How do we really make investment decisions?


If you were rational, this is how you might make a decision:

  1. Set out the alternatives – what are the choice you have?
  2. For each choice, what is the payoff – what are you likely to get?
  3. Again, for each choice, what is the probability that it will happen?
  4. What is the expected value of the option (probability x payoff)

With choices that lead to other possible choices, you need a decision tree and the ability to work out sequences of expected value.

You then choose the approach that results in the highest expected value.

This approach, however, is not intuitive, and most people are not wired to approach decision making in this way.

In addition, it’s a little old. The statistical basis for this approach lies in the work of Thomas Bayes in the mid-1700s. Our knowledge of people has moved on a bit since then.

There are two situations people face often when making personal and business decisions.

The first situation is when they know the chances of winning or losing.

For example, lets say you entered a game where you could win £10 or lose £5 on a coin toss. There is a 50% chance of either, and you might be tempted to take a punt at this level.

Most people would not take the bet if the option was between getting £1,000 or losing £500. The fear of losing would overwhelm the prospect of winning.

The other situation is when they don’t know what might happen and the risks that could emerge.

Quite often, the next thing to go wrong is completely different from the ways in which things went wrong before – and all the planning and controls that were in place to avoid the last disaster fail to prevent the next one.

A more human approach to making these decisions is based on Plausibility Theory and in particular the idea that you may take a risk as long as your downside is capped.

In other words, you may be willing to take a decision that you expect to be profitable, as long as the loss if you are wrong is limited to a certain level.

This approach became popular around 15 years ago as the concept of Value-at-Risk (VaR). Using this approach, you put in place a management system that ensures that you limit your loss to a particular level, say 1 or 2% of your portfolio value, and then work to get the most profit out of the opportunity.

So… you avoid the ugly end result, limit the worst case to a bad result and work on achieving a good result.

But… mathematicians ruin everything.

VaR was quickly adopted in many financial models – from standard portfolio markets to energy, and complex models were used to justify the products that were being introduced. They even formed part of the Basel II rules used to regulate international banking.

Which then failed rather spectacularly to prevent the global financial crisis that kicked off in 2008.

Although arguably that was down to smart people who figured that they could use the methods to try and take greater risks with other people’s money while at the same time reducing their own personal risk to almost nothing.

After all, how many executives of banks have been tried and convicted for their role in the crisis?

So it looks like the people managing your money applied Plausibility Theory rather well, except they did it to benefit themselves rather than you.

The takeaway is perhaps that the next time you make an investment decision, figure out what will happen to you if things go horribly wrong before being enticed by the promises of future returns.

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