Category: Investing Insight

Yesterday, we discussed how theoretical models can be used to draw biased conclusions by using faulty assumptions. If the models then get picked up without an understanding of those assumptions, it leads to expensive mistakes. But are empirical models free from such bias, especially if the data-set is big enough? Absolutely not.

In an article titled “Big data: are we making a big mistake?” in the FT, author Tim Harford points out that by merely finding statistical patterns in the data, data scientists are focusing too much on correlation and giving short shrift to causation.

All the problems that you had in “small” data exist in “big” data, but they are only tougher to find. When it comes to data, size isn’t everything, you still need to deal with sample error and sample bias.

For example, it is in principle possible to record and analyse every message on Twitter and use it to draw conclusions about the public mood. But while we can look at all the tweets, Twitter users are not representative of the population as a whole. According to the Pew Research Internet Project, in 2013, US-based Twitter users were disproportionately young, urban or suburban, and black.

Worse still, as the data set grows, it becomes harder to figure out if a pattern is statistically significant, i.e., can such a pattern have emerged purely by chance.

The whole article is worth read, plan to spend some time on it: Big data: are we making a big mistake?

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An engineer, a physicist and an economist are stranded on a deserted island with nothing to eat. A crate containing many cans of soup washes ashore and the three ponder how to open the cans.
Engineer: Let’s climb that tree and drop the cans on the rocks.
Physicist: Let’s heat each can over our campfire until the increase in internal
pressure causes it to open.
Economist: Let’s assume we have a can opener.

In a recent paper titled Chameleons: The Misuse of Theoretical Models in Finance and Economics Paul Pfleiderer of Stanford University lays out how some models, built on assumptions with dubious connections to the real world, end up being used to inform policy and other decision making. He terms these models “Chameleons.”

Notice how similar these two abstracts are:

– “Why High Leverage is Optimal for Banks” by Harry DeAngelo and René Stulz.

– “Why High Alcohol Consumption is Optimal for Humans” by Bacardi and Mondavi 😉

These are a good illustration that one can generally develop a theoretical model to produce any result within a wide range.

Read the whole thing at your leisure: Chameleons: The Misuse of Theoretical Models in Finance and Economics