in Chicago.

Although some traditional portfolio managers argue that decision-making, when it comes to the stock market, is different. People with chest pains do not change their symptoms based on what other people with chest pains are doing to gain an advantage. But investors, at least to some degree, change their behavior based on what other investors are doing. For this reason, many traditional judgment-based portfolio managers reject "naked strategies" because they say a rule-based method is inflexible and thus cannot change based on market conditions. This is why the best portfolio managers that rely on judgment, they argue, are able to outperform naked managers.

Phooey!

Naked strategies don't have to be static. They can be flexible and adjust to market conditions. The rules for adjusting to market conditions need to be created when the strategy is designed. The Cripps Model, discussed in The Naked Portfolio Manager, is a perfect example of this. This model measures how investors react to news on a stock and then applies those measurements to calculate a fair price. Stocks are purchased when the market price is substantially below this calculated fair price.

Note here that the Cripps Model calculates the value of a stock based on the collective judgments of all market participants and then gives a buy signal when the stock price deviates substantially from that price. This is much different than an analyst forming his own opinion about what a stock should be worth.

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Wednesday, October 14, 2009

How Many Variables Do You Need To Create A Good Naked Strategy?

As I wrote in one of my last blog posts, "naked strategies" are quite simply highly efficient, cost-effective methods of coming to a decision quickly. These methods help investors avoid many of the delays that are frequently part of the decision-making process, such as the delays that occur when human decision-makers apply their judgment.

Comparing naked portfolio managers to traditional portfolio managers, the latter often uses far more information than is used in the naked model. People assume that humans judges are more reliable and sometimes even more cost-effective in their investment decision-making because they use a lot more information. Yet ironically, naked strategies often outperform human managers despite using far less information.

There are many examples of this out side the area or portfolio managment. Orley Ashenfelter, a Princeton professor, developed a model for predicting the value of wine futures based on just two variables (rainfall and temperature). That model proved much more reliable than the decision-making of wine speculators, who had the same data available to them plus the benefit of using the old "swish and spit" method. Another example: The Goldman chest pain decision aide used to make triage decisions for patients uses just four variables, yet it made more reliable triage decisions than the cardiologists and emergency room doctors at Cook County Hospital in Chicago who had far more information. Greenblatt's Magic Formula, which I discuss in The Naked Portfolio Manager, uses just two inputs, yet has produced impressive results relative to market averages.

One would think that with more information, human judges would be able to out-predict models, but the evidence clearly indicates this is not the case. The problem that human judges have is that when you have lots of information, it's sometimes difficult to determine what data is extremely important and what is almost irrelevant. With naked decision-making strategies, on the other hand, the method's construction tells you what data is truly important.

Good models focus on only the most important criteria necessary for making the decision. Models with a huge number of factors often dilute the value of the most important variables by averaging them with inputs of lesser value.
Decision-Making Best Practice #20: If a model has a very large number of inputs, it usually means that the model's creator has not done a very good job of identifying the most important inputs. Be very skeptical of these models.

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