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The Math Professor’s Test of Market Analytics Preparedness
Traders Magazine Online News, August 30, 2017
What was the closing price for each of the components of the S&P 500 for the last three years? Getting closing prices on stocks is easy, but actually knowing all the components of an index and their closing prices takes more effort. How quickly can most financial services answer a question like that?
In college, I had heard of a math professor who started out the semester by writing an equation on the board and then observing the class to see how many students had completed the equation in one minute, in two minutes and in five minutes. He then told the class that those who finished in one minute would probably get an A. Those who finished in two minutes would probably get a B. The five-minute finishers, would likely be C students. Anyone who hadn’t finished in five minutes should drop the class, the professor said, because they would probably fail.
The speed at which financial firms can answer market questions often seems analogous to how quickly students could solve the equation in that college math class. In markets where some firms are measuring trading advantages in nanoseconds, speed in answering arbitrary analytical questions can determine whether firms pass or fail.
Which stocks have overperformed a particular index by 2 percent or more? Which bond was the most active in a particular ETF in the first six months of the year? These are the types of arbitrary questions that standard data systems are not pre-programmed to answer. Firms that can answer these questions quickly, have achieved a level of sophistication in their understanding and management of market analytics that can yield competitive advantage. In fact, getting good at answering these types of questions, whether for regulatory compliance purposes or for return on equity, may be the future of finance.
Imagine that if applied to the world of finance the math professor’s timeline was expanded, so that those who could answer these question in an hour were A students and those who could come up with an answer in a day would get Bs. The truth is, by that measure, most of the industry would be C students at best.
The failing companies don’t even bother to ask, and because they don’t ask arbitrary analytical questions they are dulling their abilities to identify the idiosyncracies in markets that can lead to trading alpha. D students, in any situation, tend to be very similar to failing students, but with a few lucky breaks.
The vast majority of the industry falls into the C student category, which is to say those who are struggling so hard to keep their heads above water that they don’t have time to lay the groundwork for real success. These firms still look at technology as an expense, and are more focused on how to minimize their expenses, than maximize the output they get from them.
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