Less Is More

Error, Human

Much market analysis operates on the assumption that more data is better – more data leads to more accurate results. More data may require more complex processing, leading to greater and greater requirements for computing power but, in principle, the idea is that more is better.

Out in the real world, however, we don’t have the luxury of this kind of analysis. This leads to errors which sometimes we call biases. But surprisingly it also, often, leads to better results. It may just be that the reason we make so many mistakes is because we’re trying to process too much information, not because we’re naturally error prone.

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The Turkey Illusion: An Audience With Gerd Gigerenzer

Thanksgiving Comes

Imagine you’re a turkey. Every day you're approached by a man with a bucket of corn who feeds you.  What kind of mental model of what happens when he appears do you think you’ll build up?

Gerd Gigerenzer, the Director of the Max Plank Institute for Human Development and the Harding Center for Risk Literacy uses this story to demonstrate the financial industry’s approach to modelling decision making under uncertainty.  We pay rich financial services organizations to make predictions based on the past that are only ever correct by chance, in order to absolve ourselves of responsibility for when they go wrong.  It’s a waste of time, money and talent.  Because, of course, Thanksgiving always comes.

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Satisficing Stockpicking

Logical Lab Rats

When we make decisions we nearly always do so in the context of something or other. In fact about the only time we’re asked to make contextless choices is in academic exams and laboratory based psychology experiments. As these are the two most familiar situations faced by the academics generating the theories that underpin most of modern finance we shouldn’t be awfully surprised if their great ideas are somewhat lacking in any understanding of … well, anything, really.

Being trained to think logically and probabilistically is a necessary part of being a modern economist, but it’s hardly a requirement for most people in most professions most of the time. You don’t find many baristas trying to make Bayesian inferences about which particular coffee to pour next. We clearly don’t rationalise most decisions, we make them quickly and effortlessly. We don’t optimise, we satisfice.

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Behavioural Finance’s Smoking Gun

Linda

Here’s a classic demonstration of behavioural finance in action, proving the irrationality of humankind. It’s the (in)famous Linda problem:

Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations. Which of these two alternatives is more probable?

(a) Linda is a bank teller.
(b) Linda is a bank teller and is active in the feminist movement.

Most subjects choose (b) and are informed that they’re irrational because the conjunction of two events – Linda is a bank teller and active in the feminist movement – is less likely than her just being a bank teller, regardless of her leisure interests. This is known as the conjunction fallacy. Unfortunately there’s a teensy little problem with this finding.

It’s wrong and it’s the smoking gun of behavioral finance.

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