Exo Investing Blog

The subconscious biases putting your investments at risk - Part 2

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Welcome to part 2 of our series on the dangers of human bias in investing. Part 1 was our breakdown of Loss Aversion, and here we’ll be covering that wonderful thing we call Hindsight.

Don’t worry, we always try to use ‘normal people’ language to help us all understand what can be quite complex psychological theory.

So why is hindsight (not) so ‘wonderful’?

It’s something we’re all guilty of, often only remembering what we got right, and conveniently forgetting what we got wrong. But why?

In the 1970s, investigation into biases was a main focus area for our psychologist friends from part 1, Amos Tversky and Daniel Kahneman.

Importantly, they identified two heuristics* that were extremely important to understanding hindsight bias. These were the availability heuristic and the representativeness heuristic.

*For those that aren’t dictionary aficionados or subscribers to Psychology Weekly, heuristics are mental shortcuts that usually involve focusing on one aspect of a complex problem and ignoring others. Meaning they can help our brain work efficiently, but equally take us down the wrong path.

Those heuristics intrigue me - tell me more

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The availability heuristic is a mental shortcut where we rely on immediate examples that come to mind when evaluating something. Effectively, our brain works on the assumption that if we can recall something quickly, then it must be important.

This can mean we have a tendency to lean towards more recent information, as it’s easier to recall, meaning we can become biased toward the latest news. So let’s hope it’s not of the fake variety.

It’s essentially our brain thinking "if I can think of it that quickly, it must be important."

One down, one to go.

That ‘clever’ person you get stuck sitting next to at dinner would (un)helpfully explain the representativeness heuristic as how we estimate the likelihood of an event by comparing it to an existing prototype that already exists in our minds. Our prototype is what we think is the most relevant or typical example of a particular event or object. 😴🛌💤

Lost? Us too.

It’s a wordy way of saying we may overestimate the likelihood that something will occur, simply because it currently appears more (its ‘representativeness’).

But in reality its occurrence likely isn’t any more probable.

What does all of this mean?

In researching this topic, we found a great bit of light reading in the September 2012 issue of Perspectives on Psychological Science, a journal of the Association for Psychological Science.

In all honesty, it’s really not that light, but it’s a really good piece by psychological scientists Neal Roese and Kathleen Vohs after they reviewed previous theories on hindsight bias.

They concluded that hindsight bias can be very damaging, because it gets in the way of us learning from our experiences.

“If you feel like you knew it all along, it means you won’t stop to examine why something really happened,” observed Roese. “It’s often hard to convince seasoned decision makers that they might fall prey to hindsight bias.”

A good example of all of this is looking back at the recent market crashes or bubbles, which became the subjects of substantial hindsight bias.

Following the dotcom bubble or that rather grim time we had in 2008, many analysts tried to demonstrate how what seemed like trivial events at the time were actually clear indicators of impending trouble. But if the bubble or crash had really been that obvious to the general public, they would likely have been avoided altogether.

Looking specifically at the dotcom bubble and subprime mortgage crisis, you’d think we’d all at least be aware that something wasn’t quite right - but that’s one for part 3, Herding.

What else should we watch out for?

As mentioned, part 1 covered Loss Aversion, and our next focus points will be the dangers of Herding and then Familiarity.

What’s the one thing that makes us (at Exo) feel a bit better about all of these issues though?

Data doesn’t get emotional.

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